Stochastic Loss Aversion for Random Medium Access

We consider a slotted-ALOHA LAN with loss-averse, noncooperative greedy users. To avoid non-Pareto equilibria, particularly deadlock, we assume probabilistic loss-averse behavior. This behavior is modeled as a modulated white noise term, in addition to the greedy term, creating a diffusion process modeling the game. We observe that when player’s modulate with their throughput, a more efficient exploration of play-space (by Gibbs sampling) results, and so finding a Pareto equilibrium is more likely over a given interval of time.