Agent-based models for animal cognition: A proposal and prototype

Animal ecologists have successfully applied agent-based models to many different problems. Often, these focus on issues conceming collective behaviors, environmental interactions, or the evolution of traits. In these cases, patterns of interest can usually be investigated by constructing the appropriate multi-agent system, and then varying or evolving model parameters. In recent years, however, the study of animal behavior has increasingly expanded to include the study of animal cognition. In this field, the question is not just how or why a particular behavior is performed, but also what its 'mental underpinnings' are. In this paper, we argue that agent-based models are uniquely suited to explore questions conceming animal cognition, as the experimenter has direct access to agents' intemal representations, control over their evolutionary history, and a perfect record of their previous leaming experience. To make this possible, a new modeling paradigm must be developed, where agents' reasoning processes are explicitly simulated, and can evolve over time. We propose that this be done in the form of "if-then" rules, where only the form is specified, not the content. This should allow qualitatively different reasoning processes to emerge, which may be more or less "cognitive" in nature. In this paper, we illustrate the potential of such an approach with a prototype model. Agents must evolve explicit rule sets to forage for food, and to escape predators. It is shown that even in this relatively simple setup, different strategies emerge, as well as unexpected outcomes.