Combinatorial Information Market Design
Information Systems Frontiers
Algorithmic Game Theory
Pari-mutuel markets: mechanisms and performance
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Subsidized Prediction Markets for Risk Averse Traders
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
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
A new understanding of prediction markets via no-regret learning
Proceedings of the 11th ACM conference on Electronic commerce
Information aggregation in smooth markets
Proceedings of the 11th ACM conference on Electronic commerce
Automated market-making in the large: the gates hillman prediction market
Proceedings of the 11th ACM conference on Electronic commerce
A practical liquidity-sensitive automated market maker
Proceedings of the 11th ACM conference on Electronic commerce
An axiomatic characterization of continuous-outcome market makers
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Liquidity-sensitive automated market makers via homogeneous risk measures
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
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 Practical Liquidity-Sensitive Automated Market Maker
ACM Transactions on Economics and Computation
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Recently, coinciding with and perhaps driving the increased popularity of prediction markets, several novel pari-mutuel mechanisms have been developed such as the logarithmic market scoring rule (LMSR), the cost-function formulation of market makers, and the sequential convex parimutuel mechanism (SCPM). In this work, we present a unified convex optimization framework which connects these seemingly unrelated models for centrally organizing contingent claims markets. The existing mechanisms can be expressed in our unified framework using classic utility functions. We also show that this framework is equivalent to a convex risk minimization model for the market maker. This facilitates a better understanding of the risk attitudes adopted by various mechanisms. The utility framework also leads to easy implementation since we can now find the useful cost function of a market maker in polynomial time through the solution of a simple convex optimization problem. In addition to unifying and explaining the existing mechanisms, we use the generalized framework to derive necessary and sufficient conditions for many desirable properties of a prediction market mechanism such as proper scoring, truthful bidding (in a myopic sense), efficient computation, controllable risk-measure, and guarantees on the worst-case loss. As a result, we develop the first proper, truthful, risk controlled, loss-bounded (in number of states) mechanism; none of the previously proposed mechanisms possessed all these properties simultaneously. Thus, our work could provide an effective tool for designing new market mechanisms.