Stochastic Loss Aversion for Random Medium Access

TL;DR

This paper models loss-averse behavior in slotted-ALOHA LANs using stochastic processes, showing that throughput-based modulation leads to more efficient exploration and higher likelihood of reaching Pareto equilibria.

Contribution

It introduces a novel stochastic modeling approach for loss-averse behavior in medium access games, enhancing understanding of equilibrium selection.

Findings

Throughput-based modulation improves exploration efficiency.

Stochastic modeling facilitates reaching Pareto equilibria.

Probabilistic loss aversion reduces deadlock occurrences.

Abstract

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 results, and so finding a Pareto equilibrium is more likely over a given interval of time.