INTRODUCTION

Computational models of cognitive agents that incorporate a wide range of cognitive functionalities (such as various types of memory/representation, various modes of learning, and sensory motor capabilities) have been developed in both AI and cognitive science (e.g., Anderson & Lebiere, 1998; Sun, 2002). In cognitive science, they are often known as cognitive architectures. Recent developments in cognitive architectures provide new avenues for precisely specifying complex cognitive processes in tangible ways (Anderson & Lebiere, 1998).

In spite of this, however, most of the work in social simulation still assumes very rudimentary cognition on the part of the agents. At the same time, although researchers in cognitive science have devoted considerable attention to the workings of individual cognition (e.g., Anderson, 1983; Klahr et al., 1987; Rumelhart & McClelland, 1986; Sun, 2002), sociocultural processes and their relations to individual cognition have generally not been sufficiently studied by cognitive scientists (with some notable exceptions; e.g., Hutchins, 1995; Resnick et al., 1991; Lave, 1988).

However, there are reasons to believe that better models of individual cognition can lead us to a better understanding of aggregate processes involving multi-agent interaction (Moss, 1999; Castelfranchi, 2001; Sun, 2001). Cognitive models that incorporate realistic tendencies, biases, and capacities of individual cognitive agents (Boyer & Ramble, 2001) can serve as a more realistic basis for understanding multi-agent interaction. This point has been made before in different contexts (e.g., Edmonds & Moss, 2001; Kahan & Rapoport, 1984; Sun, 2001).

As noted earlier, research on social simulation has mostly dealt with simplified versions of social phenomena, involving much simplified agent models (e.g., Gilbert & Doran, 1994; Levy, 1992). Such agents are clearly not cognitively realistic, and thusmayresult in important cognition-related insights being left by the wayside.