Predictive Non-equilibrium Social Science

TL;DR

This paper emphasizes the importance of predictive analysis in Non-Equilibrium Social Science, highlighting challenges and misconceptions in social dynamics prediction and proposing a focus on predictive utility over explanatory models.

Contribution

It clarifies the distinction between predictive and explanatory models in NESS and investigates real-world prediction challenges, revealing key misconceptions and practical difficulties.

Findings

Standard prediction methods often underperform due to incorrect assumptions.

Misunderstanding which social features are predictive hampers accuracy.

Practical challenges limit effective exploitation of predictive models.

Abstract

Non-Equilibrium Social Science (NESS) emphasizes dynamical phenomena, for instance the way political movements emerge or competing organizations interact. This paper argues that predictive analysis is an essential element of NESS, occupying a central role in its scientific inquiry and representing a key activity of practitioners in domains such as economics, public policy, and national security. We begin by clarifying the distinction between models which are useful for prediction and the much more common explanatory models studied in the social sciences. We then investigate a challenging real-world predictive analysis case study, and find evidence that the poor performance of standard prediction methods does not indicate an absence of human predictability but instead reflects (1.) incorrect assumptions concerning the predictive utility of explanatory models, (2.) misunderstanding regarding which features of social dynamics actually possess predictive power, and (3.) practical difficulties exploiting predictive representations.