What is it about?
We use a popular online game on Reddit (r/place, a.k.a. the "pixel war") to showcase how machine learning can predict incoming transitions in complex socio-ecological systems. We also translate these predictions into original human-readable signals of these transitions, after a careful system-specific study.
Featured Image
Photo by CHUTTERSNAP on Unsplash
Why is it important?
Sudden transitions complex social and ecological systems can have dire consequences. A large literature intends to find early warning indicators of these critical transitions, that would be as generic as possible. Here we demonstrate that a model-independent approach with machine learning can provide much better signals than a simple application of the proposed indicators. In addition, interpreting the algorithm predictions provides a human-readable understanding of the pre-transition period that generic models would oversee.
Perspectives
We show the potential of machine learning indicators for predicting regime shifts and understanding their dynamics. Applying this approach to other systems is expected to reach higher prediction accuracy than generic models, but also to provide original properties of pre-transition periods that can be incorporated in system-specific expertise. This is to me the only way forward for AI-human interactions: a true bidirectional exchange of service and knowledge.
Guillaume Falmagne
Technological University Dublin
Read the Original
This page is a summary of: Interpretable early warnings using machine learning in an online game-experiment, Proceedings of the National Academy of Sciences, January 2026, Proceedings of the National Academy of Sciences,
DOI: 10.1073/pnas.2503493122.
You can read the full text:
Contributors
The following have contributed to this page
