Probabalistic Inference for Actor Centered Models

Abstract

The analysis of relations between different levels of a system is a key issue in social science simulation. Here, I discuss the contribution of different modeling methodologies to this. Special emphasis is given to the formalism of “Probabilistic Graphical Models“, resp. “Bayesian Networks“, which is both advantageous for level transitory inference and integration of empirical data. Furthermore, issues of practicability and area of application are considered. The argumentation is exemplified by demonstration of a toy-application for which explicit level-transitory statements are inferred.