Agent-based computational modeling (ABM, for short) is a formal and supplementary methodological approach used in international relations (IR) theory and research, based on the general ABM paradigm and computational methodology as applied to IR phenomena. ABM of such phenomena varies according to three fundamental dimensions: scale of organization—spanning foreign policy, international relations, regional systems, and global politics—as well as by geospatial and temporal scales. ABM is part of the broader complexity science paradigm, although ABMs can also be applied without complexity concepts. There have been scores of peer-reviewed publications using ABM to develop IR theory in recent years, based on earlier pioneering work in computational IR that originated in the 1960s that was pre-agent based. Main areas of theory and research using ABM in IR theory include dynamics of polity formation (politogenesis), foreign policy decision making, conflict dynamics, transnational terrorism, and environment impacts such as climate change. Enduring challenges for ABM in IR theory include learning the applicable ABM methodology itself, publishing sufficiently complete models, accumulation of knowledge, evolving new standards and methodology, and the special demands of interdisciplinary research, among others. Besides further development of main themes identified thus far, future research directions include ABM applied to IR in political interaction domains of space and cyber; new integrated models of IR dynamics across domains of land, sea, air, space, and cyber; and world order and long-range models.
Lin Qiu and Riyang Phang
Political systems involve citizens, voters, politicians, parties, legislatures, and governments. These political actors interact with each other and dynamically alter their strategies according to the results of their interactions. A major challenge in political science is to understand the dynamic interactions between political actors and extrapolate from the process of individual political decision making to collective outcomes. Agent-based modeling (ABM) offers a means to comprehend and theorize the nonlinear, recursive, and interactive political process. It views political systems as complex, self-organizing, self-reproducing, and adaptive systems consisting of large numbers of heterogeneous agents that follow a set of rules governing their interactions. It allows the specification of agent properties and rules governing agent interactions in a simulation to observe how micro-level processes generate macro-level phenomena. It forces researchers to make assumptions surrounding a theory explicit, facilitates the discovery of extensions and boundary conditions of the modeled theory through what-if computational experiments, and helps researchers understand dynamic processes in the real-world. ABM models have been built to address critical questions in political decision making, including why voter turnouts remain high, how party coalitions form, how voters’ knowledge and emotion affect election outcomes, and how political attitudes change through a campaign. These models illustrate the use of ABM in explicating assumptions and rules of theoretical frameworks, simulating repeated execution of these rules, and revealing emergent patterns and their boundary conditions. While ABM has limitations in external validity and robustness, it provides political scientists a bottom-up approach to study a complex system by clearly defining the behavior of various actors and generate theoretical insights on political phenomena.