Maintaining Homeostasis by Decision-Making

Living organisms need to maintain energetic homeostasis. For many species, this implies taking actions with delayed consequences. For example, humans may have to decide between foraging for high-calorie but hard-to-get, and low-calorie but easy-to-get food, under threat of starvation. Homeostatic principles prescribe decisions that maximize the probability of sustaining appropriate energy levels across the entire foraging trajectory. Here, predictions from biological principles contrast with predictions from economic decision-making models based on maximizing the utility of the endpoint outcome of a choice. To empirically arbitrate between the predictions of biological and economic models for individual human decision-making, we devised a virtual foraging task in which players chose repeatedly between two foraging environments, lost energy by the passage of time, and gained energy probabilistically according to the statistics of the environment they chose. Reaching zero energy was framed as starvation. We used the mathematics of random walks to derive endpoint outcome distributions of the choices. This also furnished equivalent lotteries, presented in a purely economic, casino-like frame, in which starvation corresponded to winning nothing. Bayesian model comparison showed that—in both the foraging and the casino frames—participants’ choices depended jointly on the probability of starvation and the expected endpoint value of the outcome, but could not be explained by economic models based on combinations of statistical moments or on rank-dependent utility. This implies that under precisely defined constraints biological principles are better suited to explain human decision-making than economic models based on endpoint utility maximization.

所有活体生物都需要维持能量稳态（energetic homeostasis）。对于多数物种而言，这意味着需要采取会带来延迟后果的行动。例如，人类在面临饥饿威胁时，可能需要在觅食高卡路里但难以获取，与低卡路里但易于获取的食物之间做出抉择。稳态原则规定的决策，需最大化在整个觅食轨迹中维持适宜能量水平的概率。在此处，生物学原则得出的预测，与基于最大化选择终点结果效用的经济决策模型所得出的预测存在差异。为了在人类个体决策的生物学模型与经济学模型的预测之间开展实证仲裁，我们设计了一项虚拟觅食任务：参与者需在两种觅食环境间反复做出选择，随着时间流逝会损失能量，并根据所选环境的统计特征以概率形式获得能量。能量降至零点被设定为饥饿状态。我们借助随机游走（random walk）的数学方法推导了各选择的终点结果分布。这同时还生成了等价的彩票任务，以纯经济学的赌场式框架呈现，其中饥饿状态对应一无所获。贝叶斯模型比较结果显示，无论是在觅食框架还是赌场框架中，参与者的选择均同时取决于饥饿发生概率与结果的期望终点价值，但无法用基于统计矩组合或秩相依效用（rank-dependent utility）的经济学模型解释。这意味着，在严格定义的约束条件下，相较于基于终点效用最大化的经济学模型，生物学原则更适合解释人类的决策行为。