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.

翻译：生物体与环境的每次互动都涉及一场博弈。面对一个充满随机性的世界，生物体仅掌握部分知识，却必须决定下一步行动或近期策略，这一行为隐含或明确地依赖于对世界建立的模型。关于环境统计信息的更充分了解能提升博弈质量，但现实中收集信息的资源总是有限的。我们认为，最优推理理论表明，“复杂”模型在信息有限时更难推断，且会导致更大的预测误差。因此，我们提出“稳扎稳打”原则：在信息采集能力有限的情况下，生物系统应偏向于采用更简单的世界模型，从而选择风险更低的博弈策略。在贝叶斯推断框架下，我们证明了存在由贝叶斯先验决定的最优安全适应策略。随后，我们以细菌随机表型转换为例，展示了“稳扎稳打”原则的实施如何提升细菌集体的适应度（种群增长率）。我们指出，该原则广泛适用于适应、学习与进化问题，并揭示了生物体能够繁荣生长的环境类型。