Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders

• 成果类型: Article
• 署名作者: Healy, Paul J.; Linardi, Sera; Lowery, J. Richard; Ledyard, John O.
• 署名单位: University System of Ohio; Ohio State University; California Institute of Technology; University of Texas System; University of Texas Austin
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.1100.1226
• 发表日期: 2010
• 页码: 1977-1996
• 关键词: Information aggregation; prediction markets; mechanism design

摘要

Double auction prediction markets have proven successful in large-scale applications such as elections and sporting events. Consequently, several large corporations have adopted these markets for smaller-scale internal applications where information may be complex and the number of traders is small. Using laboratory experiments, we test the performance of the double auction in complex environments with few traders and compare it to three alternative mechanisms. When information is complex we find that an iterated poll (or Delphi method) outperforms the double auction mechanism. We present five behavioral observations that may explain why the poll performs better in these settings.