In this paper, we present some of our ongoing experimental research towards investigating advantages of modeling other agents in multiagent environments. We attempt to quantify the value or utility of building models about other agents using no more than the observation of others' behavior. We are interested in empirically showing that a modeler agent can take advantage of building and updating its beliefs about other agents. This advantage can make it perform better than an agent without modeling capabilities. We have been conducting a simulation-based study using a competitive game called Meeting Scheduling Game as a testbed. First, we briefly describe our multiagent simultaion testbed. Then, we describe in detail our experimental study. We explore a range of strategies from least- to most-informed, and present some of our preliminary results on the relative performance of these strategies. Decreasing the a priori knowledge about the others and increasing the modeling capabilities we are able to define a series of "modeler" agents. Finally, we present a method for using probabilistic models about the others in such a way that the expected utility is maximized.
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