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

• URL: http://arxiv.org/abs/2502.09880v1
• Date: Fri, 14 Feb 2025 03:14:50 GMT
• Title: Interpretable Early Warnings using Machine Learning in an Online Game-experiment
• Authors: Guillaume Falmagne, Anna B. Stephenson, Simon A. Levin,
• Abstract summary: A large-scale social game, Reddit's r/place, provides a unique opportunity to test statistical early warning signals. We develop a machine-learning-based early warning system that combines the predictive power of multiple system-specific time series. Our algorithm detects half of the transitions occurring within 20 minutes at a false positive rate of just 3.7%.
• Score: 0.6827423171182153
• License:
• Abstract: 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 minutes at a false positive rate of just 3.7%. 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.

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