  We study a general class of dynamic multi-agent decision problems with
asymmetric information and non-strategic agents, which includes dynamic teams
as a special case. When agents are non-strategic, 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
notions of private information that effectively compresses the agents'
information in a mutually consistent manner. Based on the notions of sufficient
information, we propose an information state for each agent that is sufficient
for decision making purposes. We present instances of dynamic multi-agent
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 multi-agent 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.
