Agent-based Modeling

Abstract

Agent-based modeling (ABM) has become widely accepted as a methodological tool to model and simulate dynamic processes of geographical phenomena. A growing number of ABM studies across a variety of domains and disciplines is partially explained by the development of agent-modeling tools and platforms, the availability of micro-data, and the advancement in computer technology and cyberinfrastructure. In addition to these technical reasons, another key motivation underlying ABM research is to address challenges embedded in conventional modeling approaches being relatively coarse, aggregate, static, normative, and inflexible across scales with a reductionist viewpoint (Batty 2005 cited under Application: Urban Systems.” With complexity science, including complex systems, complex adaptive systems, and artificial life, providing theoretical foundations and rationales, ABM is a computational methodology for simulating dynamic processes of nature and human systems driven by disaggregated, heterogeneous, and autonomous entities, i.e., agents, that interact among themselves and their environments. A key fundamental concept of the ABM framework is that a system emerges from the dynamic individual-level interactions from bottom-up, where the simulated outcome is more than the sum of its components. This bottom-up approach enables ABM to exhibit complex system dynamics, properties of which could include feedback effect, path-dependence, phase shift, non-linearity, adaptation, self-organization, tipping points, and emergence. Three key components of ABM are agents, their environment, and their decision rules. Agents are the crucial component in ABM where each individual agent has its own characteristics and agenda, assesses its surrounded situation, and makes decisions. Agents reside in an environment, which can represent a geographic space in case for spatially explicit agent-based models. Agents’ behavioral decisions and interactions within their environment are defined based on a set of rules, which can alter their status and location over time. The purpose of ABM research can be classified into theoretical exploration and empirical investigation as well as the combination of two. In the latter case, ABM can be used as an artificial laboratory experiment to explore what-if scenarios and to investigate how changes in agents, environments, and/or rules affect the macro-level outcomes. ABM has been applied to represent a wide variety of geographic processes and behaviors including but not limited to urban system, land-use/land-cover change, ecology, transportation, animal/human movement, behavioral geography, spatial cognition, transportation, and disease epidemiology. While the growing interest in ABM as a modeling methodology to simulate complex systems is remarkable, there exist various conceptual, methodological, and technical challenges.