Modeling Cognitive Agents for Social Systems and a Simulation in Urban Dynamics

Abstract

Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at least in part, by the perceived importance of fostering human-like adjustable autonomy and behaviors in social systems. But, efficient scalable and robust social systems are difficult to engineer. One difficulty exists in the design of how society and agents evolve and the other difficulties exist in how to capture the highly cognitive decision-making process that sometimes follows intuition and bounded rationality. We present a multi-agent architecture called CASE (Cognitive Agents for Social Environments). CASE provides a way to embed agent interactions in a three-dimensional social structure. It also presents a computational model for an individual agent’s intuitive and deliberative decision-making process. This chapter also presents our work on creating a multi-agent simulation which can help social and economic scientists use CASE agents to perform their tests. Finally, we test the system in an urban dynamic problem. Our experiment results suggest that intuitive decision-making allows the quick convergence of social strategies, and embedding agent interactions in a three-dimensional social structure speeds up this convergence as well as maintains the system’s stability.

Distributed Agency

“…The rapidly expanding world of the computer simulation-a paradigm with foundations generally related to Multi-Agent Systems (MAS)-is based on independent agents that strategically interact with each other (Van der Hoek & Wooldridge, 2008). The independence granted to the agents in MAS is a computational one, in the sense that each agent processes information internally, without the need to resort to the outside world to draw conclusions about the inputs it receives (for an example of advances in this field, see Zhang, Coleman, Pellon, & Leezer, 2008). In contrast, the proposed DA language allows for a broader computational description of the world, while at the same time allowing for a higher level of realism that the researcher may consider appropriate.…”