The Simulation Model as a Causal Explanation Generator

Abstract

Here we enrich Paul Weirich’s thesis holding that a simulation model can create knowledge in the form of causal explanations. We sustain the validity of exporting results from the model to the modelized world in virtue of the similarity between model and world, which is analyzable in terms of partial identity of structure, eliminating the superficial similarity that repeats empirical results by adjusting data via calibration. The structure of relations rescues from the world critical results to analyze such similarity, as so certain properties which condition the kind of relations held between the elements of the model representing entities of the world