Agent-Based Virtual Laboratories for a Novel Experimental Approach to Socio-Environmental Synthesis

Session G1: Using Simulation Models to Improve Understanding of Environmental Systems

The number of agent-based modeling (ABM) applications within the socio-environmental context has exploded over the last decade. Most of these ABMs have been designed to deepen our understanding of the decision-making processes and human-environment interactions that lead to emergent community- and/or landscape-level outcomes in specific locations and contexts. While these 'case-based' ABMs have generally been successful in this aim - to which their popularity attests - little progress has been made through this case-based approach towards the ultimate goal of building coherent theory about the structure, dynamics, and sustainability of socio-environmental systems. Moving away from case-based ABMs, this paper will introduce the agent-based virtual laboratory (ABVL) approach, which requires more generalized ABMs for cross-site experimentation, comparison, and synthesis. Broadly, the ABVL approach harnesses the process-based explanatory power of ABMs within a modeling system architecture explicitly designed for flexible, iterative experimentation and cross-site comparison. A review of practical and philosophical aspects of socio-environmental modeling purposes, epistemologies, and design and evaluation principles is presented in order to place the ABVL approach along a spectrum of existing modeling approaches. As an illustration of the novel research questions that can be asked with ABVLs, a demonstration model is used to compare a household- versus settlement-level agent representation in search of the best and most parsimonious explanation of land-use and livelihood patterns across three study sites in East and Southeast Asia. The synthesis capabilities of the ABVL approach can lead to new hypotheses and experiments to accelerate the development of theories of socio-environmental system change.