Staged Models for Interdisciplinary Research

TL;DR

This paper presents a staged modeling approach that starts with a complex model and derives simpler models to balance analytical rigor and relevance, demonstrated through a voting intention case study.

Contribution

It introduces a method for creating a chain of models from complex to simple, enhancing collaboration and insights in interdisciplinary research.

Findings

Simplified models closely match complex model predictions.

Variations of simpler models reveal hidden insights.

The approach improves interdisciplinary collaboration.

Abstract

Modellers of complex biological or social systems are often faced with an invidious choice: to use simple models with few mechanisms that can be fully analysed, or to construct complicated models that include all the features which are thought relevant. The former ensures rigour, the latter relevance. We discuss a method that combines these two approaches, beginning with a complex model and then modelling the complicated model with simpler models. The resulting "chain" of models ensures some rigour and relevance. We illustrate this process on a complex model of voting intentions, constructing a reduced model which agrees well with the predictions of the full model. Experiments with variations of the simpler model yield additional insights which are hidden by the complexity of the full model. This approach facilitated collaboration between social scientists and physicists -- the complex model was specified based on the social science literature, and the simpler model constrained to agree (in core aspects) with the complicated model.