Title: Modeling the Formation of Social Conventions in Multi-Agent Populations,
Abstract: In order to understand the formation of social conventions we need to know
the specific role of control and learning in multi-agent systems. To advance in
this direction, we propose, within the framework of the Distributed Adaptive
Control (DAC) theory, a novel Control-based Reinforcement Learning architecture
(CRL) that can account for the acquisition of social conventions in multi-agent
populations that are solving a benchmark social decision-making problem. Our
new CRL architecture, as a concrete realization of DAC multi-agent theory,
implements a low-level sensorimotor control loop handling the agent's reactive
behaviors (pre-wired reflexes), along with a layer based on model-free
reinforcement learning that maximizes long-term reward. We apply CRL in a
multi-agent game-theoretic task in which coordination must be achieved in order
to find an optimal solution. We show that our CRL architecture is able to both
find optimal solutions in discrete and continuous time and reproduce human
experimental data on standard game-theoretic metrics such as efficiency in
acquiring rewards, fairness in reward distribution and stability of convention
formation.