Social learning is an e ffective way to reduce uncertainty about the environment, helping individuals to adopt adaptive behaviour cheaply. Although this is evident for learning about temporally stable targets, such as acquisition of an avoidance of toxic foods, the utility of social learning in a temporally unstable environment is less clear, since knowledge acquired by social learning may be outdated. An individual can either depend entirely on its own foraging information (individual forager) or that provided by the environment or shared by other agents. We are interested in scenarios where individual foraging might be a useful and effective strategy and how the topology and distribution of resources in the network/environment might a ffect this.
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