  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 where 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.
