The weighted majority algorithm
Information and Computation
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Combinatorial Information Market Design
Information Systems Frontiers
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
A dynamic pari-mutuel market for hedging, wagering, and information aggregation
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Convex Optimization
Computer
Prediction, Learning, and Games
Prediction, Learning, and Games
Proceedings of the 8th ACM conference on Electronic commerce
Logarithmic regret algorithms for online convex optimization
Machine Learning
A primal-dual perspective of online learning algorithms
Machine Learning
Pricing combinatorial markets for tournaments
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Eliciting properties of probability distributions
Proceedings of the 9th ACM conference on Electronic commerce
Permutation betting markets: singleton betting with extra information
Proceedings of the 9th ACM conference on Electronic commerce
Complexity of combinatorial market makers
Proceedings of the 9th ACM conference on Electronic commerce
Parimutuel Betting on Permutations
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Surrogate regret bounds for proper losses
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Combinatorial prediction markets for event hierarchies
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Betting Boolean-style: a framework for trading in securities based on logical formulas
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Learning Permutations with Exponential Weights
The Journal of Machine Learning Research
Pari-mutuel markets: mechanisms and performance
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
A new understanding of prediction markets via no-regret learning
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 optimization-based framework for automated market-making
Proceedings of the 12th ACM conference on Electronic commerce
A Unified Framework for Dynamic Prediction Market Design
Operations Research
Liquidity-sensitive automated market makers via homogeneous risk measures
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
An efficient Monte-Carlo algorithm for pricing combinatorial prediction markets for tournaments
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
An axiomatic characterization of adaptive-liquidity market makers
Proceedings of the fourteenth ACM conference on Electronic commerce
What you jointly know determines how you act: strategic interactions in prediction markets
Proceedings of the fourteenth ACM conference on Electronic commerce
Cost function market makers for measurable spaces
Proceedings of the fourteenth ACM conference on Electronic commerce
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We propose a general framework for the design of securities markets over combinatorial or infinite state or outcome spaces. The framework enables the design of computationally efficient markets tailored to an arbitrary, yet relatively small, space of securities with bounded payoff. We prove that any market satisfying a set of intuitive conditions must price securities via a convex cost function, which is constructed via conjugate duality. Rather than deal with an exponentially large or infinite outcome space directly, our framework only requires optimization over a convex hull. By reducing the problem of automated market making to convex optimization, where many efficient algorithms exist, we arrive at a range of new polynomial-time pricing mechanisms for various problems. We demonstrate the advantages of this framework with the design of some particular markets. We also show that by relaxing the convex hull we can gain computational tractability without compromising the market institution’s bounded budget. Although our framework was designed with the goal of deriving efficient automated market makers for markets with very large outcome spaces, this framework also provides new insights into the relationship between market design and machine learning, and into the complete market setting. Using our framework, we illustrate the mathematical parallels between cost-function-based markets and online learning and establish a correspondence between cost-function-based markets and market scoring rules for complete markets.