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Kenny, Ian; Kbaier, Dhouha; Fairchild, Richard and Hinvest, Neal
(2024).
Abstract
In this paper, we present an innovative approach that integrates machine learning and game theory into a game theoretic model involving two countries, incorporating predictive citizen influence. As part of the research, we carried out two surveys to assess citizens’ attitudes towards climate change, and whether these were persistent over time. Using a machine learning model, we attempt to predict people's attitudes towards environmental risk. Using the predictions as input into a game theoretic model, encompassing factors that influence whether a country will continue to comply with their commitments made in ratifying an International Environmental Agreement (IEA).
We explore the integration of machine learning predictions and game theory dynamics, providing insights into the potential and challenges of this interdisciplinary approach. Our findings reveal a significant discrepancy between the level of concern about environmental risk in the population and the sustained level of population concern required by the game theoretic model to influence governmental commitments Also, we delve into the reasons behind this discrepancy, and discuss potential strategies to mitigate the loss of focus on the need of societal changes to address the impact of climate change. The research presented is part of the broader research project investigating the engagement of citizens and NGOs to influence governments and intergovernmental bodies to uphold their commitments on climate change.