Prediction & Decision Markets

1. Prediction & Decision Markets Robin Hanson Associate Professor of Economics, GMU

2. “Pays $1 if Paul 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 are “gambling” “prediction” “info”)

3. Today’s Current Event Prices 6-10% Bird Flu confirmed in US. By 7/2008 7-8% US war act on N. Korea by 10/2008 3-5% Bush Impeached by end of term 11-17% Bin Laden caught by 2009 40-45% US or Israel air strike on Iran by 2009 7-11% 9.0 Richter Earthquake by 2009 31-35% US max tax rate > 38% in 2009 30-39% China war act on Taiwan by 2011 20-29% Google Lunar Prize won by 2013 InTrade.com

4. Beats Alternatives • Vs. Public Opinion • I.E.M. beat presidential election polls 451/596 (Berg et al ‘01) • Re NFL, beat ave., rank 7 vs. 39 of 1947 (Pennock et al ’04) • Vs. Public Experts • Racetrack odds beat weighed track experts (Figlewski ‘79) • If anything, track odds weigh experts too much! • OJ futures improve weather forecast (Roll ‘84) • Stocks beat Challenger panel (Maloney & Mulherin ‘03) • Gas demand markets beat experts (Spencer ‘04) • Econ stat markets beat experts 2/3 (Wolfers & Zitzewitz ‘04) • Vs. Private Experts • HP market beat official forecast 6/8 (Plott ‘00) • Eli Lily markets beat official 6/9 (Servan-Schreiber ’05) • Microsoft project markets beat managers (Proebsting ’05) • XPree beat corp error, 3.5 vs 6.6%

5. Hollywood Stock Exchange Science 291:987-988, February 9 2001

6. Track Odds Beat Handicappers Figlewski (1979) Journal of Political Economy 14 Estimated on 146 races, tested on 46

7. Economic Derivatives Market Wolfers & Zitzewitz “Prediction Markets” (2004) Journal of Economic Perspectives

8. NFL Markets vs Individuals Average of Forecasts Servan-Schreiber, Wolfers, Pennock & Galebach (2004) Prediction Markets: Does Money Matter? Electronic Markets, 14(3). 1,947 Forecasters

9. “Accuracy and Forecast Standard Error of Prediction Markets” Joyce Berg, Forrest Nelson and Thomas Rietz, July 2003.

10. Iowa Electronic Markets vs. Polls “Accuracy and Forecast Standard Error of Prediction Markets” Joyce Berg, Forrest Nelson and Thomas Rietz, July 2003.

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

12. 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

13. The Fuss: Analysts often use prices from various markets as indicators of potential events. The use of petroleum futures contract prices by analysts of the Middle East is a classic example. The Policy Analysis Market (PAM) refines this approach by trading futures contracts that deal with underlying fundamentals of relevance to the Middle East. Initially, PAM will focus on the economic, civil, and military futures of Egypt, Jordan, Iran, Iraq, Israel, Saudi Arabia, Syria, and Turkey and the impact of U.S. involvement with each. [Click here for a summary of PAM futures contracts] The contracts traded on PAM will be based on objective data and observable events. These contracts will be valuable because traders who are registered with PAM will use their money to acquire contracts. A PAM trader who believes that the price of a specific futures contract under-predicts the future status of the issue on which it is based can attempt to profit from his belief by buying the contract. The converse holds for a trader who believes the price is an over-prediction – she can be a seller of the contract. This price discovery process, with the prospect of profit and at pain of loss, is at the core of a market’s predictive power. The issues represented by PAM contracts may be interrelated; for example, the economic health of a country may affect civil stability in the country and the disposition of one country’s military may affect the disposition of another country’s military. The trading process at the heart of PAM allows traders to structure combinations of futures contracts. Such combinations represent predictions about interrelated issues that the trader has knowledge of and thus may be able to make money on through PAM. Trading these trader-structured derivatives results in a substantial refinement in predictive power. [Click here for an example of PAM futures and derivatives contracts] The PAM trading interface presents A Market in the Future of the Middle East. Trading on PAM is placed in the context of the region using a trading language designed for the fields of policy, security, and risk analysis. PAM will be active and accessible 24/7 and should prove as engaging as it is informative. Became:

14. PAM Press Of 500+ articles, these gave more favorable PAM impression: Article: later in time, more words, mentioned insider, news (not Editorial) style, not anonymous Publication: finance or science specialty, many awards, many readers

15. Source:

16. Internal Applications • Sales - HP, Google, Nokia, XPree, O’Reilly, Best Buy • Deadlines - Siemens, Microsoft, Misys • Pick Project - Qualcomm, GE, Lily, Pfizer, Intercontinental Hotels • Unknown - Novartis, GSK, Motorola, ArcelorMittal, Corning, Dentsu, Masterfoods, Thomson, Yahoo, Abbott, Chrysler, Edmunds, InfoWorld, FritoLay, Erickson, IHG, NBC, HVG, RAND, SAIC, SCA, TNT, Cisco, General Mills, Swisscom

17. Inputs Outputs Prediction Markets Theory For Same Compare! Status Quo Institution

18. Not Experts vs. Self-chosen Amateurs • Forecasting Institution Goal: • Given same participants, resources, topic • Want most accurate institution forecasts • Separate question: who let participate? • Can limit who can trade in market • Markets have low penalty for add fools • Hope: get more info from amateurs?

19. Advantages • Numerically precise • Consistent across many issues • Frequently updated • Hard to manipulate • Need not say who how expert when • At least as accurate as alternatives

20. $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

21. Mechanisms • CDA (Continuous Double Auction) • make or take offers to buy or sell • Call Auction – match accumulated offers • Market maker – always small spread • Combinatorial version works great • Competitive Forecasting – like survey • Formula says consensus & score • Can be interface to market maker

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. Combinatorial Lab Experiments • 7 indep. prices from 3 folks in 4 min. • Simple Double Auction < Scoring Rule ~ Opinion Pool ~ Combinatorial Call < Market Scoring Rule • 255 indep. prices from 6 folks in 4 min. • Combinatorial Call ~ Simple Double Auction ~ Scoring Rule < Opinion Pool ~ Market Scoring Rule

24. Combo Market Maker Best of 5 Mechs 3 subjects, 7 prices, 5 minutes 6 subjects, 256 prices, 5 minutes

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

26. Concerns • 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

27. Simple Manipulation Model Kyle Style Market Microstructure Game Theory Market maker Manipulator Informed trader Noise trader Equilibrium

28. Lab Data Hanson, Oprea, Porter JEBO, 2005 • 12 subjects, value = 0,40,100 • Each clue like “Not 100”. • 6 manipulators, get bonus for • higher price • Manipulators bid higher • Others accept lower • Prices no less accurate

29. 8 traders, Value = 0,100 • Each Prob(Clue=V) = 2/3 • 4 manipulators, bonus for price to hidden target 0,100 • 5 judges see prices, predict • Manipulators bid toward target • Prices and judges predictions • no less accurate R. Oprea, D. Porter, C. Hibbert, R. Hanson, D.Tila 2006

30. Paying Employees for Info • Play money: Why should employees bother? • Real money: Shirk to trade? Hide info from team? • My Proposal: Use new “color of money” • Employees, teams give initial accounts (small, then grow) • Subsidize each topic at estimated info $ value (try less first) • Consistent increases credited in performance reviews as value added to organization (need good stat analysis) • Team, standard info accounts trade before individuals

31. Can Estimate Standard Info Values • Choose best of set of options • Value to know payoff of each option • Match price, or production/delivery, to demand level • Value to know demand level • Match personnel to project deadline • Value to know finish date given personnel

32. Play Personal Mood Real or Rated Work Outcome, Ability Incentives Strong Weak Decision Markets Key Topics Morale Markets Fun Money: Time: Shows:

33. Many possible traders Some possible traders High Value Prediction Markets Few possible traders Most Prediction Markets Most Betting Markets Cost-Value Space Fewer High Value Topics Cost per trader Info Value

34. $ Revenue if Switch $1 $1 if Switch P(S) E(R | S) E(R) Compare! $ Revenue E(R | not S) $ Revenue if not Switch $1 if not Switch Ad Agency Decision Markets

35. Presidential Decision Politimetrics.com

36. Corporate Applications E[ Revenue | Switch ad agency? ] E[ Revenue | Raise price 10%? ] E[ Project done date | Drop feature? ] E[ Project done date | Add personnel? ] E[ Stock price | Fire CEO? ] E[ Stock price | Acquire firm X? ]

37. Legal permission Outcome Measured Aggregate-enough Linear-enough Conditional-enough Decision Distinct options Important enough Enough influence Public credibility Traders Enough informed Decision-insiders Enough incentives Anonymity Prices Intermediate-enough Can show enough Decision Market Requirements

38. Coordination

39. Coordinator Aides: Schedule meetings Collect stats Monitor others Research options Write reports, talks Review proposals Screen candidates Predict outcomes Make choices Coordination But: Lose Control, Leak Info, Ins. Tr. Law?