Competition between adaptive agents: from learning to collective efficiency and back

TL;DR

This paper investigates how adaptive agents' learning influences collective efficiency in resource competition models like the Minority Game, using statistical physics to analyze and understand the phenomena and optimization potential.

Contribution

It provides an exact analytical framework linking agent learning dynamics to collective efficiency, enabling the study of optimization in competitive environments.

Findings

Efficiency depends critically on learning strategies.

Exact results clarify the phenomenology of agent competition.

Insights into what agents can optimize for improved collective outcomes.

Abstract

We use the Minority Game and some of its variants to show how efficiency depends on learning in models of agents competing for limited resources. Exact results from statistical physics give a clear understanding of the phenomenology, and opens the way to the study of reverse problems. What agents can optimize and how well is discussed in details.