Balancing the costs of alternative decisions is a fundamental challenge for decision makers. This is especially critical in social situations, where the choices individuals face are often associated with highly asymmetric error costs---such as pedestrian groups crossing the street, police squads holding a suspect at gunpoint, or animal groups evading predation. While a broad literature has explored how individuals acting alone adapt to asymmetric error costs, little is known about how individuals in groups cope with these costs. Here we investigate adaptive decision strategies of individuals in groups facing asymmetric error costs, modeling scenarios where individuals aim to maximize group-level payoff (‘‘cooperative groups’’) or individual-level payoff (‘‘competitive groups’’). We extended the drift--diffusion model to the social domain in which individuals first gather personal information independently; they can then either wait for additional social information or decide early, thereby potentially influencing others. We combined this social drift--diffusion model with an evolutionary algorithm to derive adaptive behavior. Under asymmetric costs, small cooperative groups evolved response biases to avoid the costly error. Large cooperative groups, however, did not evolve response biases, since the danger of response biases triggering false information cascades increases with group size. We show that individuals in competitive groups face a social dilemma: They evolve higher response biases and wait for more information, thereby undermining group performance. Our results have broad implications for understanding social dynamics in situations with asymmetric costs, such as crowd panics and predator detection.
Avoiding costly mistakes in groups: The evolution of error management in collective decision making