The Crowd Classification Problem: Social Dynamics of Binary-Choice Accuracy
成果类型：
Article
署名作者：
Becker, Joshua Aaron; Guilbeault, Douglas; Smith, Edward Bishop
署名单位：
University of London; University College London; University of California System; University of California Berkeley; Northwestern University
刊物名称：
MANAGEMENT SCIENCE
ISSN/ISSBN：
0025-1909
DOI：
10.1287/mnsc.2021.4127
发表日期：
2022
页码：
3949-3965
关键词：
group decision making
collective intelligence
decision theory
Wisdom of crowds
delphi method
摘要：
Decades of research suggest that information exchange in groups and organizations can reliably improve judgment accuracy in tasks such as financial forecasting, market research, and medical decision making. However, we show that improving the accuracy of numeric estimates does not necessarily improve the accuracy of decisions. For binary-choice judgments, also known as classification tasks???for example, yes/no or build/buy decisions???social influence is most likely to grow the majority vote share, regardless of the accuracy of that opinion. As a result, initially, inaccurate groups become increasingly inaccurate after information exchange, even as they signal stronger support. We term this dynamic the ???crowd classification problem.??? Using both a novel data set and a reanalysis of three previous data sets, we study this process in two types of information exchange: (1) when people share votes only, and (2) when people form and exchange numeric estimates prior to voting. Surprisingly, when people exchange numeric estimates prior to voting, the binary-choice vote can become less accurate, even as the average numeric estimate becomes more accurate. Our findings recommend against voting as a form of decision making when groups are optimizing for accuracy. For those cases where voting is required, we discuss strategies for managing communication to avoid the crowd classification problem. We close with a discussion of how our results contribute to a broader contingency theory of collective intelligence.