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- W2404046634 abstract "A prediction market allows a group of traders to form a consensus probability distribution by entering into agreements that pay off contingent on events of interest. A combinatorial prediction market allows conditional trades or trades on Boolean combinations of events to form a joint distribution over many related events. Sun et al. (2012) showed how to use a junction tree to update both the consensus joint distribution and each user's assets in a combinatorial prediction market. Because a separate asset junction tree is maintained for each user on the joint space, this approach is very inefficient in the typical case where most users trade sparsely with respect to the joint space. Further, any changes to the global junction tree must be mirrored across all users. We demonstrate large efficiency gains from divorcing the probability and asset data structures, dynamically building a separate asset junction tree for each user. The trade-based asset model has asset blocks as the basic units involving questions being traded only. We compare a simple block-iteration method against a more sophisticated user-specific junction tree, analyzing conditions under which each approach is faster. Our asset model has been deployed in SciCast, a combinatorial prediction market for science and technology forecasting." @default.
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- W2404046634 date "2014-07-27" @default.
- W2404046634 modified "2023-09-26" @default.
- W2404046634 title "Trade-based asset models for combinatorial prediction markets" @default.
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