Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Monte-Carlo approximation algorithms for enumeration problems
Journal of Algorithms
Approximation algorithms
Combinatorial Information Market Design
Information Systems Frontiers
Generating random solutions for constraint satisfaction problems
Eighteenth national conference on Artificial intelligence
On probabilistic inference by weighted model counting
Artificial Intelligence
Pricing combinatorial markets for tournaments
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Complexity of combinatorial market makers
Proceedings of the 9th ACM conference on Electronic commerce
Towards efficient sampling: exploiting random walk strategies
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Performing Bayesian inference by weighted model counting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
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
A new understanding of prediction markets via no-regret learning
Proceedings of the 11th ACM conference on Electronic commerce
A tractable combinatorial market maker using constraint generation
Proceedings of the 13th ACM Conference on Electronic Commerce
Efficient Market Making via Convex Optimization, and a Connection to Online Learning
ACM Transactions on Economics and Computation - Special Issue on Algorithmic Game Theory
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Computing the marketmaker price of a security in a combinatorial prediction market is #P-hard. We devise a fully polynomial randomized approximation scheme (FPRAS) that computes the price of any security in disjunctive normal form (DNF) within an ε multiplicative error factor in time polynomial in 1/ε and the size of the input, with high probability and under reasonable assumptions. Our algorithm is a Monte-Carlo technique based on importance sampling. The algorithm can also approximately price securities represented in conjunctive normal form (CNF) with additive error bounds. To illustrate the applicability of our algorithm, we show that many securities in Yahoo!'s popular combinatorial prediction market game called Predictalot can be represented by DNF formulas of polynomial size.