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：每个生物体与其环境的互动都涉及下注。在对随机世界的部分了解基础上，生物体必须决定其下一步或短期策略，这个决策暗示或明示着对世界模型的假设。关于环境统计信息的更好理解可以提高下注质量，但在实践中，信息收集资源总是有限的。我们认为，最优推理理论表明，使用“复杂”模型很难在有限的信息下进行推断，并导致更大的预测误差。因此，我们提出了一个“安全第一”的原则，即在有限的信息收集能力下，生物系统应该偏向于更简单的世界模型，从而采用更少冒险的投注策略。在贝叶斯推理框架下，我们展示了有一个由贝叶斯先验确定的最优安全适应策略。接着，我们证明在细菌的随机表型切换背景下，实施我们的“安全第一”的原则可以增加细菌群体的适应度（人口增长率）。我们认为这个原则广泛适用于适应、学习和进化问题，并阐明了生物体能够繁荣的环境类型。