社会折扣实现快速且可靠的集体逃生
Social Discounting Enables Fast and Reliable Collective Escape
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中文总结 AI 辅助
该研究针对动物检测威胁的权衡问题，提出社会折扣模型，发现群居动物可通过权衡邻居的动静实现快速可靠逃生，该模型能解释野生花鳉鱼群的逃生行为。
中文摘要 AI 辅助
独居动物在检测威胁时面临权衡：更快的检测意味着要接受更多的误报。我们表明，群体可以通过将未受干扰的邻居视为反对威胁的证据来更好地管理这种权衡，从而比独居个体更快、更准确。我们将每只动物建模为一个嘈杂的证据积累器，当它的信念超过阈值时就会逃跑，发现邻居的逃跑信号危险，而静止则信号安全。单纯的反应者仅对逃跑做出反应，随着群体规模增大，误报会增加；贝叶斯反应者则对两者都进行权衡，可通过单一的社会折扣率近似，该折扣率在上述两种极限之间插值。这为群体性能产生了闭式表达式，包括级联分支比，其在安全状态下保持强亚临界，在威胁下转为超临界，因此仅通过行为就能推断出动物在邻居保持静止时对威胁的折扣率，且该折扣率设定了一个上限，即动物在误报率无法再通过单独折扣维持前，能关注的邻居数量。在鸟类攻击下的野生花鳉鱼群，用远高于任何合理关注邻域内个体贝叶斯更新所提供的折扣率能得到最佳描述，且该模型在推断值下预测，误报率会随鱼群规模增大而保持恒定。
英文摘要
Solitary animals face a tradeoff when detecting threats: faster detection means accepting more false alarms. We show that groups can manage this tradeoff better by treating an undisturbed neighbor as evidence against a threat, becoming both faster and more accurate than lone individuals. Modeling each animal as a noisy evidence-accumulator that flees when its belief crosses a threshold, we find that a neighbor's flight signals danger while its stillness signals safety. A naive responder reacts only to flights and inflates false alarms as the group grows; a Bayesian responder weighs both, approximated by a single social discounting rate that interpolates between these limits. This yields closed-form expressions for group performance, including cascade branching ratios that stay strongly subcritical in safety and turn supercritical under threat, so the rate at which an animal discounts a threat while its neighbors stay still can be inferred from behavior alone, and it sets a ceiling on how many neighbors an animal can attend before discounting alone can no longer hold its false-alarm rate. Wild sulphur molly shoals under bird attack are best described by discounting rates well above what individually Bayesian updating supplies over any neighborhood they could plausibly attend, and the same model, at the inferred value, predicts a false-alarm rate that stays constant as shoals grow.
