Collective revelation: a mechanism for self-verified, weighted, and truthful predictions
Proceedings of the 10th ACM conference on Electronic commerce
Subsidized Prediction Markets for Risk Averse Traders
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Gaming Dynamic Parimutuel Markets
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Composition of markets with conflicting incentives
Proceedings of the 11th ACM conference on Electronic commerce
Proceedings of the 11th ACM conference on Electronic commerce
A multi-agent system for predicting future event outcomes
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Decision markets with good incentives
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Proceedings of the 13th ACM Conference on Electronic Commerce
Rational market making with probabilistic knowledge
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A multi-agent prediction market based on partially observable stochastic game
Proceedings of the 13th International Conference on Electronic Commerce
Pay by the bit: an information-theoretic metric for collective human judgment
Proceedings of the 2013 conference on Computer supported cooperative work
What you jointly know determines how you act: strategic interactions in prediction markets
Proceedings of the fourteenth ACM conference on Electronic commerce
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We study the equilibrium behavior of informed traders interacting with market scoring rule (MSR) market makers. One attractive feature of MSR is that it is myopically incentive compatible: it is optimal for traders to report their true beliefs about the likelihood of an event outcome provided that they ignore the impact of their reports on the profit they might garner from future trades. In this paper, we analyze non-myopic strategies and examine what information structures lead to truthful betting by traders. Specifically, we analyze the behavior of risk-neutral traders with incomplete information playing in a dynamic game. We consider finite-stage and infinite-stage game models. For each model, we study the logarithmic market scoring rule (LMSR) with two different information structures: conditionally independent signals and (unconditionally) independent signals. In the finite-stage model, when signals of traders are independent conditional on the state of the world, truthful betting is a Perfect Bayesian Equilibrium (PBE). Moreover, it is the unique Weak Perfect Bayesian Equilibrium (WPBE) of the game. In contrast, when signals of traders are unconditionally independent, truthful betting is not a WPBE. In the infinite-stage model with unconditionally independent signals, there does not exist an equilibrium in which all information is revealed in a finite amount of time. We propose a simple discounted market scoring rule that reduces the opportunity for bluffing strategies. We show that in any WPBE for the infinite-stage market with discounting, the market price converges to the fully-revealing price, and the rate of convergence can be bounded in terms of the discounting parameter. When signals are conditionally independent, truthful betting is the unique WPBE for the infinite-stage market with and without discounting.