Understanding Climate Policy Using Participatory Agent-Based Social Simulation

Abstract

Integrated assessment models (IAMs) have been widely applied to questions of climate change policy—such as the effects of abating greenhouse gas emissions, balancing impacts, adaptation and mitigation costs, understanding processes of adaptation, and evaluating the potential for technological solutions. In almost all cases, the social dimensions of climate policy are poorly represented. Econometric models look for efficient optimal solutions. Decision making perspectives might reflect broadscale cultural theory, but not the diversity of cognitive models in practice. Technological change is often ignored or exogenous, and without understanding of stakeholder strategies for innovation and diffusion. Policy measures are proposed from idealised perspectives, with little understanding of the constraints of individual decision makers. We suggest a set of criteria for IAMs that can be used to evaluate the choice and structure of models with respect to their suitability for understanding key climate change debates. The criteria are discussed for three classes of models— optimising econometric models, dynamic simulation models and a proposed agent-based strategy. A prototype agent-based IAM is reported to demonstrate the usefulness and power of the agent based approach and to indicate concretely how that approach meets the criteria for good IAMs and to complex social issues more generally.