  Every interaction of a living organism with its environment involves the
placement of a bet. Armed with partial knowledge about a stochastic world, the
organism must decide its next step or near-term strategy, an act that
implicitly or explicitly involves the assumption of a model of the world.
Better information about environmental statistics can improve the bet quality,
but in practice resources for information gathering are always limited. We
argue that theories of optimal inference dictate that ``complex'' models are
harder to infer with bounded information and lead to larger prediction errors.
Thus, we propose a principle of ``playing it safe'' where, given finite
information gathering capacity, biological systems should be biased towards
simpler models of the world, and thereby to less risky betting strategies. In
the framework of Bayesian inference, we show that there is an optimally safe
adaptation strategy determined by the Bayesian prior. We then demonstrate that,
in the context of stochastic phenotypic switching by bacteria, implementation
of our principle of ``playing it safe'' increases fitness (population growth
rate) of the bacterial collective. We suggest that the principle applies
broadly to problems of adaptation, learning and evolution, and illuminates the
types of environments in which organisms are able to thrive.
