Description:

Dr. Michael J. North, Deputy Director, Center for Complex Adaptive Agent Systems Simulation
Decision and Information Sciences Division, Argonne National Laboratory

Abstract

Agent-based modeling has been successfully used to model complex adaptive systems in diverse disciplines. Many of these models were implemented using agent-based modeling software such as Swarm, Repast 3, Repast Simphony, Repast for High-Performance Computing, MASON, NetLogo, or StarLogo. All of these options use modular imperative architectures with factored agents, spaces, a scheduler, and logs. Many custom agent-based models also use this kind of architecture. This talk will introduce and apply a theoretical formalism for analyzing modular imperative agent-based models of complex adaptive systems. The talk will include a discussion of an analytical proof that the asymptotic time and space performance of modular imperative agent-based modeling studies is computationally optimal for a common class of problems. Here ‘optimal’ means that no other technique can solve the same problem computationally using less asymptotic time or space. Given that agent-based modeling is both computationally optimal and a natural structural match for many modeling problems, it follows that it is the best modeling method for such problems. Several other proofs about the time and space performance of modular imperative agent-based models will also be discussed along with validated predictions of the performance of three implemented models.