PUTTING MECHANISMS IN FORAGING THEORY: the role of computational mechanisms in optimal decision making

Classic optimization models explain adaptive behaviours in terms of optimal cost-benefit trade-offs. They assume flexibility and make testable predictions about what animals should do in order to maximize their fitness. But they usually do not ask how animals can do it. Since flexible behaviours can be globally optimal but locally sub-optimal, the optimality models should directly focus on the underlying mechanisms of behavioural flexibility, such as learning and decision making. In this paper, I use the classic “diet” model of Optimal Foraging Theory (OFT) to investigate the evolution of decision-making mechanisms at both the computational and the algorithmic level. At the computational level, I define benefits (the expected rate of net energy intake) and costs (lost opportunity) and formalize the decision rule. At the algorithmic level, I present two sequential-sampling models, which differ in the way information is internally represented and used. The first model represents the prospective items along a one-dimension scale of values (benefit-cost differences) and it uses a fixed amount of sensory information (and time) to make decisions. The second model represents items in the 2-dimension plane of benefits and costs, and it uses a variable amount of information. I test the models along a gradient of resource abundance. In each environment, I use OFT to classify resources as either profitable or unprofitable, and describe the model performance in terms of decision time and accuracy. At high resource density, both models predict foraging choice to be more selective, but less accurate than at low density, because decisions are strongly biased in the false-positive direction. At low resource density, the 2-dimension model performs better than the 1-dimension alternative, because it takes less time to make more accurate decisions. These differences, however, disappear when resources are abundant.