Mice dynamically adapt to opponents in competitive multi-player games

Competing for resources in dynamic social environments is fundamental for survival, and requires continuous monitoring of both 'self' and 'others' to guide effective choices. Yet our understanding of value-based decision-making comes primarily from studying individuals in isolation, leaving open fundamental questions about how animals adapt their strategies during social competition. Here, we developed an ethologically relevant multi-player game, in which freely-moving mice make value-based decisions in a competitive spatial foraging task. We found that mice integrate real-time spatial information about 'self' and the opponent to flexibly shift their preference towards safer, low-payout options when appropriate. Analyses of mice and reinforcement learning agents reveal that these behavioural adaptations cannot be explained by simple reward learning, but are instead consistent with optimal decision strategies guided by opponent features. Using a dynamical model of neural activity, we found that in addition to opponent effects, decisions under competition were also noisier and more sensitive to initial conditions, generating testable predictions for neural recordings and perturbations. Together, this work reveals a fundamental mechanism for competitive foraging, and proposes novel quantitative frameworks towards understanding value-based decision-making in a fast-changing social environment.