Bayes' theorem and its applications in animal behaviour
Bayes' theorem and its applications in animal behaviour
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DOI：
10.1111/j.0030-1299.2006.14228.x
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发表时间：
2006-02-01
期刊：
影响因子：
3.4
通讯作者：
Olsson, O
中科院分区：
文献类型：
--
作者：
McNamara, JM;Green, RF;Olsson, O
贝叶斯决策理论可以用来模拟动物的行为。在本文中，我们概述了这些模型中的理论概念。我们还回顾了贝叶斯模型应用的生物学背景，并概述了未来研究的一些方向。贝叶斯决策理论，当应用于动物行为时，是基于一个假设，即个体对世界的可能状态有某种“先验意见”。例如，这可能是以前经历过的食物块的质量分布，或潜在配偶的质量分布。然后假定动物能够使用采样信息来得出“后验意见”，例如关于给定食物块的质量，或一年中配偶的平均质量。一个正确表述的贝叶斯模型预测了动物如何结合以往的经验和采样信息来做出最佳决策。我们认为动物可能有“先见”的假设是合理的。他们的先验可能来自两种来源中的一种或两种：要么来自他们在对环境进行采样时获得的个人经验，要么来自前几代人对环境的适应经验。这意味着我们应该经常期待在自然界中看到“贝叶斯式”决策。
Bayesian decision theory can be used to model animal behaviour. In this paper we give an overview of the theoretical concepts in such models. We also review the biological contexts in which Bayesian models have been applied, and outline some directions where future studies would be useful. Bayesian decision theory, when applied to animal behaviour, is based on the assumption that the individual has some sort of "prior opinion" of the possible states of the world. This may, for example, be a previously experienced distribution of qualities of food patches, or qualities of potential mates. The animal is then assumed to be able use sampling information to arrive at a "posterior opinion", concerning e.g. the quality of a given food patch, or the average qualities of mates in a year. A correctly formulated Bayesian model predicts how animals may combine previous experience with sampling information to make optimal decisions. We argue that the assumption that animals may have "prior opinions" is reasonable. Their priors may come from one or both of two sources: either from their own individual experience, gained while sampling the environment, or from an adaptation to the environment experienced by previous generations. This means that we should often expect to see "Bayesian-like" decision-making in nature.