Computational evolution of decision-making strategies [article]

Peter Kvam, Joseph Cesario, Jory Schossau, Heather Eisthen, Arend Hintze
2015 arXiv   pre-print
Most research on adaptive decision-making takes a strategy-first approach, proposing a method of solving a problem and then examining whether it can be implemented in the brain and in what environments it succeeds. We present a method for studying strategy development based on computational evolution that takes the opposite approach, allowing strategies to develop in response to the decision-making environment via Darwinian evolution. We apply this approach to a dynamic decision-making problem
more » ... here artificial agents make decisions about the source of incoming information. In doing so, we show that the complexity of the brains and strategies of evolved agents are a function of the environment in which they develop. More difficult environments lead to larger brains and more information use, resulting in strategies resembling a sequential sampling approach. Less difficult environments drive evolution toward smaller brains and less information use, resulting in simpler heuristic-like strategies.
arXiv:1509.05646v1 fatcat:4tlpfwis2fepxdvqgi55whybw4