Decision Markets with Good Incentives

  • Yiling Chen
  • Ian Kash
  • Mike Ruberry
  • Victor Shnayder
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7090)


Decision markets both predict and decide the future. They allow experts to predict the effects of each of a set of possible actions, and after reviewing these predictions a decision maker selects an action to perform. When the future is independent of the market, strictly proper scoring rules myopically incentivize experts to predict consistent with their beliefs, but this is not generally true when a decision is to be made. When deciding, only predictions for the chosen action can be evaluated for their accuracy since the other predictions become counterfactuals. This limitation can make some actions more valuable than others for an expert, incentivizing the expert to mislead the decision maker. We construct and characterize decision markets that are – like prediction markets using strictly proper scoring rules – myopic incentive compatible. These markets require the decision maker always risk taking every available action, and reducing this risk increases the decision maker’s worst-case loss. We also show a correspondence between strictly proper decision markets and strictly proper sets of prediction markets, creating a formal connection between the incentives of prediction and decision markets.


Decision Maker Decision Rule Multiagent System Market Maker Full Support 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Berg, J.E., Forsythe, R., Nelson, F.D., Rietz, T.A.: Results from a dozen years of election futures markets research. In: Plott, C.A., Smith, V. (eds.) Handbook of Experimental Economic Results (2001)Google Scholar
  2. 2.
    Berg, J.E., Rietz, T.A.: Prediction markets as decision support systems. Information Systems Frontier 5, 79–93 (2003)CrossRefGoogle Scholar
  3. 3.
    Chen, K.-Y., Plott, C.R.: Information aggregation mechanisms: Concept, design and implementation for a sales forecasting problem. Working paper No. 1131, California Institute of Technology, Division of the Humanities and Social Sciences (2002)Google Scholar
  4. 4.
    Chen, Y., Dimitrov, S., Sami, R., Reeves, D.M., Pennock, D.M., Hanson, R.D., Fortnow, L., Gonen, R.: Gaming prediction markets: Equilibrium strategies with a market maker. Algorithmica 58(4), 930–969 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Chen, Y., Gao, X.A., Goldstein, R., Kash, I.A.: Market manipulation with outside incentives. In: AAAI 2011: Proceedings of the 25th Conference on Artificial Intelligence (2011)Google Scholar
  6. 6.
    Chen, Y., Kash, I.A.: Information elicitation for decision making. In: AAMAS 2011: Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (2011)Google Scholar
  7. 7.
    Chen, Y., Pennock, D.M.: A utility framework for bounded-loss market makers. In: UAI 2007: Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, pp. 49–56 (2007)Google Scholar
  8. 8.
    Debnath, S., Pennock, D.M., Giles, C.L., Lawrence, S.: Information incorporation in online in-game sports betting markets. In: EC 2003: Proceedings of the 4th ACM Conference on Electronic Commerce, pp. 258–259. ACM, New York (2003)Google Scholar
  9. 9.
    Dimitrov, S., Sami, R.: Composition of markets with conflicting incentives. In: EC 2010: Proceedings of the 11th ACM Conference on Electronic Commerce, pp. 53–62. ACM, New York (2010)Google Scholar
  10. 10.
    Gneiting, T., Raftery, A.E.: Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 102(477), 359–378 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Hanson, R.: Decision markets. IEEE Intelligent Systems 14(3), 16–19 (1999)CrossRefGoogle Scholar
  12. 12.
    Hanson, R.D.: Combinatorial information market design. Information Systems Frontiers 5(1), 107–119 (2003)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Hanson, R.D.: Logarithmic market scoring rules for modular combinatorial information aggregation. Journal of Prediction Markets 1(1), 1–15 (2007)Google Scholar
  14. 14.
    McCarthy, J.: Measures of the value of information. PNAS: Proceedings of the National Academy of Sciences of the United States of America 42(9), 654–655 (1956)CrossRefzbMATHGoogle Scholar
  15. 15.
    Ostrovsky, M.: Information aggregation in dynamic markets with strategic traders. In: EC 2009: Proceedings of the Tenth ACM Conference on Electronic Commerce, p. 253. ACM, New York (2009)Google Scholar
  16. 16.
    Othman, A., Sandholm, T.: Decision rules and decision markets. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 625–632 (2010)Google Scholar
  17. 17.
    Savage, L.J.: Elicitation of personal probabilities and expectations. Journal of the American Statistical Association 66(336), 783–801 (1971)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Wolfers, J., Zitzewitz, E.: Prediction markets. Journal of Economic Perspective 18(2), 107–126 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yiling Chen
    • 1
  • Ian Kash
    • 1
  • Mike Ruberry
    • 1
  • Victor Shnayder
    • 1
  1. 1.Harvard UniversityUSA

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