Evolution of hardwired behavioral strategies through competitive population dynamics

Normative approaches are commonly used to predict an organism's hardwired behaviors by optimizing utility functions such as sensory information or reward, which are loosely interpreted as proxies for evolutionary fitness. However, the validity of the assumption that utility function optimization confers true evolutionary success has seldom been explored. Here we develop mechanistic evolutionary models to investigate whether normative principles can predict the most evolutionarily advantageous strategies. With mean-field approximations and agent-based stochastic simulations, we show that the most competitive strategies that emerged from these models in environments of randomly distributed resources conform well with normative predictions, but only when organisms are sparsely distributed and interactions are rare. These results suggest that normative approaches can predict the evolutionarily most competitive innate behavioral strategies, at least when the optimized utility is directly relevant for the organisms’ survival and accounts for environmental constraints. This work bridges the gap between normative approaches and the underlying fundamental evolutionary dynamics.