# Interpretable Early Warnings using Machine Learning in an Online Game-experiment

arXiv: https://arxiv.org/abs/2502.09880 · HTML: https://mlsanity.com/paper/2502.09880/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 93 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Machine learning dismantling and early-warning signals of disintegration in complex systems](https://mlsanity.com/paper/2101.02453/) (2021)
- [Universal Early Warning Signals of Phase Transitions in Climate Systems](https://mlsanity.com/paper/2206.00060/) (2022)
- [Latent Structure in Collaboration: the Case of Reddit r/place](https://mlsanity.com/paper/1804.05962/) (2018)
- [With Flying Colors: Predicting Community Success in Large-scale Collaborative Campaigns](https://mlsanity.com/paper/2307.09650/) (2023)
- [A Machine Learning Early Warning System: Multicenter Validation in Brazilian Hospitals](https://mlsanity.com/paper/2006.05514/) (2020)
- [Deep Learning for Disease Outbreak Prediction: A Robust Early Warning Signal for Transcritical Bifurcations](https://mlsanity.com/paper/2501.07764/) (2025)

## Abstract (arXiv)

Stemming from physics and later applied to other fields such as ecology, the theory of critical transitions suggests that some regime shifts are preceded by statistical early warning signals. Reddit's r/place experiment, a large-scale social game, provides a unique opportunity to test these signals consistently across thousands of subsystems undergoing critical transitions. In r/place, millions of users collaboratively created ''compositions'', or pixel-art drawings, in which transitions occur when one composition rapidly replaces another. We develop a machine-learning-based early warning system that combines the predictive power of multiple system-specific time series via gradient-boosted decision trees with memory-retaining features. Our method significantly outperforms standard early warning indicators. Trained on the 2022 r/place data, our algorithm detects half of the transitions occurring within 20 min at a false positive rate of just 3.6%. Its performance remains robust when tested on the 2023 r/place event, demonstrating generalizability across different contexts. Using SHapley Additive exPlanations (SHAP) for interpreting the predictions, we investigate the underlying drivers of warnings, which could be relevant to other complex systems, especially online social systems. We reveal an interplay of patterns preceding transitions, such as critical slowing down or speeding up, a lack of innovation or coordination, turbulent histories, and a lack of image complexity. These findings show the potential of machine learning indicators in socio-ecological systems for predicting regime shifts and understanding their dynamics.