Information-Driven Phase Changes in Multi-agent Coordination

Large systems of agents deployed in a real-world environment face threats to their problem solving performance that are independent of the complexity of the problem or the characteristics of their specific solution mechanism. One such threat is the degrading of the quality of agent coordination mechanisms when faced with delays in the flow of critical information among the agents introduced by communication latencies. In this paper we demonstrate in a simple model of locally interacting agents that the emerging system-level performance may degrade very suddenly as the rate of individual decision making increases against the availability of up-to-date information. We present results from extensive simulation experiments that lead us to select a locally accessible metric to adapt the agent's individual decision rate to values that are below this phase change. Given the generic nature of the coordination mechanism that is analyzed and the information-theoretic metric, the adaptation mechanism may increase the deployability of large-scale agent systems in real-world applications.