Problems With Multi-Scale-Models

Abstract

This paper analyses typical experimental set-ups for multi-scale models on a not-aggregated level of model description in comparison to conventionally aggregated models. It postulates that for real-world-applications additional assumptions become necessary which concern to the type and the parameters of the data transformation between the aggregated and the non-aggregated level. The structure of the problem is analysed and typical scenarios for model usage and validation are listed. General methodological deliberations for each of these scenarios are made that offer a guideline for correct experimental design in order to validate the corresponding models.