[submitted to the Journal of Complexity, Governance and Networks on November 29, 2018]
Policy-making exhibits nonlinear patterns which emerge from the interaction of heterogeneous bounded-rational actors. How to understand such complexity with both clarity and parsimony? We disaggregate policy-making into different parts to develop a conceptual agent-based model. We define agents with attributes, cognitive rules, and strategies drawing from the literature on public policy, sociology, and behavioral psychology. We present a set of parameters that constrain agent adaptation, such as environmental variables; policy processes; and network structures. We use bit-strings to encode information and the fundamentals of evolutionary biology to conceptualize learning and adaptation mechanisms. We present a set of hypothetical results for illustration purposes. This disaggregation and conceptualization serves as the groundwork to formalize, implement, and simulate the current understanding of policy-making. Among other applications, the model is designed to inject dynamism into static theory; shed light on how individual behavior self-organizes and scales to the group and the institutional levels; and conduct evaluative research in data-scarce contexts.
Keywords: policy-making, decision-making, complex systems, agent-based model, conceptualization