LAUSR

We study a general class of dynamic multi- agent decision problems with asymmetric information and nonstrategic agents, which include dynamic teams as a special case. When agents are nonstrategic, an agent’s strategy is known to the other agents. Nevertheless, the agents’ strategy choices and beliefs are interdependent over times, a phenomenon known as signaling. We introduce the notion of sufficient information that effectively compresses the agents’ information in a mutually consistent manner. Based on the notion of sufficient information, we propose an information state for each agent that is sufficient for decision-making purposes. We present instances of dynamic multiagent decision problems where we can determine an information state with a time-invariant domain for each agent. Furthermore, we present a generalization of the policy-independence property of belief in partially observed Markov decision processes (POMDP) to dynamic multiagent decision problems. Within the context of dynamic teams with asymmetric information, the proposed set of information states leads to a sequential decomposition that decouples the interdependence between the agents’ strategies and beliefs over time and enables us to formulate a dynamic program to determine a globally optimal policy via backward induction.