Information Markets II: Theory, Outputs, Inputs, Foul Play, Combinatorics, Applications

1. Information Markets II: Theory, Outputs, Inputs, Foul Play, Combinatorics, Applications Robin Hanson Economics George Mason University

2. Theory I - Old • 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

3. 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!

4. 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, …

5. Outputs • What price is best estimate? • last? median? an average? Reweight trades? • If not last, auto-trader to fix makes $! • This good discipline re if really can fix • Imagine Govt agency fixing stock prices! • Require post comment with each trade? • Use trade record in performance review? • Reward contribution vs. infer other abilities • Crunch trade data to see who thinks what • Give more a feeling of participation? • Don’t let these issues distract you from:

6. 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 • Can let many know best estimates • Not fear estimates reveal secrets • Not using uncertainty, biases to motivate • Avoid inducing foul play

7. 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 • Clear decision time and use prices then • Choose instrumental variables • E.g., condition on random decision

8. 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 • Mainly want people can get relevant info • Diversity helps, but only of info • Trading experience a plus, but not the key • Standard trading needs min traders/claim • Fools are fine, up to a point

9. 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. brag 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 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 • When pay: cash upfront, per trade, market maker • Subsidized market maker pays only for new info

10. Foul Play I • Generic fix: limit who/when trade • Lying • If advisors can bet, may talk less • Fix?: Let advisors show bet stake • Manipulation • Idea: lose on trades, gain in decisions • Field: Effect rare, short-lived • Lab: no net effect? (see conf talks) • Theory: trading on any consideration other than asset value is noise trading

11. 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 (eg finish project) • Embezzlement – • Stat insiders windfall? Keep info from team? • Fix: Special accounts trade first • Fix?: new color of $, subsidy at info value est. • Retribution – anonymity helps at a cost • Can still brag re overall record

12. 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

13. 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

14. 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

15. Accuracy (95% C.L.)

16. Applications • Private Policy • Sales (own and others) • Project completion, quality (bug rate) • Decisions: mergers, subcontractor choice, regional expansions, … • Public Policy • Epidemics, • monetary policy, health policy, ... • School & job applicants …