An Axiomatic Study of Scoring Rule Markets

Prediction markets are well-studied in the case where predictions are probabilities or expectations of future random variables. In 2008, Lambert, et al. proposed a generalization, which we call "scoring rule markets" (SRMs), in which traders predict the value of arbitrary statistics of the random variables, provided these statistics can be elicited by a scoring rule. Surprisingly, despite active recent work on prediction markets, there has not yet been any investigation into the properties of more general SRMs. To initiate such a study, we ask the following question: in what sense are SRMs "markets"? We classify SRMs according to several axioms that capture potentially desirable qualities of a market, such as the ability to freely exchange goods (contracts) for money. Not all SRMs satisfy our axioms: once a contract is purchased in any market for prediction the median of some variable, there will not necessarily be any way to sell that contract back, even in a very weak sense. Our main result is a characterization showing that slight generalizations of cost-function-based markets are the only markets to satisfy all of our axioms for finite-outcome random variables. Nonetheless, we find that several SRMs satisfy weaker versions of our axioms, including a novel share-based market mechanism for ratios of expected values.

[1]  Yoav Shoham,et al.  Eliciting properties of probability distributions , 2008, EC '08.

[2]  T. Gneiting Making and Evaluating Point Forecasts , 2009, 0912.0902.

[3]  David M. Pennock,et al.  A Utility Framework for Bounded-Loss Market Makers , 2007, UAI.

[4]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[5]  L. J. Savage Elicitation of Personal Probabilities and Expectations , 1971 .

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

[7]  W. Newey,et al.  Asymmetric Least Squares Estimation and Testing , 1987 .

[8]  Jennifer Wortman Vaughan,et al.  Efficient Market Making via Convex Optimization, and a Connection to Online Learning , 2013, TEAC.

[9]  Jacob D. Abernethy,et al.  A Characterization of Scoring Rules for Linear Properties , 2012, COLT.

[10]  G. Brier VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .

[11]  Yoav Shoham,et al.  Eliciting truthful answers to multiple-choice questions , 2009, EC '09.

[12]  J. Wolfers,et al.  Prediction Markets , 2003 .

[13]  Arpit Agarwal,et al.  On Consistent Surrogate Risk Minimization and Property Elicitation , 2015, COLT.

[14]  Jacob D. Abernethy,et al.  A Collaborative Mechanism for Crowdsourcing Prediction Problems , 2011, NIPS.

[15]  Ian A. Kash,et al.  General Truthfulness Characterizations Via Convex Analysis , 2012, WINE.

[16]  Jacob D. Abernethy,et al.  Information aggregation in exponential family markets , 2014, EC.

[17]  Xiaolong Li,et al.  A general volume-parameterized market making framework , 2014, EC.

[18]  Ian A. Kash,et al.  Vector-Valued Property Elicitation , 2015, COLT.