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 most of our understanding of value-based decision-making comes from studying individuals in isolation, leaving open key questions about how animals adapt their strategies during social competition. Here, we developed an ethologically relevant multi-player game in which freely moving mice made valuebased decisions in a competitive spatial foraging task. We found that mice integrated real-time spatial information about both themselves and their opponents to flexibly shift their preferences toward safer, low-payout options when appropriate. Analyses of mice behaviour and reinforcement learning agents revealed that these adaptations could not be explained by simple reward learning, but were instead consistent with optimal decision strategies guided by opponent features. Using a dynamical model of decision-making, we found that competition increased choice variability and sensitivity to initial conditions, generating testable predictions for future neural recordings and perturbations. Together, our findings reveal a fundamental mechanism for flexible competitive foraging and propose novel frameworks for quantitatively understanding value-based decision-making in fast-changing social environments.