Oversimplification issues in social sciences research models

Organizational responses to strategic surprises like the credit crunch in 2008 and the pandemic in 2020, are increasingly reliant on scientific insights. As a result, model accuracy has become more critical than ever, and model complexity has increased to help us capture the real-world phenomena we study. So much so, that appeals for simplification are beginning to surface. But unfortunately, simplification has its issues.

Too simple models are so generic that they no longer accurately describe or predict real-world cause-effect relationships. On the other hand, too complex models are accurate for a specific context and are difficult to generalize. Somewhere on the continuum between too simple and too complex lies the optimal model.

In this article, the authors contribute to the ongoing discussion on model complexity by classifying the problems with mismatches between real-world and model complexity. They present a framework of four levels of model complexity and possible oversimplification problems.

The framework can help scholars within the social sciences to detect possible oversimplification from literature reviews and inform choices for either in- or decreases in model complexity.