贝叶斯网络在组合预测市场价格更新中的应用
Price Updating in Combinatorial Prediction Markets with Bayesian Networks
摘要：
为了克服基于对数市场评分规则（LMSR-based）的组合预测市场中计算/更新价格的#P-困难，Chen等人。[5]最近使用一个简单的贝叶斯网络来表示锦标赛组合预测市场中证券的价格，并表明两种类型的流行证券是结构保持的。在本文中，我们显着扩展这一想法，采用贝叶斯网络在一般的组合预测市场。我们揭示了一个非常自然的连接基于LMSR的组合预测市场和概率信念聚合，这导致了一个完整的表征所有结构保持证券可分解的网络结构。值得注意的是，Chen等人[5]的主要结果是我们特征的推论。然后，我们证明，为了一个非常基本的证券集是结构保持，贝叶斯网络的图必须是可分解的。我们还讨论了一些近似技术的证券结构不保持。
英文摘要：
To overcome the #P-hardness of computing/updating prices in logarithm market scoring rule-based (LMSR-based) combinatorial prediction markets, Chen et al. [5] recently used a simple Bayesian network to represent the prices of securities in combinatorial predictionmarkets for tournaments, and showed that two types of popular securities are structure preserving. In this paper, we significantly extend this idea by employing Bayesian networks in general combinatorial prediction markets. We reveal a very natural connection between LMSR-based combinatorial prediction markets and probabilistic belief aggregation,which leads to a complete characterization of all structure preserving securities for decomposable network structures. Notably, the main results by Chen et al. [5] are corollaries of our characterization. We then prove that in order for a very basic set of securities to be structure preserving, the graph of the Bayesian network must be decomposable. We also discuss some approximation techniques for securities that are not structure preserving.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
