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.

翻译：设计增强多智能体系统合作的协议仍然是一个重大挑战。廉价磋商，即在正式行动前进行的无成本、无约束力的沟通，是一个关键解决方案。然而，现有理论框架通常为了分析的易处理性而排除了随机探索（或称噪声），导致其对系统性能的功能性影响在很大程度上未被探索。为弥补这一空白，我们提出了一个两阶段演化博弈论模型，将信号传递与捐赠博弈相结合，并将探索明确纳入决策过程。我们在不同拓扑结构上进行的基于智能体的模拟揭示了一个普遍存在的最优探索率，该探索率能最大化系统范围的合作。从机制上看，适度的探索破坏了背叛策略的稳定性，并催化了自组织的合作联盟，促进了它们的周期性成功。此外，合作峰值是通过振荡周期与放大效应之间的微妙平衡实现的。我们的研究结果表明，与其追求确定性的刚性，不如接纳战略性探索（作为一种工程化的随机性形式），这对于在基于通信的智能系统中维持合作并实现最优性能至关重要。