# Evolutionary Dynamics

When honeybees choose a nest site, individuals blindly switch to the option advertised most strongly by neighbors, and this local imitation is shown to be equivalent, at the group level, to a single online reinforcement-learning agent solving a multi-armed bandit. That macro-agent updates nest-site probabilities by a rule named Maynard-Cross Learning, with each scout acting as one parallel action-reward sample. The derivation covers the weighted voter model, a simplified description, and simulations indicate roughly 500 scouts suffice to track the macro-dynamics and converge on the best site.

When honeybees choose a nest site, individuals blindly switch to the option advertised most strongly by neighbors, and this local imitation is shown to be equivalent, at the group level, to a single online reinforcement-learning agent solving a multi-armed bandit. That macro-agent updates nest-site probabilities by a rule named Maynard-Cross Learning, with each scout acting as one parallel action-reward sample. The derivation covers the weighted voter model, a simplified description, and simulations indicate roughly 500 scouts suffice to track the macro-dynamics and converge on the best site.

This paper develops a co-evolutionary multilayer potential game in which boundedly rational agents update mixed strategies while market, information, and institutional links adapt to observed compatibility and diffusion signals, and proposes a stability-guarded mirror-replicator (SGMR) dynamic combining entropy-regularized strategy revision, projected link rewiring, and a spectral safeguard; the authors prove that the mirror step recovers replicator dynamics in the small-step limit and establish the exact-potential property, monotone potential improvement, sublinear stationarity, and local input-to-state stability under observation disturbances, with computational experiments on synthetic economic networks showing faster convergence, higher collective welfare, stronger noise resilience, an

This paper develops a co-evolutionary multilayer potential game in which boundedly rational agents update mixed strategies while market, information, and institutional links adapt to observed compatibility and diffusion signals, and proposes a stability-guarded mirror-replicator (SGMR) dynamic combining entropy-regularized strategy revision, projected link rewiring, and a spectral safeguard; the authors prove that the mirror step recovers replicator dynamics in the small-step limit and establish the exact-potential property, monotone potential improvement, sublinear stationarity, and local input-to-state stability under observation disturbances, with computational experiments on synthetic economic networks showing faster convergence, higher collective welfare, stronger noise resilience, an