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. Accuracy.001.01.1110 100 Estimates per trader Market Scoring Rules Pushing the Limit Simple Info Markets thin market problem Scoring Rules opinion pool problem

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 …