policy

An agent-based model of policy-making: disaggregation and conceptualization

An agent-based model of policy-making: disaggregation and conceptualization

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.

An Introduction to Complexity Science for Social Sciences

An Introduction to Complexity Science for Social Sciences

In both social sciences and policy-making, researchers and practitioners tackle multifaceted phenomena. Examples are (armed) conflicts, migration, the emergence of populism, the automation of professions, financial crises, international trade, or social integration. Efforts in research and practice have led to various approaches and processes to analyse these phenomena and make decisions under uncertainty.

I argue that the toolbox used to tackle these real-world problems could benefit a lot from the growing field of complexity science, i.e. the study of complex systems in the physical, biological, and social worlds. I believe complexity science is a vital tool that yields a more honest and granular understanding of social phenomena. Complexity science is not revolutionary, it is the middle-ground between the assemblage of (a) insights and methods from many scientific disciplines and (b) the dilution of disciplinary boundaries.