Manipulation in Conditional Decision Markets

Conditional decision markets concurrently predict the future and decide on it. These markets price the impact of decisions, conditional on them being executed. After the markets close, a principal decides which decisions are executed based on the prices in the markets. As some decisions are not executed, the respective outcome cannot be observed, and the markets predicting the impact of non-executed decisions are void. This allows ex-post costless manipulation of such markets. We conduct two versions of an online experiment to explore scenarios in which a principal runs conditional decision markets to inform her choice among a set of a risky alternatives. We find that the level of manipulation depends on the simplicity of the market setting. When a trader is alone, has the power to move prices far enough, and the decision is deterministically tied to market prices or a very high correlation between prices and decision is implied, only then manipulation occurs. As soon as another trader is present to add risk to manipulation, manipulation is eliminated. Our results contrast theoretical work on conditional decision markets in two ways: First, our results suggest that manipulation may not be as meaningful an issue. Second, probabilistic decision rules are used to add risk to manipulation; when manipulation is not a meaningful issue, deterministic decisions provide the better decision with less noise. To the best of our knowledge, this is the first experimental analysis isolating the effects of the conditional nature of decision markets.

[1]  Lian Jian,et al.  Aggregation and Manipulation in Prediction Markets: Effects of Trading Mechanism and Information Distribution , 2012, Manag. Sci..

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

[3]  Thomas A. Rietz,et al.  Information Systems Frontiers 5:1, 79–93, 2003 C ○ 2003 Kluwer Academic Publishers. Manufactured in The Netherlands. Prediction Markets as Decision Support Systems , 2022 .

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

[5]  Stanko Dimitrov,et al.  Composition of markets with conflicting incentives , 2010, EC '10.

[6]  S. Chinn.  A simple method for converting an odds ratio to effect size for use in meta-analysis. , 2000, Statistics in medicine.

[7]  A. Gibbard.  Manipulation of Schemes That Mix Voting with Chance , 1977 .

[8]  Bryan R. Routledge,et al.  Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders , 2010, Manag. Sci..

[9]  David Rothschild,et al.  Trading Strategies and Market Microstructure: Evidence from a Prediction Market , 2015 .

[10]  Vincent Conitzer,et al.  Optimal False-Name-Proof Voting Rules with Costly Voting , 2008, AAAI.

[11]  Koleman Strumpf,et al.  Manipulating political stock markets: A field experiment and a century of observational data , 2006 .

[12]  David Porter,et al.  Affecting Policy by Manipulating Prediction Markets: Experimental Evidence , 2013 .

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

[14]  David M. Pennock,et al.  Algorithmic Game Theory: Computational Aspects of Prediction Markets , 2007 .

[15]  Tuomas Sandholm,et al.  Decision rules and decision markets , 2010, AAMAS.

[16]  R. Hanson,et al.  Information aggregation and manipulation in an experimental market , 2006 .

[17]  Henner Gimpel,et al.  Measuring the impact of information aggregation mechanisms: An experimental investigation , 2011 .

[18]  Thomas A. Rietz,et al.  Prediction market accuracy in the long run , 2008 .

[19]  Robin Hanson,et al.  Can Manipulators Mislead Market Observers , 2007 .

[20]  David G. Rand,et al.  Economic Games on the Internet: The Effect of $1 Stakes , 2011, PloS one.

[21]  David G. Stork,et al.  Building intelligent systems one e-citizen at a time , 1999, IEEE Intell. Syst..

[22]  Ian A. Kash,et al.  Information elicitation for decision making , 2011, AAMAS.

[23]  Christof Weinhardt,et al.  Short-selling in prediction markets , 2012 .

[24]  Florian Teschner,et al.  Collective Intelligence in Market-Based Social Decision Making , 2013, ICIS.

[25]  Lance Fortnow,et al.  Gaming Prediction Markets: Equilibrium Strategies with a Market Maker , 2010, Algorithmica.

[26]  David G. Rand,et al.  The online laboratory: conducting experiments in a real labor market , 2010, ArXiv.

[27]  R. Hanson.  LOGARITHMIC MARKETS CORING RULES FOR MODULAR COMBINATORIAL INFORMATION AGGREGATION , 2012 .

[28]  R. Hanson,et al.  An experimental test of combinatorial information markets , 2009 .

[29]  J McCarthy,et al.  MEASURES OF THE VALUE OF INFORMATION. , 1956, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Ian A. Kash,et al.  Decision Markets with Good Incentives , 2011, WINE.

[31]  Stanko Dimitrov,et al.  Non-myopic strategies in prediction markets , 2008, EC '08.

[32]  Siddharth Suri,et al.  Conducting behavioral research on Amazon’s Mechanical Turk , 2010, Behavior research methods.