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Five Problems in Information Market Design

Five Problems in Information Market Design. Robin Hanson George Mason University Talk at Microsoft Research, 9Feb04. I Had A Dream. 1989, at Xanadu, foresaw web, & that not enough “Info” tech helps find bits , but info still elusive What effect gun control on crime, Iraq war on terror

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Five Problems in Information Market Design

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  1. Five Problems in Information Market Design Robin Hanson George Mason University Talk at Microsoft Research, 9Feb04

  2. I Had A Dream • 1989, at Xanadu, foresaw web, & that not enough • “Info” tech helps find bits, but info still elusive • What effect gun control on crime, Iraq war on terror • Honest & rational agents need few bits to agree • 100 bits can give 90% chance opinion diff 10% • 106 bits can give 99% chance opinion diff 1% • I imagined “Idea Futures” • Betting odds as consensus, fund research by subsidize • Disputants expected to “put where is”

  3. “Pays $1 if Bush wins” Will price rise or fall? sell E[ price change | ?? ] buy price sell Lots of ?? get tried, price includes all! buy Buy Low, Sell High (All speculation is “gambling”!)

  4. Today’s Prices 63-64% President Bush re-elected 2004 91-93% Kerry is Democratic nominee 2004 82-83%  LOTR Oscar best picture of 2003 44-47% Bin Laden captured by 2005 25-30% Palestinian State by 2006 33-40%  Michael Jackson guilty of lewd acts TradeSports.com

  5. Information Markets • Most speculative markets aggregate info well • Very hard to find info to beat average return • Some make markets for info aggregate purpose • In direct comparisons, beat other institutions • Racetrack odds beat track experts (Figlewski 1979) • OJ futures improve weather forecast (Roll 1984) • I.E.M. beat president polls 451/596 (Berg et al 2001) • HP market beat sales forecast 6/8 (Plott 2000) • Stocks beat Challenger panel (Maloney & Mulherin 2003)

  6. Info Markets Benefits • People self-select as experts – we need not choose • Incentive to be honest with yourself, and to stay out if you don’t know. • If not honest, eventually lose money & leave. • Precise and continually updated • Consistent across diverse contexts • Can specialize correct any bias that you see

  7. Corporate Applications • Past Trials: • Product Delivery: Xanadu, Seimens • Sales: HP, Tradesports • Current Trials: Drug, Insurance, Bank, … • Key issue: ask high value questions • Cost varies little, benefit varies much • HP dropped printer sales markets

  8. Potential Problems • Self-fulfilling prophecies • Decision selection bias • Price manipulation • Thin markets • Combinatorial explosions • Moral hazard • Regulation • Secrecy • Bozos Today’s talk • Reduce info sharing • Rich more “votes” • Risk distortion • Bubbles

  9. 1. Self-Fulfilling Prophecies • Problem: self-fulfilling/defeating forecasts • Expect high sales, so raise marketing budget • Expect not make deadline, so quit trying hard • Fear terror attack on flight, so cancel it • Solution: forecast outcome given effort • Predict sales given marketing budget • Predict completion date given hours/week work • Predict terror attack given allow flight

  10. $1 if Bad Virus & Cut Feature P(Cut) $1 if Cut Feature P(Virus | Cut) $1 Compare! P(Virus | not Cut) $1 if Not Cut Feature $1 if Bad Virus & Not Cut Feature P(not Cut) If Cut Feature, Avoid Bad Virus?

  11. Decision Market Applications E[ stock price | fire CEO? ] E[ stock price | acquire company? ] E[ product sales | hire ad agency X? ] E[ crime rate | gun control bill pass? ] E[Democrat win | Nominate Dean? ] E[ GDP | Bush re-elected? ] E[ SA civil war | US moves troops out? ]

  12. 2. Decision Selection Bias • If traders think deciders will use info traders do not have • Market advice may contradict trader info • Related to “Newcomb’s Problem”

  13. Best to keep in this case Stock if keep CEO Better to dump Best to dump in this case Expected value over distribution is center of mass Stock if dump CEO A Decision State Space Imagine a uniform distribution over this area

  14. If Deciders Have Same Info Market prices here if decision not correlated with state Stock if keep CEO Better to dump Stock if dump CEO

  15. Well-Informed Deciders Keep Stock if keep CEO Apparent center Dump True center Stock if dump CEO

  16. Problem Seems Uncommon

  17. Avoiding Selection Bias • Problem scenarios unusual, but ... • Let decision makers & their advisors trade • Make decision time clear to traders • Focus on prices just before decision time

  18. 3. Manipulation Fears (e.g. PAM) • Bad guys gain $ by giving info, changing acts • More plausible if bet on specific details, thick market • PAM not on specifics, max gain/trade < $100 • A good deal for us if give few $, gain much info • Terror & corporate sabotage now effects big markets • Bad guys lose $ to obscure market info • If slow adjust to track record, worst case is get no info • $1M PAM worth it if 0.1% chance gain 0.1% of $400B/yr • We see little effect in lab, field experiments • If small, “noise traders” attract others, net add info

  19. Kyle Style Market Microstructure Price Manipulation Model Market maker Manipulator Informed trader Noise trader Equilibrium

  20. Bias Model Implications • Manipulators are essentially noise traders • If informed traders pockets too shallow to counter, noise trading hurts accuracy • Else, noise trading induces info effort, helps! • Robust to standard irrationality model (QRE) • Average result – “your mileage may vary”

  21. 4. Thin Market Problem • Trade requires coordinate in Assets and • Time: waiting offers suffer adverse selection • Call markets, combo match, can help some, but • Most possible info markets do not exist • Most are illegal, and for most of the rest • Expect few traders, so don’t make offer • If known that only one person has opinion on a topic, price of simple market not reveal it!

  22. Simple Info Markets Market Scoring Rules Scoring Rules opinion pool problem thin market problem 100 .001 .01 .1 1 10 Best of Both Accuracy Estimates per trader

  23. $ ei if i $ s(1)-s(0) Market Scoring Rules • Scoring rule: report r, state is i, reward si(r) p = argmaxrSi pi si(r), Si pi si(p)  0 • MSR: User t faces $ rule: Dsi = si(pt) - si(pt-1) “Anyone can use scoring rule if pay off last user” • Is auto market maker, price from net sales s • Tiny sale fee:  pi(s) ei (sisi+ei) • Big sale fee: 01 Sipi(s(t)) si´(t) dt • Log rule modular, cost  entropy  # dims

  24. Every nation*quarter: • Political stability • Military activity • Economic growth • US $ aid • US military activity • & global, special • & all combinations

  25. Return to Focus ? Trade IQcs4 IQcs4 < 85 85 03 03 SAum3 105-125 03 Update Payoffs: If & Ave. pay Select New Price 65% Max Up 95.13% +$34.74 -$85.18 -$19.72 Buy 10% Up 68.72% +$2.74 -$3.28 -$1.07 You Pick 65 % +1.43 -2.04 +0.34 Saudi Arabian Economic Health No Trade 62.47% $0.00 $0.00 $0.00 125 30 15 10% Dn 56.79% -$2.61 +$2.74 -$1.12 65 70 Sell Exit Issue 48.54% -$15.34 +$26.02 -$6.31 35 40 100 94 100 Max Dn 22.98% -$120.74 +$96.61 -$22.22 < 85 25 35 35 30 10 10 75 1 2 3 4 1 2 > 03 03 03 03 04 04 ? Return to Form Execute a Trade If US military involvement in Saudi Arabia in 3rd Quarter 2003 is not between 105 and 125, this trade is null and void. Otherwise, if Iraq civil stability in 4th Quarter 2003 is below 85, then I will receive $1.43, but if it is not below 85, I will pay $2.04. Abort trade if price has changed? Execute Trade Scenario

  26. Laboratory Tests • Joint work with John Ledyard (Caltech), Takashi Ishida (Net Exchange) • Caltech students, get ~$30/session • 6 periods/session, 12-15 minutes each • Trained in 3var session, return for 8var • Metric: Kulback-Leibleri qilog(pi /qi) distance from market prices to Bayesian beliefs given all group info

  27. Environments: Goals, Training (Actually: X Z Y ) • Want in Environment: • Many variables, few directly related • Few people, each not see all variables • Can compute rational group estimates • Explainable, fast, neutral • Training Environment: • 3 binary variables X,Y,Z, 23 = 8 combos • P(X=0) = .3, P(X=Y) = .2, P(Z=1)= .5 • 3 people, see 10 cases of: AB, BC, AC • Random map XYZ to ABC Case A B C 1 1 - 1 2 1 - 0 3 1 - 0 4 1 - 0 5 1 - 0 6 1 - 1 7 1 - 1 8 1 - 0 9 1 - 0 10 0 - 0 Sum: 9 - 3 Same A B C A -- -- 4 B -- -- -- C -- -- --

  28. Experiment Environment (Really: W V X S U Z Y T ) • 8 binary vars: STUVWXYZ • 28 = 256 combinations • 20% = P(S=0) = P(S=T) = P(T=U) = P(U=V) = … = P(X=Y) = P(Y=Z) • 6 people, each see 10 cases: ABCD, EFGH, ABEF, CDGH, ACEG, BDFH • random map STUVWXYZ to ABCDEFGH Case A B C D E F G H 1 0 1 0 1 - - - - 2 1 0 0 1 - - - - 3 0 0 1 1 - - - - 4 1 0 1 1 - - - - 5 0 1 1 1 - - - - 6 1 0 0 1 - - - - 7 0 1 1 1 - - - - 8 1 0 0 1 - - - - 9 1 0 0 1 - - - - 10 1 0 0 1 - - - - Sum 6 3 4 10 - - - - Same A B C D E F G H A -- 1 2 6 -- -- -- -- B -- -- 7 3 -- -- -- -- C -- -- -- 4 -- -- -- -- D -- -- -- -- -- -- -- -- …

  29. Mechanisms Compared • Survey Mechanisms (# cases: 3var, 8var) • Individual Scoring Rule (72,144) • Log Opinion Pool (384,144) • Market Mechanisms • Simple Double Auction (24,18) • Combined Value Call Market (24,18) • MSR Market Maker (36,17)

  30. Accuracy (95% C.L.)

  31. KL(prices,group) 1- KL(uniform,group) MSR Info vs. Time – 3 Variables 1 % Info Agg. = 0 0 5 10 15 Minutes -1

  32. KL(prices,group) 1- KL(uniform,group) MSR Info vs. Time – 8 Variables 1 % Info Agg. = 0 0 5 10 15 Minutes -1

  33. Experiment Conclusions • Controlled experiments on complex info problems • Bayesian estimates too high a standard • 7 indep. prices from 3 people in 5 minutes • Simple DA < Indiv. Score Rule ~ Opinion Pool ~ Combined Value < Market Scoring Rule • 255 indep. prices from 6 people in 5 minutes • Combined Value ~ Simple DA ~ Indiv. Score Rule < Market Scoring Rule ~ Opinion Pool

  34. 5. Combinatorial Explosion • MSR code easy if store all 2N states, but … • Compute Task #1: Update prices after trade • Prices = full joint probability distribution • Bayes rule updates aid modularity, risks $ pump • Compute Task #2: Find user net trade stake • 2A: Can user assets from old trades back new? • 2B: What is average net stake on new trade?

  35. D A C G F B E H Overlapping Patches • Simple all-combo MSR per patch • Allow trade only if vars in same patch • Is Markov network, much research re update • Exact update rule possible, but only if net is tree • But any predictable error allows money pump • Monte Carlo sampling update solves this? • Arbitrage neighbors is simple, robust, not Bayes rule • Need adapt tech to share user assets across patches • How change or add patches?

  36. Potential Problems • Self-fulfilling prophecies • Decision selection bias • Price manipulation • Thin markets • Combinatorial explosions • Moral hazard • Regulation • Secrecy • Bozos Today’s talk • Reduce info sharing • Rich more “votes” • Risk distortion • Bubbles

  37. Sweepstake Sports Bet Racetrack Lottery Casino Regulation of Speculation Gamble Capitalize Firms Hedge Individual Risk “Entertain” Hedge Common Risk Aggregate Information

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