An alternative to the typical application of rational choice models to climate policy is the coupling of agent-based modeling and exploratory approaches. Agent-based models (ABMs) represent the world as made up of heterogeneous, boundedly-rational agents who act in their own interests and yet engage in substantive communication. Rather than focusing on optimal outcomes, agent-based models are primarily concerned with the evolution of large-scale properties that 'emerge' from the lower-level behavior. Consequently, ABMs have the potential to address complex system properties and generate a wider array of plausible storylines than more traditional integrated assessment modeling methodologies. We provide an overview of a new agent-based model of economic growth, energy technology, and climate change, and demonstrate use of the model for scenario discovery. Scenario discovery generates ensembles of plausible futures under alternative assumptions and hypotheses concerning system behavior. Such scenarios can help identify policy vulnerabilities and opportunities, thus supporting the design of robust climate change mitigation strategies.
Integrated assessment of mitigation strategies using an agent-based model of the linked energy, economic, and climate system