Training LSTM-neural networks on early warning signals of declining cooperation in simulated repeated public good games

We present results of attempts to expand and enhance the predictive power of Early Warning Signals (EWS) for Critical Transitions (Scheffer et al. 2009) through the deployment of a Long-Short-Term-Memory (LSTM) Neural Network on agent-based simulations of a Repeated Public Good Game, which due to positive feedbacks on experience and social entrainment transits abruptly from majority cooperation to majority defection and back. Our method extension is inspired by several known deficiencies of EWS and by lacking possibilities to consider micro-level interaction in the so far primarily used simulation methods. We find that: • The method is applicable to agent-based simulations (as an extension of equation-based methods). • The LSTM yields signals of imminent transitions that can complement statistical indicators of EWS. • The less tensely connected part of an agent population could take a larger role in causing a tipping than the well-connected part.