報告題目：基于“輕型”預測市場的群體智慧
報告人：曾大軍，中國科學院自動化研究所研究員
復雜系統(tǒng)管理與控制國家重點實驗室副主任
報告時間：2017年5月31日上午9:30-11:00
報告地點：經(jīng)濟與管理學院704室
報告人簡介：
曾大軍，中國科學院自動化研究所研究員，復雜系統(tǒng)管理與控制國家重點實驗室副主任，博士生導師，IEEE Fellow， IEEE智能交通學會主席(2016-2017)，IEEE Intelligent Systems主編(2013-2016)。主要研究方向是大數(shù)據(jù)解析學、安全信息學、多智能體系統(tǒng)、復雜經(jīng)濟與社會系統(tǒng)優(yōu)化與控制。發(fā)表學術(shù)論文300余篇，谷歌學術(shù)索引總引用數(shù)8千余次。曾大軍研究員系國家杰出青年科學基金獲得者，入選中國科學院 “百人計劃”、及國家“萬人計劃”科技創(chuàng)新領(lǐng)軍人才。他領(lǐng)導的科研團隊獲基金委創(chuàng)新研究群體資助。
報告摘要：
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.
主辦單位：經(jīng)濟與管理學院 能源與環(huán)境經(jīng)濟研究所
