What is Agent-based Modeling?
Contents
What is Agent-based Modeling?#
Agent-based modeling (ABM for short) is a simulation method where interactions between agents of a system and the interplay of these interactions with an environment are modeled and simulated. These interactions should ideally produce emergent patterns that may correspond to real-world phenomena. Agents of the real system need to be explicitly and individually represented in the model.
The goal of this method is to link the emergent patterns and phenomena at the systemic macro-level with the individual micro-level behavior of the agents.
Agent-based modeling is a form of individual-based (sometimes also called micro-level) approach that differs from equation-based (or macro-level) approaches, in which only macro processes and at most aggregated groups of actors are modeled, not single agents or individuals. Davidsson and Verhagen [2017]. Agent-based modeling in turn differs from other micro-level approaches in that each agent is “explicitly and individually represented in the model” and the intensity and variability of the interaction between the agents is particularly high. Apart from this explicit, individual representation, “no further assumptions are made.” Galán et al. [2017].
While mathematics can still play a major role in it, ABM is not as dependent on equations as other approaches and hence is more flexible. Purely mathematical approaches, while very effective, are limiting in a number of ways that are especially relevant for history. Some of the aspects that are much harder to model mathematically rather than with programming languages are agent heterogeneity, interdependencies between processes, out-of-equilibrium dynamics, local interactions, the role of physical space and finally the micro-macro link, so how macro-level phenomena are interconnected to individual behavior. Epstein and Axtell [1996].
Section Bibliography#
- DV17
Paul Davidsson and Harko Verhagen. Types of Simulation. In Bruce Edmonds and Ruth Meyer, editors, Simulating Social Complexity: A Handbook, Understanding Complex Systems, pages 23–37. Springer International Publishing, Cham, 2017. URL: https://doi.org/10.1007/978-3-319-66948-9_3 (visited on 2021-06-13), doi:10.1007/978-3-319-66948-9_3.
- EA96
Joshua M. Epstein and Robert Axtell. Growing Artificial Societies. Social Science from the Bottom Up. Brookings Institution Press, Washington, 1996.
- GII+17
José M. Galán, Luis R. Izquierdo, Segismundo S. Izquierdo, José I. Santos, Ricardo del Olmo, and Adolfo López-Paredes. Checking Simulations: Detecting and Avoiding Errors and Artefacts. In Bruce Edmonds and Ruth Meyer, editors, Simulating Social Complexity: A Handbook, Understanding Complex Systems, pages 119–140. Springer International Publishing, Cham, 2017. URL: https://doi.org/10.1007/978-3-319-66948-9_7 (visited on 2021-06-13), doi:10.1007/978-3-319-66948-9_7.