Nonlinearity in Social Service Evaluation: A Primer on Agent-based Modeling

Measurement of nonlinearity in social service research and evaluation relies primarily on spatial analysis and, to a lesser extent, social network analysis. Recent advances in geographic methods and computing power, however, allow for the greater use of simulation methods. These advances now enable evaluators and researchers to simulate complex adaptive systems (CASs) by applying agent-based modeling (ABM). CASs reflect the interactions of competitive and cooperative tendencies found in agents. ABM simulations create and test generated observable patterns using the fewest number of plausible decision rules and agents. This primer presents essential concepts for understanding ABM as social service applications of complexity theory shift from a metaphorical perspective to a formalized evaluation method. Further developments in ABM methods need to focus on concepts emanating from the study of complexity science, including the concepts of the wisdom of groups, strengths found in diverse perspectives, robustness, interconnectedness, sustainability, and conflict and cooperation. Appropriate software programs for developing and testing agent-based models are provided.