KnitCity: a machine learning-based, game-theoretical framework for prediction assessment and seismic risk policy design

TL;DR

This paper introduces a novel framework combining machine learning and game theory to predict seismic-like events in knitted fabric, evaluate predictors, and optimize decision policies for seismic risk management in a simulated city.

Contribution

It presents a comprehensive framework for designing, evaluating, and comparing predictors and decision policies using reinforcement learning in a physically motivated seismic risk scenario.

Findings

Effective predictors identified through the framework.

Reinforcement learning policies optimize evacuation decisions.

Quantitative assessment of predictor relevance in decision-making.

Abstract

Knitted fabric exhibits avalanche-like events when deformed: by analogy with eathquakes, we are interested in predicting these "knitquakes". However, as in most analogous seismic models, the peculiar statistics of the corresponding time-series severely jeopardize this endeavour, due to the time intermittence and scale-invariance of these events. But more importantly, such predictions are hard to {\it assess}: depending on the choice of what to predict, the results can be very different and not easily compared. Furthermore, forecasting models may be trained with various generic metrics which ignore some important specificities of the problem at hand, in our case seismic risk. Finally, these models often do not provide a clear strategy regarding the best way to use these predictions in practice. Here we introduce a framework that allows to design, evaluate and compare not only predictors but also decision-making policies: a model seismically active {\it city} subjected to the crackling dynamics observed in the mechanical response of knitted fabric. We thus proceed to study the population of KnitCity, introducing a policy through which the mayor of the town can decide to either keep people in, which in case of large events cause human loss, or evacuate the city, which costs a daily fee. The policy only relies on past seismic observations. We construct efficient policies using a reinforcement learning environment and various time-series predictors based on artificial neural networks. By inducing a physically motivated metric on the predictors, this mechanism allows quantitative assessment and comparison of their relevance in the decision-making process.