Interpretable Early Warning Signals in Large Human Groups, using Machine Learning in an Online Game-experiment

Understanding the emergent dynamics – in particular critical transitions – in complex social-ecological systems is key to foster positive social transformations in the Anthropocene era. Regime shifts in some ecosystems may be preceded by statistical early warning signals, but systems where such signals can be tested systematically are elusive. The r/place game hosted by Reddit is a social experiment that provides data for thousands of subsystems that can undergo critical transitions. It is therefore an excellent testbed for comparing the performance of various warning indicators. In r/place, millions of users collaborated to build many discernible drawings on a canvas of pixels. A drawing undergoes a transition when it is rapidly replaced by another. We build an early warning signal indicator that uses machine learning to combine the predictive power of a number of time-dependent and system-specific variables, and we show that its performance far exceeds that of standard indicators. For example, when training the algorithm and testing its performance on separate parts of the 2022 r/place, we detect half of the transitions coming in less than 20 minutes with only a 0.6% false positive rate. The performance only slightly decreases when training on 2022 data and testing on the 2023 experiment, showing that the predictive power holds across significantly different setups. We use SHAP values to elucidate the drivers of any given warning and highlight generic properties of warnings in online social systems. Some properties, such as a decreasing return time, are at odds with standard statistical indicators. Where sufficient data is available, our tool and resulting insights can contribute to warn of – and possibly trigger or avoid – macroscopic social and ecological change.