Exploration enhances cooperation in the multi-agent communication system

TL;DR

This paper demonstrates that incorporating strategic exploration into multi-agent communication protocols significantly enhances cooperation, revealing an optimal exploration rate that balances stability and alliance formation.

Contribution

It introduces a novel two-stage evolutionary game model that explicitly includes exploration, showing how strategic randomness promotes cooperation in multi-agent systems.

Findings

Optimal exploration rate maximizes cooperation

Moderate exploration destabilizes defection

Balance between oscillation and amplification enables cooperation peak

Abstract

Designing protocols enhancing cooperation for multi-agent systems remains a grand challenge. Cheap talk, defined as costless, non-binding communication before formal action, serves as a pivotal solution. However, existing theoretical frameworks often exclude random exploration, or noise, for analytical tractability, leaving its functional impact on system performance largely unexplored. To bridge this gap, we propose a two-stage evolutionary game-theoretical model, integrating signalling with a donation game, with exploration explicitly incorporated into the decision-making. Our agent-based simulations across topologies reveal a universal optimal exploration rate that maximises system-wide cooperation. Mechanistically, moderate exploration undermines the stability of defection and catalyses the self-organised cooperative alliances, facilitating their cyclic success. Moreover, the cooperation peak is enabled by the delicate balance between oscillation period and amplification. Our findings suggest that rather than pursuing deterministic rigidity, embracing strategic exploration, as a form of engineered randomness, is essential to sustain cooperation and realise optimal performance in communication-based intelligent systems.