An Efficient Monte-Carlo Algorithm for Pricing Combinatorial Prediction Markets for Tournaments

Lirong Xia, David M. Pennock

Abstract: 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 e multiplicative error factor in time polynomial in 1/e 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.