Betting boolean-style: a framework for trading in securities based on logical formulas

We develop a framework for trading in compound securities: financial instruments that pay off contingent on the outcomes of arbitrary statements in propositional logic. Buying or selling securities---which can be thought of as betting on or against a particular future outcome---allows agents both to hedge risk and to profit (in expectation) on subjective predictions. A compound securities market allows agents to place bets on arbitrary boolean combinations of events, enabling them to more closely achieve their optimal risk exposure, and enabling the market as a whole to more closely achieve the social optimum.The tradeoff for allowing such expressivity is in the complexity of the agents' and auctioneer's optimization problems.We develop and motivate the concept of a compound securities market, presenting the framework through a series of formal definitions and examples. We then analyze in detail the auctioneer's matching problem. We show that, with numevents events, the matching problem is co-NP-complete in the divisible case and complete in the indivisible case. We show that the latter hardness result holds even under severe language restrictions on bids. With events, and numevents securities, the problem is polynomial in the divisible case and NP-complete in the indivisible case. We briefly discuss matching algorithms and tractable special cases.

[1]  David M. Pennock,et al.  The Real Power of Artificial Markets , 2001, Science.

[2]  Winston C. Yang,et al.  Parimutuel betting markets as information aggregation devices: experimental results , 2003 .

[3]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[4]  H. Jeffreys,et al.  The Theory of Probability , 1896 .

[5]  H. Varian The Arbitrage Principle in Financial Economics , 1987 .

[6]  C. Plott,et al.  Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets , 1988 .

[7]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[8]  R. Roll Orange Juice and Weather , 1984 .

[9]  R. L. Winkler,et al.  Separating probability elicitation from utilities , 1988 .

[10]  Robin Hanson,et al.  Combinatorial Information Market Design , 2003, Inf. Syst. Frontiers.

[11]  Gregory F. Cooper,et al.  The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..

[12]  Noam Nisan,et al.  Bidding and allocation in combinatorial auctions , 2000, EC '00.

[13]  Bernardo A. Huberman,et al.  Forecasting uncertain events with small groups , 2001, EC '01.

[14]  R. Hanson Could gambling save science? Encouraging an honest consensus , 1995 .

[15]  Axel Werwatz,et al.  How accurate do markets predict the outcome of an event? The Euro 2000 soccer championships experiment , 2002 .

[16]  C. Plott Markets as Information Gathering Tools , 2000 .

[17]  Jacques Drèze Essays on economic decisions under uncertainty: Market allocation under uncertainty , 1987 .

[18]  Russell J. Lundholm,et al.  Information Aggregation in an Experimental Market. , 1990 .

[19]  A. Mas-Colell,et al.  Microeconomic Theory , 1995 .

[20]  Thomas A. Rietz,et al.  Wishes, expectations and actions: a survey on price formation in election stock markets , 1999 .

[21]  Leslie R. Fine,et al.  Inducing liquidity in thin financial markets through combined-value trading mechanisms , 2002 .

[22]  David M. Pennock,et al.  Extracting collective probabilistic forecasts from web games , 2001, KDD '01.

[23]  Raymond E. Miller,et al.  Complexity of Computer Computations , 1972 .

[24]  Sven de Vries,et al.  Combinatorial Auctions: A Survey , 2003, INFORMS J. Comput..

[25]  M. Rubinstein.,et al.  Recovering Probability Distributions from Option Prices , 1996 .

[26]  J. Gandar,et al.  Informed Traders and Price Variations in the Betting Market for Professional Basketball Games , 1998 .

[27]  Ronald Fagin,et al.  A logic for reasoning about probabilities , 1988, [1988] Proceedings. Third Annual Information Symposium on Logic in Computer Science.

[28]  K. Arrow The Role of Securities in the Optimal Allocation of Risk-bearing , 1964 .

[29]  Jacques Drèze Essays on economic decisions under uncertainty: Markets and prices , 1987 .

[30]  David Levine,et al.  Winner determination in combinatorial auction generalizations , 2002, AAMAS '02.

[32]  Robert Forsythe,et al.  Anatomy of an Experimental Political Stock Market , 1992 .

[33]  W. Warmuth De Finetti, B.: Theory of Probability - A Critical Introductory Treatment, Volume 2. John Wiley & Sons, London-New York-Sydney-Toronto 1975. XIV, 375 S., £ 10.50 , 1977 .

[34]  Sandip Debnath,et al.  Information incorporation in online in-Game sports betting markets , 2003, EC '03.

[35]  Jacques H. Dreze,et al.  Market allocation under uncertainty , 1970 .

[36]  Michael P. Wellman,et al.  Compact Securities Markets for Pareto Optimal Reallocation of Risk , 2000, UAI.

[37]  R. Thaler,et al.  Anomalies Parimutuel Betting Markets: Racetracks and Lotteries , 1988 .

[38]  Paula J. Brewer Decentralized computation procurement and computational robustness in a smart market , 1999 .

[39]  A. H. Murphy,et al.  “Good” Probability Assessors , 1968 .

[40]  M. Quinzii,et al.  Theory of incomplete markets , 1996 .