Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany.
Science of Intelligence, Technische Universität Berlin, Berlin, Germany.
PLoS Comput Biol. 2022 Aug 19;18(8):e1010442. doi: 10.1371/journal.pcbi.1010442. eCollection 2022 Aug.
Individuals continuously have to balance the error costs of alternative decisions. A wealth of research has studied how single individuals navigate this, showing that individuals develop response biases to avoid the more costly error. We, however, know little about the dynamics in groups facing asymmetrical error costs and when social influence amplifies either safe or risky behavior. Here, we investigate this by modeling the decision process and information flow with a drift-diffusion model extended to the social domain. In the model individuals first gather independent personal information; they then enter a social phase in which they can either decide early based on personal information, or wait for additional social information. We combined the model with an evolutionary algorithm to derive adaptive behavior. We find that under asymmetric costs, individuals in large cooperative groups do not develop response biases because such biases amplify at the collective level, triggering false information cascades. Selfish individuals, however, undermine the group's performance for their own benefit by developing higher response biases and waiting for more information. Our results have implications for our understanding of the social dynamics in groups facing asymmetrical errors costs, such as animal groups evading predation or police officers holding a suspect at gunpoint.
个体需要不断平衡不同决策的错误成本。大量研究已经探讨了个体如何应对这一问题,表明个体形成了响应偏差来避免更高成本的错误。然而,我们对于面临不对称错误成本的群体中的动态变化以及社会影响何时放大安全或风险行为知之甚少。在这里,我们通过使用扩展到社会领域的漂移扩散模型来模拟决策过程和信息流来研究这个问题。在模型中,个体首先收集独立的个人信息;然后进入社会阶段,他们可以根据个人信息尽早做出决定,也可以等待额外的社会信息。我们将模型与进化算法相结合,以得出适应性行为。我们发现,在不对称成本下,大型合作群体中的个体不会形成响应偏差,因为这种偏差在集体层面上会放大,引发虚假的信息级联。然而,自私的个体为了自身利益会形成更高的响应偏差并等待更多信息,从而破坏群体的表现。我们的研究结果对于理解面临不对称错误成本的群体中的社会动态具有启示意义,例如逃避捕食者的动物群体或持枪看守嫌疑人的警察。