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原文連結
論文資訊
- 類型：已發表論文
- 日期：2018
摘要
Animal behaviors can often be challenging to model and predict, though optimality theory has improved our ability to do so. While many qualitative predictions of behavior exist, accurate quantitative models, tested by empirical data, are often lacking. This is likely due to variation in biases across individuals and variation in the way new 資訊 is gathered and used. We propose a modeling framework based on a novel interpretation of Bayes’s theorem to integrate optimization of energetic constraints with both prior biases and specific sources of new 資訊 gathered by individuals. We present methods for inferring distributions of prior biases within 族群s rather than assuming known priors, as is common in 貝氏 approaches to modeling behavior, and for evaluating the goodness of fit of overall model de
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