Creating Wise Crowds: Diversity Maintenance Through Incentives

民主、市场和组织的成功往往取决于一群人的准确预测。集体预测可能涉及董事会中的少数人预测商业趋势，国会中的数百人做出政策预测，或数百万人通过股票市场确定价格。 在每种情况下，更准确的预测都会带来更好的结果，而偏离目标的预测可能会导致危机。在这个项目中，研究小组将开发一个新的分析框架，用于理解集体预测，该框架结合了认知心理学和数学模型。在这个框架中，个人构建他们用来进行预测的模型。 这些模型依赖于属性--个人认为与未来发展最相关的现实成分。 例如，一个人可能会认为公司在研发上的支出预示着更好的长期增长。 在这种方法中，不同的人选择不重叠的属性集，从而产生不同的预测。 集体准确性取决于多样性的结合-看到问题的不同部分以及它们之间的联系-和复杂性。 这种方法扩展了标准的统计框架，在这个框架中，通过消除误差来提高集体准确性。 通过解包的贡献，个人的认知深度和集体的多样性对预测的准确性，该框架可以用来研究如何创造激励，以产生集体的准确性的棘手问题。 准确的群体、团队或社会需要个人层面的复杂性和集体的多样性。 每一个的多少将取决于预测任务。因此，有效的激励结构必须考虑到任务的复杂性。 为了测试激励结构，该团队将开发一套具有真实的世界特征的预测问题。 理想情况下，那些表现最好的结构可以在真实的应用中进行测试。

The success of democracies, markets and organizations often depends upon accurate forecasts by collections of individuals. Collective forecasts can involve a handful of people on a board of directors forecasting business trends, hundreds of people in congress making a policy forecast, or millions of people determining prices through a stock market. In each case, more accurate forecasts lead to better outcomes and forecasts that are way off the mark can lead to crises. In this project, the research team will develop a new analytic framework for understanding collective forecasting that combines models from cognitive psychology and mathematics. In this framework, individuals construct models that they use to make forecasts. These models rely on attributes -- components of reality that the individual sees as most relevant in the unfolding future. For example, an individual might see a company's spending on research and development as portending better long run growth. In this approach, different people choose non-overlapping sets of attributes creating a diversity of forecasts. Collective accuracy hinges on a combination of diversity -- seeing different parts of the problem and connections between them -- and sophistication. This approach extends the standard statistical framework in which collective accuracy arises through the cancellation of errors. By unpacking the contributions of individual cognitive depth and collective diversity on forecast accuracy, the framework can be used to examine the knotty problem of how to create incentives to produce collective accuracy. Accurate groups, teams, or societies need individual level sophistication and collective diversity. How much of each will depend on the forecasting task. An effective incentive structure must therefore take into account the complexity of the task. To test incentive structures, the team will develop a suite of forecasting problems with real world features. Ideally, those structures that perform best can be tested in real applications.