Simulation-based exploratory data generation and analysis (data farming): a critical reflection on its validity and methodology

Abstract

‘Data farming’ is based on the idea that simulation models run thousands of times can provide insights into the possible consequences of different options. However, the validity of the models used for data farming, especially in the context of HSCB (human, social, cultural and behavioural) modelling for decision-making and future studies, is at least questionable. This paper first reflects on the epistemological aspects of this predicament in order to illustrate its fundamental severity. Then, a possible solution is presented that is based on the notion of ‘bad models’, the concept of plausibility, and the method of simulation-based weak point analysis. The approach can be complemented by interactive war gaming. Such a systematic approach appears more defendable than most attempts to use HSCB models for affirmative purposes, and is methodologically easier to implement since it solely requires focusing on the validation of empirically amenable micro-processes.