Crowd Wisdom Relies on Agents' Ability in Small Groups with a Voting Aggregation Rule

• 成果类型: Article
• 署名作者: Keuschnigg, Marc; Ganser, Christian
• 署名单位: University of Munich
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2015.2364
• 发表日期: 2017
• 页码: 818-828
• 关键词: Averaging; combining judgments; diversity; social choice; voting

摘要

In the last decade, interest in the wisdom of crowds effect has gained momentum in both organizational research and corporate practice. Crowd wisdom relies on the aggregation of independent judgments. The accuracy of a group's aggregate prediction rises with the number, ability, and diversity of its members. We investigate these variables' relative importance for collective prediction using agent-based simulation. We replicate the diversity trumps ability proposition for large groups, showing that samples of heterogeneous agents outperform same-sized homogeneous teams of high ability. In groups smaller than approximately 16 members, however, the effects of group composition depend on the social decision function employed: diversity is key only in continuous estimation tasks (averaging) and much less important in discrete choice tasks (voting), in which agents' individual abilities remain crucial. Thus, strategies to improve collective decision making must adapt to the predictive situation at hand.