Bayesian decision theory can be used to model animal behaviour. When applied to animal behaviour, this theory 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. It is often reasonable to assume that animals may have ‘‘prior opinions’’. 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.
Bayesian foraging deals with the theories and empirical studies of animals that need to estimate the quality of their food patches as they forage.
In the last decade or so we have published a series of papers dealing with Bayesian foraging, and related topics. The latest paper is
The general model of Bayesian foraging described in this paper is available as Matlab code below. You are free to download and use that code in your own work, provided you cite the relevant publications, when appropriate. All citations are given in the m-files. In the files there is hopefully enough annotations to help you understand what they do.
The file runBayes.m is a script that calls the other functions as needed. BayesForaG.m is the main function that finds the optimal Bayesian policy.