# Revisiting the Predictability of Performative, Social Events

arXiv: https://arxiv.org/abs/2503.11713 · HTML: https://mlsanity.com/paper/2503.11713/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Outside the Echo Chamber: Optimizing the Performative Risk](https://mlsanity.com/paper/2102.08570/) (2021)
- [Stochastic Optimization for Performative Prediction](https://mlsanity.com/paper/2006.06887/) (2020)
- [Performative Prediction in a Stateful World](https://mlsanity.com/paper/2011.03885/) (2020)
- [Outcome Indistinguishability](https://mlsanity.com/paper/2011.13426/) (2020)
- [Stochastic optimization with decision-dependent distributions](https://mlsanity.com/paper/2011.11173/) (2020)
- [Loss Minimization through the Lens of Outcome Indistinguishability](https://mlsanity.com/paper/2210.08649/) (2022)
- [Data Feedback Loops: Model-driven Amplification of Dataset Biases](https://mlsanity.com/paper/2209.03942/) (2022)
- [Performative Prediction: Past and Future](https://mlsanity.com/paper/2310.16608/) (2023)
- [Online Multivalid Learning: Means, Moments, and Prediction Intervals](https://mlsanity.com/paper/2101.01739/) (2021)
- [Difficult Lessons on Social Prediction from Wisconsin Public Schools](https://mlsanity.com/paper/2304.06205/) (2023)
- [Performative Prediction with Neural Networks](https://mlsanity.com/paper/2304.06879/) (2023)
- [Making Decisions under Outcome Performativity](https://mlsanity.com/paper/2210.01745/) (2022)
- [Near-Optimal Algorithms for Omniprediction](https://mlsanity.com/paper/2501.17205/) (2025)
- [Omnipredictors for Regression and the Approximate Rank of Convex Functions](https://mlsanity.com/paper/2401.14645/) (2024)
- [Sample Efficient Omniprediction and Downstream Swap Regret for Non-Linear Losses](https://mlsanity.com/paper/2502.12564/) (2025)
- [From Fairness to Infinity: Outcome-Indistinguishable (Omni)Prediction in Evolving Graphs](https://mlsanity.com/paper/2411.17582/) (2024)
- [Incentivizing honest performative predictions with proper scoring rules](https://mlsanity.com/paper/2305.17601/) (2023)
- [Tight Lower Bounds and Improved Convergence in Performative Prediction](https://mlsanity.com/paper/2412.03671/) (2024)

## Built on by (most cited first, 11 of 11)

- [Generalization Bounds and Stopping Rules for Learning with Self-Selected Data](https://mlsanity.com/paper/2505.07367/) (2025)
- [In Defense of Defensive Forecasting](https://mlsanity.com/paper/2506.11848/) (2025)
- [Statistical Inference under Performativity](https://mlsanity.com/paper/2505.18493/) (2025)
- [The Stability of Online Algorithms in Performative Prediction](https://mlsanity.com/paper/2602.24207/) (2026)
- [Dissecting Performative Prediction: A Comprehensive Survey](https://mlsanity.com/paper/2602.10176/) (2026)
- [Partially Performative Prediction](https://mlsanity.com/paper/2606.07890/) (2026)
- [Unbiased Canonical Set-Valued Oracles Via Lattice Theory](https://mlsanity.com/paper/2606.26418/) (2026)
- [Retraining Seeks Stable Signals](https://mlsanity.com/paper/2607.15623/) (2026)
- [Performative Learning Theory](https://mlsanity.com/paper/2602.04402/) (2026)
- [Off-Policy Evaluation with Strategic Agents via Local Disclosure](https://mlsanity.com/paper/2606.07308/) (2026)
- [Stabilizing Performative Feedback Loops with Minimal Model Deployments](https://mlsanity.com/paper/2609.14065/) (2026)

## Abstract (arXiv)

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.