Agent-based modeling and simulation

Abstract

How is it possible that a whole ancient civilization disappeared? Was this caused by climate changes? What type of recruitment strategy among social insects is best adapted to their particular environment? How much time does it take to evacuate an airport if people have limited perception caused by smoke as well as restricted mobility? What if many of these people travel in groups or families? How long does it take for commuters to reach their destinations if an important arterial in the Los Angeles area is closed? These are the kind of questions that can be and are answered by agent-based modeling and simulation (ABMS). In this paradigm, simulated human beings or animals are modeled as agents, interacting with some of their peers as well as with their environment. The environment, as in many multiagent systems, plays a key role and must therefore be carefully taken into account. For instance, passengers seeking to leave the airport just mentioned try to find the shortest way to an exit, which may be partially hindered by debris. These are only some examples of scenarios - also characterized as complex adaptive systems - that can be investigated using ABMS. The core idea here is to use simulated agents for producing a phenomenon that shall be analyzed, reproduced, or predicted. This generative, bottom-up nature of modeling and simulation provides great potential for dealing with problems in which conventional modeling and simulation paradigms have difficulties capturing the core features of the original system. In what follows, this particular modeling and simulation paradigm, its concept, properties, and application are introduced and discussed. To this end, concepts about modeling and simulation in general, and about ABMS in particular, are introduced and discussed in the next two sections. Then, some popular environments for ABMS are briefly presented. Applications and case studies are then discussed. We remark that, due to lack of space, we have opted to focus on two particular domains: social science simulation (one of the earliest application domains) and traffic simulation (given the increasing interest in transportation- and traffic-related applications). Readers interested in a broader view on applications may refer to Phan and Amblard (2007) or Uhrmacher and Weyns (2009). We then conclude the article with a discussion on future challenges. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.