CYLGTHMLMar 12, 2025

Revisiting the Predictability of Performative, Social Events

arXiv:2503.11713v28 citationsh-index: 10ICML
Originality Incremental advance
AI Analysis

This addresses a central methodological issue in social science regarding the predictability of performative events, though it appears incremental by building on recent ideas.

The paper tackles the problem of predicting social events that are influenced by predictions themselves, establishing that accurate prediction is always efficiently achievable regardless of such influence, but also showing these predictions are often undesirable.

Social predictions do not passively describe the future; they actively shape it. They inform actions and change individual expectations in ways that influence the likelihood of the predicted outcome. Given these dynamics, to what extent can social events be predicted? This question was discussed throughout the 20th century by authors like Merton, Morgenstern, Simon, and others who considered it a central issue in social science methodology. In this work, we provide a modern answer to this old problem. Using recent ideas from performative prediction and outcome indistinguishability, we establish that one can always efficiently predict social events accurately, regardless of how predictions influence data. While achievable, we also show that these predictions are often undesirable, highlighting the limitations of previous desiderata. We end with a discussion of various avenues forward.

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