Evolving Behavioural Choice: An Investigation into Herrnstein's Matching Law
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
EPICURE: Spatial and Knowledge Limitations in Group Foraging
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Directed evolution of communication and cooperation in digital organisms
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
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Foraging strategies in uncertain environments is the subject of a great deal of biological investigation, much of which is grounded in mathematical models. One theoretical prediction with wide empirical support is the ideal free distribution (IFD), where agents distribute themselves among patches of resources in proportion to their suitability. However, the IFD assumes that agents have perfect information of the environment. In nature, this assumption is often violated, yet the IFD is still observed. Insights into evolved mechanisms and behaviors that result in the IFD show how such efficient outcomes may emerge from little information. In this study, the artificial life platform Avida is used to observe populations of digital organisms as they evolved to optimize resource intake in an environment with unpredictable resource distributions. It is shown that the ideal free distribution can emerge from simple foraging strategies that require minimal information. It is demonstrated that this distribution is a result of choices made by the organisms, and not simply due to those in a more advantageous setting producing more offspring. Deviations from the IFD appear to be correlated with reduced information or foraging aggregation. Distributions with organisms of differing abilities are also investigated, demonstrating further correspondence with theoretical predictions.