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Prediction Markets: Applications, Theory, Outputs, Inputs, Foul Play, Combinatorics

Prediction Markets: Applications, Theory, Outputs, Inputs, Foul Play, Combinatorics. Robin Hanson Economics George Mason University Presented at Supernova 2005 Workshop. Speculative Markets Collect Info. Hard find info not in market prices In direct compare, beat institutions

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Prediction Markets: Applications, Theory, Outputs, Inputs, Foul Play, Combinatorics

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  1. Prediction Markets: Applications, Theory, Outputs, Inputs, Foul Play, Combinatorics Robin Hanson Economics George Mason University Presented at Supernova 2005 Workshop

  2. Speculative Markets Collect Info • Hard find info not in market prices • In direct compare, beat 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) • Oscar market beats columnists (Pennock et al 2001) • Stocks beat Challenger panel (Maloney & Mulherin 2003) • Gas demand market beats experts (Spencer 2004)

  3. Vast Range of Applications • Corporate Policy • Sales (own and others) • Project completion, quality (bug rate) • Decisions: mergers, subcontractor choice, regional expansions, … • Public Policy • Epidemics, Security, Monetary policy • Scientific controversy • School & job applicants …

  4. Potential Problems • Self-defeating prophecies • Decision selection bias • Price manipulation • Inform enemies • Share less info • Combinatorics • Moral hazard • Alarm public • Embezzle • Bozos • Lies • Rich more “votes” • Risk distortion • Bubbles

  5. Prediction Markets Inputs Theory Outputs Foul Play Compare! Status Quo Institution

  6. $1 if A p(A) $1 $1 if A&B p(A&B) $1 $ x if A E[x|A]*p(A) $1 $ x E[x] $1 $1 if A&B p(B|A) $1 if A $ x if A E[x|A] $1 if A Estimates from Prices

  7. Theory I - Old • “Strong Efficient Markets” is straw man • No info - Supply and Demand • Assume beliefs not respond to prices • Price is weighted average of beliefs • More influence: risk takers, rich • Info, Static - Rational Expectations • Price clears, but beliefs depend on price • No trade if not expect “noise traders” • Price not reveal all info • More influence: info holders

  8. Theory II - Market Microstruture • Info, Dynamic – Game Theory • Example – Kyle ’85 • X - Informed trader(s) – risk averse • Y - Noise trader – fool or liquidity pref • Market makers – no info, deep pockets • If many compete, Price = E[value|x+y] • Info markets – use risk-neutral limit • If Y larger, X larger to compensate more info gathered, so more accuracy!

  9. Theory III – Behavioral Finance • Humans are overconfident • Far more speculative trade than need • Mere fact of disagreement shows • Overconfidence varies with person, experience, consequence severity • Implications • Price in part an ave of beliefs? • Adds noise to price aggregates? • Prices more honest than talk, polls, …

  10. Outputs • Prices as Estimates • Last? median? average? Reweight trades? • If not last, auto-trader to fix makes • Can hide prices (don’t know price now) • Require post comment with each trade? • Use trade record in performance review? • Reward contribution vs. infer other abilities • Crunch trade data to see group divisions? • Give more a feeling of participation? • Don’t let these issues distract you from:

  11. Ask the Right Questions • High value to more accurate estimates! • Relevant standard: beat existing institutions • Where suspect more accuracy is possible • Suspect info is withheld, or not sure who has it • Prefer fun, easy to explain and judge • Prefer can let many know best estimates • Not fear estimates reveal secrets • Not using uncertainty, biases to motivate • Avoid inducing foul play

  12. Conditional Estimates • Can avoid self-defeating predictions • Condition on decision, advises it • Don’t confuse correlation and cause • Bias if decision makers will know more • Solutions: • Let insiders trade, clear decide time, or • Let market decide, clear decide time, or • Condition on random decision, or • Find instrumental variable, condition on

  13. Inputs I • Final Judging – using prices risks gaming! • Audit lotteries reduce ave cost, but more risk • Refine claim – central vs. decentralized • Credentialing as compromise? • Participants • Want people can get relevant info • Trading experience a plus, but not key • Standard rules need min traders/claim • Fools help, up to a point • Optional: personal risk adjust, stake limit

  14. Inputs II • Cash, play money, or prizes to best traders? • Recent paper: on football, real vs. play-prizes same • Note: prizes risk inducing large random trades! • Real Choice: stuff vs. bragging rights vs. fun • Fun risks them not caring enough to be honest • Scale economies of bragging rights? • “Info $” concept: brag of $ value of info add to org • How much must pay? (If fun enough, nada!) • If many have info, just need induce them to tell • If traders must do research, must be paid more • Bigger trader pool helps find low cost providers • How pay: cash upfront, per trade, market maker • Subsidized market maker pays only for new info

  15. Foul Play I • Generic fix: limit participation • Lying • If advisors can bet, may talk less • Fix: Let advisors bet, or show bet stake • Manipulation • Idea: lose on trades, gain in decisions • Field: Effect rare, short-lived • Lab: accuracy no worse • Theory: trading on any consideration other than asset value is noise trading, helps!

  16. Foul Play II • Sabotage (Moral Hazard) • Rare (Not 9/11, ’82 Tylenol, ’02 PaineWebber) • Hard match willing capital & skilled labor • Fix: Avoid thick market on small events • Fix: Bound individual stakes (e.g. project late) • Embezzlement • Stat insiders windfall? Topics chosen for favorites? Withhold info from team? • Fix: Special/team accounts, trade first • Fix: new color of $, subsidy at info value est. • Retribution – anonymity helps at a cost

  17. Combinatorics I – The problem • Each trader wants to trade on his info, be insured against all other issues • Ex: what weather can we forecast? • Per hour per zip code? • Distribution over wind, rain amount? • Conditional on recent, nearby weather? • Old story: • Vast # possible Arrow-Debreu assets • But fixed costs, traders avoid thin • But regulation is biggest cost by far • Many computing tricks not tried

  18. Combinatorics II - Approaches • All: decompose trades into state assets • Example: Win, place, show overlaps • Call markets • Compute to find matches in offer pool • Related markets thicken each other • Recent computational complexity results • Market makers • Stands ready to trade all assets • Requires subsidy per base claim, but not for adding all combos of base • Open issues re combinatorial explosion

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

  20. 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

  21. Market Scoring Rules Scoring Rules opinion pool problem 100 .001 .01 .1 1 10 Pushing the Limit Simple Info Markets Accuracy thin market problem Estimates per trader

  22. 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)

  23. Environments: Goals, Training • 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 (Actually: X Z Y ) 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 -- -- --

  24. KL(prices,group) 1- KL(uniform,group) MSR Info vs. Time – 7 prices 1 % Info Agg. = 0 0 5 10 15 Minutes -1

  25. Experiment Environment • 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 (Really: W V X S U Z Y T ) 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 -- -- -- -- -- -- -- -- …

  26. KL(prices,group) 1- KL(uniform,group) MSR Info vs. Time – 255 prices 1 % Info Agg. = 0 0 5 10 15 Minutes -1

  27. Accuracy (95% C.L.)

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