TeorinABM

TeorinABM is a theoretical framework used in the social sciences to integrate agent-based modeling (ABM) with theory development and testing. It emphasizes using micro-level simulations to explain macro-level social patterns, linking individual behavior and interaction structures to emergent outcomes. The aim is to produce mechanistic, testable explanations of social phenomena rather than solely descriptive accounts.

Core concepts include agents with state, heterogeneous populations, interaction networks, and adaptive environments. The theory requires explicit assumptions about agent behavior and environment, and treats models as experiments to explore how micro-level processes generate macro-level effects. It supports iterative cycles of model construction, calibration, validation, and refinement of theory based on results and empirical data.

Methodologically, TeorinABM combines formal theory with computational experiments. Researchers specify agent rules grounded in theory, build ABMs, perform sensitivity analyses, and compare outcomes with real data or experiments. The approach enables scenario analysis and counterfactuals to evaluate mechanisms and policy implications.

Applications span economics, sociology, urban planning, epidemiology, and political science. It has been used to study diffusion, segregation and cultural dynamics, crowd behavior, market dynamics, and collective decision-making.

Strengths include transparent mechanisms, the ability to model heterogeneity and interactions, and the production of testable predictions. Limitations involve model complexity, risks of overfitting, validation challenges, and substantial computational requirements. Critics warn against equating simulation results with real-world causality without solid theory and data.

Emerging in the 2010s and 2020s, TeorinABM has a growing body of methodological guidance and best practices for theory-driven ABMs.

See also: Agent-based modeling; computational social science.