曾大军，中国科学院自动化研究所研究员，复杂系统管理与控制国家重点实验室副主任，博士生导师，IEEE Fellow， IEEE智能交通学会主席(2016-2017)，IEEE Intelligent Systems主编(2013-2016)。主要研究方向是大数据解析学、安全信息学、多智能体系统、复杂经济与社会系统优化与控制。发表学术论文300余篇，GOOGLE学术索引总引用数8千余次。曾大军研究员系国家杰出青年科学基金获得者，入选中国科学院 “百人计划”、及国家“万人计划”科技创新领军人才。他领导的科研团队获基金委创新研究群体资助。
Prediction markets provide a promising approach for future event prediction. Most existing prediction market approaches are based on auction mechanisms. Despite their theoretical appeal and success in various application settings, these mechanisms suffer from several major drawbacks. First, opinions from experts and amateurs are treated equally. Second, continuous attention from participants is assumed. Third, such mechanisms are subject to various forms of market manipulation. To alleviate these limitations, we propose to employ the classic fixed odds betting as an alternative prediction market mechanism. We build a structural model based on a Belief-Decision framework as the event probability estimator. This Belief-Decision framework models bettors’ beliefs with mixed Beta distributions and bettors’ decisions with prospect theory. A maximum likelihood approach is applied to estimate the model parameters. We conducted experiments on three real-world betting datasets to evaluate our proposed approach. Experimental results show that fixed odds betting-based prediction outperforms the reduced form models based on odds and betting results, and achieves a comparable performance with auction-based prediction markets. The results suggest the possibility of employing fixed odds betting as a prediction market in a variety of application contexts where the assumptions made by auction-based approaches do not hold.