Abstract for: How do system dynamic models (SD) capture path dependent and complex evolutionary behaviour in social science analysis?
The aim of social scientists is to capture the causal mechanisms that explain behavior of people and groups of people, such as communities, societies or firms. Such an endeavor becomes increasingly difficult as theorizing concerns patterns of behavior. A theory of behavior explains how the cause-effect structure of interaction among specific variables leads to emergent paths of behavior of these variables. Thus, building theories of behavior implies creating a narrative that connects a deep theoretical structure to a repertoire of plausible behaviors that encompass the observed critical events and behaviors. A problem challenging discursive theories of behavior is the quality and robustness of inferred connections between causal structure and emerging behaviors. Equally difficult is to understand how modifications of theoretical assumptions, crystallized into a model, lead to modifications of the phenomenon under study. To make the described endeavor even more challenging, observed patterns of behavior are often produced by path-dependent processes that amplify non-systematic and stochastic disturbances. In this essay, we suggest that the interaction between field research, computer simulation and System Dynamics allows to elicit causal models from the rich texture of everyday life.