Abstract

Modelling complex systems in engineering, science or social science involves selection of measurements on many levels including observability (determined e.g. by technical equipment, cost, confidentiality, existing records) and need for interpretability. Among the initially selected variables, the frequency and quality of observation may be altered by censoring and sampling biases. A model is, by definition, a simplification, and the question one asks is often not whether a certain effect exists, but whether it matters. This crucially depends on the research objective or perspective. Biased conclusions occur when the research question is interwoven with the mechanisms in which the variables for the analysis are selected or weighted. Such effects can occur in any applications that involve observational data. I will give some examples from a few of my own research projects involving quality assessment, decision making, financial trading, genomics and microscopy.