People often think that choices in a game only depend on what others do. But the environment also plays a role. When players change their actions, the surroundings can shift, and those shifts can push players to pick different actions later. In many real situations, the setting is shaped by both nearby conditions and the overall state of the whole system.
To study this back‑and‑forth, we created a model with many agents placed on a grid. Each agent follows a simple rule based on its payoff and the local environment. We let the environment update according to what the agents do nearby and also according to a global average. We explored the model using random simulations and a mean‑field approach that averages over space.
When we looked at the outcomes, we saw that keeping the agents in fi