1. Smarter Markets: Bringing Intelligence into the Exchange Example: Smarter Prediction Markets David Pennock Microsoft Research

2. NYSE 1926 http://online.wsj.com/article/SB10001424052748704858404576134372454343538.html

3. NYSE 1987 http://online.wsj.com/article/SB10001424052748704858404576134372454343538.html

4. NYSE 2006-2011 2011 Deutsche Börse AG 2007 Euronext 2006 Archipelago, ipo

5. NYSE 7pm Sep 10, 2012

6. Phase 0 Mechanism (Rules) e.g. Auction, Exchange,...

7. Phase 1.0 Mechanism (Rules) e.g. Auction, Exchange,...

8. Phase 1.5 Mechanism (Rules) e.g. Auction, Exchange,... Stats/ML/Opt Engine

9. Phase 2.0 Mechanism (Rules) e.g. Auction, Exchange,... Stats/ML/Opt Engine

10. Phase 2.0 Mechanism (Rules) e.g. Auction, Exchange,... Stats/ML/Opt Engine Advertising ✓, Finance ✗,...

11. How is automation happening? Phase 0: Invention, manual execution Auctions Finance WALL STREET Advertising bookstores, banks, grocery stores,...

12. How is automation happening? Phase 1.0: Computers mimic it (Cheaper, faster) Auctions Finance ECN Advertising Amazon, ATMs, auto checkout,...

13. How is automation happening? Phase 2.0: Computers improve it (Cheaper, faster, better) Auctions Finance Advertising Source: Sandholm, T. “Expressive Commerce and Its Application to Sourcing: How We Conducted $35 Billion of Generalized Combinatorial Auctions.” AI Magazine, 28(3): 45-58, 2007 Expressive auctions for chemicals, packaging, ingredients, technology, services, medical, transport, materials,... custom Amazon, e-banking, RFID,...

14. An Example Prediction A random variable, e.g. Will US go into recession in 2012? (Y/N)

15. An Example Prediction Market A random variable, e.g. Turned into a financial instrument payoff = realized value of variable $1 if$0 if I am entitled to: Will US go into recession in 2012? (Y/N) Recession in 2012 No Recession in 2012

16. New Markets – Same CDA

17. 2012 September 10 7:23 p.m. ET

18. Between 3.0% and 3.7% chance

19. 2012 July 22 8:57 a.m. ET Between 13.6% and 14.4% chance

21. http://www.predictwise.com/maps/2012president

22. Design for Prediction Auctions/FinancialPrediction Markets PrimaryGains from tradeInformation SecondaryInformationGains from trade

23. Design for Prediction Goals for trade – Efficiency (gains) – Inidiv. rationality – Budget balance – Revenue – Comp. complexity Equilibrium – General, Nash,...

24. Design for Prediction Goals for trade – Efficiency (gains) – Inidiv. rationality – Budget balance – Revenue – Comp. complexity Equilibrium – General, Nash,... Goals for prediction – Info aggregation – 1. Liquidity – 2. Expressiveness – Bounded budget – Indiv. rationality – Comp. complexity Equilibrium – Rational expectations Competes with: experts, scoring rules, opinion pools, ML/stats, polls, Delphi

25. Design for Prediction Goals for trade – Efficiency (gains) – Inidiv. rationality – Budget balance – Revenue – Comp. complexity Equilibrium – General, Nash,... Goals for prediction – Info aggregation – 1. Liquidity – 2. Expressiveness – Bounded budget – Indiv. rationality – Comp. complexity Equilibrium – Rational expectations Competes with: experts, scoring rules, opinion pools, ML/stats, polls, Delphi

26. Prediction Markets Versus... model it - baseline model it - baseline++ poll a crowd - mTurk pay a crowd - probSports contest pay a crowd - Vegas market pay a crowd - TradeSports market guess

27. Why Liquidity?

28. Low liquidity takes the prediction out of markets http://blog.oddhead.com/2010/07/08/why-automated-market-makers/ Between 0.2% and 99.8% chance

29. Why Expressiveness?

32. Call option and put options are redundant Range bets require four trades ( “ butterfly spread ” ) Bid to buy call option @strike 15 can ’ t match with ask to sell @strike 10 Can ’ t set own strike Bottom line: Lacks expressiveness

33. Why Expressiveness? Dem Pres, Dem Senate, Dem House Dem Pres, Dem Senate, GOP House Dem Pres, GOP Senate, Dem House Dem Pres, GOP Senate, GOP House... Dem Pres Dem House Dem wins >=270 electoral votes Dem wins >=280 electoral votes...

34. Industry Standard Ignore relationships: Treat them as independent markets Las Vegas sports betting Kentucky horseracing Wall Street stock options High Streetspread betting

35. A Better Way (Or,... Bringing trading into digital age) Expressiveness – Linear programming – Bossaerts, Fine, Ledyard: Combined Value Trading – http://bit.ly/multipm Expressiveness + Liquidity – Automated market maker – Always quote a price on anything – Downside: requires subsidy/risk

36. Example: Liquidity and Expressiveness

37. Getting Greedy Design a market for information on exponentially many things “Combinatorial prediction market”

38. Combinatorial securities: More information, more fun Payoff is function of common variables, e.g. 50 states elect Dem or Rep

39. Combinatorial securities: More information, more fun Dem will win California

40. Combinatorial securities: More information, more fun Dem will lose FL but win election Dem will win >8 of 10 Northeastern states Same party will win OH & PA

41. Combinatorial securities: More information, more fun There will be a path of blue from Canada to Mexico

42. Some Counting 54 “states”: 48 + DC + Maine (2), Nebraska (3) 2 54 = 18 quadrillion possible outcomes 2 2 54  10 18008915383333500 distinct predictions More than a googol, less than a googolplex NOT independent

43. A tractable combinatorial market maker using constraint generation ACM EC 2012 MIROSLAV DUDÍK, SEBASTIEN LAHAIE, DAVID M. PENNOCK Thanks: David Rothschild, Dan Osherson, Arvid Wang, Jake Abernethy, Rafael Frongillo, Rob Schapire

44. Automated Market Maker ExchangeMarket Maker IndependentTractable No risk No info propagation Industry standard Tractable Exponential loss bound No info propagation CombinatorialNP-hard No risk Full info propagation Major liquidity problem #P-hard Linear/Const loss bound Full info propagation Info propagation  Reward traders for information, not computational power

45. Automated Market Maker ExchangeMarket Maker IndependentTractable No risk No info propagation Industry standard Tractable Exponential loss bound No info propagation Our approachTractable Good loss bound Some info propagation CombinatorialNP-hard No risk Full info propagation Major liquidity problem #P-hard Linear/Const loss bound Full info propagation Info propagation  Reward traders for information, not computational power

46. Our Approach: Approx Combo Market Maker Independent market makers for securities and small groups Parallel constraint generation to find and remove arbitrage Embedded in a convex optimization framework Deterministic: Better user experience (Previous Predictalot: Monte Carlo)

47. Our Contributions Separates pricing (must be fast) and information propagation New method to derive loss bound Empirical evaluation on over 300 thousand complex predictions Building this this for real! Fantasy Politics on PredictWise.com for 2012 US Presidential election

48. Consistent pricing 0 1 01 A&B’&C Independent markets

49. Consistent pricing 0 1 01 A&B’&C Independent markets Prices p

50. Consistent pricing 0 1 01 A&B’&C Independent markets

51. Consistent pricing 0 1 01 A&B’&C B = 0.6 A = 0.8 C = 0.9 Independent markets

52. Consistent pricing 0 1 01 0.6B = 0.6 0.8A = 0.8 A&B’&C 0.9C = 0.9

53. Consistent pricing 0 1 01 0.6B = 0.6 0.4 0.8A = 0.8 0.8 A&B’&C 0.9C = 0.9 0.9

54. Consistent pricing 0 1 01 0.6B = 0.6 0.4 0.8A = 0.8 0.8 A&B’&C 0.9C = 0.9 0.9

55. Consistent pricing 0 1 01 0.6B = 0.6 0.4 0.8A = 0.8 0.8 A&B’&C 0.9C = 0.9 0.9 A=B A=C A=B’

56. Consistent pricing 0 1 01 0.6B = 0.6 0.4 0.8A = 0.8 0.8 A&B’&C 0.9C = 0.9 0.9 A&C = 0.5

57. Consistent pricing 0 1 01 0.6B = 0.6 0.4 0.8A = 0.8 0.8 A&B’&C 0.9C = 0.9 0.9

58. Approximate pricing 0 1 01 0.6B = 0.6 0.4 0.8A = 0.8 0.8 A&B’&C 0.9C = 0.9 0.9

59. Approximate pricing 0 1 01 0.6B = 0.6 0.4 Prices p 0.8A = 0.8 0.8 A&B’&C 0.9C = 0.9 0.9

60. Approximate pricing 0 1 01 0.5B = 0.5 0.5  Buy NotB Prices p 0.8A = 0.8 0.8 A&B’&C 0.9C = 0.9 0.9

61. Approximate pricing 0 1 01 0.5B = 0.5 0.5 Prices p 0.8A = 0.8 0.8 A&B’&C 0.9C = 0.9 0.9

62. Approximate pricing 0 1 01 0.8 0.5 A = 0.8 B = 0.5 0.5 0.8 Prices p A&B’&C 0.9C = 0.9 0.9

63. For Election Create 50 states – initialize with prior Create all groups of 2 – init as indep For conjunctions of 3 or more, group with it opposite disjunction: A&B&C, A’|B’|C’ Each group is indep MM – fast In parallel: Generate, find, and fix constraints

64. Constraints Clique lower bound P(L1|...|Lm) ≥Σ C P(Li) – Σ C P(Li&Lj) Spanning tree upper bound P(L1|...|Lm) ≤ Σ P(Li) – Σ T P(Li&Lj) Threshold constraints TBA Choosing constraints is key! – Depends on bets (unlike Monte Carlo) – An art

65. Does it work? Tested on over 300K complex predictions from Princeton study Budget

66. Does it work? Tested on over 300K complex predictions from Princeton study

67. No really, does it work? Building this thing for real Fantasy Politics for Election 2012 on PredictWise.com

68. http://PredictWise.com/fantasy

72. Predictalot Mar 16 2011 Over 4000 variations of this 3-team prediction were placed

73. Predictalot alpha

74. Combo Prediction Markets: More Info Gory details What is (and what good is) a combinatorial prediction market? http://bit.ly/combopm More accessible Guest post on Freakonomics http://bit.ly/combopmfreak