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

NYSE

NYSE

NYSE Deutsche Börse AG 2007 Euronext 2006 Archipelago, ipo

NYSE 7pm Sep 10, 2012

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

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

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

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

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

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

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

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,...

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

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

New Markets – Same CDA

2012 September 10 7:23 p.m. ET

Between 3.0% and 3.7% chance

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

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

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

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

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

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

Why Liquidity?

Low liquidity takes the prediction out of markets Between 0.2% and 99.8% chance

Why Expressiveness?

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

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...

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

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

Example: Liquidity and Expressiveness

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

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

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

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

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

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

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

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

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

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)

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

Consistent pricing A&B’&C Independent markets

Consistent pricing A&B’&C Independent markets Prices p

Consistent pricing A&B’&C Independent markets

Consistent pricing A&B’&C B = 0.6 A = 0.8 C = 0.9 Independent markets

Consistent pricing B = A = 0.8 A&B’&C 0.9C = 0.9

Consistent pricing B = A = A&B’&C 0.9C =

Consistent pricing B = A = A&B’&C 0.9C =

Consistent pricing B = A = A&B’&C 0.9C = A=B A=C A=B’

Consistent pricing B = A = A&B’&C 0.9C = A&C = 0.5

Consistent pricing B = A = A&B’&C 0.9C =

Approximate pricing B = A = A&B’&C 0.9C =

Approximate pricing B = Prices p 0.8A = A&B’&C 0.9C =

Approximate pricing B =  Buy NotB Prices p 0.8A = A&B’&C 0.9C =

Approximate pricing B = Prices p 0.8A = A&B’&C 0.9C =

Approximate pricing A = 0.8 B = Prices p A&B’&C 0.9C =

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

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

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

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

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

Predictalot Mar Over 4000 variations of this 3-team prediction were placed

Predictalot alpha

Combo Prediction Markets: More Info Gory details What is (and what good is) a combinatorial prediction market? More accessible Guest post on Freakonomics