Modeling Social Systems: Transparency, Reproducibility, and Responsibility

TL;DR

This paper discusses the importance of transparency, reproducibility, and responsibility in modeling complex social systems, emphasizing challenges and strategies for responsible scientific modeling in data-scarce environments.

Contribution

It highlights the unique challenges of social system modeling and proposes principles for transparent, reproducible, and responsible modeling practices.

Findings

Models can inform policy and theory despite data limitations.

Transparency and reproducibility are crucial for responsible social modeling.

Challenges include sparse, noisy, and incomplete data.

Abstract

Mathematical models of complex social systems can enrich social scientific theory, inform interventions, and shape policy. From voting behavior to economic inequality and urban development, such models influence decisions that affect millions of lives. Thus, it is especially important to formulate and present them with transparency, reproducibility, and humility. Modeling in social domains, however, is often uniquely challenging. Unlike in physics or engineering, researchers often lack controlled experiments or abundant, clean data. Observational data is sparse, noisy, partial, and missing in systematic ways. In such an environment, how can we build models that can inform science and decision-making in transparent and responsible ways?