Bluffing and Strategic Reticence in Prediction Markets

  • Yiling Chen
  • Daniel M. Reeves
  • David M. Pennock
  • Robin D. Hanson
  • Lance Fortnow
  • Rica Gonen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4858)


We study the equilibrium behavior of informed traders interacting with two types of automated market makers: market scoring rules (MSR) and dynamic parimutuel markets (DPM). Although both MSR and DPM subsidize trade to encourage information aggregation, and MSR is myopically incentive compatible, neither mechanism is incentive compatible in general. That is, there exist circumstances when traders can benefit by either hiding information (reticence) or lying about information (bluffing). We examine what information structures lead to straightforward play by traders, meaning that traders reveal all of their information truthfully as soon as they are able. Specifically, we analyze the behavior of risk-neutral traders with incomplete information playing in a finite-period dynamic game. We employ two different information structures for the logarithmic market scoring rule (LMSR): conditionally independent signals and conditionally dependent signals. When signals of traders are independent conditional on the state of the world, truthful betting is a Perfect Bayesian Equilibrium (PBE) for LMSR . However, when signals are conditionally dependent, there exist joint probability distributions on signals such that at a PBE in LMSR traders have an incentive to bet against their own information—strategically misleading other traders in order to later profit by correcting their errors. In DPM, we show that when traders anticipate sufficiently better-informed traders entering the market in the future, they have incentive to partially withhold their information by moving the market probability only partway toward their beliefs, or in some cases not participating in the market at all.


Market Maker Independent Signal Informed Trader Double Auction Prediction Market 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yiling Chen
    • 1
  • Daniel M. Reeves
    • 1
  • David M. Pennock
    • 1
  • Robin D. Hanson
    • 2
  • Lance Fortnow
    • 3
  • Rica Gonen
    • 1
  1. 1.Yahoo! Research 
  2. 2.George Mason University 
  3. 3.University of Chicago 

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