Making Uncertainty Visible: Multiverse Analysis for Robust Computational Social Science

2026-05-19

AI Summary

This study addresses the pervasive issue in computational social science wherein researchers’ considerable freedom in methodological choices often undermines the robustness of empirical conclusions, while computational failures are routinely overlooked. For the first time, the authors systematically apply a multiverse analysis framework to this domain, evaluating how diverse yet plausible analytical pipelines—including Bayesian inference, network generative modeling, and machine learning with and without large language models—affect the findings of three published studies. Their results demonstrate that substantive conclusions are highly sensitive to method selection. The work not only identifies specific method combinations prone to computational failure but also offers practical recommendations for selecting defensible analytical paths and transparently reporting the full spectrum of multiverse outcomes, underscoring the critical role of disclosing failed analyses in enhancing research reproducibility.

Abstract

Through case studies, we demonstrate how multiverse analysis can strengthen the robustness and transparency of computational social science findings against alternative methodological decisions. We conduct multiverse analyses of three published social science studies that use the following computational methods: Bayesian analysis, network generative modeling, and machine learning with or without large language models. These methods are applied frequently in computational social science studies, yet entail a greater degree of arbitrariness in terms of methodological choices, or "researcher degrees of freedom." Our multiverse analyses reveal how the empirical findings in these studies vary as a function of various plausible decision combinations. Our three case studies also expose an often-ignored motivation for conducting multiverse analysis: Showing which methodological combinations lead to computational failure. These failed cases are usually not communicated in the published reports, even though these sophisticated computational methods have a much higher likelihood of failure. We end our paper with suggestions on how to find defensible decision combinations for multiverse analysis of computational social science studies and how to communicate multiverse analysis findings fairly.

Problem

• researcher degrees of freedom
• computational social science
• robustness
• methodological uncertainty
• computational failure

Innovation

• multiverse analysis
• computational social science
• researcher degrees of freedom
• methodological robustness
• computational failure