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原文連結
論文資訊
- 類型：已發表論文
- 日期：2016-02-19
摘要
Markets, democracies, and organizations rely on accurate aggregate predictions to function properly. A large literature explains how accuracy can arise from diverse predictive models, typically captured as independent or non-perfectly correlated signals. Yet, that literature largely ignores how the 多樣性 of models arises and is maintained. In this paper, we derive equilibrium levels of model 多樣性 as a function of 社會 structure, 族群 size, the probability of experimentation, and the number of available models by building on a theoretical framework used to study 生物多樣性. We then link model 多樣性 to collective accuracy by generalizing the bias-variance decomposition formula. Assuming equally accurate models, we find that for large 族群s collective accuracy depends primarily on the 多樣性 of available models
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