prediction markets

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Infotopia: How Many Minds Produce Knowledge by Cass R. Sunstein

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affirmative action, Andrei Shleifer, availability heuristic, Build a better mousetrap, c2.com, Cass Sunstein, cognitive bias, cuban missile crisis, Daniel Kahneman / Amos Tversky, Edward Glaeser, en.wikipedia.org, feminist movement, framing effect, hindsight bias, information asymmetry, Isaac Newton, Jean Tirole, jimmy wales, market bubble, market design, minimum wage unemployment, prediction markets, profit motive, rent control, Richard Stallman, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, slashdot, stem cell, The Wisdom of Crowds, winner-take-all economy

Recall that under the Condorcet Jury Theorem, the average vote of a large group will be wrong if most group members are likely to err. In a prediction market, the existence of incentives greatly increases the likelihood that each investor will prove to be right. Those without information will not participate; those with a lot of information will Money, Prices, and Prediction Markets / 105 participate a great deal. Crucially, the problems that infect deliberating groups are reduced in prediction markets. As a result, such markets have often proved remarkably accurate. Perhaps most important, prediction markets have been found not to amplify individual errors but to eliminate them; the prices that result from trading prove reliable even if many individual traders err. Because of their accuracy, prediction markets are receiving a great deal of attention in the private sector, as we shall see, and it is easy to find software and services to support their use.3 Of course, investors, like everyone else, are subject to cognitive biases and to the informational pressure imposed by the views of others.

A prediction market might be used to make forecasts about the future progress of the disease.57 Such markets might generally be used to make forecasts about the likely effects of development projects, such as those involving vaccinations and mortality reductions.58 The Central Intelligence Agency might want to know about the outcome of elections in Iraq, or the likelihood of a feared event in the Middle East. The CIA might create an internal prediction market, designed to aggregate the information held by its own employees. The White House might seek to predict the likelihood and magnitude of damage from natural disasters, including tornadoes and earthquakes. Accurate information could greatly assist in advance planning. Prediction markets could easily be created to help in that task. Some of these examples involve private behavior. Others involve the judgments of public institutions. Some might seem fanciful. Others involve predictions on which prediction markets are already flourishing. Money, Prices, and Prediction Markets / 133 Failed Predictions? Of Manipulation, Bias, and Bubbles / In what circumstances might prediction markets fail? Let’s begin with some noteworthy blunders.

Among its most impressive achievements to date is its uncanny accuracy in predicting Oscar winners in 2005, with correct judgments in all eight of the categories for which trading was allowed. An interesting counterpoint: Tradesports.com, a prediction market that uses real rather than virtual money, predicted only six of eight Oscar winners in that year. In fact, prediction markets that use virtual money have been found, in many circumstances, to do as well as markets that rely on real money.23 When entertainment and law meet, prediction markets do well. Days before the ultimate verdict in the Michael Jackson case, insiders knew what would happen. As one reporter noted in advance, “Whether or not Michael Jackson’s jurors still have a reasonable doubt about his guilt, Money, Prices, and Prediction Markets / 111 the wild world of Internet betting has rendered judgment: the smart money is on acquittal.”24 Many people believe that “you can’t predict the weather,” but the National Weather Service does quite well, and orange juice futures do even better.25 The markets for the demand for gas outperform the experts on the demand for gas.26 A large prediction market focuses on the likelihood that economic data released later in the week will show specific values;27 the market has performed at least as well as the consensus forecasts of a survey of about fifty professional forecasters.


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Blockchain: Blueprint for a New Economy by Melanie Swan

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The result is that there can be de facto investors in cryptocurrency projects who are not getting much more than early access to open source software. A better way to crowdfund cryptocurrency projects in a decentralized yet legal way, with more effective checks and balances, is needed. Bitcoin Prediction Markets One example of new tech with old tech is Bitcoin prediction markets like Predictious and Fairlay.50 Bitcoin prediction markets offer a betting venue for the usual real-world outcomes as prediction markets always have, such as elections, political legislation, sports matches, and technology product releases, and also serve as a good source of information about the developing blockchain industry. Bitcoin prediction markets are one way to see what insiders think about Bitcoin’s future price directions, the success of different altcoin and protocol 2.0 projects, and industry issues more generally (e.g., technical development issues with Bitcoin, such as when there will be a hard fork—significant change—of the code, and the level of difficulty of the mining algorithm).

-M2M/IoT Bitcoin Payment Network to Enable the Machine Economy and consensus models, Blockchain AI: Consensus as the Mechanism to Foster “Friendly” AI-Blockchain Consensus Increases the Information Resolution of the Universe extensibility of, Extensibility of Blockchain Technology Concepts for facilitating big data predictive task automation, Blockchain Layer Could Facilitate Big Data’s Predictive Task Automation future applications, Blockchain AI: Consensus as the Mechanism to Foster “Friendly” AI-Blockchain Consensus Increases the Information Resolution of the Universe limitations of (see limitations) organizational capabilities, Blockchain Technology Is a New and Highly Effective Model for Organizing Activity tracking capabilities, Fundamental Economic Principles: Discovery, Value Attribution, and Exchange-Fundamental Economic Principles: Discovery, Value Attribution, and Exchange blockchain-recorded marriage, Decentralized Governance Services BlockCypher, Blockchain Development Platforms and APIs BOINC, DAOs and DACs bond deposit postings, Technical Challenges Brin, David, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel BTCjam, Financial Services business model challenges, Business Model Challenges Buttercoin, Financial Services Byrne, Patrick, Financial Services C Campus Cryptocurrency Network, Campuscoin Campuscoin, Campuscoin-Campuscoin censorship, Internet (see decentralized DNS system) Chain, Blockchain Development Platforms and APIs challenges (see see limitations) charity donations, Charity Donations and the Blockchain—Sean’s Outpost China, Relation to Fiat Currency ChromaWallet, Wallet Development Projects Chronobit, Virtual Notary, Bitnotar, and Chronobit Circle Internet Financial, eWallet Services and Personal Cryptosecurity Codius, Financial Services coin drops, Coin Drops as a Strategy for Public Adoption coin mixing, eWallet Services and Personal Cryptosecurity coin, defining, Terminology and Concepts, Currency, Token, 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Increases the Information Resolution of the Universe contagious disease relief, Global Public Health: Bitcoin for Contagious Disease Relief contracts, Blockchain 2.0: Contracts-The Blockchain as a Path to Artificial Intelligence (see also smart contracts) crowdfunding, Crowdfunding-Crowdfunding financial services, Financial Services-Financial Services marriage, Decentralized Governance Services prediction markets, Bitcoin Prediction Markets smart property, Smart Property-Smart Property wallet development projects, Wallet Development Projects copyright protection, Monegraph: Online Graphics Protection Counterparty, Blockchain 2.0 Protocol Projects, Counterparty Re-creates Ethereum’s Smart Contract Platform Counterparty currency (XCP), Currency, Token, Tokenizing Counterwallet, Wallet Development Projects crowdfunding, Crowdfunding-Crowdfunding cryptocurrencies benefits of, Currency, Token, Tokenizing cryptosecurity, eWallet Services and Personal Cryptosecurity eWallet services, eWallet Services and Personal Cryptosecurity mechanics of, How a Cryptocurrency Works-Merchant Acceptance of Bitcoin merchant acceptance, Merchant Acceptance of Bitcoin cryptosecurity challenges, eWallet Services and Personal Cryptosecurity cryptowallet, Blockchain Neutrality currency, Technology Stack: Blockchain, Protocol, Currency-Regulatory Status, Currency, Token, Tokenizing-Extensibility of Demurrage Concept and Features Campuscoin, Campuscoin-Campuscoin coin drops, Coin Drops as a Strategy for Public Adoption Communitycoin, Communitycoin: Hayek’s Private Currencies Vie for Attention-Communitycoin: Hayek’s Private Currencies Vie for Attention cryptocurrencies, How a Cryptocurrency Works-Merchant Acceptance of Bitcoin decentralizing, Communitycoin: Hayek’s Private Currencies Vie for Attention defining, Currency, Token, Tokenizing-Currency, Token, Tokenizing, Currency: New Meanings demurrage, Demurrage Currencies: Potentially Incitory and Redistributable-Extensibility of Demurrage Concept and Features double-spend problem, The Double-Spend and Byzantine Generals’ Computing Problems fiat currency, Relation to Fiat Currency-Relation to Fiat Currency monetary and nonmonetary, Currency Multiplicity: Monetary and Nonmonetary Currencies-Currency Multiplicity: Monetary and Nonmonetary Currencies new meanings, Currency: New Meanings technology stack, Technology Stack: Blockchain, Protocol, Currency-Technology Stack: Blockchain, Protocol, Currency currency mulitplicity, Currency Multiplicity: Monetary and Nonmonetary Currencies-Currency Multiplicity: Monetary and Nonmonetary Currencies D DAOs, DAOs and DACs-DAOs and DACs DAOs/DACs, DAOs and DACs-DAOs and DACs, Batched Notary Chains as a Class of Blockchain Infrastructure, Blockchain Government Dapps, Dapps-Dapps, Extensibility of Demurrage Concept and Features Dark Coin, eWallet Services and Personal Cryptosecurity dark pools, Technical Challenges Dark Wallet, eWallet Services and Personal Cryptosecurity DASs, DASs and Self-Bootstrapped Organizations DDP, Crowdfunding decentralization, Smart Contracts, Centralization-Decentralization Tension and Equilibrium decentralized applications (Dapps), Dapps-Dapps decentralized autonomous organization/corporation (DAO) (see DAOs/DACs) decentralized autonomous societies (DASs), DASs and Self-Bootstrapped Organizations decentralized autonomy, eWallet Services and Personal Cryptosecurity decentralized DNS, Namecoin: Decentralized Domain Name System-Decentralized DNS Functionality Beyond Free Speech: Digital Identity challenges of, Challenges and Other Decentralized DNS Services and digital identity, Decentralized DNS Functionality Beyond Free Speech: Digital Identity-Decentralized DNS Functionality Beyond Free Speech: Digital Identity DotP2P, Challenges and Other Decentralized DNS Services decentralized file storage, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation decentralized secure file serving, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation deeds, Decentralized Governance Services demurrage currencies, Demurrage Currencies: Potentially Incitory and Redistributable-Extensibility of Demurrage Concept and Features action-incitory features, Extensibility of Demurrage Concept and Features limitations of, Demurrage Currencies: Potentially Incitory and Redistributable digital art, Digital Art: Blockchain Attestation Services (Notary, Intellectual Property Protection)-Personal Thinking Blockchains (see also blockchain attestation services) hashing and timestamping, Hashing Plus Timestamping-Limitations online graphics protection, Monegraph: Online Graphics Protection digital cryptography, Ethereum: Turing-Complete Virtual Machine, Public/Private-Key Cryptography 101 digital divide, defining, Digital Divide of Bitcoin digital identity verification, Blockchain 2.0: Contracts, Smart Property, Wallet Development Projects, Digital Identity Verification-Digital Divide of Bitcoin, Limitations, Decentralized Governance Services, Liquid Democracy and Random-Sample Elections, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy, Privacy Challenges for Personal Records dispute resolution, PrecedentCoin: Blockchain Dispute Resolution DIYweathermodeling, Community Supercomputing DNAnexus, Genomecoin, GenomicResearchcoin Dogecoin, Technology Stack: Blockchain, Protocol, Currency, Currency Multiplicity: Monetary and Nonmonetary Currencies, Scandals and Public Perception DotP2P, Challenges and Other Decentralized DNS Services double-spend problem, The Double-Spend and Byzantine Generals’ Computing Problems DriveShare, DAOs and DACs dynamic redistribution of currency (see demurrage currency) E education (see learning and literacy) Electronic Freedom Foundation (EFF), Distributed Censorship-Resistant Organizational Models EMR (electronic medical record) system, EMRs on the Blockchain: Personal Health Record Storage Ethereum, Crowdfunding, Blockchain 2.0 Protocol Projects, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Ethereum: Turing-Complete Virtual Machine-Counterparty Re-creates Ethereum’s Smart Contract Platform eWallet services, eWallet Services and Personal Cryptosecurity ExperimentalResultscoin, Blockchain Academic Publishing: Journalcoin F Fairlay, Bitcoin Prediction Markets fiat currency, Relation to Fiat Currency-Relation to Fiat Currency file serving, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Ethereum: Turing-Complete Virtual Machine file storage, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation financial services, Regulatory Status, Financial Services-Financial Services, Blockchain Technology Is a New and Highly Effective Model for Organizing Activity, Government Regulation Fitbit, Personal Thinking Blockchains, Blockchain Health Research Commons, Extensibility of Demurrage Concept and Features Florincoin, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel Folding@Home, DAOs and DACs, Blockchain Science: Gridcoin, Foldingcoin, Community Supercomputing franculates, Blockchain Government freedom of speech, Namecoin: Decentralized Domain Name System, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel (see also decentralized DNS system) Freicoin, Demurrage Currencies: Potentially Incitory and Redistributable fundraising (see crowdfunding) futarchy, Futarchy: Two-Step Democracy with Voting + Prediction Markets-Futarchy: Two-Step Democracy with Voting + Prediction Markets G GBIcoin, Demurrage Currencies: Potentially Incitory and Redistributable GBIs (Guaranteed Basic Income initiatives), Demurrage Currencies: Potentially Incitory and Redistributable Gems, Blockchain Development Platforms and APIs, Dapps Genecoin, Blockchain Genomics Genomecoin, Genomecoin, GenomicResearchcoin Genomic Data Commons, Genomecoin, GenomicResearchcoin genomic sequencing, Blockchain Genomics 2.0: Industrialized All-Human-Scale Sequencing Solution-Genomecoin, GenomicResearchcoin GenomicResearchcoin, Genomecoin, GenomicResearchcoin genomics, consumer, Blockchain Genomics-Genomecoin, GenomicResearchcoin Git, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation GitHub, Blockchain Academic Publishing: Journalcoin, Currency Multiplicity: Monetary and Nonmonetary Currencies global public health, Global Public Health: Bitcoin for Contagious Disease Relief GoCoin, Financial Services GoToLunchcoin, Terminology and Concepts governance, Blockchain Government-Societal Maturity Impact of Blockchain Governance decentralized services, Decentralized Governance Services-Decentralized Governance Services dispute resolution, PrecedentCoin: Blockchain Dispute Resolution futarchy, Futarchy: Two-Step Democracy with Voting + Prediction Markets-Futarchy: Two-Step Democracy with Voting + Prediction Markets Liquid Democracy system, Liquid Democracy and Random-Sample Elections-Liquid Democracy and Random-Sample Elections personalized governance services, Blockchain Government random-sample elections, Random-Sample Elections societal maturity impact of blockchain governance, Societal Maturity Impact of Blockchain Governance government regulation, Regulatory Status, Government Regulation-Government Regulation Gridcoin, Blockchain Science: Gridcoin, Foldingcoin-Blockchain Science: Gridcoin, Foldingcoin H hashing, Hashing Plus Timestamping-Limitations, Batched Notary Chains as a Class of Blockchain Infrastructure, Technical Challenges Hayek, Friedrich, Communitycoin: Hayek’s Private Currencies Vie for Attention, Demurrage Currencies: Potentially Incitory and Redistributable, Conclusion, The Blockchain Is an Information Technology health, Blockchain Health-Virus Bank, Seed Vault Backup as demurrage currency, Extensibility of Demurrage Concept and Features doctor vendor RFP services, Doctor Vendor RFP Services and Assurance Contracts health notary services, Blockchain Health Notary health research commons , Blockchain Health Research Commons health spending, Healthcoin healthcare decision making and advocacy, Liquid Democracy and Random-Sample Elections personal health record storage, EMRs on the Blockchain: Personal Health Record Storage virus bank and seed vault backup, Virus Bank, Seed Vault Backup Healthcoin, Healthcoin, Demurrage Currencies: Potentially Incitory and Redistributable I identity authentication, eWallet Services and Personal Cryptosecurity, Blockchain 2.0: Contracts, Smart Property, Smart Property, Wallet Development Projects, Digital Identity Verification-Digital Divide of Bitcoin, Limitations, Decentralized Governance Services, Liquid Democracy and Random-Sample Elections, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy, Privacy Challenges for Personal Records Indiegogo, Crowdfunding, Dapps industry scandals, Scandals and Public Perception infrastructure needs and issues, Technical Challenges inheritance gifts, Smart Contracts intellectual property, Monegraph: Online Graphics Protection (see also digital art) Internet administration, Distributed Censorship-Resistant Organizational Models Internet Archive, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Personal Thinking Blockchains Internet censorship prevention (see Decentralized DNS system) Intuit Quickbooks, Merchant Acceptance of Bitcoin IP protection, Hashing Plus Timestamping IPFS project, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation J Johnston, David, Blockchain Technology Could Be Used in the Administration of All Quanta Journalcoin, Blockchain Academic Publishing: Journalcoin Judobaby, Crowdfunding justice applications for censorship-resistant organizational models, Distributed Censorship-Resistant Organizational Models-Distributed Censorship-Resistant Organizational Models digital art, Digital Art: Blockchain Attestation Services (Notary, Intellectual Property Protection)-Personal Thinking Blockchains (see also digital art, blockchain attestation services) digital identity verification, Blockchain 2.0: Contracts, Smart Property, Wallet Development Projects, Digital Identity Verification-Digital Divide of Bitcoin, Limitations, Decentralized Governance Services, Liquid Democracy and Random-Sample Elections, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy, Privacy Challenges for Personal Records freedom of speech/anti-censorship, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel governance, Blockchain Government-Societal Maturity Impact of Blockchain Governance (see also governance) Namecoin, Namecoin: Decentralized Domain Name System-Decentralized DNS Functionality Beyond Free Speech: Digital Identity, Monegraph: Online Graphics Protection (see also decentralized DNS) K Kickstarter, Crowdfunding, Community Supercomputing Kipochi, Blockchain Neutrality, Global Public Health: Bitcoin for Contagious Disease Relief, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy Koinify, Crowdfunding, Dapps Kraken, Financial Services L latency, Blockchain 2.0 Protocol Projects, Technical Challenges, Technical Challenges, Scandals and Public Perception LaZooz, Dapps, Campuscoin, Extensibility of Demurrage Concept and Features Learncoin, Learncoin learning and literacy, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy-Learning Contract Exchanges learning contract exchanges, Learning Contract Exchanges Ledra Capital, Blockchain 2.0: Contracts, Ledra Capital Mega Master Blockchain List legal implications crowdfunding, Crowdfunding smart contracts, Smart Contracts lending, trustless, Smart Property Lighthouse, Crowdfunding limitations, Limitations-Overall: Decentralization Trends Likely to Persist business model challenges, Business Model Challenges government regulation, Government Regulation-Government Regulation personal records privacy challenges, Privacy Challenges for Personal Records scandals and public perception, Scandals and Public Perception-Scandals and Public Perception technical challenges, Technical Challenges-Technical Challenges Liquid Democracy system, Liquid Democracy and Random-Sample Elections-Liquid Democracy and Random-Sample Elections Litecoin, Technology Stack: Blockchain, Protocol, Currency, Technology Stack: Blockchain, Protocol, Currency, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel, Currency Multiplicity: Monetary and Nonmonetary Currencies, Technical Challenges literacy (see learning and literacy) LTBcoin, Wallet Development Projects, Currency, Token, Tokenizing M M2M/IoT infrastructure, M2M/IoT Bitcoin Payment Network to Enable the Machine Economy, Blockchain Development Platforms and APIs, Blockchain Academic Publishing: Journalcoin-The Blockchain Is Not for Every Situation, The Blockchain Is an Information Technology Maidsafe, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Technical Challenges Manna, Crowdfunding marriage, blockchain recorded, Decentralized Governance Services Mastercoin, Blockchain 2.0 Protocol Projects mechanics of cryptocurrencies, How a Cryptocurrency Works Medici, Financial Services mega master blockchain list, Ledra Capital Mega Master Blockchain List-Ledra Capital Mega Master Blockchain List Melotic, Crowdfunding, Wallet Development Projects merchant acceptance, Merchant Acceptance of Bitcoin merchant payment fees, Summary: Blockchain 1.0 in Practical Use messaging, Ethereum: Turing-Complete Virtual Machine, Dapps, Challenges and Other Decentralized DNS Services, Technical Challenges MetaDisk, DAOs and DACs mindfiles, Personal Thinking Blockchains MIT Bitcoin Project, Campuscoin Monegraph, Monegraph: Online Graphics Protection money (see currency) MOOCs (massive open online courses), Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy Moroz, Tatiana, Communitycoin: Hayek’s Private Currencies Vie for Attention multicurrency systems, Demurrage Currencies: Potentially Incitory and Redistributable N Nakamoto, Satoshi, Blockchain 2.0: Contracts, Blockchain 2.0: Contracts Namecoin, Namecoin: Decentralized Domain Name System-Decentralized DNS Functionality Beyond Free Speech: Digital Identity, Monegraph: Online Graphics Protection Nationcoin, Coin Drops as a Strategy for Public Adoption, Demurrage Currencies: Potentially Incitory and Redistributable notary chains, Batched Notary Chains as a Class of Blockchain Infrastructure notary services, Hashing Plus Timestamping, Blockchain Health Notary NSA surveillance, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel NXT, Technology Stack: Blockchain, Protocol, Currency, Blockchain 2.0 Protocol Projects O offline wallets, Technical Challenges OneName, Digital Identity Verification-Digital Identity Verification OneWallet, Wallet Development Projects online graphics protection, Monegraph: Online Graphics Protection-Monegraph: Online Graphics Protection Open Assets, Blockchain 2.0 Protocol Projects Open Transactions, Blockchain 2.0 Protocol Projects OpenBazaar, Dapps, Government Regulation Ostel, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel P passports, Decentralized Governance Services PayPal, The Double-Spend and Byzantine Generals’ Computing Problems, Financial Services, Distributed Censorship-Resistant Organizational Models peer-to-peer lending, Financial Services Peercoin, Technology Stack: Blockchain, Protocol, Currency personal cryptosecurity, eWallet Services and Personal Cryptosecurity personal data rights, Blockchain Genomics personal mindfile blockchains, Personal Thinking Blockchains personal thinking chains, Personal Thinking Blockchains-Personal Thinking Blockchains physical asset keys, Blockchain 2.0: Contracts, Smart Property plagiarism detection/avoidance, Blockchain Academic Publishing: Journalcoin Precedent, PrecedentCoin: Blockchain Dispute Resolution, Terminology and Concepts prediction markets, Bitcoin Prediction Markets, DASs and Self-Bootstrapped Organizations, Decentralized Governance Services, Futarchy: Two-Step Democracy with Voting + Prediction Markets-Futarchy: Two-Step Democracy with Voting + Prediction Markets Predictious, Bitcoin Prediction Markets predictive task automation, Blockchain Layer Could Facilitate Big Data’s Predictive Task Automation privacy challenges, Privacy Challenges for Personal Records private key, eWallet Services and Personal Cryptosecurity Proof of Existence, Proof of Existence-Proof of Existence proof of stake, Blockchain 2.0 Protocol Projects, PrecedentCoin: Blockchain Dispute Resolution, Technical Challenges proof of work, PrecedentCoin: Blockchain Dispute Resolution, Technical Challenges-Technical Challenges property ownership, Smart Property property registration, Decentralized Governance Services public documents registries, Decentralized Governance Services public health, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Global Public Health: Bitcoin for Contagious Disease Relief public perception, Scandals and Public Perception-Scandals and Public Perception public/private key cryptography, Public/Private-Key Cryptography 101-Public/Private-Key Cryptography 101 publishing, academic, Blockchain Academic Publishing: Journalcoin-Blockchain Academic Publishing: Journalcoin pull technology, eWallet Services and Personal Cryptosecurity push technology, eWallet Services and Personal Cryptosecurity R random-sample elections, Random-Sample Elections Realcoin, Relation to Fiat Currency redistribution of currency (see demurrage currency) regulation, Government Regulation-Government Regulation regulatory status, Regulatory Status reputation vouching, Ethereum: Turing-Complete Virtual Machine Researchcoin, Blockchain Academic Publishing: Journalcoin REST APIs, Technical Challenges Ripple, Technology Stack: Blockchain, Protocol, Currency, Relation to Fiat Currency, Blockchain 2.0 Protocol Projects Ripple Labs, Financial Services Roadcoin, Blockchain Government S Saldo.mx, Blockchain Neutrality scandals, Scandals and Public Perception science, Blockchain Science: Gridcoin, Foldingcoin-Charity Donations and the Blockchain—Sean’s Outpost community supercomputing, Community Supercomputing global public health, Global Public Health: Bitcoin for Contagious Disease Relief Sean's Outpost, Charity Donations and the Blockchain—Sean’s Outpost secret messaging, Ethereum: Turing-Complete Virtual Machine security issues, Technical Challenges self-bootstrapped organizations, DASs and Self-Bootstrapped Organizations self-directing assets, Automatic Markets and Tradenets self-enforced code, Smart Property self-sufficiency, Smart Contracts SETI@home, Blockchain Science: Gridcoin, Foldingcoin, Community Supercomputing size and bandwidth, Technical Challenges smart contracts, Smart Contracts-Smart Contracts, Smart Contract Advocates on Behalf of Digital Intelligence automatic markets and tradenets, Automatic Markets and Tradenets Counterparty, Counterparty Re-creates Ethereum’s Smart Contract Platform DAOs/DACs, DAOs and DACs-DAOs and DACs Dapps, Dapps-Dapps DASs, DASs and Self-Bootstrapped Organizations Ethereum, Ethereum: Turing-Complete Virtual Machine increasingly autonomous, Dapps, DAOs, DACs, and DASs: Increasingly Autonomous Smart Contracts-Automatic Markets and Tradenets smart literacy contracts, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy-Learning Contract Exchanges smart property, Smart Property-Smart Property, Monegraph: Online Graphics Protection smartwatch, Extensibility of Demurrage Concept and Features Snowden, Edward, Distributed Censorship-Resistant Organizational Models social contracts, Smart Contracts social network currencies, Currency Multiplicity: Monetary and Nonmonetary Currencies Stellar, Blockchain Development Platforms and APIs stock market, Financial Services Storj, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Dapps, Technical Challenges Stripe, Blockchain Development Platforms and APIs supercomputing, Community Supercomputing Svalbard Global Seed Vault, Virus Bank, Seed Vault Backup Swancoin, Smart Property swaps exchange, Financial Services Swarm, Crowdfunding, Dapps Swarm (Ethereum), Ethereum: Turing-Complete Virtual Machine Swarmops, Crowdfunding T Tatianacoin, Communitycoin: Hayek’s Private Currencies Vie for Attention technical challenges, Technical Challenges-Technical Challenges Tendermint, Technical Challenges Tera Exchange, Financial Services terminology, Terminology and Concepts-Terminology and Concepts 37Coins, Global Public Health: Bitcoin for Contagious Disease Relief throughput, Technical Challenges timestamping, Hashing Plus Timestamping-Limitations titling, Decentralized Governance Services tradenets, Automatic Markets and Tradenets transaction fees, Summary: Blockchain 1.0 in Practical Use Tribecoin, Coin Drops as a Strategy for Public Adoption trustless lending, Smart Property Truthcoin, Futarchy: Two-Step Democracy with Voting + Prediction Markets Turing completeness, Ethereum: Turing-Complete Virtual Machine Twister, Dapps Twitter, Monegraph: Online Graphics Protection U Uber, Government Regulation unbanked/underbanked markets, Blockchain Neutrality usability issues, Technical Challenges V value chain composition, How a Cryptocurrency Works versioning issues, Technical Challenges Virtual Notary, Virtual Notary, Bitnotar, and Chronobit voting and prediction, Futarchy: Two-Step Democracy with Voting + Prediction Markets-Futarchy: Two-Step Democracy with Voting + Prediction Markets W wallet APIs, Blockchain Development Platforms and APIs wallet companies, Wallet Development Projects wallet software, How a Cryptocurrency Works wasted resources, Technical Challenges Wayback Machine, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation Wedbush Securities, Financial Services Whatevercoin, Terminology and Concepts WikiLeaks, Distributed Censorship-Resistant Organizational Models Wikinomics, Community Supercomputing World Citizen project, Decentralized Governance Services X Xapo, eWallet Services and Personal Cryptosecurity Z Zennet Supercomputer, Community Supercomputing Zooko's Triangle, Decentralized DNS Functionality Beyond Free Speech: Digital Identity About the Author Melanie Swan is the Founder of the Institute for Blockchain Studies and a Contemporary Philosophy MA candidate at Kingston University London and Université Paris VIII.

As articulated by cryptographer David Chaum,120 the idea is that (like the ideal of a poll) randomly sampled voters would be more representative (or could at least include underrepresented voters) and give voters more time to deliberate on issues privately at home, seeking their own decision-making resources rather than being swayed by advertising.121 Blockchain technology could be a means of implementing random-sample elections in a large-scale, trustable, pseudonymous way. Futarchy: Two-Step Democracy with Voting + Prediction Markets Another concept is futarchy, a two-level process by which individuals first vote on generally specified outcomes (like “increase GDP”), and second, vote on specific proposals for achieving these outcomes. The first step would be carried out by regular voting processes, the second step via prediction markets. Prediction market voting could be by different cryptocurrencies (the EconomicVotingCoin or EnvironmentalPolicyVotingCoin) or other economically significant tokens. Prediction market voting is investing/speculating, taking a bet on one or the other side of a proposal, betting on the proposal that you want to win. For example, you might buy the “invest in new biotechnologies contract” as what you think is the best means of achieving the “increase in GDP” objective, as opposed to other contracts like the “invest in automated agriculture contract”).


pages: 327 words: 103,336

Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts

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active measures, affirmative action, Albert Einstein, Amazon Mechanical Turk, Black Swan, butterfly effect, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, collapse of Lehman Brothers, complexity theory, correlation does not imply causation, crowdsourcing, death of newspapers, discovery of DNA, East Village, easy for humans, difficult for computers, edge city, en.wikipedia.org, Erik Brynjolfsson, framing effect, Geoffrey West, Santa Fe Institute, George Santayana, happiness index / gross national happiness, high batting average, hindsight bias, illegal immigration, industrial cluster, interest rate swap, invention of the printing press, invention of the telescope, invisible hand, Isaac Newton, Jane Jacobs, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, lake wobegon effect, Long Term Capital Management, loss aversion, medical malpractice, meta analysis, meta-analysis, Milgram experiment, natural language processing, Netflix Prize, Network effects, oil shock, packet switching, pattern recognition, performance metric, phenotype, Pierre-Simon Laplace, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, ultimatum game, urban planning, Vincenzo Peruggia: Mona Lisa, Watson beat the top human players on Jeopardy!, X Prize

MARKETS, CROWDS, AND MODELS One increasingly popular method is to use what is called a prediction market—meaning a market in which buyers and sellers can trade specially designed securities whose prices correspond to the predicted probability that a specific outcome will take place. For example, the day before the 2008 US presidential election, an investor could have paid $0.92 for a contract in the Iowa Electronic Markets—one of the longest-running and best-known prediction markets—that would have yielded him or her $1 if Barack Obama had won. Participants in prediction markets therefore behave much like participants in financial markets, buying and selling contracts for whatever price is on offer. But in the case of prediction markets, the prices are explicitly interpreted as making a prediction about the outcome in question—for example, the probability of an Obama victory on the eve of Election Day was predicted by the Iowa Electronic Markets to be 92 percent.

Possibly then regulators would have had a more accurate assessment of those risks and been more inclined to crack down on the oil industry before a disaster took place. Possibly the disaster could have been averted. These are the sorts of claims that the proponents of prediction markets tend to make, and it’s easy to see why they’ve generated so much interest. In recent years, in fact, prediction markets have been set up to make predictions as varied as the likely success of new products, the box office revenues of upcoming movies, and the outcomes of sporting events. In practice, however, prediction markets are more complicated than the theory suggests. In the 2008 presidential election, for example, one of the most popular prediction markets, Intrade, experienced a series of strange fluctuations when an unknown trader started placing very large bets on John McCain, generating large spikes in the market’s prediction for a McCain victory.

In spite of all the statistics and analysis, in other words, and in spite of the absence of meaningful salary caps in baseball and the resulting concentration of superstar players on teams like the New York Yankees and Boston Red Sox, the outcomes of baseball games are even closer to random events than football games. Since then, we have either found or learned about the same kind of result for other kinds of events that prediction markets have been used to predict, from the opening weekend box office revenues for feature films to the outcomes of presidential elections. Unlike sports, these events occur without any of the rules or conditions that are designed to make sports competitive. There is also a lot of relevant information that prediction markets could conceivably exploit to boost their performance well beyond that of a simple model or a poll of relatively uninformed individuals. Yet when we compared the Hollywood Stock Exchange (HSX)—one of the most popular prediction markets, which has a reputation for accurate prediction—with a simple statistical model, the HSX did only slightly better.7 And in a separate study of the outcomes of five US presidential elections from 1988 to 2004, political scientists Robert Erikson and Christopher Wlezien found that a simple statistical correction of ordinary opinion polls outperformed even the vaunted Iowa Electronic Markets.8 TRUST NO ONE, ESPECIALLY YOURSELF So what’s going on here?


pages: 589 words: 147,053

The Age of Em: Work, Love and Life When Robots Rule the Earth by Robin Hanson

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8-hour work day, artificial general intelligence, augmented reality, Berlin Wall, bitcoin, blockchain, brain emulation, business process, Clayton Christensen, cloud computing, correlation does not imply causation, creative destruction, demographic transition, Erik Brynjolfsson, ethereum blockchain, experimental subject, fault tolerance, financial intermediation, Flynn Effect, hindsight bias, information asymmetry, job automation, job satisfaction, John Markoff, Just-in-time delivery, lone genius, Machinery of Freedom by David Friedman, market design, meta analysis, meta-analysis, Nash equilibrium, new economy, prediction markets, rent control, rent-seeking, reversible computing, risk tolerance, Silicon Valley, smart contracts, statistical model, stem cell, Thomas Malthus, trade route, Turing test, Vernor Vinge

Combinatorial versions can even allow a small number of users to manage billions of consistent interconnected estimates, so that updates on some topics automatically improve the accuracy of estimates of quite different topics (Sun et al. 2012). Head-to-head comparisons between prediction markets and other forecasting mechanisms, given similar resources on the same questions, find prediction markets to be consistently either about as accurate, or substantially more accurate than, other mechanisms. Compared with other mechanisms, prediction markets are more robust to situations where no one knows anything useful, where most invited participants are ignorant or fools, and where some participants are willing to lie or lose money to distort resulting estimates. Prediction markets can tell a firm how likely a project is to make its deadline, how likely a supplier is to deliver as promised, or how many units of a particular product will be sold in a particular region. Prediction markets can also help combinatorial auctions by choosing between proposed auction rules and mechanisms.

As better auction mechanisms could plausibly deliver great value, and as the relevant research community has well-established ways to develop better mechanisms, it seems safe to guess that such mechanisms will be available if desired. Prediction Markets I have also been personally involved in developing the new institution of “prediction markets.” These are variations on speculative and betting markets that can encourage and facilitate the aggregation of information on important outcomes. By subsidizing trading on particular questions of interest, one can induce people able to learn about those questions to self-select into improving visible consensus estimates (Hanson 2003). Prediction markets give clear precise continually updated estimates that are consistent across many topics. Combinatorial versions can even allow a small number of users to manage billions of consistent interconnected estimates, so that updates on some topics automatically improve the accuracy of estimates of quite different topics (Sun et al. 2012).

In fact, the most valuable innovations in an em world might be better laws and institutions of intellectual property, to more strongly encourage innovation. One possible way to promote innovation is to use prediction markets to separate two forecasting tasks, one on the feasibility of an innovation, and the other on the demand for that innovation. Today, investing in a new firm is usually a bet on two factors: its product idea, and its team. Investors who are good at estimating if a product would sell well are often not good at estimating which team has the best chance of delivering the product. When there are prediction markets on which products would be successful, if produced, those who can predict product ideas well can just focus on betting in those markets. Those who can predict teams well can instead hedge their product risk in these prediction markets, and then invest in particular ventures with the teams they favor. Widespread recording of em activities allows for interesting changes.


pages: 515 words: 126,820

Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World by Don Tapscott, Alex Tapscott

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Airbnb, altcoin, asset-backed security, autonomous vehicles, barriers to entry, bitcoin, blockchain, Bretton Woods, business process, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, cloud computing, cognitive dissonance, commoditize, corporate governance, corporate social responsibility, creative destruction, Credit Default Swap, crowdsourcing, cryptocurrency, disintermediation, distributed ledger, Donald Trump, double entry bookkeeping, Edward Snowden, Elon Musk, Erik Brynjolfsson, ethereum blockchain, failed state, fiat currency, financial innovation, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, future of work, Galaxy Zoo, George Gilder, glass ceiling, Google bus, Hernando de Soto, income inequality, informal economy, information asymmetry, intangible asset, interest rate swap, Internet of things, Jeff Bezos, jimmy wales, Kickstarter, knowledge worker, Kodak vs Instagram, Lean Startup, litecoin, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, means of production, microcredit, mobile money, money market fund, Network effects, new economy, Oculus Rift, off grid, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, performance metric, Peter Thiel, planetary scale, Ponzi scheme, prediction markets, price mechanism, Productivity paradox, QR code, quantitative easing, ransomware, Ray Kurzweil, renewable energy credits, rent-seeking, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Satoshi Nakamoto, Second Machine Age, seigniorage, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, smart grid, social graph, social software, Stephen Hawking, Steve Jobs, Steve Wozniak, Stewart Brand, supply-chain management, TaskRabbit, The Fortune at the Bottom of the Pyramid, The Nature of the Firm, The Wisdom of Crowds, transaction costs, Turing complete, Turing test, Uber and Lyft, unbanked and underbanked, underbanked, unorthodox policies, wealth creators, X Prize, Y2K, Zipcar

Prediction markets can serve as a hedge against global uncertainty and “black swan” events: “Will Greece’s economy shrink by more than 15 percent this year?”84 Today, we rely on a few talking heads to sound the alarms; a prediction market would act more impartially as an early warning system for investors globally. Prediction markets could complement and ultimately transform many aspects of the financial system. Consider prediction markets on the outcomes of corporate actions—earnings reports, mergers, acquisitions, and changes in management. Prediction markets would inform the insurance of value and the hedging of risk, potentially even displacing esoteric financial instruments like options, interest rate swaps and credit default swaps. Of course, not everything needs a prediction market. Enough people need to care to make it liquid enough to attract attention. Still, the potential is vast, the opportunity significant, and access available to all.

Consider the farmer in Nicaragua or Kenya who has no robust tools to hedge against currency risk, political risk, or changes to the weather and climate. Accessing prediction markets would allow that person to mitigate the risk of drought or disaster. For example, he could buy a prediction contract that pays out if a crop yield is below a certain level, or if the country gets less than a predetermined amount of rain. Prediction markets are useful for investors who want to place bets on the outcome of specific events such as “Will IBM beat its earnings by at least ten cents this quarter?” Today the reported “estimate” for corporate earnings is nothing more than the mean or median of a few so-called expert analysts. By harnessing the wisdom of the crowds, we can form more realistic predictions of the future, leading to more efficient markets. Prediction markets can serve as a hedge against global uncertainty and “black swan” events: “Will Greece’s economy shrink by more than 15 percent this year?”

Scenario Planning: Building scenarios with simulation and modeling software to project future policy needs and to understand the long-term consequences of decisions. Politicians, bureaucrats, and citizens could assess the potential impacts on a range of factors, ranging from health, to the environment, to the economy. Prediction Markets: As we explained in the case of Augur, there are countless opportunities to use prediction markets for trading the outcome of events. Governments can use them to gain insight into many substantive questions: When will the bridge actually be built? What will be the unemployment level in twelve months? Will there be a National Party prime minister after the next election—an actual question from an iPredict market in New Zealand. Blockchain technologies could supercharge all of these tools. To begin, contributions from citizens could be private, opening up the possibilities of engagement. This is bad for repressive governments but good for democracy as it makes it more difficult for government authorities to censor, suppress, and track down opposition.


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Superforecasting: The Art and Science of Prediction by Philip Tetlock, Dan Gardner

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Affordable Care Act / Obamacare, Any sufficiently advanced technology is indistinguishable from magic, availability heuristic, Black Swan, butterfly effect, cloud computing, cuban missile crisis, Daniel Kahneman / Amos Tversky, desegregation, drone strike, Edward Lorenz: Chaos theory, forward guidance, Freestyle chess, fundamental attribution error, germ theory of disease, hindsight bias, index fund, Jane Jacobs, Jeff Bezos, Kenneth Arrow, Mikhail Gorbachev, Mohammed Bouazizi, Nash equilibrium, Nate Silver, obamacare, pattern recognition, performance metric, Pierre-Simon Laplace, place-making, placebo effect, prediction markets, quantitative easing, random walk, randomized controlled trial, Richard Feynman, Richard Feynman, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, Silicon Valley, Skype, statistical model, stem cell, Steve Ballmer, Steve Jobs, Steven Pinker, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!

By aggregating all these judgments, the contract price should, in theory, closely track the true probability of Hillary Clinton winning. Prediction markets like the famous Iowa Electronic Markets have an impressive track record. And they have a theory, backed by a battalion of Nobel laureates, going for them. So who would win in a battle between superteams and prediction markets? Most economists would say it’s no contest. Prediction markets would mop the floor with the superteams. We put that proposition to the test by randomly assigning regular forecasters to one of three experimental conditions. Some worked alone. Others worked in teams. And some were traders in prediction markets run by companies such as Inkling and Lumenogic. Of course, after year 1—when the value of teams was resoundingly demonstrated—nobody expected forecasters working alone to compete at the level of teams or prediction markets, so we combined all their forecasts and calculated the unweighted average to get the “wisdom of the crowd.”

And of course we had one more competitor: superteams. The results were clear-cut each year. Teams of ordinary forecasters beat the wisdom of the crowd by about 10%. Prediction markets beat ordinary teams by about 20%. And superteams beat prediction markets by 15% to 30%. I can already hear the protests from my colleagues in finance that the only reason the superteams beat the prediction markets was that our markets lacked liquidity: real money wasn’t at stake and we didn’t have a critical mass of traders. They may be right. It is a testable idea, and one worth testing. It’s also important to recognize that while superteams beat prediction markets, prediction markets did a pretty good job of forecasting complex global events. How did superteams do so well? By avoiding the extremes of groupthink and Internet flame wars.

And they always judge the same way: they look at which side of “maybe”—50%—the probability was on. If the forecast said there was a 70% chance of rain and it rains, people think the forecast was right; if it doesn’t rain, they think it was wrong. This simple mistake is extremely common. Even sophisticated thinkers fall for it. In 2012, when the Supreme Court was about to release its long-awaited decision on the constitutionality of Obamacare, prediction markets—markets that let people bet on possible outcomes—pegged the probability of the law being struck down at 75%. When the court upheld the law, the sagacious New York Times reporter David Leonhardt declared that “the market—the wisdom of the crowds—was wrong.”15 The prevalence of this elementary error has a terrible consequence. Consider that if an intelligence agency says there is a 65% chance that an event will happen, it risks being pilloried if it does not—and because the forecast itself says there is a 35% chance it will not happen, that’s a big risk.


pages: 472 words: 117,093

Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson

3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, backtesting, barriers to entry, bitcoin, blockchain, book scanning, British Empire, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Dean Kamen, discovery of DNA, disintermediation, distributed ledger, double helix, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, ethereum blockchain, everywhere but in the productivity statistics, family office, fiat currency, financial innovation, George Akerlof, global supply chain, Hernando de Soto, hive mind, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, law of one price, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, moral hazard, multi-sided market, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, Plutocrats, plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Ronald Coase, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, TaskRabbit, Ted Nelson, The Market for Lemons, The Nature of the Firm, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, two-sided market, Uber and Lyft, Uber for X, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

Results from prediction markets confirm Hayek’s insights about the knowledge-aggregating power of prices within markets. In markets like the ones just described, events with final share prices of about $0.70 tend to actually happen about 70% of the time, making these prices pretty accurate probability estimates. There are active debates about whether prediction markets provide more accurate forecasts than other methods (such as properly weighted averages of polls, or reliance on the superforecasters identified by Philip Tetlock and discussed in Chapter 2), but few people anymore doubt that prediction markets can be very effective under the right conditions. As economist Robin Hanson, the scholar who has done the most to advance both the theory and practice of prediction markets, puts it, “Prediction markets reflect a fundamental principle underlying the value of market-based pricing: Because information is often widely dispersed among economic actors, it is highly desirable to find a mechanism to collect and aggregate that information.

Market-Based Solutions Groups often behave in ways that are emergent and thus generate knowledge. As groups went online and became the crowd, innovators found different ways to detect and harvest this knowledge. Prediction markets were one of the earliest of these, and the ones that built most directly from Hayek’s insights. These are markets not for goods and services, but for future events, such as a particular person being elected US president in 2020, an upcoming movie making between $50 million and $100 million in the box office in its first week, or the official US inflation rate averaging more than 3% over the next quarter. Here’s how prediction markets work. First, the market maker creates a set of securities that participants can buy and sell, just like they sell a company’s shares on the New York Stock Exchange or Nasdaq.

Hayek, “The Use of Knowledge in Society,” American Economic Review 35, no. 4 (1945): 519–30. 236 “secure the best use of resources”: Ibid. 236 Orwellian: Geoffrey Nunberg, “Simpler Terms; If It’s ‘Orwellian,’ It’s Probably Not,” New York Times, June 22, 2003, http://www.nytimes.com/2003/06/22/weekinreview/simpler-terms-if-it-s-orwellian-it-s-probably-not.html. 236 Kafkaesque: Joe Fassler, “What It Really Means to Be ‘Kafkaesque,’ ” Atlantic, January 15, 2014, https://www.theatlantic.com/entertainment/archive/2014/01/what-it-really-means-to-be-kafkaesque/283096. 237 “The marvel [of prices]”: Hayek, “Use of Knowledge in Society.” 239 “Prediction markets reflect a fundamental principle”: Kenneth J. Arrow et al., “The Promise of Prediction Markets,” Science 320 (May 16, 2008): 877–78, http://mason.gmu.edu/~rhanson/PromisePredMkt.pdf. 240 “Hello everybody out there using minix”: Derek Hildreth, “The First Linux Announcement from Linus Torvalds,” Linux Daily, April 15, 2010, http://www.thelinuxdaily.com/2010/04/the-first-linux-announcement-from-linus-torvalds. 241 over 1.5 billion Android phones and tablets: Linus Torvalds, “The Mind behind Linux,” TED Talk, February 2016, 21:30, https://www.ted.com/talks/linus_torvalds_the_mind_behind_linux?


pages: 230 words: 61,702

The Internet of Us: Knowing More and Understanding Less in the Age of Big Data by Michael P. Lynch

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Affordable Care Act / Obamacare, Amazon Mechanical Turk, big data - Walmart - Pop Tarts, bitcoin, Cass Sunstein, Claude Shannon: information theory, crowdsourcing, Edward Snowden, Firefox, Google Glasses, hive mind, income inequality, Internet of things, John von Neumann, meta analysis, meta-analysis, Nate Silver, new economy, patient HM, prediction markets, RFID, sharing economy, Steve Jobs, Steven Levy, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, WikiLeaks

In a certain obvious sense, markets like this can be seen as encoding the information of the network of investors that make it up—not only about what may happen in the future but about the state of an election at any given time. But the way in which this information is aggregated is not, as in the cases above, statistical. Prediction markets don’t average the views of their participants, rather they work in the same way other markets work: the more confident buyers are that a given candidate will win, the higher his or her “stock” goes—the more value is attached to it, no matter how many people may own that stock. But prediction markets also have their limits. A well-known example, noted by David Leonhardt of the New York Times, was the 2012 Supreme Court decision on the Affordable Care Act.9 Right up to the last minute, Intrade was indicating a 75 percent chance that the Act’s mandate would be declared unconstitutional.

And in those situations where people are worse than chance at judging a situation, then the more people you get to answer the question, the higher the probability that you’ll get the wrong answer. In that case, the crowd is not wise but unwise. This brings us to a key point: whether or not a network “knows” something (even in the nonliteral sense) depends on the cognitive capacities (and incapacities) of the nodes on that network—the individual people who make it up. A good example is prediction markets. Markets like this trade in futures, but participants aren’t betting on whether a given company’s monetary value will rise but whether, for example, a politician will win an election or a particular movie will win an Oscar. These markets had some notable early successes; Intrade, for example, was famously better at predicting the 2006 midterm elections than cable news. (Intrade was one of the most widely cited before it closed in 2013.)

After all, the interviewer example presumes that the tacit commitment in question is itself a product of the individuals’ beliefs. If so, then perhaps the best we can say is that what we can loosely call the group’s implicit commitment “supervenes” or is a product of the individuals’ commitments. 8. Surowiecki, The Wisdom of Crowds, xii. 9. David Leonhardt, “When the Crowd Isn’t Wise,” New York Times, July 7, 2012. 10. Nate Silver, “The Virtues and Vices of Election Prediction Markets,” New York Times, October 24, 2012. 11. I was helped to see these points in discussions with Sandy Goldberg and Nate Sheff. The example in the text is similar to that in Goldberg, “The Division of Epistemic Labor,” 117. 12. Weinberger, Too Big to Know, 21. 13. Descartes, Meditations, 103. 14. Weinberger, Too Big to Know, 23. 15. Sosa, Reflective Knowledge, chs. 7 and 8.. 16. Pritchard, Epistemic Luck, 225.


pages: 261 words: 10,785

The Lights in the Tunnel by Martin Ford

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Albert Einstein, Bill Joy: nanobots, Black-Scholes formula, call centre, cloud computing, collateralized debt obligation, commoditize, creative destruction, credit crunch, double helix, en.wikipedia.org, factory automation, full employment, income inequality, index card, industrial robot, inventory management, invisible hand, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, knowledge worker, low skilled workers, mass immigration, moral hazard, pattern recognition, prediction markets, Productivity paradox, Ray Kurzweil, Search for Extraterrestrial Intelligence, Silicon Valley, Stephen Hawking, strong AI, superintelligent machines, technological singularity, Thomas L Friedman, Turing test, Vernor Vinge, War on Poverty

The Predictive Nature of Markets One of the more interesting developments to arise out of the Internet has been the appearance of online prediction markets. A prediction market is really just another name for a betting market, and it operates in a similar fashion to the futures markets that allow traders to place bets on the future direction of things like oil prices and stock market indexes. Prediction markets, such as the Iowa Electronic Markets (IEM) and Intrade, allow participants to bet real Copyrighted Material – Paperback/Kindle available @ Amazon THE LIGHTS IN THE TUNNEL / 108 money on things like elections, economic developments (such as recessions), or specific events in the business or entertainment worlds. While prediction markets are specifically set up to predict future events, we know that we can expand this idea and say that all free markets are, in essence, prediction markets. If you buy a particular company’s stock, then you are placing a bet that, in the future, that stock will trade at a higher value.


Adaptive Markets: Financial Evolution at the Speed of Thought by Andrew W. Lo

Albert Einstein, Alfred Russel Wallace, algorithmic trading, Andrei Shleifer, Arthur Eddington, Asian financial crisis, asset allocation, asset-backed security, backtesting, bank run, barriers to entry, Berlin Wall, Bernie Madoff, bitcoin, Bonfire of the Vanities, bonus culture, break the buck, Brownian motion, business process, butterfly effect, capital asset pricing model, Captain Sullenberger Hudson, Carmen Reinhart, Chance favours the prepared mind, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, Diane Coyle, diversification, diversified portfolio, double helix, easy for humans, difficult for computers, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, interest rate derivative, invention of the telegraph, Isaac Newton, James Watt: steam engine, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, merger arbitrage, meta analysis, meta-analysis, Milgram experiment, money market fund, moral hazard, Myron Scholes, Nick Leeson, old-boy network, out of africa, p-value, paper trading, passive investing, Paul Lévy, Paul Samuelson, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, quantitative hedge fund, quantitative trading / quantitative finance, RAND corporation, random walk, randomized controlled trial, Renaissance Technologies, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Robert Shiller, Robert Shiller, short selling, sovereign wealth fund, statistical arbitrage, Steven Pinker, stochastic process, survivorship bias, The Great Moderation, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Malthus, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, Triangle Shirtwaist Factory, ultimatum game, Upton Sinclair, US Airways Flight 1549, Walter Mischel, Watson beat the top human players on Jeopardy!, WikiLeaks, Yogi Berra, zero-sum game

And not just about the Red Sox; imagine creating prediction markets for collecting information about terrorist events, flu epidemics, nuclear meltdowns, and presidential elections. This may sound a little like science fiction, but prediction markets were widely used in the United States in the nineteenth and early Are We All Homo economicus Now? • 39 twentieth centuries to forecast elections before the advent of modern polling techniques.44 Paul W. Rhode and Koleman Strumpf have documented these early prototypes—they were betting markets with standardized contracts. These markets were so popular that major newspapers carried their daily price quotations as the election season heated up. In the 1920s and 1930s, specialist firms of “betting commissioners” operated out of offices on Wall Street. Prediction markets were widely viewed as producing the most accurate information about the state of the presidential races and were usually successful in forecasting the winner well in advance of Election Day.

The reason is simple. The Wall Street odds represent a large body of extremely impartial opinion that talks with money and approaches Coolidge and Davis as dispassionately as it pronounces judgment on Anaconda and Bethlehem Steel.”45 With the advent of the Internet, prediction markets have experienced something of a renaissance. The Iowa Electronic Markets—originally the Iowa Presidential Stock Market—is perhaps the best-known research prediction market, dating back to the 1988 presidential election,46 but commercial prediction markets have also become popular. Many of these markets have done as well or better than conventional forecasting methods; for instance, in midsummer of 2010 the Iowa Electronic Markets correctly predicted that the Republican Party would take control of the House of Representatives, but that the Democratic Party would keep control of the Senate, a result few people expected.

On the other hand, in some particularly rancorous political races, such as the 2012 presidential election, people have tried to manipulate prediction markets in attempts to gain “momentum” for their preferred candidate, in much the same way an unscrupulous trader might try to run up the price of a penny stock. While this had no practical way of affecting the outcome of the election (at least, not given the small size of 40 • Chapter 1 these markets), it did briefly damage the usefulness of the prediction market before it corrected itself. In the end, however, people with the more accurate prediction gained more money at the manipulators’ expense, just as the Efficient Markets Hypothesis would predict. But prediction markets are only one possible use of the Efficient Markets Hypothesis. A casual conversation I had with a former MIT colleague, a marketing professor named Ely Dahan, led to one unexpected use.


pages: 829 words: 186,976

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't by Nate Silver

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airport security, availability heuristic, Bayesian statistics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, big-box store, Black Swan, Broken windows theory, Carmen Reinhart, Claude Shannon: information theory, Climategate, Climatic Research Unit, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, computer age, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, Daniel Kahneman / Amos Tversky, diversification, Donald Trump, Edmond Halley, Edward Lorenz: Chaos theory, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, everywhere but in the productivity statistics, fear of failure, Fellow of the Royal Society, Freestyle chess, fudge factor, George Akerlof, haute cuisine, Henri Poincaré, high batting average, housing crisis, income per capita, index fund, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, invention of the printing press, invisible hand, Isaac Newton, James Watt: steam engine, John Nash: game theory, John von Neumann, Kenneth Rogoff, knowledge economy, locking in a profit, Loma Prieta earthquake, market bubble, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, Monroe Doctrine, mortgage debt, Nate Silver, negative equity, new economy, Norbert Wiener, PageRank, pattern recognition, pets.com, Pierre-Simon Laplace, prediction markets, Productivity paradox, random walk, Richard Thaler, Robert Shiller, Robert Shiller, Rodney Brooks, Ronald Reagan, Saturday Night Live, savings glut, security theater, short selling, Skype, statistical model, Steven Pinker, The Great Moderation, The Market for Lemons, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, transfer pricing, University of East Anglia, Watson beat the top human players on Jeopardy!, wikimedia commons

The more fundamental problem is that we have a demand for experts in our society but we don’t actually have that much of a demand for accurate forecasts.” Hanson, in order to address this deficiency, is an advocate of prediction markets—systems where you can place bets on a particular economic or policy outcome, like whether Israel will go to war with Iran, or how much global temperatures will rise because of climate change. His argument for these is pretty simple: they ensure that we have a financial stake in being accurate when we make forecasts, rather than just trying to look good to our peers. We will revisit the idea of prediction markets in chapter 11; they are not a panacea, particularly if we make the mistake of assuming that they can never go wrong. But as Hansen says, they can yield some improvement by at least getting everyone’s incentives in order.

To the extent that markets are reflections of our collective judgment, they are fallible too. In fact, a market that makes perfect predictions is a logical impossibility. Justin Wolfers, Prediction Markets Cop If there really were a Bayesland, then Justin Wolfers, a fast-talking, ponytailed polymath who is among America’s best young economists, would be its chief of police, writing a ticket anytime he observed someone refusing to bet on their forecasts. Wolfers challenged me to a dinner bet after I wrote on my blog that I thought Rick Santorum would win the Iowa caucus, bucking the prediction market Intrade (as well as my own predictive model), which still showed Mitt Romney ahead. In that case, I was willing to commit to the bet, which turned out well for me after Santorum won by literally just a few dozen votes after a weeks-long recount.* But there have been other times when I have been less willing to accept one of Wolfers’ challenges.

Robin Hanson, an economist at George Mason University, is an advocate of the supply-side alternative. I met him for lunch at one of his favorite Moroccan places in northern Virginia. He’s in his early fifties but looks much younger (despite being quite bald), and is a bit of an eccentric. He plans to have his head cryogenically frozen when he dies.71 He is also an advocate of a system he calls “futarchy” in which decisions on policy issues are made by prediction markets72 rather than politicians. He is clearly not a man afraid to challenge the conventional wisdom. Instead, Hanson writes a blog called Overcoming Bias, in which he presses his readers to consider which cultural taboos, ideological beliefs, or misaligned incentives might constrain them from making optimal decisions. “I think the most interesting question is how little effort we actually put into forecasting, even on the things we say are important to us,” Hanson told me as the food arrived.


pages: 288 words: 16,556

Finance and the Good Society by Robert J. Shiller

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Alvin Roth, bank run, banking crisis, barriers to entry, Bernie Madoff, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, cognitive dissonance, collateralized debt obligation, collective bargaining, computer age, corporate governance, Daniel Kahneman / Amos Tversky, Deng Xiaoping, diversification, diversified portfolio, Donald Trump, Edward Glaeser, eurozone crisis, experimental economics, financial innovation, financial thriller, fixed income, full employment, fundamental attribution error, George Akerlof, income inequality, information asymmetry, invisible hand, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, land reform, loss aversion, Louis Bachelier, Mahatma Gandhi, Mark Zuckerberg, market bubble, market design, means of production, microcredit, moral hazard, mortgage debt, Myron Scholes, Occupy movement, passive investing, Ponzi scheme, prediction markets, profit maximization, quantitative easing, random walk, regulatory arbitrage, Richard Thaler, Right to Buy, road to serfdom, Robert Shiller, Robert Shiller, Ronald Reagan, selection bias, self-driving car, shareholder value, Sharpe ratio, short selling, Simon Kuznets, Skype, Steven Pinker, telemarketer, The Market for Lemons, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, Vanguard fund, young professional, zero-sum game, Zipcar

With the advent of high-scope trading, many more things are traded. An example would be the “prediction markets” that set prices based on the probabilities that a specified event will transpire. The earliest prediction markets, dating back to 1988 at the Iowa Electronic Markets, traded contracts that paid out according to the outcome of a speci ed event, such as a candidate receiving a certain share of the popular vote. The price of such a contract turns out to be a useful estimate of the probability of that outcome.11 Since prediction markets focus on a well-de ned event in the near future and give quick feedback to participants, they are probably less likely to be in uenced by speculative excesses than other markets, such as stock markets. Today there are many such prediction markets, including Intrade.com, Lumenogic at newsfutures.com, and the Foresight Exchange at ideasphere.com.

Analogous markets have since been trading at the CME Group in Chicago.13 The European Investment Bank in 2004 attempted to start a market for longevity risk— the risk that, because of developments in medical research and changing environmental conditions, people will on average live longer than expected (a problem for de nedbene t pension funds, which have to make payments for as long as retirees live) or less long than expected (a problem for life insurance companies, which will have to pay benefits early).14 Many of these markets have not yet achieved liquidity, and there is little volume of trade. Often, as in prediction markets, the volume is so low that the market seems more a game among enthusiasts than a signi cant economic institution. This problem poses a challenge for market makers, and it is instructive to understand the role they play in launching such markets. For example, there was, until recently, no derivatives market for residential real estate prices, for single-family home prices. There are markets for commercial real estate prices, but not for the much bigger category of real estate represented by our homes.

., Shared Responsibility, Shared Risk, Chapter 7. New York: Oxford University Press. Weyl, Hermann. 1952. Symmetry. Princeton, NJ: Princeton University Press. Whitman, Walt. 1892. Leaves of Grass. Project Gutenberg Ebook, http://www.gutenberg.org/files/1322/1322-h/1322-h.htm. Wittgenstein, Ludwig. 1953. Philosophical Investigations. Oxford: Blackwell. Wolfers, Justin, and Eric Zitzewitz. 2004. “Prediction Markets.” Journal of Economic Perspectives 18(2):107–26. Woodward, Bob. 2001. Maestro: Greenspan’s Fed and the American Boom. New York: Simon and Schuster. Working, Holbrook. 1934. “A Random-Di erence Series for Use in the Analysis of Time Series.” Journal of the American Statistical Association 29(185):11–24. Yavlinsky, Grigory. 2011. Realpolitik: The Hidden Cause of the Great Recession (And How to Avert the Next One), trans.

Trend Commandments: Trading for Exceptional Returns by Michael W. Covel

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Albert Einstein, Bernie Madoff, Black Swan, commodity trading advisor, correlation coefficient, delayed gratification, diversified portfolio, en.wikipedia.org, Eugene Fama: efficient market hypothesis, family office, full employment, Lao Tzu, Long Term Capital Management, market bubble, market microstructure, Mikhail Gorbachev, moral hazard, Myron Scholes, Nick Leeson, oil shock, Ponzi scheme, prediction markets, quantitative trading / quantitative finance, random walk, Sharpe ratio, systematic trading, the scientific method, transaction costs, tulip mania, upwardly mobile, Y2K, zero-sum game

I thought a different approach to get this If you are not story out was required, for very few are yet criticized, you might aware of what’s in these pages. not be doing much. Sadly, many still see making money wrong. They make wildly inaccurate assumptions about what constitutes a winning trader: • Do they possess a unique talent? • A special inborn gene or divine gift? • The innate talent of a child prodigy? • Inside knowledge? • Ability to predict markets? • Degrees in finance or an MBA? • Huge starting capital? One answer: No. Why do we not know that? Instant gratification is our Achilles’ heel. Multitask this and that. Kardashians. Now. Faster. Easier. Patience is a fourletter word. How does the latest iPad help you to make money trading the markets? How does attending a Code Pink or Tea Party rally help? How does connected 24/7 help you? TweetDeck, favorite blog, fancy broker tools…all will do what?

The only reason you take a trade is because the market is doing something.4 Technical analysis, the other market theory, operates in stark contrast. It is based on the belief that at any given point in time, market prices reflect all known fundamentals for that particular market. Instead of trying to evaluate fundamental factors, technical analysis looks at market prices themselves. The illusion has been There are essentially two forms of technical analysis. The first is based on reading charts and using indicators to supposedly predict market direction. For example, here is a mixing of fundamentals and predictive technical analysis: created that there is an explanation for everything with the primary task to find that explanation.6 Grains all made subtle bull breakout technical moves last week as continued fear of a global grain panic builds premium into these markets. I do believe that the grain rally should be sold into as it will be short-lived in 2011.

Trend followers wait for a market to move first and then they follow it.1 Now, some argue that the term trend following is too imprecise. Others use terms like global statistical financial analysis or managed futures to describe the strategy. That debate will not be solved here. If you do not like the phrase trend following, substitute your term as you keep reading. Trend following trading is reactive. It does not predict market direction. Trend trading demands self-discipline to follow precise rules (no guessing or wild emotions). It involves a certain risk management that uses the current market price, equity level in your account, and current market volatility. We decided that systematic trading was best. Fundamental trading gave me ulcers.2 Trend traders use an initial risk rule to determine their trading size at entry.


pages: 329 words: 95,309

Digital Bank: Strategies for Launching or Becoming a Digital Bank by Chris Skinner

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algorithmic trading, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, bank run, Basel III, bitcoin, business intelligence, business process, business process outsourcing, call centre, cashless society, clean water, cloud computing, corporate social responsibility, credit crunch, crowdsourcing, cryptocurrency, demand response, disintermediation, don't be evil, en.wikipedia.org, fault tolerance, fiat currency, financial innovation, Google Glasses, high net worth, informal economy, Infrastructure as a Service, Internet of things, Jeff Bezos, Kevin Kelly, Kickstarter, M-Pesa, margin call, mass affluent, mobile money, Mohammed Bouazizi, new economy, Northern Rock, Occupy movement, Pingit, platform as a service, Ponzi scheme, prediction markets, pre–internet, QR code, quantitative easing, ransomware, reserve currency, RFID, Satoshi Nakamoto, Silicon Valley, smart cities, software as a service, Steve Jobs, strong AI, Stuxnet, trade route, unbanked and underbanked, underbanked, upwardly mobile, We are the 99%, web application, Y2K

In the same way, banks can use transaction data combined with search trends and other data to predict and then proactively offer service in real time. That service might be offering car loans as you drive by the showroom of the BMW dealership you happened to be Googling last night or mortgages as you drive towards the real estate office of the broker you found. Now that’s all well and good, but it goes further than this as prediction marketing can now be embedded into the internet of things. For example, a few years ago the Metro store in Germany built a prototype of the grocery outlet of a few years ahead using NFC and RFID technologies. The concept store included the idea of dynamic pricing as you walk through the aisles, based upon your loyalty, shopping habits and more. Your smartphone would emanate your preferences and change prices dynamically for you based upon sensing your presence through the mobile network and your smartphone.

Like algorithmic trading in capital markets where algorithmic news feeds allow trading in equities to move in real-time high frequency blackbox strategies that maximise returns, we’re talking of applying the same technologies to retail transaction services for customer loyalty and wallet share. That’s the battle about to begin as we move from managing data to using information as a competitive weapon and, at the core of predictive marketing is Big Data. If the battleground is augmented service, then Big Data is the weaponry to compete. What is Big Data? The term Big Data stems from the Second World War, when the phrase Big Science was used to describe the rapid cycle of changes that occurred in scientific disciplines during and after World War II. A good example of this rapid cycle of change during World War II was the invention of computing.

If you search for headache tablets side effects, they might recommend that I switch to paracetamol and direct me via Google maps to my nearest pharmacist. If you search for pricing TVs, they might offer you a special deal with a retailer’s discount code. If you don’t think that Google Analytics are the key to predictive, proactive marketing, just checkout the results of research of three academics who find that Google predicts stock market movements pretty accurately: “Debt” was the most reliable term for predicting market ups and downs, the researchers found. By going long when “debt” searches dropped and shorting the market when “debt” searches rose, the researchers were able to increase their hypothetical portfolio by 326 percent. (In comparison, a constant buy-and-hold strategy yielded just a 16 percent return.)[24] In the same way, banks can use transaction data combined with search trends and other data to predict and then proactively offer service in real time.

Future Files: A Brief History of the Next 50 Years by Richard Watson

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Albert Einstein, bank run, banking crisis, battle of ideas, Black Swan, call centre, carbon footprint, cashless society, citizen journalism, commoditize, computer age, computer vision, congestion charging, corporate governance, corporate social responsibility, deglobalization, digital Maoism, disintermediation, epigenetics, failed state, financial innovation, Firefox, food miles, future of work, global supply chain, global village, hive mind, industrial robot, invention of the telegraph, Jaron Lanier, Jeff Bezos, knowledge economy, linked data, low skilled workers, M-Pesa, mass immigration, Northern Rock, peak oil, pensions crisis, precision agriculture, prediction markets, Ralph Nader, Ray Kurzweil, rent control, RFID, Richard Florida, self-driving car, speech recognition, telepresence, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Turing test, Victor Gruen, white flight, women in the workforce, Zipcar

The technology these devices use is E Ink, which mimics actual ink by using a series of tiny pixels. Interestingly, the technology does not need any power to display the type unless the “page” is turned, so up to 20 books can be read before the reader needs recharging. I’d expect Apple to launch something similar, not least because the iTunes music-store model could easily be adapted to e-books rather than the current audiobooks. Prediction marketing Given how much the advertising industry goes on about creativity and strategy, it’s ironic that the big agencies have been so slow to embrace the brave new world of digital media. Perhaps this is because many prefer to kid themselves that they are in the movie business, or maybe many are still in denial about losing the strategic high ground to management consultancies. Advertising has already started to shift away from traditional media such as television and newspapers to online and this migration will substantially increase.

So ads for soft drinks will “magically” appear on your cellphone as you walk past a vending machine on a hot day. Localization will also involve placing “real” car ads inside virtual street-racing games when your virtual car is off the road, or triggering a short animation on a washing-powder pack when you pass by the packs in the supermarket and they recognize you as a lapsed user. Call this “now marketing” or prediction marketing if you like. However, it would be a mistake to assume that the internet will take over from old media entirely. The internet is primarily a place where people go to find information or entertainment, or other like-minded individuals. This means that advertising will be repackaged to look like information or entertainment and it will be used to facilitate conversations between the people who know about things (such as brands) and the people who don’t.

Gen Y is also hyper-connected, so virtual and collaborative networks will grow in importance as a way of getting things done. Workforces will also become more balanced. There will be a greater spread of ages, more ethnic diversity and more women in the workforce, the latter significantly contributing to a shift away from the white middle-aged alpha male culture that has Work and Business 277 been dominant for so long. Decisions will be made using prediction markets and innovation will be run using open or distributed innovation principles. Work/life balance Instead of working less and enjoying a leisure society, we are working more. We are also commuting for longer periods. Being busy is a modern mark of prestige. This will all change. The open-all-hours work culture will be challenged by parents seeking more time with their kids and there will be law suits and regulation concerning the social costs of long work hours.

Trade Your Way to Financial Freedom by van K. Tharp

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asset allocation, commodity trading advisor, compound rate of return, computer age, Elliott wave, high net worth, margin call, market fundamentalism, pattern recognition, prediction markets, random walk, risk tolerance, short selling, statistical model, transaction costs

When you read and thoroughly understand the subsequent chapters, you’ll probably realize that there are vast untapped areas to apply neural networks to-such as exits and position sizing. Human Behavior Has a Cycle THERE’S AN ORDER TO THE UNIVERSE The idea that there is an order to the universe is extremely popular. People want to understand how the markets work, so it is most appealing to them to be able to find some underlying structure. They believe, of course, that once you know the underlying structure, you can predict market movements. In many cases, such theories are even more exact because they attempt to predict market turning points. This naturally appeals to the psychological bias that most people have of wanting to be right and to have control over the markets. As a result, they want to catch market turning points. In addition, it’s a highly marketable idea to sell to the public. There are a number of different types of theories involving market order, including Gann, Elliott Wave, astrological theories, etc.

This endeavor can be extremely time consuming and labor intensive, even for a group of experts working collectivelv. I speak from experience. In 1991 the Predictive Technologies Group, the research and development division of my firm, introduced VantagePoint Intermarket Analysis software, after experimenting with intermarket analysis since the mid-1980s. VantagePoint applies neural networks to intermarket analysis in order to predict market trends, moving averages, and next-day prices for various financial futures markets. Currently there are 21 custom-designed VantagePoint programs for currencies, interest rate markets, stock indices, and the energy complex which allow traders to benefit from intermarket analysis, without having to reinvent the wheel or become rocket scientists, Practical Applications While used extensively by traders to identify trends, simple moving averages, due to their mathematical construction, are considered a lagging indicator.

In addition, the most intense periods of sunspot activity, as one might expect if this theory were true, seem to correlate with the high points in civilization. We’re currently in one! In contrast, low periods of sunspot activity seem to correlate with what might be termed declines in civilization. Obviously, if such a theory is valid and if sunspot activity is predictable, then one would expect sunspot activity to have a strong effect on what happens in the market. There are numerous attempts to correlate and predict markets based upon major physical systems such as the activity of the sun. It is very easy to put together enough best-case examples to prove’ to others--or yourself-that these theories are correct. I’ve seen it happen hundreds of times because there is a simple perceptual bias that will convince people of certain relationships from just a few well-chosen examples. Nevertheless, there is a big difference between theory and reality.


pages: 272 words: 64,626

Eat People: And Other Unapologetic Rules for Game-Changing Entrepreneurs by Andy Kessler

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23andMe, Andy Kessler, bank run, barriers to entry, Berlin Wall, Bob Noyce, British Empire, business process, California gold rush, carbon footprint, Cass Sunstein, cloud computing, collateralized debt obligation, collective bargaining, commoditize, computer age, creative destruction, disintermediation, Douglas Engelbart, Eugene Fama: efficient market hypothesis, fiat currency, Firefox, Fractional reserve banking, George Gilder, Gordon Gekko, greed is good, income inequality, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, Joseph Schumpeter, knowledge economy, knowledge worker, libertarian paternalism, low skilled workers, Mark Zuckerberg, McMansion, Netflix Prize, packet switching, personalized medicine, pets.com, prediction markets, pre–internet, profit motive, race to the bottom, Richard Thaler, risk tolerance, risk-adjusted returns, Silicon Valley, six sigma, Skype, social graph, Steve Jobs, The Wealth of Nations by Adam Smith, transcontinental railway, transfer pricing, wealth creators, Yogi Berra

With cap and trade, someone sets the amount of emissions each firm is allowed and then trading begins between those that need more emission permits and those that have extras because they reduced emissions. But this is flawed in that people, not the market, set the actual cap or maximum amount of emissions, creating an almost artificial market for extra carbon credits to trade until the right price is set. The more human intervention overriding the market, the tougher true price discovery becomes. Lots of companies are playing around with prediction markets, using the Intelligence at the Edge and running surveys and varying pricing to figure out what people will pay for new products. Like it or not, markets will be inserted into more and more of our lives and Free Radicals ought to be the ones doing it, driving markets, making them happen everywhere. I can’t tell you all the things that will be market driven. Think it through. Transactions, decisions, everything that has to allocate your money and your time and your energy and your attention can have a market applied to it.

Duke Power Grove, Andy Guns, Germs, and Steel (Diamond) Halstead, Maurice Halstead length Haves and Have-nots Health care the edge in personalized medicine Hedge funds, abundance, finding for Helú, Carlos Slim Henne, Albert Hersov, Rob Hewlett-Packard Hierarchy of needs Hoff, Ted Horizontal integration benefits of computers and voice communication and global economy and innovation and intellectual property ownership meaning of and price United States example How Capitalism Saved America (DiLorenzo) How We Got Here (Kessler) Hulu Humans, adapting technology to Hybrid autos IBM, vertical integration ICQ instant messaging Imperialism Income, generational differences Industrialization, and specialization Innovation, and horizontal integration Instant messaging, virtual pipe of Insurance companies, as Thieves Integration, horizontal Intel Intellectual property and horizontal integration and price cuts Intelligence (IQ), parameters of Intelligence at the edge cloud computing in health care social networking Interest rates, and Fed Internet digitized products, lack of protection of evolution of horizontal layers peer to peer (P2P) virtual pipes See also Networks; specific companies Internet stocks Investment capital, money/highest returns connection iPad iPhone iPod iTunes Jenkins, Holman Jobs Creators eliminating with technology licensed occupations replacing with technology Servers Slackers Slimers Sloppers Sponges Super Sloppers Thieves Jobs, Steve Junk bonds Kamangar, Salar Katzenberg, Jeffrey Keynes, John Maynard Kindle Kittler, Fred Kluge, John Lawyers, as Sponges Lehman Brothers bankruptcy Licenses, employment-related LinkedIn Livingston, Robert Longshoremen, as Sloppers McCaw, Craig McKnight, Dr. Jerry McNary, Robert Malone, John Maps, Google Market entrepreneurs Vanderbilt as example as winners Marketers, as Super Sloppers Markets benefits of for information for politics prediction markets price discovery by stock markets Mashups Maslow, Abraham Media defined empires, building of relationship to virtual pipe versus technology Media companies, vertical integration of Medicine, personalized Memory, chunks Mickos, Mårten Microcosm (Gilder) Microsoft employee interviews at Microsoft Word Midgley, Thomas Milken, Michael Mirabilis Money supply classic formula filled bucket comparison gold standard Monopoly, Vanderbilt fight against Moore, Gordon Moore’s law Mozilla Foundation Murdoch, Rupert Mushet, Robert Music, digital piracy virtual pipes for MySpace MySQL Nantell, Jim Napoleon Napster Needs, hierarchy of Netflix recommendations to customers Netscape Networks cloud computing dumb edge of network, intelligence at social networking See also Internet 99% Conference Nintendo Novelists, compared to programmers Noyce, Bob Nudge (Thaler and Sunstein) Obama, Barack Obama, Michelle Ofoto Open-source software Oracle Organic foods Organizational charts Orman, Suze O’Rourke, P.

Outliers (Gladwell) Page, Larry Palm Pre Pandora Peer to Peer (P2P) Personal Genome Project Personalized technology, examples of Physicians, as Thieves Pipes. See Virtual pipe Piracy, of digital products Pirate Bay Political entrepreneurs becoming examples of failure of media moguls as operation of Sponges created by Prague Stock Exchange Prediction markets Prevailing wage laws Price, and horizontal integration Procter & Gamble Productivity defined jobs, eliminating with technology wealth accumulation with. See Productivity and wealth Productivity and wealth Creators of economic principles and efficiency and exceptionalism guaranteed profits and horizontal integration jobs, hierarchy of jobs, replacing with technology and money supply non-productive workers, types of workweek over time Profit as business driver.

The Economic Singularity: Artificial intelligence and the death of capitalism by Calum Chace

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3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, Douglas Engelbart, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Flynn Effect, full employment, future of work, gender pay gap, gig economy, Google Glasses, Google X / Alphabet X, ImageNet competition, income inequality, industrial robot, Internet of things, invention of the telephone, invisible hand, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, lifelogging, lump of labour, Lyft, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, new economy, Occupy movement, Oculus Rift, PageRank, pattern recognition, post scarcity, post-industrial society, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, The Future of Employment, Thomas Malthus, transaction costs, Tyler Cowen: Great Stagnation, Uber for X, universal basic income, Vernor Vinge, working-age population, Y Combinator, young professional

Hanson is less impressed by these demonstrations of rapidly improving AI: “We do expect automation to take most jobs eventually, so we should work to better track the situation. But for now, Ford's reading of the omens seems to me little better than fortune telling with entrails or tarot cards.” Having unburdened himself of this cynicism, Hanson proceeds to offer a constructive suggestion. He advocates forecasting by means of prediction markets, where people place bets on particular economic or policy outcomes, like the level of unemployment at some future date. He argues that prediction markets give us a financial stake in being accurate when we make forecasts, rather than just trying to look good to our peers. Tyler Cowen A professor at George Mason University and co-author of an extremely popular blog, Tyler Cowen was New Jersey's youngest ever chess champion. He is a man with prodigiously broad knowledge and interests, and although he proposes some key ideas forcefully, there is always some nuance, and he dislikes simplistic and modish solutions.

We should be employing economists and others to monitor the available data for signs of technological unemployment, and devising new ways to detect it. The economist Robin Hanson thinks that machines will eventually render most humans unemployed, but that it will not happen for many decades, probably centuries. Despite this scepticism, he proposes an interesting way to watch out for the eventuality: prediction markets. People make their best estimates when they have some skin in the forecasting game. Offering people the opportunity to bet real money on when they see their own jobs or other peoples’ jobs being automated may be an effective way to improve our forecasting.[cccliv] Planning We don’t have sufficient information to draw up detailed plans for the way we would like our economies and societies to evolve.


pages: 326 words: 106,053

The Wisdom of Crowds by James Surowiecki

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AltaVista, Andrei Shleifer, asset allocation, Cass Sunstein, Daniel Kahneman / Amos Tversky, experimental economics, Frederick Winslow Taylor, George Akerlof, Howard Rheingold, I think there is a world market for maybe five computers, interchangeable parts, Jeff Bezos, John Meriwether, Joseph Schumpeter, knowledge economy, lone genius, Long Term Capital Management, market bubble, market clearing, market design, moral hazard, Myron Scholes, new economy, offshore financial centre, Picturephone, prediction markets, profit maximization, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Coase, Ronald Reagan, shareholder value, short selling, Silicon Valley, South Sea Bubble, The Nature of the Firm, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Toyota Production System, transaction costs, ultimatum game, Yogi Berra, zero-sum game

The data on the Iowa Electronic Markets’ performance comes from Joyce Berg, Robert Forsythe, Forrest Nelson, and Thomas Rietz, “Results from a Dozen Years of Election Futures Market Research,” University of Iowa working paper (2000), http://www.biz.uiowa.edu/iem/archive/BFNR_2000.pdf. See also Robert Forsythe, Forrest Nelson, George R. Neumann, and Jack Wright, “Anatomy of an Experimental Political Stock Market,” American Economic Review 82 (1992): 1142–61; and Joyce Berg, Forrest Nelson, and Thomas Rietz, “Accuracy and Forecast Standard Error of Prediction Markets,” Tippie College of Business mimeo (2001), http://www.biz.uiowa.edu/iem/archive/forecasting.pdf. The IEM Web site has a page with links to research into the potential efficacy of prediction markets: http://www.biz.uiowa.edu/iem/archive/references.html. One important thing to note is that most of these papers, while they document the relative accuracy of the IEM’s forecasts, offer a different explanation for that accuracy than the one I offer here. These authors attribute the success of the IEM not to the collective wisdom of the crowd of all IEM traders but rather to a small minority of rational and foresighted investors—the “marginal investors” or “marginal traders”—who keep the market smart by buying or selling whenever IEM prices start to get out of whack (that is, whenever they deviate from what their true value should be).

NetExchange was careful to make clear that the goal of the market would not be to predict terrorist incidents but rather to forecast broader economic, social, and military trends in the region. So perhaps the promise of PAM will actually get tested against reality, instead of being dismissed out of hand. It also seems plausible, and even likely, that the U.S. intelligence community will eventually return to the idea of using internal prediction markets—limited to analysts and experts—as a means of aggregating dispersed pieces of information and turning them into coherent forecasts and policy recommendations. Perhaps that would mean that the CIA would be running what Senators Wyden and Dorgan scornfully called “a betting parlor.” But we know one thing about betting markets: they’re very good at predicting the future. 5. SHALL WE DANCE?


pages: 502 words: 107,657

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

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Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, call centre, commoditize, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, lifelogging, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mass immigration, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, software as a service, speech recognition, statistical model, Steven Levy, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra, zero-sum game

By coming together as a group, our limited capacities as individuals are overcome. Moreover, we no longer need to take on the challenging task of identifying the best person for the job. It doesn’t matter which person is smartest. A diverse mix best does the trick. The collective intelligence of a crowd emerges on many occasions, as explored thoroughly by James Surowiecki in his book The Wisdom of Crowds. Examples include: Prediction markets, wherein a group of people together estimate the prospects for a horse race, political event, or economic occurrence by way of placing bets (unfortunately, this adept forecasting method cannot usually scale to the domain of PA, in which thousands or millions of predictions are generated by a predictive model). The audience of the TV quiz show Who Wants to Be a Millionaire?, whom contestants may poll to weigh in on questions.

Anisimov, “Statistical Modelling of Clinical Trials (Recruitment and Randomization),” Communications in Statistics—Theory and Methods 40, issue 19–20 (2011): 3684–3699. www.tandfonline.com/toc/lsta20/40/19–20. MultiCare Health System (four hospitals in Washington): Karen Minich-Pourshadi for HealthLeaders Media, “Hospital Data Mining Hits Paydirt,” HealthLeaders Media Online, November 29, 2010. www.healthleadersmedia.com/page-1/FIN-259479/Hospital-Data-Mining-Hits-Paydirt. Medical centers and healthcare providers: Customer Potential Management Marketing Group, “Predictive Market Segmentation in Healthcare: Increasing the Effectiveness of Disease Prevention and Early Intervention,” CMP White Paper, CiteSeerxB Online, January 14, 2002. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.134.756. Blue Cross Blue Shield of Tennessee: Rick Whiting, “Businesses Mine Data to Predict What Happens Next,” InformationWeek, May 29, 2006. www.informationweek.com/businesses-mine-data-to-predict-what-hap/188500520.

See crime prediction for law enforcement politics, PA for See also electoral politics Portrait Software Post hoc, ergo propter hoc Power of Habit: Why We Do What We Do in Life and Business (Duhigg) PragmaticTheory team prediction benefits of choosing what to predict collective obsession with future predictions good vs. bad limits of organizational learning and prediction, effects of and on about Data Effect, The Ensemble Effect, The Induction Effect, The Persuasion Effect, The Prediction Effect, The Prediction Effect, The prediction markets predictive analytics. See PA (predictive analytics) Predictive Analytics World (PAW) conferences predictive models defined marketing models overlearning and assuming response modeling response uplift modeling univariate vs. multivariate See also ensemble models predictive models, launching about action and decision making causality and deployment phase Elder’s success in going live machine learning and building observation and personalization and risks in uplift modeling predictive technology See also machine learning predictor variables pregnancy and birth, predicting customer pregnancy and buying behavior premature births prejudice, risk of PREMIER Bankcard privacy Google policies on insight vs. intrusion regarding predicted consumer data and profiling customers Progressive Insurance psychology emotions, cause and effect of Freud on emotions predictive analysis in schizophrenia, predicting psychopathy, predicting Psych (TV show) purchases, predicting Q Quadstone R Radcliffe, Nicholas Radica Games Ralph’s random forests Rebellion Research recency recidivism prediction for law enforcement recommendation systems Reed Elsevier reliability modeling REO Speedwagon (band) response modeling drawbacks of examples of targeted marketing with response rates response uplift modeling retail websites, behavior on retirement, health and Richmond (VA) Police Department Rio Salado Community College Riskprediction.org.uk risk management risk scores Risky Business (film) Robin, Leo Romney, Mitt Royal Astronomy Society R software Russell, Bertrand Rutter, Brad S Saaf, Randy safety and efficiency, PA for Safeway sales leads, predicting Salford Systems Salsburg, David Santa Cruz (CA) Police Department sarcasm, in reviews Sartre, Jean-Paul SAS satellites, predicting fault in satisficing Schamberg, Lisa schizophrenia, predicting Schlitz, Don Schmidt, Eric Schwartz, Ari Science magazine security levels, predicting self-driving cars Selfridge, Oliver Semisonic (band) sepsis, predicting Sessions, Roger Shakespeare, William Shaw, George Bernard Shearer, Colin shopping habits, predicting sickness, predicting Siegel, Eric silence, concept of Silver, Nate Simpsons, The (TV show) Siri Sisters of Mercy Health Systems small business credit risks Smarr, Larry smoking and smokers health problems and causation for motion disorders and social effect and quitting SNTMNT Sobel, David social computing social effect social media networks data glut on happiness as contagious on healthcare LinkedIn PA for spam filtering on Twitter viral tweets and posts on YouTube See also Facebook sociology, uplift modeling applications for SpaceShipOne spam filtering Spider-Man (film) sporting events, crime rates and sports cars Sprint SPSS staff behavior.


pages: 187 words: 62,861

The Penguin and the Leviathan: How Cooperation Triumphs Over Self-Interest by Yochai Benkler

business process, California gold rush, citizen journalism, Daniel Kahneman / Amos Tversky, East Village, Everything should be made as simple as possible, experimental economics, experimental subject, framing effect, informal economy, invisible hand, jimmy wales, job satisfaction, Joseph Schumpeter, Kenneth Arrow, knowledge economy, laissez-faire capitalism, loss aversion, Murray Gell-Mann, Nicholas Carr, peer-to-peer, prediction markets, Richard Stallman, Scientific racism, Silicon Valley, Steven Pinker, telemarketer, Toyota Production System, ultimatum game, Washington Consensus, zero-sum game, Zipcar

What people say to one another about their motivations and intentions, economists claim, is more or less worthless information—it doesn’t tell us anything about how people would actually act if something real were at stake. Because talk is supposedly “cheap” in this sense—it doesn’t commit you to anything—a whole strand of work in economics has developed to place people in situations where they have to actually act, rather than talk: buy or not buy, invest or not invest, as this is assumed to be the only way to reveal their true preferences. That’s what prediction markets, which have become so popular in the run-up to elections, for example, do; prediction markets are speculative markets—betting exchanges—created to make predictions. They are just one example of a field called “mechanism design,” built on the assumption that it is not what people say, but what they do, that counts. It has been so influential that it won its inventors the Nobel Prize in economics in 2007. While much of the work being done in the field is of course laudable, and is sound in theory, talk in both experiments and the real world is in fact not at all “cheap” and is very far from meaningless.


pages: 247 words: 81,135

The Great Fragmentation: And Why the Future of All Business Is Small by Steve Sammartino

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3D printing, additive manufacturing, Airbnb, augmented reality, barriers to entry, Bill Gates: Altair 8800, bitcoin, BRICs, Buckminster Fuller, citizen journalism, collaborative consumption, cryptocurrency, David Heinemeier Hansson, Elon Musk, fiat currency, Frederick Winslow Taylor, game design, Google X / Alphabet X, haute couture, helicopter parent, illegal immigration, index fund, Jeff Bezos, jimmy wales, Kickstarter, knowledge economy, Law of Accelerating Returns, lifelogging, market design, Metcalfe's law, Metcalfe’s law, Minecraft, minimum viable product, Network effects, new economy, peer-to-peer, post scarcity, prediction markets, pre–internet, profit motive, race to the bottom, random walk, Ray Kurzweil, recommendation engine, remote working, RFID, Rubik’s Cube, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, social graph, social web, software is eating the world, Steve Jobs, survivorship bias, too big to fail, US Airways Flight 1549, web application, zero-sum game

The market doesn’t care when you finished school Getting your digital on It’s not my job Welcome to med school Note Chapter 5: Rehumanisation: words define our future Dead-end products A future of unfinished products The malleable marketplace Corporate skulduggery? Outsourcing logic The end of bison hunting The tastemakers The selfish era So how do we survive? Creative types Collaboration, creative orientation and counter intuition Note Chapter 6: Demographics is history: moving on from predictive marketing How to get profiled The price of pop culture The best average The weapon of choice Don’t fence me in How do you define a teenager? Stealing music or connecting? Marketing 1.0 Marketing revised The new intersection Social + interests = intention The story of cities Do I know you? The interest graph in action The anti-demographic recommendation engine Chapter 7: The truth about pricing: technology and omnipresent deflation Technology deflation Real-world technology deflation The free super computer The crux is human It’s getting quicker Technology curve jumping Technology stacking Omnipresent deflation Consumer price index trickery Connections and the impact on prices Economic border hopping The new minimum wage Notes Chapter 8: A zero-barrier world: how access to knowledge is breaking down barriers So what’s changed?

What it means for business ‘Tell and sell’ is over. Now it’s about connecting in a non-corporate, human manner. Corporations have to start acting more like people and less like evil organisms with their own agenda. Note 1 Driverless car prediction dates by various respected auto and technology firms: www.driverless-future.com/?page_id=384 CHAPTER 6 Demographics is history: moving on from predictive marketing If you learned your marketing trade any time in the past 60 years, there’s a very good chance a large part of what you learned was related to demographic profiling, the statistical art of putting people into behaviour buckets. These were clusters created to define what people believe and how they’re likely to behave so that they could be ‘targeted’ with financial efficiency. It was the marketing diet I was brought up on, and I believed it to be accurate in most ways, until I realised that everything I saw in the ‘real world’ flew in the face of demographics.

The Disciplined Trader: Developing Winning Attitudes by Mark Douglas

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Albert Einstein, conceptual framework, fear of failure, financial independence, prediction markets, risk tolerance, the market place

In any case, you cannot negate their significance in how they will determine and affect your behavior as a trader. Many traders will try to circumvent confronting these limiting beliefs by becoming an expert market analyst. It doesn't matter how good a market analyst you become; if you don't release yourself from the effects of these beliefs, you won't be successful to the extent these limiting beliefs have power in your mental system. There are many market gurus who can predict market moves with uncanny accuracy but can't make money as a trader. Either they don't know the nature of beliefs and how they affect and determine behavior, or they don't want to confront the issues surrounding these beliefs. You have to want to do it or nothing will happen. And if you choose not to, you will be subjecting yourself to the same recurring cycles of negative experiences again and again until you either decide to work through whatever issues are necessary or lose all your money and have to give it up.

You also can't learn anything new because fear will force you to perceive the environmental "now" from your individual past. You will experience that past regardless of the opportunities the environment may have to offer in that same moment. Your individual history will repeat itself until you change your history, which will then allow you to learn and experience something new. Evolving beyond your fears is also the best way to learn how to predict market behavior. The more fearful traders are, the fewer the choices they perceive as available to themselves and the easier it is to predict their behavior. You will be able to recognize this clearly in others when you recognize it yourself and work your way out of the condition where you trade with fear. However, you will need some resource to limit yourself so that you don't get reckless. Getting reckless is exactly what people have a tendency to do if they don't feel any fear, especially if there is a potential for thrilling results, as there is in trading.


pages: 233 words: 66,446

Bitcoin: The Future of Money? by Dominic Frisby

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3D printing, altcoin, bank run, banking crisis, banks create money, barriers to entry, bitcoin, blockchain, capital controls, Chelsea Manning, cloud computing, computer age, cryptocurrency, disintermediation, ethereum blockchain, fiat currency, fixed income, friendly fire, game design, Isaac Newton, Julian Assange, land value tax, litecoin, M-Pesa, mobile money, money: store of value / unit of account / medium of exchange, Occupy movement, Peter Thiel, Ponzi scheme, prediction markets, price stability, QR code, quantitative easing, railway mania, Ronald Reagan, Satoshi Nakamoto, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, Stephen Hawking, Steve Jobs, Ted Nelson, too big to fail, transaction costs, Turing complete, War on Poverty, web application, WikiLeaks

What we did with Ethereum is we kind of unified a lot of the 2.0 actors and put them into a big bucket and we’re building a completely new block chain and we’re building a completely new scripting language that basically adds in those missing features. ‘The end result is you can do things now like have Wall Street on a block chain. So any financial contract that you would see in Wall Street can now be put on a block chain just like money can. You can do prediction markets. You can have a Las Vegas gambling system living on a block chain. You can also take traditional server client internet apps like YouTube or Facebook or Netflix and you can actually now make all of these services run in a decentralized way with no central actor controlling them. You can do decentralized Dropbox, so instead of having all your files stored on a server; you can actually store them in a decentralized network and instead provide them with a token system just like Bitcoin.

Car ownership and land ownership could be registered on a block chain (and in the UK this is not before time. Both the DVLA and the Land Registry badly need to pull their socks up, particularly the latter – 50% of land in the UK is still unregistered). This system of ownership and smart contracts has the potential to dramatically transform the legal system and slash costs. As we’ve suggested, insurance can be put on a block chain, prediction markets, and identity systems (username and password systems are on their way out), even services like Facebook, YouTube or Netflix. Why would you want YouTube, Facebook or Netflix running in a decentralized way with no central body in charge? It eliminates the problem of excessive personal information on Facebook, or your YouTube viewing habits being monitored and marketed to. And think about the copyright implications for the TV and movie industries.


pages: 252 words: 72,473

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil

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Affordable Care Act / Obamacare, Bernie Madoff, big data - Walmart - Pop Tarts, call centre, carried interest, cloud computing, collateralized debt obligation, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, Emanuel Derman, housing crisis, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, illegal immigration, Internet of things, late fees, mass incarceration, medical bankruptcy, Moneyball by Michael Lewis explains big data, new economy, obamacare, Occupy movement, offshore financial centre, payday loans, peer-to-peer lending, Peter Thiel, Ponzi scheme, prediction markets, price discrimination, quantitative hedge fund, Ralph Nader, RAND corporation, recommendation engine, Rubik’s Cube, Sharpe ratio, statistical model, Tim Cook: Apple, too big to fail, Unsafe at Any Speed, Upton Sinclair, Watson beat the top human players on Jeopardy!, working poor

wean families from Section 8: Giselle Routhier, “Mayor Bloomberg’s Revolving Door of Homelessness,” Safety Net, Spring 2012, www.​coalition​forthehomeless.​org/​mayor-​bloombergs-​revolving-​door-​of-​homelessness/. Made in a Free World: Issie Lapowsky, “The Next Big Thing You Missed: Software That Helps Businesses Rid Their Supply Chains of Slave Labor,” Wired, February 3, 2015, www.​wired.​com/​2015/​02/​frdm/. Eckerd, a child and family services nonprofit: Darian Woods, “Who Will Seize the Child Abuse Prediction Market?,” Chronicle for Social Change, May 28, 2015, https://​chronicle​ofsocialchange.​org/​featured/​who-​will-​seize-​the-​child-​abuse-​prediction-​market/​10861. Boston Globe: Michael Levenson, “Can Analytics Help Fix the DCF?,” Boston Globe, November 7, 2015, www.​bostonglobe.​com/​2015/​11/​07/​childwelfare-​bostonglobe-​com/​AZ2kZ7ziiP8c​BMOite2KKP/story.​html. ABOUT THE AUTHOR Cathy O’Neil is a data scientist and the author of the blog mathbabe.​org. She earned a PhD in mathematics from Harvard and taught at Barnard College before moving to the private sector, where she worked for the hedge fund D.

The Intelligent Asset Allocator: How to Build Your Portfolio to Maximize Returns and Minimize Risk by William J. Bernstein

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asset allocation, backtesting, capital asset pricing model, commoditize, computer age, correlation coefficient, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, fixed income, index arbitrage, index fund, intangible asset, Long Term Capital Management, p-value, passive investing, prediction markets, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, South Sea Bubble, survivorship bias, the rule of 72, the scientific method, time value of money, transaction costs, Vanguard fund, Yogi Berra, zero-coupon bond

I am continually amazed at the amount of time the financial and mass media devote to well-regarded analysts attempting to divine the movements of the market from political and economic events. This is a fool’s errand. Almost always these analysts are the employees of large brokerage houses; one would think that these organizations would tire of looking foolish on so regular a basis. (If you are not convinced of the futility of trying to predict market direction from economic conditions, then consider that the biggest money is made by buying when things look the bleakest: 1932, 1937, 1975, and 1982 were all great times to buy stocks. Then consider that the most dangerous times to buy or own stocks is when there is plenty of economic blue sky; those who bought in 1928, 1936, or 1966 were soon sorry.) In the end, it is easy to understand why the aggregate efforts of all of the nation’s professional money managers fail to best the market: They are the market.

Prolonged market declines will make rebalancing seem a frustrating waste of money; in the end, however, asset prices almost always turn around, and you usually will be rewarded handsomely for your patience. 7. The markets are smarter than you are; they are also smarter than the experts. Remember that a stopped clock is right twice a day. Even the most inept analyst occasionally makes a good call, and he will probably be interviewed by Lou Rukeyser soon after he has made it. Nobody consistently predicts market direction. Very few money managers beat the market in the long run; those that have done so in the recent past are unlikely to do so in the future. Do not run with the crowd; those who follow the elephant herd often get dirty and squashed. 8. Know how expensive the tomatoes are. Keep an eye on market valuation. Changes in your policy allocation should be made only in response to valuation changes, and they should be made in a direction opposite to the price of the asset.


pages: 240 words: 65,363

Think Like a Freak by Steven D. Levitt, Stephen J. Dubner

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Albert Einstein, Anton Chekhov, autonomous vehicles, Barry Marshall: ulcers, call centre, Cass Sunstein, colonial rule, Edward Glaeser, Everything should be made as simple as possible, food miles, Gary Taubes, income inequality, Internet Archive, Isaac Newton, medical residency, Metcalfe’s law, microbiome, prediction markets, randomized controlled trial, Richard Thaler, Scramble for Africa, self-driving car, Silicon Valley, Tony Hsieh, transatlantic slave trade, éminence grise

All the team leaders were then asked to pick one of three signals—a green light, yellow light, or red light—to indicate their confidence in an on-time opening. All seven of them picked the green light. Great news! As it happens, this firm had also set up an internal prediction market, where any employee could anonymously place a small bet on various company directives. One bet asked whether the Chinese store would open on time. Considering that all seven team leaders had given it a green light, you might expect bettors to be similarly bullish. They weren’t. The prediction market showed a 92 percent chance the store wouldn’t open on time. Guess who was right—the anonymous bettors or the team leaders who had to stand in front of their bosses? The Chinese store did not open on time. It’s easy to identify with the leaders who gave the project the green light.


pages: 205 words: 18,208

The Transparent Society: Will Technology Force Us to Choose Between Privacy and Freedom? by David Brin

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affirmative action, airport security, Ayatollah Khomeini, clean water, cognitive dissonance, corporate governance, data acquisition, death of newspapers, Extropian, Howard Rheingold, illegal immigration, informal economy, information asymmetry, Iridium satellite, Jaron Lanier, John Markoff, John von Neumann, Kevin Kelly, means of production, mutually assured destruction, offshore financial centre, open economy, packet switching, pattern recognition, pirate software, placebo effect, Plutocrats, plutocrats, prediction markets, Ralph Nader, RAND corporation, Robert Bork, Saturday Night Live, Search for Extraterrestrial Intelligence, Steve Jobs, Steven Levy, Stewart Brand, telepresence, trade route, Vannevar Bush, Vernor Vinge, Whole Earth Catalog, Whole Earth Review, Yogi Berra, zero-sum game, Zimmermann PGP

If so, Plato will turn out to be right in predicting that the final outcome of democracy is mob rule, followed by a takeover of his preferred approach to government; dictatorship by a “noble” elite. 266 ... modern observers who think we have entered an era of unpredictability ... See Kevin Kelly, Out of Control (Reading, Mass.: Addison-Wesley, 1994), and Edward Tenner. Why Things Bite Back (New York: Knopf, 1996). 267 ... a “predictions market” ... University of California economist Robin Hanson calls his system a “betting pool on disputed science questions, where the current odds-on favorites are treated as the current intellectual consensus. Ideas futures markets let you bet on the future settlement of a scientific controversy. [See http://www.ideosphere.com/ and http://hanson.berkeley.edulideafutures.html.] But the method may have wider applications.” Note: a form of predictions market was depicted in John Brunnerʼs wonderfully prescient science fiction novel The Shockwave Rider in 1974, a work that illustrated principles of transparency and also invented the terminology, possibly the very concepts, of computer “viruses” and “worms.” 267 ...

The idea of a predictions registry may have originated when Sir Francis Galton (1822—1911) attempted to perform experiments statistically measuring the efficacy of prayer. (He discovered what skeptics now call the “placebo effect.”) In the 1970s, efforts were made to catalog predictions using the crude technique of mailing postcards to a post office box in New York City, but sorting through shoe boxes did not prove an efficient or comprehensive method of correlating results, and the effort collapsed. The Internet has changed all that. For example, a “predictions market” has been set up by Robin Hanson, a researcher at the University of California at Berkeley. In his Web space, visitors bet against each other about future trends in science, much like Vegas odds makers, or gamblers on the Chicago commodities exchange. Winners are those whose guesses (or sage insights) prove correct most often. The step to a more general registry would be simple. Anyone claiming to have special foresight should be judged by a simple standard: success or failure.


pages: 519 words: 102,669

Programming Collective Intelligence by Toby Segaran

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always be closing, correlation coefficient, Debian, en.wikipedia.org, Firefox, full text search, information retrieval, PageRank, prediction markets, recommendation engine, slashdot, Thomas Bayes, web application

Sites like Amazon and Netflix use information about the things people buy or rent to determine which people or items are similar to one another, and then make recommendations based on purchase history. Other sites like Pandora and Last.fm use your ratings of different bands and songs to create custom radio stations with music they think you will enjoy. Chapter 2 covers ways to build recommendation systems. Prediction markets are also a form of collective intelligence. One of the most well known of these is the Hollywood Stock Exchange (http://hsx.com), where people trade stocks on movies and movie stars. You can buy or sell a stock at the current price knowing that its ultimate value will be one millionth of the movie's actual opening box office number. Because the price is set by trading behavior, the value is not chosen by any one individual but by the behavior of the group, and the current price can be seen as the whole group's prediction of box office numbers for the movie.

, Learning from Clicks, Learning from Clicks, Setting Up the Database, Feeding Forward, Training with Backpropagation, Training Test, Training Test artificial, Learning from Clicks, Learning from Clicks, Setting Up the Database, Feeding Forward, Training with Backpropagation, Training Test, Training Test backpropagation, Training with Backpropagation connecting to search engine, Training Test designing click-training network, Learning from Clicks feeding forward, Feeding Forward setting up database, Setting Up the Database training test, Training Test neural network classifier, Exercises neural networks, Neural Networks, Neural Networks, Neural Networks, Neural Networks, Training a Neural Network, Training a Neural Network, Training a Neural Network, Strengths and Weaknesses, Strengths and Weaknesses backpropagation, and, Training a Neural Network black box method, Strengths and Weaknesses combinations of words, and, Neural Networks multilayer perceptron network, Neural Networks strengths and weaknesses, Strengths and Weaknesses synapses, and, Neural Networks training, Training a Neural Network using code, Training a Neural Network news sources, A Corpus of News newsfeatures.py, Selecting Sources, Downloading Sources, Downloading Sources, Downloading Sources, Converting to a Matrix, Using NumPy, The Algorithm, Displaying the Results, Displaying the Results, Displaying by Article, Displaying by Article getarticlewords function, Downloading Sources makematrix function, Converting to a Matrix separatewords function, Downloading Sources shape function, The Algorithm showarticles function, Displaying the Results, Displaying by Article showfeatures function, Displaying the Results, Displaying by Article stripHTML function, Downloading Sources transpose function, Using NumPy nn.py, Setting Up the Database, Setting Up the Database, Setting Up the Database, Setting Up the Database searchnet class, Setting Up the Database, Setting Up the Database, Setting Up the Database, Setting Up the Database generatehiddennode function, Setting Up the Database getstrength method, Setting Up the Database setstrength method, Setting Up the Database nnmf.py, The Algorithm difcost function, The Algorithm non-negative matrix factorization (NMF), Supervised versus Unsupervised Learning, Clustering, Non-Negative Matrix Factorization, Non-Negative Matrix Factorization, Non-Negative Matrix Factorization, Using Your NMF Code factorization, Supervised versus Unsupervised Learning goal of, Non-Negative Matrix Factorization update rules, Non-Negative Matrix Factorization using code, Using Your NMF Code normalization, Normalization Function numerical predictions, Building Price Models numpredict.py, Building a Sample Dataset, Building a Sample Dataset, Defining Similarity, Defining Similarity, Defining Similarity, Defining Similarity, Subtraction Function, Subtraction Function, Weighted kNN, Weighted kNN, Cross-Validation, Cross-Validation, Cross-Validation, Heterogeneous Variables, Scaling Dimensions, Optimizing the Scale, Optimizing the Scale, Uneven Distributions, Estimating the Probability Density, Graphing the Probabilities, Graphing the Probabilities, Graphing the Probabilities createcostfunction function, Optimizing the Scale createhiddendataset function, Uneven Distributions crossvalidate function, Cross-Validation, Optimizing the Scale cumulativegraph function, Graphing the Probabilities distance function, Defining Similarity dividedata function, Cross-Validation euclidian function, Defining Similarity gaussian function, Weighted kNN getdistances function, Defining Similarity inverseweight function, Subtraction Function knnestimate function, Defining Similarity probabilitygraph function, Graphing the Probabilities probguess function, Estimating the Probability Density, Graphing the Probabilities rescale function, Scaling Dimensions subtractweight function, Subtraction Function testalgorithm function, Cross-Validation weightedknn function, Weighted kNN wineprice function, Building a Sample Dataset wineset1 function, Building a Sample Dataset wineset2 function, Heterogeneous Variables NumPy, Using NumPy, Using NumPy, Simple Usage Example, NumPy, Installation on Other Platforms, Installation on Other Platforms installation on other platforms, Installation on Other Platforms installation on Windows, Simple Usage Example usage example, Installation on Other Platforms using, Using NumPy O online technique, Strengths and Weaknesses Open Web APIs, Open APIs optimization, Optimization, Group Travel, Representing Solutions, Representing Solutions, Representing Solutions, Representing Solutions, The Cost Function, The Cost Function, The Cost Function, Random Searching, Hill Climbing, Simulated Annealing, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Optimizing for Preferences, Optimizing for Preferences, The Cost Function, The Cost Function, Network Visualization, Network Visualization, Counting Crossed Lines, Drawing the Network, Exercises, Exercises, Exercises, Exercises, Exercises, Exercises, Exercises, Optimizing the Scale, Exercises, Optimization, Optimization annealing starting points, Exercises cost function, The Cost Function, Optimization exercises, Exercises genetic algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms crossover or breeding, Genetic Algorithms generation, Genetic Algorithms mutation, Genetic Algorithms population, Genetic Algorithms genetic optimization stopping criteria, Exercises group travel cost function, Exercises group travel planning, Group Travel, Representing Solutions, Representing Solutions, Representing Solutions, The Cost Function, The Cost Function car rental period, The Cost Function departure time, Representing Solutions price, Representing Solutions time, Representing Solutions waiting time, The Cost Function hill climbing, Hill Climbing line angle penalization, Exercises network visualization, Network Visualization, Counting Crossed Lines, Drawing the Network counting crossed lines, Counting Crossed Lines drawing networks, Drawing the Network layout problem, Network Visualization network vizualization, Network Visualization pairing students, Exercises preferences, Optimizing for Preferences, Optimizing for Preferences, The Cost Function, The Cost Function cost function, The Cost Function running, The Cost Function student dorm, Optimizing for Preferences random searching, Random Searching representing solutions, Representing Solutions round-trip pricing, Exercises simulated annealing, Simulated Annealing where it may not work, Genetic Algorithms optimization.py, Group Travel, Representing Solutions, Representing Solutions, The Cost Function, Random Searching, Hill Climbing, Simulated Annealing, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Optimizing the Scale annealingoptimize function, Simulated Annealing geneticoptimize function, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms elite, Genetic Algorithms maxiter, Genetic Algorithms mutprob, Genetic Algorithms popsize, Genetic Algorithms getminutes function, Representing Solutions hillclimb function, Hill Climbing printschedule function, Representing Solutions randomoptimize function, Random Searching schedulecost function, The Cost Function P PageRank algorithm, Real-Life Examples, The PageRank Algorithm pairing students, Exercises Pandora, Real-Life Examples parse tree, Programs As Trees Pearson correlation, Hierarchical Clustering, Viewing Data in Two Dimensions hierarchical clustering, Hierarchical Clustering multidimensional scaling, Viewing Data in Two Dimensions Pearson correlation coefficient, Pearson Correlation Score, Pearson Correlation Coefficient, Pearson Correlation Coefficient code, Pearson Correlation Coefficient Pilgrim, Mark, Universal Feed Parser polynomial transformation, The Kernel Trick poplib, Exercises population, Genetic Algorithms, What Is Genetic Programming?, Creating the Initial Population, Genetic Algorithms diversity and, Creating the Initial Population Porter Stemmer, Finding the Words on a Page Pr(Document), Exercises prediction markets, Real-Life Examples price models, Building a Sample Dataset, Building a Sample Dataset, k-Nearest Neighbors, Exercises, Exercises, Exercises, Exercises, Exercises, Exercises, Exercises, Exercises building sample dataset, Building a Sample Dataset eliminating variables, Exercises exercises, Exercises item types, Exercises k-nearest neighbors (kNN), k-Nearest Neighbors laptop dataset, Exercises leave-one-out cross-validation, Exercises optimizing number of neighbors, Exercises search attributes, Exercises varying ss for graphing probability, Exercises probabilities, Calculating Probabilities, Starting with a Reasonable Guess, Probability of a Whole Document, A Quick Introduction to Bayes' Theorem, Combining the Probabilities, Graphing the Probabilities, Conditional Probability assumed probability, Starting with a Reasonable Guess Bayes' Theorem, A Quick Introduction to Bayes' Theorem combining, Combining the Probabilities conditional probability, Calculating Probabilities graphing, Graphing the Probabilities of entire document given classification, Probability of a Whole Document product marketing, Other Uses for Learning Algorithms public message boards, Filtering Spam pydelicious, Simple Usage Example, Simple Usage Example, pydelicious installation, Simple Usage Example usage example, Simple Usage Example pysqlite, Building the Index, Persisting the Trained Classifiers, Installation on All Platforms, Installation on All Platforms, pysqlite, Simple Usage Example importing, Persisting the Trained Classifiers installation on other platforms, Installation on All Platforms installation on Windows, Installation on All Platforms usage example, Simple Usage Example Python, Style of Examples, Python Tips advantages of, Style of Examples tips, Python Tips Python Imaging Library (PIL), Drawing the Dendrogram, Python Imaging Library, Installation on Windows, Installation on Windows, Installation on Windows installation on other platforms, Installation on Windows usage example, Installation on Windows Windows installation, Installation on Windows Python, genetic programming and, Programs As Trees, Programs As Trees, Representing Trees in Python, Building and Evaluating Trees, Displaying the Program building and evaluating trees, Building and Evaluating Trees displaying program, Displaying the Program representing trees, Representing Trees in Python traversing complete tree, Programs As Trees Q query layer, Design of a Click-Tracking Network querying, Querying, Querying query function, Querying R radial-basis function, The Kernel Trick random searching, Random Searching random-restart hill climbing, Hill Climbing ranking, What's in a Search Engine?


pages: 338 words: 106,936

The Physics of Wall Street: A Brief History of Predicting the Unpredictable by James Owen Weatherall

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Albert Einstein, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Asian financial crisis, bank run, beat the dealer, Benoit Mandelbrot, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, Brownian motion, butterfly effect, capital asset pricing model, Carmen Reinhart, Claude Shannon: information theory, collateralized debt obligation, collective bargaining, dark matter, Edward Lorenz: Chaos theory, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, George Akerlof, Gerolamo Cardano, Henri Poincaré, invisible hand, Isaac Newton, iterative process, John Nash: game theory, Kenneth Rogoff, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, martingale, Myron Scholes, new economy, Paul Lévy, Paul Samuelson, prediction markets, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk-adjusted returns, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Coase, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, statistical arbitrage, statistical model, stochastic process, The Chicago School, The Myth of the Rational Market, tulip mania, V2 rocket, Vilfredo Pareto, volatility smile

In 1996, Sornette’s work on earthquakes earned him a part-time professor-in-residence position in UCLA’s earth and space sciences department, and at the Institute of Geophysics and Planetary Physics. By this time, though, at least half of his energy was devoted to finance. That same year, Sornette, Bouchaud, and Sornette’s postdoctoral researcher, Anders Johansen, realized that Sornette’s earlier work on predicting earthquakes and ruptures could be extended to predicting market crashes. They published a paper together in another physics journal. Amazingly, just a few months later, Sornette detected the log-periodic pattern that he had determined should presage a crash. The success of October 1997 deepened his belief that he was on to something important, and he redoubled his efforts on economics and financial modeling. As with his theories of material rupture and earthquakes, the central idea behind Sornette’s market-crash-as-critical-event hypothesis involves collective action, or herding behavior.

For this reason, assuming normal or log-normal distributions can lead to extremely misleading results and, moreover, produce a false sense of confidence regarding the likelihood of certain kinds of extreme events. For another, despite the fact that extreme events happen infrequently on both models, in Mandelbrot’s models of financial markets, they happen often enough that it is the extreme events that dominate market behavior in the long run. And so, even if there are similarities in how the models predict markets on a typical day, there is a significant difference in how one should view the importance of a “typical day” for the long-term behavior of markets. “It is also too simple to say that Mandelbrot was ignored . . .”: For instance, see Fama (1964). “Today, the best evidence indicates . . .”: See, for instance, Cont (2001) and references therein; this point was also emphasized in conversation by Didier Sornette, whose work is the subject of Chapter 7

The Smartest Investment Book You'll Ever Read: The Simple, Stress-Free Way to Reach Your Investment Goals by Daniel R. Solin

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asset allocation, corporate governance, diversification, diversified portfolio, index fund, market fundamentalism, money market fund, Myron Scholes, passive investing, prediction markets, random walk, risk tolerance, risk-adjusted returns, risk/return, transaction costs, Vanguard fund, zero-sum game

The (Swedroe), 182 Index 191 Ontario Securities Commission, 83,89, 11 9, 160, 164 Ontario securities regulations, 142,170 O'Reilly, William E., 162 overconfidence in Hype ractive Investor, 32 overseas stocks. Su international stocks Palaro, Helder P. , 166 Palmer, RosseJl E., 73 PAS (Porrfolio Allocation Score), 178 Su also Asset Allocation Q uestionnaire passive mvestmenrs index funds and ETFs as, 8, 18 See also ETFs (exchange traded funds); index funds Patalon, William, 111, 62 pension funds, as Smarr InvestOrs, predictions, market. Se, market timing (predicting the future) principal-protected notes (PPNs),I64 Professional Financial Advisor, The(De Gocy) , 155-56, 181 prospectuS for a fund, 60 Prudent Investor Rule, 99-101 Prudential house funds , 77, 163 psychology of investing desire for order, 3 1 gambling, 30-3 1, 153-54 irrational exuberance in, 49-50, 157 market psychology, 49 marketing and, 30 overconfidence, 32 personal values and, 34-35 research on, 152-54 ~s jz.zle" and excitement, 32-33 stress of, 25 Su also Asset Allocation Questionnaire 105-6, 139-40, 168 pension plans (RRPs and RRSPs) ETFs in, 139-40, 169-70 Porrfolio Allocation Score (PAS), 178 Su also ruse t Allocation Questionnaire Portfolio ~kction (Markowitz), 67,162 poHfolios, inveSlment.


pages: 1,088 words: 228,743

Expected Returns: An Investor's Guide to Harvesting Market Rewards by Antti Ilmanen

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Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, Bernie Madoff, Black Swan, Bretton Woods, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, central bank independence, collateralized debt obligation, commoditize, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, George Akerlof, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, information asymmetry, interest rate swap, invisible hand, Kenneth Rogoff, laissez-faire capitalism, law of one price, Long Term Capital Management, loss aversion, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, Myron Scholes, negative equity, New Journalism, oil shock, p-value, passive investing, Paul Samuelson, performance metric, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, Robert Shiller, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, survivorship bias, systematic trading, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs, tulip mania, value at risk, volatility arbitrage, volatility smile, working-age population, Y2K, yield curve, zero-coupon bond, zero-sum game

The data confirm that including M&A yield would reduce the predictive ability of a broad carry composite, and is best left out. These data start only in 1985, but academic studies document good market-timing ability using total payout yield and net buyback yield since the 1920s. The strong results may partly reflect corporate managers successfully market timing their equity issuance to coincide with expensive market levels. Table 8.3. Predictive market-timing correlations of narrow and broad carry measures, 1985–2009 Sources: Haver Analytics, Michael Afreh (Nomura). 8.4.2 Value measures: Earnings yield and the Fed Model Absolute valuation A stock market’s price–earnings (P/E) ratio and its inverse, the earnings yield (E/P), are the most popular equity market valuation indicators. The spread (arithmetic difference) or, alternatively, the ratio of government bond yield (Y) over earnings yield (E/P) is the most popular measure of relative valuation between the two major asset classes—and thus shorthand for the equity–bond premium.

The profit/GDP ratio has a longer history and appears related to both earnings growth expectations and the market P/E ratio.) Disappointments following excessive growth optimism may be a key reason for the success of contrarian stock selection and market-timing strategies. The share of prime-age savers in the population peaked in Japan in the late 1980s and in the U.S. around 2000, consistent with broad patterns in market valuations. Some observers call the 1990s “the baby-boomer rally” in the U.S. and predict market declines when the retiring boomers begin to dissave (i.e., use past savings for current consumption). The story rings true but demographic developments are at best only one influence on asset prices. Moreover, there is some evidence of retirees continuing to save rather than dissave and the story ignores the entry of savers from younger emerging market countries. 8.4.3 Ex ante equity premia based on the DDM While the yield ratio is useful shorthand for the equity–bond premium, the dividend discount model (DDM) gives us directly what we want to see: a numerical estimate of the difference between stocks’ and bonds’ expected long-run returns.

This average correlation turns out to have a significantly positive relation to next quarter equity market returns, while average variance has virtually no relation (and indeed a negative one once the correlation’s predictive ability is controlled for). Apparently, the average variance component offsets and conceals the market-timing ability of correlation, leaving stock market variance unable to predict market returns. Table 19.1. Correlations between monthly S&P 500 returns and contemporaneous and lagged realized one-month volatility levels (Vol) and changes (dVol), 1928–2009 (and VIX 1986–2009) Sources: Bloomberg. Bollerslev–Zhou (2007) present the most positive evidence for a conditional risk–reward relation so far. Their ex ante variance risk premium proxy—the gap between implied and realized variances, using model-free implied variance and realized variance measures based on high-frequency intraday data—gives positive-signed and significant predictions of next quarter equity market returns.


pages: 467 words: 154,960

Trend Following: How Great Traders Make Millions in Up or Down Markets by Michael W. Covel

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Albert Einstein, asset allocation, Atul Gawande, backtesting, beat the dealer, Bernie Madoff, Black Swan, buy low sell high, capital asset pricing model, Clayton Christensen, commodity trading advisor, computerized trading, correlation coefficient, Daniel Kahneman / Amos Tversky, delayed gratification, deliberate practice, diversification, diversified portfolio, Edward Thorp, Elliott wave, Emanuel Derman, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, fiat currency, fixed income, game design, hindsight bias, housing crisis, index fund, Isaac Newton, John Meriwether, John Nash: game theory, linear programming, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, mental accounting, money market fund, Myron Scholes, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Renaissance Technologies, Richard Feynman, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, survivorship bias, systematic trading, the scientific method, Thomas L Friedman, too big to fail, transaction costs, upwardly mobile, value at risk, Vanguard fund, volatility arbitrage, William of Occam, zero-sum game

Instead of evaluating fundamental factors, technical analysis looks at the market prices themselves. Technical traders believe that a careful analysis of daily price action is an effective means of trading for profit. Now here is where an understanding of technical analysis becomes complicated. There are essentially two forms of technical analysis. One form is based on an ability to “read” charts and use “indicators” to predict market direction. Here is an example of the mentality behind a predictive view of technical analysis: 9 But I think our ace in the hole is that the governments usually screw things up and don’t maintain their sound money and policy coordination. And about the time we’re ready to give up on what usually has worked, and proclaim that the world has now changed, the governments help us out by creating unwise policy that helps produce dislocations and trends.

He is nimble when, relying on his system, he reacts to the Japanese yen move with alacrity because he trusts his trading plan and his management of risk. The second chart is the British pound (Chart 2.4) where, unlike the Japanese yen, the market proved unfavorable for Dunn. It was a typical whipsaw market, which is always difficult for trend followers because small losses add up. You can see how he entered and was stopped out; then entered and was stopped out again. Remember, trend followers don’t predict markets, they react to them—so the small losses were part of the game. He managed the small losses because the British pound was only a portion of his 37 Chapter 2 • Great Trend Followers portfolio. His yen trade more than made up for his losses on the British pound trade, because no matter how uncomfortable others are with his approach, for Dunn, big winners offset small losers in the long run.


pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

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agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, Donald Knuth, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, Gödel, Escher, Bach, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, Menlo Park, meta analysis, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Norbert Wiener, NP-complete, nuclear winter, optical character recognition, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, transaction costs, Turing machine, Vernor Vinge, Watson beat the top human players on Jeopardy!, World Values Survey, zero-sum game

The same could happen if fixes are found for some of the bureaucratic deformations that warp organizational life—wasteful status games, mission creep, concealment or falsification of information, and other agency problems. Even partial solutions to these problems could pay hefty dividends for collective intelligence. The technological and institutional innovations that could contribute to the growth of our collective intelligence are many and various. For example, subsidized prediction markets might foster truth-seeking norms and improve forecasting on contentious scientific and social issues.78 Lie detectors (should it prove feasible to make ones that are reliable and easy to use) could reduce the scope for deception in human affairs.79 Self-deception detectors might be even more powerful.80 Even without newfangled brain technologies, some forms of deception might become harder to practice thanks to increased availability of many kinds of data, including reputations and track records, or the promulgation of strong epistemic norms and rationality culture.

Gordon, and Gemmell, Jim. 2009. Total Recall: How the E-Memory Revolution Will Change Everything. New York: Dutton. Benyamin, B., Pourcain, B. St., Davis, O. S., Davies, G., Hansell, M. K., Brion, M.-J. A., Kirkpatrick, R. M., et al. 2013. “Childhood Intelligence is Heritable, Highly Polygenic and Associated With FNBP1L.” Molecular Psychiatry (January 23). Berg, Joyce E., and Rietz, Thomas A. 2003. “Prediction Markets as Decision Support Systems.” Information Systems Frontiers 5 (1): 79–93. Berger, Theodore W., Chapin, J. K., Gerhardt, G. A., Soussou, W. V., Taylor, D. M., and Tresco, P. A., eds. 2008. Brain–Computer Interfaces: An International Assessment of Research and Development Trends. Springer. Berger, T. W., Song, D., Chan, R. H., Marmarelis, V. Z., LaCoss, J., Wills, J., Hampson, R. E., Deadwyler, S.


pages: 457 words: 128,838

The Age of Cryptocurrency: How Bitcoin and Digital Money Are Challenging the Global Economic Order by Paul Vigna, Michael J. Casey

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3D printing, Airbnb, altcoin, bank run, banking crisis, bitcoin, blockchain, Bretton Woods, California gold rush, capital controls, carbon footprint, clean water, collaborative economy, collapse of Lehman Brothers, Columbine, Credit Default Swap, cryptocurrency, David Graeber, disintermediation, Edward Snowden, Elon Musk, ethereum blockchain, fiat currency, financial innovation, Firefox, Flash crash, Fractional reserve banking, hacker house, Hernando de Soto, high net worth, informal economy, intangible asset, Internet of things, inventory management, Julian Assange, Kickstarter, Kuwabatake Sanjuro: assassination market, litecoin, Long Term Capital Management, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, McMansion, means of production, Menlo Park, mobile money, money: store of value / unit of account / medium of exchange, Network effects, new economy, new new economy, Nixon shock, offshore financial centre, payday loans, Pearl River Delta, peer-to-peer, peer-to-peer lending, pets.com, Ponzi scheme, prediction markets, price stability, profit motive, QR code, RAND corporation, regulatory arbitrage, rent-seeking, reserve currency, Robert Shiller, Robert Shiller, Satoshi Nakamoto, seigniorage, shareholder value, sharing economy, short selling, Silicon Valley, Silicon Valley startup, Skype, smart contracts, special drawing rights, Spread Networks laid a new fibre optics cable between New York and Chicago, Steve Jobs, supply-chain management, Ted Nelson, The Great Moderation, the market place, the payments system, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, tulip mania, Turing complete, Tyler Cowen: Great Stagnation, Uber and Lyft, underbanked, WikiLeaks, Y Combinator, Y2K, zero-sum game, Zimmermann PGP

But from a narrative perspective it doesn’t feel as good. There are individual losses and socialized gains.” Asked to describe the job market if and when the kinds of decentralized autonomous companies envisaged by his firm become prevalent, BitShares CEO Daniel Larimer confidently predicts that these projects “can create millions of information-based jobs.” What’s more, he says, blockchain-based prediction markets, where people buy and sell contracts that pay out depending on how accurately they predict an event, will create new moneymaking opportunities in the intermediary industries destined for disruption. “If you’re a middleman in the lending industry or a middleman in commodities, or have medical knowledge, you know that industry better than anyone else, which means you can take the knowledge you have and turn it into value,” Larimer says.

These, he insists, are not “make-work jobs” in which people “dig holes and fill them in”; they are “high-end value-producing jobs.” Larimer’s jobs-for-everyone utopianism—the pervading ethos of Silicon Valley, shared by many bitcoiners—glosses over how many, if not most, people find change difficult. Not all, and perhaps not many, laid-off workers can easily pick themselves up and parlay their knowledge into making an income from speculative trading on a BitShares prediction market. To many it will seem like a form of gambling. To subject their lives to such uncertainty is anathema to people who’ve expected a salaried job to last a lifetime and to provide security and permanence. People will have to figure out how to apply their particular skills to this Brave New World and, if they can’t apply them, how to rapidly acquire the right skills. As Tyler Cowen noted in his book Average Is Over, “The key questions will be: Are you good at working with intelligent machines or not?

How I Became a Quant: Insights From 25 of Wall Street's Elite by Richard R. Lindsey, Barry Schachter

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Albert Einstein, algorithmic trading, Andrew Wiles, Antoine Gombaud: Chevalier de Méré, asset allocation, asset-backed security, backtesting, bank run, banking crisis, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, Brownian motion, business process, buy low sell high, capital asset pricing model, centre right, collateralized debt obligation, commoditize, computerized markets, corporate governance, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, discounted cash flows, disintermediation, diversification, Donald Knuth, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, full employment, George Akerlof, Gordon Gekko, hiring and firing, implied volatility, index fund, interest rate derivative, interest rate swap, John von Neumann, linear programming, Loma Prieta earthquake, Long Term Capital Management, margin call, market friction, market microstructure, martingale, merger arbitrage, Myron Scholes, Nick Leeson, P = NP, pattern recognition, Paul Samuelson, pensions crisis, performance metric, prediction markets, profit maximization, purchasing power parity, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Richard Feynman, Richard Feynman, Richard Stallman, risk-adjusted returns, risk/return, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, sorting algorithm, statistical arbitrage, statistical model, stem cell, Steven Levy, stochastic process, systematic trading, technology bubble, The Great Moderation, the scientific method, too big to fail, trade route, transaction costs, transfer pricing, value at risk, volatility smile, Wiener process, yield curve, young professional

For all of you except the fourth group, we are pretty sure this book will provide considerable satisfaction. (For the fourth group, who knows?) By way of introduction, we will explain from our perspective the roots, roles, and contributions of the Wall Street quant. We begin by defining the Quant. Mark Joshi, a famous quant, has proposed this definition: A quant designs and implements mathematical models for the pricing of derivatives, assessment of risk, or predicting market movements.1 1 JWPR007-Lindsey 2 May 7, 2007 18:27 h ow i b e cam e a quant Perhaps some of the terms used in this definition require definition themselves. A mathematical model is a formula, equation, group of equations, or computational algorithm that attempts to explain some type of relationship. For example, Einstein’s famous e = mc 2 is a model that describes the relationship between energy and mass.

The Chicago Board Options Exchange (CBOE) became the first organized exchange to have regular trading in equity options. A humble beginning, certainly, as only 911 option contracts were traded on 16 different equities. Now, each year, hundreds of millions of equity option contracts on thousands of companies trade on dozens of exchanges (both physical and electronic) around the world. The key ingredient that ties quants to derivatives and the other two functions identified by Joshi (risk assessment and predicting markets) is mathematical know-how. The Black-Scholes option pricing formula is a good example of this. JWPR007-Lindsey May 7, 2007 18:27 Introduction 3 The model, as it was first presented, was obtained by employing a result from physics, the solution to a particular partial differential equation called the heat-transfer equation. The level of abstractness involved in this work frequently inspires awe, fear, and even derision among nonquants.


pages: 200 words: 54,897

Flash Boys: Not So Fast: An Insider's Perspective on High-Frequency Trading by Peter Kovac

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bank run, barriers to entry, bash_history, Bernie Madoff, computerized markets, computerized trading, Flash crash, housing crisis, index fund, locking in a profit, London Whale, market microstructure, merger arbitrage, prediction markets, price discovery process, Sergey Aleynikov, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, zero day

The value of its ability to buy Microsoft from you at $30 a share and to hold the shares for a few microseconds – knowing that, even if the Microsoft share price began to fall, it could turn around and sell the shares at $30.01 – was determined by how likely it was that Microsoft’s share price, in those magical microseconds, would rise in price.” Lewis faces a dilemma: how can you argue that volatility – the unpredictability of a stock’s price – somehow benefits high-frequency traders, whom he has been arguing make all their money by predicting market moves? It seems that unpredictability would ruin whatever scam they have going. It’s a very difficult argument to make, even more so given the principle of “adverse selection.” Adverse selection is the fancy economic term that means that a market-maker who sticks his neck out is the first one to get stuck with a losing position when the market drops. The more volatile the markets, the more likely he gets stuck with losing positions.


pages: 188 words: 9,226

Collaborative Futures by Mike Linksvayer, Michael Mandiberg, Mushon Zer-Aviv

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4chan, Benjamin Mako Hill, British Empire, citizen journalism, cloud computing, collaborative economy, corporate governance, crowdsourcing, Debian, en.wikipedia.org, Firefox, informal economy, jimmy wales, Kickstarter, late capitalism, loose coupling, Marshall McLuhan, means of production, Naomi Klein, Network effects, optical character recognition, packet switching, postnationalism / post nation state, prediction markets, Richard Stallman, semantic web, Silicon Valley, slashdot, Slavoj Žižek, stealth mode startup, technoutopianism, the medium is the message, The Wisdom of Crowds, web application

“Open Innovation” is a practice that is somewhere between open science and business. Open Innovation refers to a collection of tools and methods for enabling more collaboration. Some of these Open Innovation tools include crowdsourcing of research expertise which is being lead by a company called InnoCentive, patent pools, end-user innovation which Erik von Hippel documented in Democratizing Innovation, and wisdom of the crowds methods such as prediction markets. 117 Reputation is an important question for many forms of collaboration, but particularly in science, where careers are determined primarily by one narrow metric of reputation—publication. If the above phenomena are to reach their full potential, they will have to be aligned with scientific career incentives. This means new reputation systems that take into account, for example, re-use of published data and code, and the impact of granular online contributions, must be developed and adopted.


pages: 161 words: 44,488

The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology by William Mougayar

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Airbnb, airport security, Albert Einstein, altcoin, Amazon Web Services, bitcoin, Black Swan, blockchain, business process, centralized clearinghouse, Clayton Christensen, cloud computing, cryptocurrency, disintermediation, distributed ledger, Edward Snowden, en.wikipedia.org, ethereum blockchain, fault tolerance, fiat currency, fixed income, global value chain, Innovator's Dilemma, Internet of things, Kevin Kelly, Kickstarter, market clearing, Network effects, new economy, peer-to-peer, peer-to-peer lending, prediction markets, pull request, QR code, ride hailing / ride sharing, Satoshi Nakamoto, sharing economy, smart contracts, social web, software as a service, too big to fail, Turing complete, web application

New Companies & Behaviors Online identity and reputation will be decentralized. We will own the data that belongs to us. We will self-manage our online reputations, and as we interact with various people or businesses, only the relevant slices of data will be revealed to them. Cryptocurrency-only banks will emerge, offering a variety of financial services based on virtual currencies. Decentralized prediction markets will enter the mainstream and offer frequent and credible predictions. Distributed Autonomous Organizations (DAOs) will become viable, with self-governed operations and user-generated value creation that tie-back directly to services and financial rewards. Spontaneous and trusted commerce will happen between peers, without central intermediaries, and with little to no friction. Content distribution and attributions will be signed on the blockchain in irrefutable ways.

The Handbook of Personal Wealth Management by Reuvid, Jonathan.

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asset allocation, banking crisis, BRICs, collapse of Lehman Brothers, correlation coefficient, credit crunch, cross-subsidies, diversification, diversified portfolio, estate planning, financial deregulation, fixed income, high net worth, income per capita, index fund, interest rate swap, laissez-faire capitalism, land tenure, market bubble, merger arbitrage, negative equity, new economy, Northern Rock, pattern recognition, Ponzi scheme, prediction markets, Right to Buy, risk tolerance, risk-adjusted returns, risk/return, short selling, side project, sovereign wealth fund, statistical arbitrage, systematic trading, transaction costs, yield curve

Art loans are also a practice long offered by Christie’s and at Bank of America. Contemporary art accounts for much of the current interest in this field, owing to greater liquidity, frequent trades and a growing belief in its long-term value. Developing these activities, some have proposed an art exchange as a mechanism for the issuing of securities with art or collections as the underlying asset. So far, it is possible to trade art in the prediction market via the website Intrade. A related development has been the continued growth in rental income from art. In 1972, the Canadian government was the first to set up an art rental project aimed at government agencies. It allocated $5m over five years. With some 18,000 paintings, prints, photographs and sculptures by over 2,500 artists, the Canada Council Art Bank is now home to the largest collection of contemporary Canadian art and is the most important player in the Canadian art market.


pages: 260 words: 76,223

Ctrl Alt Delete: Reboot Your Business. Reboot Your Life. Your Future Depends on It. by Mitch Joel

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3D printing, Amazon Web Services, augmented reality, call centre, clockwatching, cloud computing, Firefox, future of work, ghettoisation, Google Chrome, Google Glasses, Google Hangouts, Khan Academy, Kickstarter, Kodak vs Instagram, Lean Startup, Marc Andreessen, Mark Zuckerberg, Network effects, new economy, Occupy movement, place-making, prediction markets, pre–internet, QR code, recommendation engine, Richard Florida, risk tolerance, self-driving car, Silicon Valley, Silicon Valley startup, Skype, social graph, social web, Steve Jobs, Steve Wozniak, Thomas L Friedman, Tim Cook: Apple, Tony Hsieh, white picket fence, WikiLeaks, zero-sum game

The only real question is this: If a multimillion-dollar business can be developed and managed by one person with a laptop in an apartment, what happens to your business and your job as this rapid innovation and digitization continues to ripple through every industry? There is a startling realization in Arment’s story: There’s a good chance that your current business (or job) may not even be around in the next five years. Wacky prediction? Marketing hype to sell a book? It isn’t. Arment’s story isn’t an anomaly. Did you ever think it was even possible to have a one-person company with well over one million customers and a multimillion-dollar valuation? Welcome to purgatory (or heaven, if you’re Marco Arment). Apple co-founder and former CEO Steve Jobs once said, “If you don’t cannibalize yourself, someone else will.” That’s what Arment did… that’s what you need to start thinking about as well—whether it’s your job, your business, or your career.


pages: 309 words: 78,361

Plenitude: The New Economics of True Wealth by Juliet B. Schor

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Asian financial crisis, big-box store, business climate, carbon footprint, cleantech, Community Supported Agriculture, creative destruction, credit crunch, Daniel Kahneman / Amos Tversky, decarbonisation, dematerialisation, demographic transition, deskilling, Edward Glaeser, en.wikipedia.org, Gini coefficient, global village, income inequality, income per capita, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Joseph Schumpeter, Kenneth Arrow, knowledge economy, life extension, McMansion, new economy, peak oil, pink-collar, post-industrial society, prediction markets, purchasing power parity, ride hailing / ride sharing, Robert Shiller, Robert Shiller, sharing economy, Simon Kuznets, single-payer health, smart grid, The Chicago School, Thomas L Friedman, Thomas Malthus, too big to fail, transaction costs, Zipcar

Each year, the U.S. government underwrites the oil, gas, nuclear, and coal industries to the tune of tens (or, depending on one’s definition, hundreds) of billions, allows them to emit carbon without financial liability, and supports a massive automobile infrastructure and chemical-intensive farming. Finally, the assumption of an all-knowing market requires almost superhuman informational and processing demands. Investors have to understand the scientific evidence, combine it with economic, political, social, psychological, and demographic data, and predict market movements. Relying on market prices as indicators of the state of the natural world is risky, if not reckless. Trade-off Economics: The Unbearable Costliness of Nature Accounts that emphasize the ability of the market to develop alternatives for nature or to clean up the messes caused by growth tend to be thinking in the medium or long term. In the Environmental Kuznets Curve story, it’s only after decades of economic development that people begin to take action to clean up their air, water, and soil.


pages: 300 words: 77,787

Investing Demystified: How to Invest Without Speculation and Sleepless Nights by Lars Kroijer

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Andrei Shleifer, asset allocation, asset-backed security, Bernie Madoff, bitcoin, Black Swan, BRICs, Carmen Reinhart, cleantech, compound rate of return, credit crunch, diversification, diversified portfolio, equity premium, estate planning, fixed income, high net worth, implied volatility, index fund, intangible asset, invisible hand, Kenneth Rogoff, market bubble, money market fund, passive investing, pattern recognition, prediction markets, risk tolerance, risk/return, Robert Shiller, Robert Shiller, selection bias, sovereign wealth fund, too big to fail, transaction costs, Vanguard fund, yield curve, zero-coupon bond

During crashes like 2008 there is a natural tendency for everyone to have a view on the markets. The markets will dominate the headlines and be a topic of conversation at work, the gym, meals out, in homes and everywhere else. How can you not have a view? The point is that we still don’t have a view. While many people with the benefit of hindsight say they saw the rebound, ‘just’ because there is great market turbulence does not mean that an investor is better able to predict market movements. We don’t consider ourselves smarter than the average dollar invested in the market, and that average dollar put the S&P 500 at an index value of under 700 in March 2009. The fact that four years later we see that same index trading around its all-time highs does not mean that we could predict in March 2009 that this would be the case. When you look back at the 2008–09 crises or any crises preceding it, many people have a sense that there is a bottom somewhere, and great profits to be made for investors who find the bottom.

The Techno-Human Condition by Braden R. Allenby, Daniel R. Sarewitz

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airport security, augmented reality, carbon footprint, clean water, cognitive dissonance, conceptual framework, creative destruction, Credit Default Swap, decarbonisation, facts on the ground, friendly fire, industrial cluster, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Isaac Newton, Jane Jacobs, land tenure, life extension, Long Term Capital Management, market fundamentalism, mutually assured destruction, nuclear winter, Peter Singer: altruism, planetary scale, prediction markets, Ralph Waldo Emerson, Ray Kurzweil, Silicon Valley, smart grid, source of truth, stem cell, Stewart Brand, technoutopianism, the built environment, The Wealth of Nations by Adam Smith, transcontinental railway, Whole Earth Catalog

Might human-enhancement technologies threaten even that fundamental assumption? (Put another way, could we design "selfishness" out of the human?) We doubt it, yet the larger point is this: Even if it seems as if we are simply modifying the constitution of humanness at the individual level, the systems-level effects of tens of millions of such modifications may plausibly begin to manifest in system-wide changes in human values and behaviors that cannot possibly be predicted. Markets are an information-processing mechanism built on, and assuming, stable legal, cultural, and institutional foundations, and a stable idea of what humans are; enhancement renders those foundations contingent and unpredictably changing. Markets assume a particular context; enhancement transforms it. Like the network of vaccination that provides group immunity, or the network of communications that makes your cell phone more than just an over-engineered paperweight, enhancement technologies do not live quietly at one particular level.

Economic Gangsters: Corruption, Violence, and the Poverty of Nations by Raymond Fisman, Edward Miguel

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accounting loophole / creative accounting, Andrei Shleifer, Asian financial crisis, barriers to entry, blood diamonds, clean water, colonial rule, congestion charging, crossover SUV, Donald Davies, European colonialism, failed state, feminist movement, George Akerlof, income inequality, income per capita, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Live Aid, mass immigration, megacity, oil rush, prediction markets, random walk, Scramble for Africa, selection bias, Silicon Valley, South China Sea, unemployed young men

The value of futures of the Standard and Poors 500 Index— a measure of what investors expected to happen to stock values the next morning—jumped by over 1 percent at around 2 a.m. on election night in 2000 when Bush was declared the winner in Florida, only to drop back down a couple of hours later when the Florida outcome was rescinded. Refer to “Partisan Impacts on the Economy: Evidence from Prediction Markets and Close Elections,” by Erik Snowberg, Justin Wolfers, and Eric Zitzewitz in the Quarterly Journal of Economics (2007) for details. 5. “Health matters for Suharto’s children,” Financial Times, January 3, 1998. 6. A question that we often get when discussing these results is whether we personally profited in some way from our interest in 217 N O TES the stock market reaction to Suharto’s health.


pages: 297 words: 103,910

Free culture: how big media uses technology and the law to lock down culture and control creativity by Lawrence Lessig

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Brewster Kahle, Cass Sunstein, creative destruction, future of journalism, George Akerlof, Innovator's Dilemma, Internet Archive, invention of the printing press, Kenneth Arrow, Kevin Kelly, knowledge economy, Louis Daguerre, new economy, prediction markets, prisoner's dilemma, profit motive, rent-seeking, Richard Florida, Richard Stallman, Ronald Coase, Ronald Reagan, Saturday Night Live, Silicon Valley, software patent, transaction costs

Because here we have hundreds of thousands of webcasters who want to pay, and that should establish the market rate, and if you set the rate so high, you're going to drive the small webcasters out of business… ." And the RIAA experts said, "Well, we don't really model this as an industry with thousands of webcasters, we think it should be an industry with, you know, five or seven big players who can pay a high rate and it's a stable, predictable market." (Emphasis added.) Translation: The aim is to use the law to eliminate competition, so that this platform of potentially immense competition, which would cause the diversity and range of content available to explode, would not cause pain to the dinosaurs of old. There is no one, on either the right or the left, who should endorse this use of the law. And yet there is practically no one, on either the right or the left, who is doing anything effective to prevent it. 3.


pages: 317 words: 84,400

Automate This: How Algorithms Came to Rule Our World by Christopher Steiner

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23andMe, Ada Lovelace, airport security, Al Roth, algorithmic trading, backtesting, big-box store, Black-Scholes formula, call centre, cloud computing, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, Donald Trump, Douglas Hofstadter, dumpster diving, Flash crash, Gödel, Escher, Bach, High speed trading, Howard Rheingold, index fund, Isaac Newton, John Markoff, John Maynard Keynes: technological unemployment, knowledge economy, late fees, Marc Andreessen, Mark Zuckerberg, market bubble, medical residency, money market fund, Myron Scholes, Narrative Science, PageRank, pattern recognition, Paul Graham, Pierre-Simon Laplace, prediction markets, quantitative hedge fund, Renaissance Technologies, ride hailing / ride sharing, risk tolerance, Sergey Aleynikov, side project, Silicon Valley, Skype, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, upwardly mobile, Watson beat the top human players on Jeopardy!, Y Combinator

In the case of a stock trader, an algorithm that seeks to capture the spread between a stock’s bid and offer prices can be thwarted when the market moves, which can leave the trader and his algorithm with only one side—the wrong side—of a trade. If an algorithm bought Microsoft at the bid of $50.00 and then failed to sell it within the same several seconds at the ask of $50.02 before the market went down, the trader will be taking a loss when he sells at the new ask of $49.99. These algorithms are built to predict market direction. To be successful, the algorithm has to be right only 51 percent of the time. But a 51 percent probability could still leave a high-frequency trader open to big losses if he trades only ten times a day. There could be plenty of days where he loses on seven of ten trades. But high-frequency traders are called “high frequency” for a reason. They make tens of thousands of trades a day representing millions of shares.


pages: 327 words: 91,351

Traders at Work: How the World's Most Successful Traders Make Their Living in the Markets by Tim Bourquin, Nicholas Mango

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algorithmic trading, automated trading system, backtesting, commodity trading advisor, Credit Default Swap, Elliott wave, fixed income, Long Term Capital Management, paper trading, pattern recognition, prediction markets, risk tolerance, Small Order Execution System, statistical arbitrage, The Wisdom of Crowds, transaction costs, zero-sum game

There’s only one reason: people are willing to pay higher prices than you paid and will continue to pay higher prices, for whatever reason. They might say the reason is because they like the company, or they feel good about the economy. But it doesn’t matter what the reason is. The action is what counts. The action that’s required is other people have to pay higher prices. As soon as other people—fund managers, hedge funds, whoever these people are—decide to not pay higher prices, stock XYZ will no longer go up. In order to predict markets, the people who really figured out the game are predicting people, and I can tell you that some of the more successful hedge funds understand this. They’re looking at the market not so much through fundamental analysis and technical analysis. They are trying to sense it. They want to predict what people are going to do. I’ll tell you, there’s a real interesting piece of research that came out a couple of years ago from the California Institute of Technology.


pages: 299 words: 91,839

What Would Google Do? by Jeff Jarvis

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23andMe, Amazon Mechanical Turk, Amazon Web Services, Anne Wojcicki, barriers to entry, Berlin Wall, business process, call centre, cashless society, citizen journalism, clean water, commoditize, connected car, credit crunch, crowdsourcing, death of newspapers, disintermediation, diversified portfolio, don't be evil, fear of failure, Firefox, future of journalism, Google Earth, Googley, Howard Rheingold, informal economy, inventory management, Jeff Bezos, jimmy wales, Kevin Kelly, Mark Zuckerberg, moral hazard, Network effects, new economy, Nicholas Carr, old-boy network, PageRank, peer-to-peer lending, post scarcity, prediction markets, pre–internet, Ronald Coase, search inside the book, Silicon Valley, Skype, social graph, social software, social web, spectrum auction, speech recognition, Steve Jobs, the medium is the message, The Nature of the Firm, the payments system, The Wisdom of Crowds, transaction costs, web of trust, Y Combinator, Zipcar

Rather than buying seats only from the airline, if late-booking passengers could also buy seats from fellow customers in an open marketplace, that could solve some of the airlines’ overbooking problems, reducing the need to pay bumped fliers. Yes, speculators could arbitrage seats, but if they’re paid-for and nonrefundable, what problem is that for the airline? Resellers become market makers. This exchange sets a new market value for seats that in some cases will be higher than the airlines’ own fares. The airline could use the exchange as a prediction market to forecast and maximize load. It might see a surge in demand for a destination, perhaps for reasons it could not predict (a new conference or festival, good media coverage for a getaway, a travel bargain, or currency fluctuations unleashing pent-up demand). With sufficient notice, the airline could add capacity, which would keep it ahead of arbitrageurs. The airline always controls supply and now it would know more about demand.


pages: 422 words: 104,457

Dragnet Nation: A Quest for Privacy, Security, and Freedom in a World of Relentless Surveillance by Julia Angwin

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AltaVista, Ayatollah Khomeini, barriers to entry, bitcoin, Chelsea Manning, Chuck Templeton: OpenTable, clean water, crowdsourcing, cuban missile crisis, data is the new oil, David Graeber, Debian, Edward Snowden, Filter Bubble, Firefox, GnuPG, Google Chrome, Google Glasses, informal economy, Jacob Appelbaum, John Markoff, Julian Assange, Marc Andreessen, market bubble, market design, medical residency, meta analysis, meta-analysis, mutually assured destruction, prediction markets, price discrimination, randomized controlled trial, RFID, Robert Shiller, Ronald Reagan, security theater, Silicon Valley, Silicon Valley startup, Skype, smart meter, Steven Levy, Upton Sinclair, WikiLeaks, Y2K, zero-sum game, Zimmermann PGP

In 1996, self-proclaimed Internet anarchist Jim Bell posted on an Internet forum an essay titled “Assassination Politics,” describing how anonymous cash could enable the establishment of cash prizes to people who correctly “predict” somebody’s death. “It would be possible to make such awards in such a way so that nobody knows who is getting awarded the money, only that the award is being given.” Bell described this death prediction market as a way to punish “violators of rights” by putting a price on their heads. “Consider how history might have changed if we’d been able to ‘bump off’ Lenin, Stalin, Hitler, Mussolini, Tojo, Kim Il Sung, Ho Chi Minh, Ayatollah Khomeini, Saddam Hussein, Moammar Khadafi, and various others, along with all of their replacements if necessary, all for a measly few million dollars,” he wrote. Bell’s idea of placing “bounties” on the heads of government officials wasn’t well received.


pages: 292 words: 85,151

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It) by Salim Ismail, Yuri van Geest

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23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, bioinformatics, bitcoin, Black Swan, blockchain, Burning Man, business intelligence, business process, call centre, chief data officer, Chris Wanstrath, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, commoditize, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, Dean Kamen, dematerialisation, discounted cash flows, distributed ledger, Edward Snowden, Elon Musk, en.wikipedia.org, ethereum blockchain, Galaxy Zoo, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, Hyperloop, industrial robot, Innovator's Dilemma, intangible asset, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, lifelogging, loose coupling, loss aversion, Lyft, Marc Andreessen, Mark Zuckerberg, market design, means of production, minimum viable product, natural language processing, Netflix Prize, Network effects, new economy, Oculus Rift, offshore financial centre, p-value, PageRank, pattern recognition, Paul Graham, peer-to-peer, peer-to-peer model, Peter H. Diamandis: Planetary Resources, Peter Thiel, prediction markets, profit motive, publish or perish, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, subscription business, supply-chain management, TaskRabbit, telepresence, telepresence robot, Tony Hsieh, transaction costs, Tyler Cowen: Great Stagnation, urban planning, WikiLeaks, winner-take-all economy, X Prize, Y Combinator, zero-sum game

There is an interesting difference of opinion over how much data should be used based on the nature of the market in which the organization operates. While conventional wisdom says to gather as much data as possible (hence the term Big Data), psychologist Gerd Gigerenzer cautions that in uncertain markets, it is better to simplify, use heuristics and rely on fewer variables. In stable and predictable markets, on the other hand, he recommends organizations “complexify” and use algorithms with more variables. One of the leaders in gleaning insights from massive amounts of data is Palantir. Founded in 2004, Palantir builds government, commercial and health software solutions that empower organizations to make sense of disparate data. By handling technical problems, Palantir liberates its customers to focus on solving human ones.


pages: 313 words: 95,077

Here Comes Everybody: The Power of Organizing Without Organizations by Clay Shirky

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Andrew Keen, Berlin Wall, bioinformatics, Brewster Kahle, c2.com, crowdsourcing, en.wikipedia.org, hiring and firing, hive mind, Howard Rheingold, Internet Archive, invention of agriculture, invention of movable type, invention of the printing press, invention of the telegraph, jimmy wales, Kuiper Belt, liberation theology, lump of labour, Mahatma Gandhi, means of production, Merlin Mann, Metcalfe’s law, Nash equilibrium, Network effects, Nicholas Carr, Picturephone, place-making, Pluto: dwarf planet, prediction markets, price mechanism, prisoner's dilemma, profit motive, Richard Stallman, Robert Metcalfe, Ronald Coase, Silicon Valley, slashdot, social software, Stewart Brand, supply-chain management, The Nature of the Firm, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, transaction costs, ultimatum game, Vilfredo Pareto, Yogi Berra

As a result, such groups are better able to produce what James Surowiecki has called “the wisdom of crowds.” In his book of that name he identified the ways distributed groups whose members aren’t connected can often generate better answers, by pooling their knowledge or intuition without having to come to an agreement. We have many ways of achieving this kind of aggregation, from market pricing mechanisms to voting to the prediction markets Surowiecki champions, but these methods all have two common characteristics: they work better in large groups, and they don’t require direct communication as the norm among members. (Indeed, in the case of markets, such communication is often forbidden, on the grounds that small clusters of collaborators can actually pervert the workings of the large system.) Small and large are relative rather than absolute.


pages: 357 words: 91,331

I Will Teach You To Be Rich by Sethi, Ramit

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Albert Einstein, asset allocation, buy low sell high, diversification, diversified portfolio, index fund, late fees, money market fund, mortgage debt, mortgage tax deduction, prediction markets, random walk, risk tolerance, Robert Shiller, Robert Shiller, shareholder value, Silicon Valley, survivorship bias, the rule of 72, Vanguard fund

See Financial expertise f Federal Deposit Insurance Corporation (FDIC), 52 FICO scores. See Credit scores Fidelity, 187, 192 Financial advisers, 153–55 Financial expertise, 143–58 active vs. passive management and, 155–58 engineering a perfect stock-picking record and, 151 legendary investors and, 149 market-timing newsletters and, 145 personal-finance blogs and, 152 pundits’ and fund managers’ inability to predict market and, 2–3, 145–50, 165, 168 ratings of stocks and funds and, 148–52 529s, 217 Fixed costs, 104–6, 107, 130 Flexo, 44–45 401(k)s, 77–82, 176 amount to contribute to, 76, 77, 89 automatic contributions to, 79–80, 82, 129, 132, 136 common concerns about, 80–81 early withdrawal of money from, 80, 81, 85, 212 employer match and, 71, 76, 78, 79, 81, 82, 89 investing money in, 4, 81, 83, 185–86, 189, 198, 201, 209 paying credit card debt with, 46 setting up, 77, 82, 90 statistics on, 71, 72 switching jobs and, 80–81 tax-deferred growth of, 78, 80, 81, 210, 211, 221 Freelancing, 120, 139–41 Conscious Spending Plan and, 141 quarterly estimated tax payments and, 135 Friends, money issues with, 221 Frugality: cheapness vs., 94–96 prioritizing spending and, 97 Fund managers, poor performance of, 144–51, 155 Fun money, 107, 108, 130 g Get Rich Slowly, 152 Gifts, saving money for, 106–7 Girlfriends.


pages: 345 words: 87,745

The Power of Passive Investing: More Wealth With Less Work by Richard A. Ferri

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asset allocation, backtesting, Bernie Madoff, capital asset pricing model, cognitive dissonance, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, endowment effect, estate planning, Eugene Fama: efficient market hypothesis, fixed income, implied volatility, index fund, intangible asset, Long Term Capital Management, money market fund, passive investing, Paul Samuelson, Ponzi scheme, prediction markets, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, survivorship bias, too big to fail, transaction costs, Vanguard fund, yield curve, zero-sum game

TrimTabs Investment Research, a consolidator of mutual fund flow data, concluded that equity prices tend to fall after equity exchange-traded funds (ETFs) rake in large sums of money and rise after equity ETFs post heavy outflows. Regression analysis suggests the probability that equity ETF flows are a contrary leading indicator of equity prices is more than 99 percent. This means the flow of ETF money predicts market changes with high accuracy—in the opposite direction!6 One mutual fund cash flow study after another has consistently shown the same performance chasing phenomenon. Fund styles with superior performance and high fund ratings raked in the most money, and this usually occurs close to the time when these investment styles peak in performance. Institutions Are Also Trend Followers Performance chasing isn’t limited to individual investors.


pages: 322 words: 84,752

Pax Technica: How the Internet of Things May Set Us Free or Lock Us Up by Philip N. Howard

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Affordable Care Act / Obamacare, Berlin Wall, bitcoin, blood diamonds, Bretton Woods, Brian Krebs, British Empire, call centre, Chelsea Manning, citizen journalism, clean water, cloud computing, corporate social responsibility, creative destruction, crowdsourcing, digital map, Edward Snowden, en.wikipedia.org, failed state, Fall of the Berlin Wall, feminist movement, Filter Bubble, Firefox, Francis Fukuyama: the end of history, Google Earth, Howard Rheingold, income inequality, informal economy, Internet of things, Julian Assange, Kibera, Kickstarter, land reform, M-Pesa, Marshall McLuhan, megacity, Mikhail Gorbachev, mobile money, Mohammed Bouazizi, national security letter, Network effects, obamacare, Occupy movement, packet switching, pension reform, prediction markets, sentiment analysis, Silicon Valley, Skype, spectrum auction, statistical model, Stuxnet, trade route, uranium enrichment, WikiLeaks, zero day

As we learned in the previous chapter, this is not just about governments but about ways of organizing civic life. It won’t matter whether China’s government adopts an open-data policy any time soon—the internet of things will generate enough data to keep many China watchers busy. People are creatively playing with data, some of it gleaned from reluctant governments. Some of that data feeds interesting predictive markets. Such markets don’t always work well, and because they trade in odds, you can count the number of times they don’t work at all. The models are getting better, and specialists use them to gauge popular and expert opinion on likely political outcomes. When will a dictator fall from power? Will Russia claim more territory from neighboring countries? Someone is taking bets, and someone else is data mining and running experimental conflict models.71 With global supplies of data about us being used globally, we are going to have to rethink our assumptions of national sovereignty.


pages: 356 words: 105,533

Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market by Scott Patterson

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algorithmic trading, automated trading system, banking crisis, bash_history, Bernie Madoff, butterfly effect, buttonwood tree, Chuck Templeton: OpenTable, cloud computing, collapse of Lehman Brothers, computerized trading, creative destruction, Donald Trump, fixed income, Flash crash, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, Joseph Schumpeter, latency arbitrage, Long Term Capital Management, Mark Zuckerberg, market design, market microstructure, pattern recognition, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, Sergey Aleynikov, Small Order Execution System, South China Sea, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stochastic process, transaction costs, Watson beat the top human players on Jeopardy!, zero-sum game

He was so skilled at discovering patterns in the market’s daily ebb and flow that he’d risen to the top of the trading world, working first at an elite Chicago firm, packed with math and physics Ph.D.s, called Hull Trading, then inside a top secret quantitative derivatives operation at Goldman Sachs, before taking over a powerful global desk at UBS, the giant Swiss bank. In 2007, he broke out on his own and convinced twenty-five top-notch traders, programmers, and quants (an industry term for mathematicians who use quantitative techniques to predict markets) from across Wall Street to join him. He set up shop in Stamford and launched Trading Machines just as signs emerged of an impending global financial crisis. It had amounted to one of the most ambitious trading projects outside a large investment bank in years. Despite the bad timing, Trading Machines had fared well in its debut, posting a tidy profit during a time when most of Wall Street was imploding.


pages: 440 words: 108,137

The Meritocracy Myth by Stephen J. McNamee

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affirmative action, Affordable Care Act / Obamacare, Bernie Madoff, British Empire, collective bargaining, computer age, conceptual framework, corporate governance, deindustrialization, delayed gratification, demographic transition, desegregation, deskilling, equal pay for equal work, estate planning, failed state, fixed income, gender pay gap, Gini coefficient, glass ceiling, helicopter parent, income inequality, informal economy, invisible hand, job automation, joint-stock company, labor-force participation, low-wage service sector, marginal employment, Mark Zuckerberg, mortgage debt, mortgage tax deduction, new economy, New Urbanism, obamacare, occupational segregation, old-boy network, pink-collar, Plutocrats, plutocrats, Ponzi scheme, post-industrial society, prediction markets, profit motive, race to the bottom, random walk, school choice, Scientific racism, Steve Jobs, The Bell Curve by Richard Herrnstein and Charles Murray, The Spirit Level, The Wealth of Nations by Adam Smith, too big to fail, trickle-down economics, upwardly mobile, We are the 99%, white flight, young professional

For all practical purposes, investing in stocks and bonds is equivalent to gambling. The biggest difference between playing the stock market and other forms of gambling is that the odds for winning in the stock market are much better, although the return is usually much less. The odds of “winning” in the stock market can be increased beyond random chance by knowing as much as possible about the companies in which one invests and by being able to predict market changes. Knowing about companies and markets, in part, is a matter of social and cultural capital—being in a loop composed of people with accurate and current information. As recent investment scandals have underscored, these odds can be increased illegally through insider trading. Apart from such schemes, however, there is still an element of investor risk. Indeed, the willingness to take chances and to risk capital is the primary justification for capitalism.


pages: 296 words: 86,610

The Bitcoin Guidebook: How to Obtain, Invest, and Spend the World's First Decentralized Cryptocurrency by Ian Demartino

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3D printing, AltaVista, altcoin, bitcoin, blockchain, buy low sell high, capital controls, cloud computing, corporate governance, crowdsourcing, cryptocurrency, distributed ledger, Edward Snowden, Elon Musk, ethereum blockchain, fiat currency, Firefox, forensic accounting, global village, GnuPG, Google Earth, Haight Ashbury, Jacob Appelbaum, Kevin Kelly, Kickstarter, litecoin, M-Pesa, Marc Andreessen, Marshall McLuhan, Oculus Rift, peer-to-peer, peer-to-peer lending, Ponzi scheme, prediction markets, QR code, ransomware, Satoshi Nakamoto, self-driving car, Skype, smart contracts, Steven Levy, the medium is the message, underbanked, WikiLeaks, Zimmermann PGP

Ethereum is more than a coin, describing itself thus: “Ethereum is a decentralized platform that runs smart contracts: applications that run exactly as programmed without any possibility of downtime, censorship, fraud or third party interference. Ethereum is how the Internet was supposed to work.” As with Nxt and CounterParty, it allows for asset/token creation. But more than that, Ethereum has its own programming language built in, which has resulted in some very exciting projects. These include Augur, a crowd-powered prediction market where people with the Augur coin are incentivized to vote on whether a prediction came true or not. (How this works and how it keeps people honest is beyond the scope of this chapter.) There is a Bitcoin-like coin in Ethereum, called Ether, that makes the system run, secures the network and settles contracts. What has really made headlines, however, is that major technology companies, banks and financial institutions around the world have announced they are working on projects using Ethereum.

Griftopia: Bubble Machines, Vampire Squids, and the Long Con That Is Breaking America by Matt Taibbi

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affirmative action, Affordable Care Act / Obamacare, Bernie Sanders, Bretton Woods, carried interest, clean water, collateralized debt obligation, collective bargaining, computerized trading, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, David Brooks, desegregation, diversification, diversified portfolio, Donald Trump, financial innovation, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, illegal immigration, interest rate swap, laissez-faire capitalism, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, medical malpractice, money market fund, moral hazard, mortgage debt, obamacare, passive investing, Ponzi scheme, prediction markets, quantitative easing, reserve currency, Ronald Reagan, Sergey Aleynikov, short selling, sovereign wealth fund, too big to fail, trickle-down economics, Y2K, Yom Kippur War

Months later, as the bad news continued, Greenspan soldiered on: “Those who argue that we are already in a recession I think are reasonably certain to be wrong.” By October, with the U.S. in the sixth of what would ultimately be ten consecutive months of job losses, Greenspan remained stubborn. “The economy,” he said, “has not yet slipped into recession.” The economy has a lot in common with the weather, and even very good economists charged with the job of predicting market swings can become victims of unexpected turns, just like meteorologists. But Greenspan’s errors were often historic, idiotic blunders, evidence of a fundamental misunderstanding of problems that led to huge disasters. In fact, if you dig under almost every one of the major financial crashes of our time, you can find some kind of Greenspan quote cheerfully telling people not to worry about where the new trends in the economy were leading.


pages: 275 words: 84,980

Before Babylon, Beyond Bitcoin: From Money That We Understand to Money That Understands Us (Perspectives) by David Birch

agricultural Revolution, Airbnb, bank run, banks create money, bitcoin, blockchain, Bretton Woods, British Empire, Broken windows theory, Burning Man, capital controls, cashless society, Clayton Christensen, clockwork universe, creative destruction, credit crunch, cross-subsidies, crowdsourcing, cryptocurrency, David Graeber, dematerialisation, Diane Coyle, distributed ledger, double entry bookkeeping, ethereum blockchain, facts on the ground, fault tolerance, fiat currency, financial exclusion, financial innovation, financial intermediation, floating exchange rates, Fractional reserve banking, index card, informal economy, Internet of things, invention of the printing press, invention of the telegraph, invention of the telephone, invisible hand, Irish bank strikes, Isaac Newton, Jane Jacobs, Kenneth Rogoff, knowledge economy, Kuwabatake Sanjuro: assassination market, large denomination, M-Pesa, market clearing, market fundamentalism, Marshall McLuhan, Martin Wolf, mobile money, money: store of value / unit of account / medium of exchange, new economy, Northern Rock, Pingit, prediction markets, price stability, QR code, quantitative easing, railway mania, Ralph Waldo Emerson, Real Time Gross Settlement, reserve currency, Satoshi Nakamoto, seigniorage, Silicon Valley, smart contracts, social graph, special drawing rights, technoutopianism, the payments system, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, tulip mania, wage slave, Washington Consensus, wikimedia commons

For my retirement plan I need to find something unique to the virtual, which is why I was sufficiently interested in the novel concept of assassination markets to write about them. I was excited to find that an enterprising chap by the name of Kuwabatake Sanjuro had taken advantage of the invention of Bitcoin to set one up (Greenberg 2013). In case you are wondering what an assassination market is, it is a prediction market where any party can place a bet (using anonymous electronic money, and pseudonymous remailers) on the date of death of a given individual and collect a payoff if they ‘guess’ the date accurately. This would incentivize the assassination of specific individuals because the assassin, knowing when the action would take place, could profit by making an accurate bet on the time of the subject’s death.


pages: 437 words: 105,934

#Republic: Divided Democracy in the Age of Social Media by Cass R. Sunstein

A Declaration of the Independence of Cyberspace, affirmative action, Affordable Care Act / Obamacare, Bernie Sanders, Cass Sunstein, choice architecture, Donald Trump, drone strike, Erik Brynjolfsson, Filter Bubble, friendly fire, global village, illegal immigration, immigration reform, income inequality, Jane Jacobs, loss aversion, Mark Zuckerberg, obamacare, prediction markets, road to serfdom, Ronald Reagan, Silicon Valley, Skype, Snapchat, stem cell, The Chicago School, The Death and Life of Great American Cities, The Wisdom of Crowds

With social media, any one of us might be able to make a picture, a story, or a video clip available to all of us; YouTube is merely one example. In this way, social media have a powerful democratizing function.16 Countless websites are now aggregating diverse knowledge. For diverse products—books, movies, cars, doctors, and computers—it is easy to find sources that tell you what most people think, and it is easy as well to contribute to that collective knowledge. Prediction markets, for example, aggregate the judgments of numerous forecasters, and they are proving to be remarkably accurate. There is much to be said about the growing ability of consumers to be producers too.17 But that is not my topic here. I will provide little discussion of monopolistic behavior by suppliers, or manipulative practices by them. Undoubtedly some suppliers do try to monopolize, and some do try to manipulate; consider, for example, the fact that Google provides paid links for certain sites (but not others) or tailors search algorithms to present certain search results (over others).


Starstruck: The Business of Celebrity by Currid

barriers to entry, Bernie Madoff, Donald Trump, income inequality, index card, industrial cluster, labour mobility, Mark Zuckerberg, Metcalfe’s law, natural language processing, place-making, Ponzi scheme, post-industrial society, prediction markets, Renaissance Technologies, Richard Florida, Robert Metcalfe, rolodex, shareholder value, Silicon Valley, slashdot, transaction costs, upwardly mobile, urban decay, Vilfredo Pareto, winner-take-all economy

(Studios at least try to remain unconvinced in order to keep the salaries down.)56 If you believe Anita Elberse, a star is worth approximately $3 million in box office revenue. Elberse studied twelve hundred film-casting announcements and observed the influence of new information about a film (e.g., Matt Damon starring in the latest Bourne film or Julia Roberts joining Ocean’s Eleven) on the Hollywood Stock Exchange, an online prediction market that looks at a film’s expected revenue based on casting and financial decisions. Elberse found that the stars’ biggest impact is their ability to draw other stars to act in the film and to drum up financial backing for production. Stars do not, however, increase a studio’s valuation. Stars certainly obtain more value from their paycheck than the studios get in ticket sales. Elberse notes that stars are unable to generate substantial additional film revenue individually; the real revenue generation is produced through hiring a cast of stars.


pages: 391 words: 22,799

To Serve God and Wal-Mart: The Making of Christian Free Enterprise by Bethany Moreton

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affirmative action, American Legislative Exchange Council, anti-communist, Berlin Wall, big-box store, Bretton Woods, Buckminster Fuller, collective bargaining, corporate personhood, creative destruction, deindustrialization, desegregation, Donald Trump, estate planning, Fall of the Berlin Wall, Frederick Winslow Taylor, George Gilder, global village, informal economy, invisible hand, liberation theology, market fundamentalism, Mont Pelerin Society, mortgage tax deduction, Naomi Klein, new economy, New Urbanism, post-industrial society, postindustrial economy, prediction markets, price anchoring, Ralph Nader, RFID, road to serfdom, Ronald Reagan, Silicon Valley, Stewart Brand, strikebreaker, The Wealth of Nations by Adam Smith, union organizing, walkable city, Washington Consensus, white flight, Whole Earth Catalog, Works Progress Administration

Since the early 1970s, Wal-Â�Mart had courted investors with laid-Â� back annual meetings featuring fishing trips and barbecues, and by the mid-Â�1980s the national analysts could not ignore the home ofÂ�fice’s overtures. The result was an irresistible target for the Arkansans: an audience of slightly bewildered city folk, struggling to comprehend the company’s magic. With encouragement from Walton, Senior Vice President Ron Loveless elaborated on one of management’s typical in-Â�house gags 6 OUR FATHERS’ AMER I CA and presented it to the attentive crowd. “People often ask us how we predict market demand for discount merchandise,” Loveless began, and you’ve heard a lot of numbers today. But there is more to it€than that. We raise a good many chickens in Northwest Arkansas, and we’ve come to depend on them for what we call the Loveless Economic Indicator Report. You see, when times are good, you find plenty of dead chickens by the side of the road, ones that have fallen off the trucks.


pages: 561 words: 120,899

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy by Sharon Bertsch McGrayne

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Bayesian statistics, bioinformatics, British Empire, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, double helix, Edmond Halley, Fellow of the Royal Society, full text search, Henri Poincaré, Isaac Newton, John Markoff, John Nash: game theory, John von Neumann, linear programming, meta analysis, meta-analysis, Nate Silver, p-value, Pierre-Simon Laplace, placebo effect, prediction markets, RAND corporation, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, Ronald Reagan, speech recognition, statistical model, stochastic process, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Turing test, uranium enrichment, Yom Kippur War

Brown and Robert L. Mercer. They became comanagers of RenTech’s portfolio and technical trading. For several years, their Medallion Fund, limited to former and current employees, averaged annual returns of about 35%. The fund bought and sold shares so rapidly one day in 1997 that it accounted for more than 10% of all NASDAQ trades. To search for the nonrandom patterns and movements that will help predict markets, RenTech gathers as much information as possible. It begins with prior knowledge about the history of prices and how they fluctuate and correlate with each other. Then the company continuously updates that prior base. As Mercer explained, “RenTec gets a trillion bytes of data a day, from newspapers, AP wire, all the trades, quotes, weather reports, energy reports, government reports, all with the goal of trying to figure out what’s going to be the price of something or other at every point in the future. . . .


pages: 402 words: 110,972

Nerds on Wall Street: Math, Machines and Wired Markets by David J. Leinweber

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AI winter, algorithmic trading, asset allocation, banking crisis, barriers to entry, Big bang: deregulation of the City of London, butterfly effect, buttonwood tree, buy low sell high, capital asset pricing model, citizen journalism, collateralized debt obligation, corporate governance, Craig Reynolds: boids flock, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Danny Hillis, demand response, disintermediation, distributed generation, diversification, diversified portfolio, Emanuel Derman, en.wikipedia.org, experimental economics, financial innovation, fixed income, Gordon Gekko, implied volatility, index arbitrage, index fund, information retrieval, intangible asset, Internet Archive, John Nash: game theory, Kenneth Arrow, Khan Academy, load shedding, Long Term Capital Management, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, market fragmentation, market microstructure, Mars Rover, Metcalfe’s law, moral hazard, mutually assured destruction, Myron Scholes, natural language processing, negative equity, Network effects, optical character recognition, paper trading, passive investing, pez dispenser, phenotype, prediction markets, quantitative hedge fund, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Renaissance Technologies, Richard Stallman, risk tolerance, risk-adjusted returns, risk/return, Robert Metcalfe, Ronald Reagan, Rubik’s Cube, semantic web, Sharpe ratio, short selling, Silicon Valley, Small Order Execution System, smart grid, smart meter, social web, South Sea Bubble, statistical arbitrage, statistical model, Steve Jobs, Steven Levy, Tacoma Narrows Bridge, the scientific method, The Wisdom of Crowds, time value of money, too big to fail, transaction costs, Turing machine, Upton Sinclair, value at risk, Vernor Vinge, yield curve, Yogi Berra, your tax dollars at work

Trading in related securities or commodities ranks high. Macroeconomic indicators like inflation, interest rates, housing starts, and oil reserves all seem reasonable in the right context. For an idea of the many ways to predict, look for books on regression analysis, times series methods, classification and regression trees (CARTs), neural nets, and wavelets, to name a few. This doesn’t touch ideas like collective intelligence and prediction markets, which bring people into the mix. Doing this correctly can put you on the fast track to that hockey rink in the yard or the Forbes 400, or both. Doing this incorrectly will keep you off the streets for a while, but will prove detrimental to your financial health if you follow your own advice. In the next chapter, “Stupid Data Miner Tricks,” we explore the dark side of maximizing predictability.


pages: 369 words: 128,349

Beyond the Random Walk: A Guide to Stock Market Anomalies and Low Risk Investing by Vijay Singal

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3Com Palm IPO, Andrei Shleifer, asset allocation, capital asset pricing model, correlation coefficient, cross-subsidies, Daniel Kahneman / Amos Tversky, diversified portfolio, endowment effect, fixed income, index arbitrage, index fund, information asymmetry, liberal capitalism, locking in a profit, Long Term Capital Management, loss aversion, margin call, market friction, market microstructure, mental accounting, merger arbitrage, Myron Scholes, new economy, prediction markets, price stability, profit motive, random walk, Richard Thaler, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, survivorship bias, transaction costs, Vanguard fund

The second use of insider information is in trying to predict future direction of the broader market. If insiders can accurately predict how their company is likely to perform, then an aggregation of all insider recommendations should suggest whether the overall market or an industry is a buy, sell, or hold. The Vickers weekly insider report (go to vickers-stock.com) computes an “Insider Index” based on insider trading among all companies that is supposed to predict market direction. Similarly, Thomson provides its assessment of the market through its “Market Tearsheet” (go to insider.thomsonfn.com). The third source is WallStreetCity (go to www.wallstreetcity.com), which lists insider trading by industry. While these indices may be useful, the construction of the indices is critical. Detailed information on the construction of these indices is not available, and it is unclear whether these indices follow the criteria described above.


pages: 512 words: 162,977

New Market Wizards: Conversations With America's Top Traders by Jack D. Schwager

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backtesting, beat the dealer, Benoit Mandelbrot, Berlin Wall, Black-Scholes formula, butterfly effect, commodity trading advisor, computerized trading, Edward Thorp, Elliott wave, fixed income, full employment, implied volatility, interest rate swap, Louis Bachelier, margin call, market clearing, market fundamentalism, money market fund, paper trading, pattern recognition, placebo effect, prediction markets, Ralph Nelson Elliott, random walk, risk tolerance, risk/return, Saturday Night Live, Sharpe ratio, the map is not the territory, transaction costs, War on Poverty

Only by acting and thinking independently can a trader hope to know when a trade isn’t working out. If you ever find yourself tempted to seek out someone else’s opinion on a trade, that’s usually a sure sign that you should get out of your position. What are your goals? There’s no better satisfaction than playing a piece well, whether the instrument is a piano or the markets. I measure my progress not in dollars but in my skill in predicting market patterns—that is, in how close I can come to pinpointing my entries and exits to the market turns. I believe that I can go into any market with just a quote machine and out-trade 98 percent of the other traders. Over the next ten years, I would like to significantly step up my trading size. I really believe that I can become one of the best traders around. 310 / The New Market Wizard Certainly one of the primary common characteristics I have found among the great traders is an almost compelling sense of confidence in their ability to succeed.


pages: 528 words: 146,459

Computer: A History of the Information Machine by Martin Campbell-Kelly, William Aspray, Nathan L. Ensmenger, Jeffrey R. Yost

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Ada Lovelace, air freight, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Apple's 1984 Super Bowl advert, barriers to entry, Bill Gates: Altair 8800, borderless world, Buckminster Fuller, Build a better mousetrap, Byte Shop, card file, cashless society, cloud computing, combinatorial explosion, computer age, deskilling, don't be evil, Donald Davies, Douglas Engelbart, Douglas Engelbart, Dynabook, fault tolerance, Fellow of the Royal Society, financial independence, Frederick Winslow Taylor, game design, garden city movement, Grace Hopper, informal economy, interchangeable parts, invention of the wheel, Jacquard loom, Jacquard loom, Jeff Bezos, jimmy wales, John Markoff, John von Neumann, light touch regulation, linked data, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, natural language processing, Network effects, New Journalism, Norbert Wiener, Occupy movement, optical character recognition, packet switching, PageRank, pattern recognition, Pierre-Simon Laplace, pirate software, popular electronics, prediction markets, pre–internet, QWERTY keyboard, RAND corporation, Robert X Cringely, Silicon Valley, Silicon Valley startup, Steve Jobs, Steven Levy, Stewart Brand, Ted Nelson, the market place, Turing machine, Vannevar Bush, Von Neumann architecture, Whole Earth Catalog, William Shockley: the traitorous eight, women in the workforce, young professional

During 1980, with dozens of spreadsheet and word-processing packages on the market and the launch of the first database products, the potential of the personal computer as an office machine became clearly recognizable. At this point the traditional business-machine manufacturers, such as IBM, began to take an interest. THE IBM PC AND THE PC PLATFORM IBM was not, in fact, the giant that slept soundly during the personal-computer revolution. IBM had a sophisticated market research organization that attempted to predict market trends, and once the personal computer became clearly defined as a business machine in 1980, IBM reacted with surprising speed. The proposal that IBM should enter the personal-computer business came from William C. Lowe, a senior manager who headed the company’s “entry-level systems” division in Boca Raton, Florida. In July 1980 Lowe made a presentation to IBM’s senior management in Armonk, New York, with a radical plan: not only should IBM enter the personal-computer market but it should also abandon its traditional development processes in order to match the dynamism of the booming personal-computer industry.

Stocks for the Long Run, 4th Edition: The Definitive Guide to Financial Market Returns & Long Term Investment Strategies by Jeremy J. Siegel

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asset allocation, backtesting, Black-Scholes formula, Bretton Woods, buy low sell high, California gold rush, capital asset pricing model, cognitive dissonance, compound rate of return, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, fixed income, German hyperinflation, implied volatility, index arbitrage, index fund, Isaac Newton, joint-stock company, Long Term Capital Management, loss aversion, market bubble, mental accounting, Myron Scholes, new economy, oil shock, passive investing, Paul Samuelson, popular capitalism, prediction markets, price anchoring, price stability, purchasing power parity, random walk, Richard Thaler, risk tolerance, risk/return, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, short selling, South Sea Bubble, survivorship bias, technology bubble, The Great Moderation, The Wisdom of Crowds, transaction costs, tulip mania, Vanguard fund

Fortunately for shareholders, central bankers around the world are committed to keeping inflation low, and they have largely succeeded. But if inflation again rears its head, investors will do much better in stocks than in bonds. This page intentionally left blank 12 CHAPTER STOCKS AND THE BUSINESS CYCLE The stock market has predicted nine out of the last five recessions! PA U L S A M U E L S O N , 1 9 6 6 1 I’d love to be able to predict markets and anticipate recessions, but since that’s impossible, I’m as satisfied to search out profitable companies as Buffett is. P E T E R LY N C H , 1 9 8 9 2 A well-respected economist is about to address a large group of financial analysts, investment advisors, and stockbrokers. There is obvious concern in the audience. The stock market has been surging to new all-time highs almost daily, driving down dividend yields to record lows and sending price-to-earnings ratios skyward.


pages: 527 words: 147,690

Terms of Service: Social Media and the Price of Constant Connection by Jacob Silverman

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23andMe, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, basic income, Brian Krebs, California gold rush, call centre, cloud computing, cognitive dissonance, commoditize, correlation does not imply causation, Credit Default Swap, crowdsourcing, don't be evil, drone strike, Edward Snowden, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, information retrieval, Internet of things, Jaron Lanier, jimmy wales, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, late capitalism, license plate recognition, life extension, lifelogging, Lyft, Mark Zuckerberg, Mars Rover, Marshall McLuhan, mass incarceration, meta analysis, meta-analysis, Minecraft, move fast and break things, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, optical character recognition, payday loans, Peter Thiel, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, recommendation engine, rent control, RFID, ride hailing / ride sharing, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Snapchat, social graph, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, TaskRabbit, technoutopianism, telemarketer, transportation-network company, Turing test, Uber and Lyft, Uber for X, universal basic income, unpaid internship, women in the workforce, Y Combinator, Zipcar

A 2010 survey found that most Mechanical Turk workers are in the United States and India, and that while about 70 percent have full-time jobs, they also tend to have low incomes. Like viral media, there may be a smattering of success stories, people who rise above the herd and manage to master the system, but the most productive online laborers usually manage to make only a few dollars per hour. Premise—a company specializing in what it calls “hyperdata,” using rapidly collected photos of goods, particularly in grocery stores, to predict market conditions—pays its photographers 8 to 10 cents per snapshot. Premise then passes its analysis on to hedge funds and major conglomerates, who pay four-or five-figure monthly subscription fees. Some industry leaders argue that this kind of labor opens up new possibilities of work that otherwise wouldn’t exist without distributed micro-work and smartphone-connected laborers. A common example proposes that Coca-Cola, say, wouldn’t bother going into thousands of grocery stores around the country to make sure that its special displays are properly arranged.


pages: 537 words: 144,318

The Invisible Hands: Top Hedge Fund Traders on Bubbles, Crashes, and Real Money by Steven Drobny

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Albert Einstein, Asian financial crisis, asset allocation, asset-backed security, backtesting, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, business process, capital asset pricing model, capital controls, central bank independence, collateralized debt obligation, commoditize, Commodity Super-Cycle, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, diversification, diversified portfolio, equity premium, family office, fiat currency, fixed income, follow your passion, full employment, George Santayana, Hyman Minsky, implied volatility, index fund, inflation targeting, interest rate swap, inventory management, invisible hand, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, market microstructure, moral hazard, Myron Scholes, North Sea oil, open economy, peak oil, pension reform, Ponzi scheme, prediction markets, price discovery process, price stability, private sector deleveraging, profit motive, purchasing power parity, quantitative easing, random walk, reserve currency, risk tolerance, risk-adjusted returns, risk/return, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, special drawing rights, statistical arbitrage, stochastic volatility, survivorship bias, The Great Moderation, Thomas Bayes, time value of money, too big to fail, transaction costs, unbiased observer, value at risk, Vanguard fund, yield curve, zero-sum game

If you ask 10 different people what will happen, they will all tell you that “A” will happen. Then you post a poll saying everyone believes that “A” will happen. But if you change the 60 percent to 40 percent and ask people the same question, they will respond that “B” will happen. Changing the probability only 20 percent swung the “expected outcome” from 100 percent “A” to 100 percent “B.” This is how I see my role in terms of predicting market sentiment. I do not go around asking people how they are feeling, but I look for cases where a small change in fundamentals could cause a large change in how people perceive the fundamentals. There have been large swings in sentiment during the past few years. How do you stay ahead of these swings when they are driven by only small changes in fundamentals? It is impossible to accurately predict an outcome all the time, which is where money management discipline comes in.


pages: 405 words: 117,219

In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence by George Zarkadakis

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3D printing, Ada Lovelace, agricultural Revolution, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, anthropic principle, Asperger Syndrome, autonomous vehicles, barriers to entry, battle of ideas, Berlin Wall, bioinformatics, British Empire, business process, carbon-based life, cellular automata, Claude Shannon: information theory, combinatorial explosion, complexity theory, continuous integration, Conway's Game of Life, cosmological principle, dark matter, dematerialisation, double helix, Douglas Hofstadter, Edward Snowden, epigenetics, Flash crash, Google Glasses, Gödel, Escher, Bach, income inequality, index card, industrial robot, Internet of things, invention of agriculture, invention of the steam engine, invisible hand, Isaac Newton, Jacquard loom, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, job automation, John von Neumann, Joseph-Marie Jacquard, liberal capitalism, lifelogging, millennium bug, Moravec's paradox, natural language processing, Norbert Wiener, off grid, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, Paul Erdős, post-industrial society, prediction markets, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, speech recognition, stem cell, Stephen Hawking, Steven Pinker, strong AI, technological singularity, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Tyler Cowen: Great Stagnation, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

Increasingly, we interact with machines while expecting them to ‘know’ what we want, ‘understand’ what we mean and ‘talk’ to us in our human language. As Artificial Intelligence evolves further, it will become the driver of a new machine age that could usher our species to new economic, social and technological heights. Supercomputers endowed with intelligence will be able to accurately model and simulate almost every natural process. We will acquire the power to engineer virtually everything: from new drugs to predicting markets and solving the problems of economic scarcity, to terraforming planets. Artificial Intelligence could make us virtually omnipotent. As citizens of a free society, we have a duty to come to terms with this future, and to understand and debate its moral, legal, political and ethical ramifications today. Heated arguments about stem cells or genetics will pale in comparison to what Artificial Intelligence will allow us to do in a few years’ time.


pages: 461 words: 128,421

The Myth of the Rational Market: A History of Risk, Reward, and Delusion on Wall Street by Justin Fox

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activist fund / activist shareholder / activist investor, Albert Einstein, Andrei Shleifer, asset allocation, asset-backed security, bank run, beat the dealer, Benoit Mandelbrot, Black-Scholes formula, Bretton Woods, Brownian motion, capital asset pricing model, card file, Cass Sunstein, collateralized debt obligation, complexity theory, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, discovery of the americas, diversification, diversified portfolio, Edward Glaeser, Edward Thorp, endowment effect, Eugene Fama: efficient market hypothesis, experimental economics, financial innovation, Financial Instability Hypothesis, fixed income, floating exchange rates, George Akerlof, Henri Poincaré, Hyman Minsky, implied volatility, impulse control, index arbitrage, index card, index fund, information asymmetry, invisible hand, Isaac Newton, John Meriwether, John Nash: game theory, John von Neumann, joint-stock company, Joseph Schumpeter, Kenneth Arrow, libertarian paternalism, linear programming, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, market bubble, market design, Myron Scholes, New Journalism, Nikolai Kondratiev, Paul Lévy, Paul Samuelson, pension reform, performance metric, Ponzi scheme, prediction markets, pushing on a string, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Richard Thaler, risk/return, road to serfdom, Robert Bork, Robert Shiller, Robert Shiller, rolodex, Ronald Reagan, shareholder value, Sharpe ratio, short selling, side project, Silicon Valley, South Sea Bubble, statistical model, The Chicago School, The Myth of the Rational Market, The Predators' Ball, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, Thorstein Veblen, Tobin tax, transaction costs, tulip mania, value at risk, Vanguard fund, Vilfredo Pareto, volatility smile, Yogi Berra

“The most perfect expectations possible in economic affairs must be subject to substantial error because the outcome depends on unpredictable future events,” he wrote in a paper that he presented at the annual meeting of the American Economic Association in Cleveland in December 1948. “Market expectations, therefore, have a certain necessary inaccuracy.” His concern was how much “objectionable inaccuracy” there might be, due to speculators overreacting to news or taking too long to digest it. Any sort of persistent errors on the part of speculators would lead to persistent, predictable market patterns: If it is possible under any given set of circumstances to predict future price changes and have the predictions fulfilled, it follows that the market expectations must have been defective; ideal market expectations would have taken full account of the information which permitted successful prediction of the price change.40 Working wrote this at a time when most economists still agreed with Keynes’s depiction of securities markets as a futile exercise in “anticipating what average opinion expects average opinion to be.”


pages: 523 words: 143,139

Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian, Tom Griffiths

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4chan, Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, algorithmic trading, anthropic principle, asset allocation, autonomous vehicles, Bayesian statistics, Berlin Wall, Bill Duvall, bitcoin, Community Supported Agriculture, complexity theory, constrained optimization, cosmological principle, cryptocurrency, Danny Hillis, David Heinemeier Hansson, delayed gratification, dematerialisation, diversification, Donald Knuth, double helix, Elon Musk, fault tolerance, Fellow of the Royal Society, Firefox, first-price auction, Flash crash, Frederick Winslow Taylor, George Akerlof, global supply chain, Google Chrome, Henri Poincaré, information retrieval, Internet Archive, Jeff Bezos, John Nash: game theory, John von Neumann, knapsack problem, Lao Tzu, Leonard Kleinrock, linear programming, martingale, Nash equilibrium, natural language processing, NP-complete, P = NP, packet switching, Pierre-Simon Laplace, prediction markets, race to the bottom, RAND corporation, RFC: Request For Comment, Robert X Cringely, sealed-bid auction, second-price auction, self-driving car, Silicon Valley, Skype, sorting algorithm, spectrum auction, Steve Jobs, stochastic process, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, urban planning, Vickrey auction, Vilfredo Pareto, Walter Mischel, Y Combinator, zero-sum game

Simple games like rock-paper-scissors may have equilibria visible at a glance, but in games of real-world complexity it’s now clear we cannot take for granted that the participants will be able to discover or reach the game’s equilibrium. This, in turn, means that the game’s designers can’t necessarily use the equilibrium to predict how the players will behave. The ramifications of this sobering result are profound: Nash equilibria have held a hallowed place within economic theory as a way to model and predict market behavior, but that place might not be deserved. As Papadimitriou explains, “If an equilibrium concept is not efficiently computable, much of its credibility as a prediction of the behavior of rational agents is lost.” MIT’s Scott Aaronson agrees. “In my opinion,” he says, “if the theorem that Nash equilibria exist is considered relevant to debates about (say) free markets versus government intervention, then the theorem that finding those equilibria is [intractable] should be considered relevant also.”


pages: 666 words: 181,495

In the Plex: How Google Thinks, Works, and Shapes Our Lives by Steven Levy

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23andMe, AltaVista, Anne Wojcicki, Apple's 1984 Super Bowl advert, autonomous vehicles, book scanning, Brewster Kahle, Burning Man, business process, clean water, cloud computing, crowdsourcing, Dean Kamen, discounted cash flows, don't be evil, Donald Knuth, Douglas Engelbart, Douglas Engelbart, El Camino Real, fault tolerance, Firefox, Gerard Salton, Gerard Salton, Google bus, Google Chrome, Google Earth, Googley, HyperCard, hypertext link, IBM and the Holocaust, informal economy, information retrieval, Internet Archive, Jeff Bezos, John Markoff, Kevin Kelly, Mark Zuckerberg, Menlo Park, one-China policy, optical character recognition, PageRank, Paul Buchheit, Potemkin village, prediction markets, recommendation engine, risk tolerance, Rubik’s Cube, Sand Hill Road, Saturday Night Live, search inside the book, second-price auction, selection bias, Silicon Valley, skunkworks, Skype, slashdot, social graph, social software, social web, spectrum auction, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Ted Nelson, telemarketer, trade route, traveling salesman, turn-by-turn navigation, Vannevar Bush, web application, WikiLeaks, Y Combinator

When her workers did not respond with full compliance, she instituted another policy: if anyone who worked for her spotted someone else in the group looking at the stock ticker, all he or she had to do was walk over and tap that person on the shoulder. Then that person would have to buy you a share of stock. After a number of involuntary exchanges, people either stopped checking or learned to hide their peeking more effectively. But Googlers were affected by stock ownership. (They were, after all, human.) Bo Cowgill, a Google statistician, did a series of studies of his colleagues’ behavior, based on their participation in a “prediction market,” a setup that allowed them to make bets on the success of internal projects. He discovered that “daily stock price movements affect the mood, effort level and decision-making of employees.” As you’d expect, increases in stock performance made people happier and more optimistic—but they also led them to regard innovative ideas more warily, indicating that as Googlers became richer, they became more conservative.


pages: 798 words: 240,182

The Transhumanist Reader by Max More, Natasha Vita-More

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23andMe, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, Bill Joy: nanobots, bioinformatics, brain emulation, Buckminster Fuller, cellular automata, clean water, cloud computing, cognitive bias, cognitive dissonance, combinatorial explosion, conceptual framework, Conway's Game of Life, cosmological principle, data acquisition, discovery of DNA, Douglas Engelbart, Drosophila, en.wikipedia.org, endogenous growth, experimental subject, Extropian, fault tolerance, Flynn Effect, Francis Fukuyama: the end of history, Frank Gehry, friendly AI, game design, germ theory of disease, hypertext link, impulse control, index fund, John von Neumann, joint-stock company, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, Louis Pasteur, Menlo Park, meta analysis, meta-analysis, moral hazard, Network effects, Norbert Wiener, P = NP, pattern recognition, phenotype, positional goods, prediction markets, presumed consent, Ray Kurzweil, reversible computing, RFID, Richard Feynman, Ronald Reagan, silicon-based life, Singularitarianism, stem cell, stochastic process, superintelligent machines, supply-chain management, supply-chain management software, technological singularity, Ted Nelson, telepresence, telepresence robot, telerobotics, the built environment, The Coming Technological Singularity, the scientific method, The Wisdom of Crowds, transaction costs, Turing machine, Turing test, Upton Sinclair, Vernor Vinge, Von Neumann architecture, Whole Earth Review, women in the workforce, zero-sum game

He authored The Hidden Pattern: A Patternist Philosophy of Mind (Brown Walker Press, 2006); A Cosmist Manifesto: Practical Philosophy for the Posthuman Age (Humanity + Press, 2010); and co-edited with Cassio Pennachin Artificial General Intelligence (Springer, 2007). Robin Hanson, PhD, is Associate Professor of Economics, George Mason University. He authored “Meet the New Conflict, Same as the Old Conflict” (Journal of Consciousness Studies 19, 2012); “Enhancing our Truth Orientation” (Human Enhancement, Oxford University Press, 2009); and “Insider Trading and Prediction Markets” (Journal of Law, Economics, and Policy 4, 2008). Patrick D. Hopkins, PhD, is Associate Professor, Philosophy and Gender Studies, Millsaps College. He authored Sex/Machine: Readings in Culture, Gender, and Technology (Indiana University Press, 1999); and co-authored with Larry May et al. Rethinking Masculinity: Philosophical Explorations in Light of Feminism (Rowman & Littlefield, 1996).


pages: 843 words: 223,858

The Rise of the Network Society by Manuel Castells

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Apple II, Asian financial crisis, barriers to entry, Big bang: deregulation of the City of London, Bob Noyce, borderless world, British Empire, capital controls, complexity theory, computer age, computerized trading, creative destruction, Credit Default Swap, declining real wages, deindustrialization, delayed gratification, dematerialisation, deskilling, disintermediation, double helix, Douglas Engelbart, Douglas Engelbart, edge city, experimental subject, financial deregulation, financial independence, floating exchange rates, future of work, global village, Gunnar Myrdal, Hacker Ethic, hiring and firing, Howard Rheingold, illegal immigration, income inequality, Induced demand, industrial robot, informal economy, information retrieval, intermodal, invention of the steam engine, invention of the telephone, inventory management, James Watt: steam engine, job automation, job-hopping, John Markoff, knowledge economy, knowledge worker, labor-force participation, labour market flexibility, labour mobility, laissez-faire capitalism, Leonard Kleinrock, low skilled workers, manufacturing employment, Marc Andreessen, Marshall McLuhan, means of production, megacity, Menlo Park, moral panic, new economy, New Urbanism, offshore financial centre, oil shock, open economy, packet switching, Pearl River Delta, peer-to-peer, planetary scale, popular capitalism, popular electronics, post-industrial society, postindustrial economy, prediction markets, Productivity paradox, profit maximization, purchasing power parity, RAND corporation, Robert Gordon, Robert Metcalfe, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, social software, South China Sea, South of Market, San Francisco, special economic zone, spinning jenny, statistical model, Steve Jobs, Steve Wozniak, Ted Nelson, the built environment, the medium is the message, the new new thing, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, total factor productivity, trade liberalization, transaction costs, urban renewal, urban sprawl, zero-sum game

The performance of the model relies also on the absence of major disruptions in the overall process of production and distribution. Or, to put it in other words, it is based on the assumption of the “five zeros”: zero defect in the parts; zero mischief in the machines; zero inventory; zero delay; zero paperwork. Such performances can only be predicated on the basis of an absence of work stoppages and total control over labor, on entirely reliable suppliers, and on adequately predicted markets. “Toyotism” is a management system designed to reduce uncertainty rather than to encourage adaptability. The flexibility is in the process, not in the product. Thus, some analysts have suggested that it could be considered as an extension of “Fordism,”26 keeping the same principles of mass production, yet organizing the production process on the basis of human initiative and feedback capacity to eliminate waste (of time, work, and resources) while maintaining the characteristics of output close to the business plan.


pages: 662 words: 180,546

Never Let a Serious Crisis Go to Waste: How Neoliberalism Survived the Financial Meltdown by Philip Mirowski

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Alvin Roth, Andrei Shleifer, asset-backed security, bank run, barriers to entry, Basel III, Berlin Wall, Bernie Madoff, Bernie Sanders, Black Swan, blue-collar work, Bretton Woods, Brownian motion, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, constrained optimization, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, dark matter, David Brooks, David Graeber, debt deflation, deindustrialization, Edward Glaeser, Eugene Fama: efficient market hypothesis, experimental economics, facts on the ground, Fall of the Berlin Wall, financial deregulation, financial innovation, Flash crash, full employment, George Akerlof, Goldman Sachs: Vampire Squid, Hernando de Soto, housing crisis, Hyman Minsky, illegal immigration, income inequality, incomplete markets, information asymmetry, invisible hand, Jean Tirole, joint-stock company, Kenneth Arrow, Kenneth Rogoff, knowledge economy, l'esprit de l'escalier, labor-force participation, liberal capitalism, liquidity trap, loose coupling, manufacturing employment, market clearing, market design, market fundamentalism, Martin Wolf, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, Naomi Klein, Nash equilibrium, night-watchman state, Northern Rock, Occupy movement, offshore financial centre, oil shock, Pareto efficiency, Paul Samuelson, payday loans, Philip Mirowski, Ponzi scheme, precariat, prediction markets, price mechanism, profit motive, quantitative easing, race to the bottom, random walk, rent-seeking, Richard Thaler, road to serfdom, Robert Shiller, Robert Shiller, Ronald Coase, Ronald Reagan, savings glut, school choice, sealed-bid auction, Silicon Valley, South Sea Bubble, Steven Levy, technoutopianism, The Chicago School, The Great Moderation, the map is not the territory, The Myth of the Rational Market, the scientific method, The Wisdom of Crowds, theory of mind, Thomas Kuhn: the structure of scientific revolutions, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, Vilfredo Pareto, War on Poverty, Washington Consensus, We are the 99%, working poor

See also Chwieroth, Capital Ideas. 100 Hayek, Studies in Philosophy, Politics and Economics, p. 172. 101 See the papers and data available for download at http://elsa.berkeley.edu/~saez/. 102 Rajan, Fault Lines. 103 See Van Horn and Mirowski, “The Rise of the Chicago School and the Birth of Neoliberalism.” 104 Nace, Gangs of America. 105 Jensen and Meckling, “Theory of the Firm.” 106 Nik-Khah, “A Tale of Two Auctions.” 107 On the ill-fated DARPA “policy analysis market” project, see www.sfgate.com/cgi-bin/article.cgi?file=/c/a/2003/07/29/MN126930.DTL (accessed December 2, 2006) and Justin Wolfers and Eric Zitzowitz, “Prediction Markets in Theory and Practice,” www.dartmouth.edu/~ericz/palgrave.pdf. 108 For unabashed examples of this neoliberal argument, see Litan, “In Defense of Much, but Not All, Financial Innovation” and The Derivatives Dealer’s Club; Shiller, Finance and the Good Society. A more skeptical summary is Engelen, et al., After the Great Complacence. This constitutes a major component of the full-spectrum political response of the NTC to the crisis, as described in chapter 6. 109 Harcourt, The Illusion of Free Markets, p. 147. 110 Posner, quoted in ibid., p. 149. 111 Hayek, The Constitution of Liberty, p. 68, 69; Hayek, Studies in Philosophy, Politics and Economics, p. 155; Hartwell, A History of the Mont Pèlerin Society, p. 47.


pages: 781 words: 226,928

Commodore: A Company on the Edge by Brian Bagnall

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Apple II, belly landing, Bill Gates: Altair 8800, Byte Shop, Claude Shannon: information theory, computer age, Douglas Engelbart, Douglas Engelbart, Firefox, game design, index card, inventory management, Isaac Newton, low skilled workers, Menlo Park, packet switching, pink-collar, popular electronics, prediction markets, pre–internet, QWERTY keyboard, Robert Metcalfe, Robert X Cringely, Silicon Valley, special economic zone, Steve Jobs, Steve Wozniak, Ted Nelson

“I remember these heat problems of video sparkling back then on those early chips,” recalls Finkel. “I think it might have been, ‘Is it ready enough to show?’ And he made that decision there.” The nervous engineers displayed the result of almost two months of compressed labor. “We told him what it was, how simple it was, and what it could cost,” recalls Russell. “He said, ‘Put it on the floor.’” As Winterble predicted, marketing was not happy to learn of the project so late in development. “When these guys found out about it, and found out that they were not involved in it, then right away you can imagine: it hit the fan,” says Winterble. “It was turmoil.” With no advanced warning, Kit Spencer had to work non-stop to prepare print material for the prototype. “The marketing guys ended up claiming it was going to do everything under the sun on the charts,” says Russell.


pages: 1,737 words: 491,616

Rationality: From AI to Zombies by Eliezer Yudkowsky

Albert Einstein, Alfred Russel Wallace, anthropic principle, anti-pattern, anti-work, Arthur Eddington, artificial general intelligence, availability heuristic, Bayesian statistics, Berlin Wall, Build a better mousetrap, Cass Sunstein, cellular automata, cognitive bias, cognitive dissonance, correlation does not imply causation, cosmological constant, creative destruction, Daniel Kahneman / Amos Tversky, dematerialisation, discovery of DNA, Douglas Hofstadter, Drosophila, effective altruism, experimental subject, Extropian, friendly AI, fundamental attribution error, Gödel, Escher, Bach, hindsight bias, index card, index fund, Isaac Newton, John Conway, John von Neumann, Long Term Capital Management, Louis Pasteur, mental accounting, meta analysis, meta-analysis, money market fund, Nash equilibrium, Necker cube, NP-complete, P = NP, pattern recognition, Paul Graham, Peter Thiel, Pierre-Simon Laplace, placebo effect, planetary scale, prediction markets, random walk, Ray Kurzweil, reversible computing, Richard Feynman, Richard Feynman, risk tolerance, Rubik’s Cube, Saturday Night Live, Schrödinger's Cat, scientific mainstream, sensible shoes, Silicon Valley, Silicon Valley startup, Singularitarianism, Solar eclipse in 1919, speech recognition, statistical model, Steven Pinker, strong AI, technological singularity, The Bell Curve by Richard Herrnstein and Charles Murray, the map is not the territory, the scientific method, Turing complete, Turing machine, ultimatum game, X Prize, Y Combinator, zero-sum game

The glue that binds them to their current place has dissolved, and they can walk in some direction, hopefully forward. Lonely dissent, I called it. True dissent doesn’t feel like going to school wearing black; it feels like going to school wearing a clown suit. That’s what it takes to be the lone voice who says, “If you really think you know who’s going to win the election, why aren’t you picking up the free money on the Intrade prediction market?” while all the people around you are thinking, “It is good to be an individual and form your own opinions, the shoe commercials told me so.” Maybe in some other world, some alternate Everett branch with a saner human population, things would be different . . . but in this world, I’ve never seen anyone begin to grow as a rationalist until they make a deep emotional break with the wisdom of their pack.

Do not rely on being able to successfully think high-minded thoughts unless experimentation is so costly or dangerous that you have no other choice. But sometimes experiments are costly, and sometimes we prefer to get there first . . . so you might consider trying to train yourself in reasoning on scanty evidence, preferably in cases where you will later find out if you were right or wrong. Trying to beat low-capitalization prediction markets might make for good training in this?—though that is only speculation. As of now, at least, reasoning based on scanty evidence is something that modern-day science cannot reliably train modern-day scientists to do at all. Which may perhaps have something to do with, oh, I don’t know, not even trying? Actually, I take that back. The most sane thinking I have seen in any scientific field comes from the field of evolutionary psychology, possibly because they understand self-deception, but also perhaps because they often (1) have to reason from scanty evidence and (2) do later find out if they were right or wrong.