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Abstract
Mitigating climate change effects involves strategic decisions by individuals that may choose to limit their emissions at a cost. Everyone shares the ensuing benefits and thereby individuals can free ride on the effort of others, which may lead to the tragedy of the commons. For this reason, climate action can be conveniently formulated in terms of Public Goods Dilemmas often assuming that a minimum collective effort is required to ensure any benefit, and that decision-making may be contingent on the risk associated with future losses. Here we investigate the impact of reward and punishment in this type of collective endeavors — coined as collective-risk dilemmas — by means of a dynamic, evolutionary approach. We show that rewards (positive incentives) are essential to initiate cooperation, mostly when the perception of risk is low. On the other hand, we find that sanctions (negative incentives) are instrumental to maintain cooperation. Altogether, our results are gratifying, given the a-priori limitations of effectively implementing sanctions in international agreements. Finally, we show that whenever collective action is most challenging to succeed, the best results are obtained when both rewards and sanctions are synergistically combined into a single policy.
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1 INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, IST-Taguspark, Porto, Salvo, Portugal; ATP-group, Porto, Salvo, Portugal; Unbabel, R. Visc. de Santarém 67B, Lisboa, Portugal
2 Department of Ecology and Evolutionary Biology, Princeton University, Princeton, USA; INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, IST-Taguspark, Porto, Salvo, Portugal; ATP-group, Porto, Salvo, Portugal
3 Centro de Biologia Molecular e Ambiental, Universidade do Minho, Braga, Portugal; Departamento de Matemática e Aplicações, Universidade do Minho, Braga, Portugal; ATP-group, Porto, Salvo, Portugal
4 INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, IST-Taguspark, Porto, Salvo, Portugal; ATP-group, Porto, Salvo, Portugal; Machine Learning Group, Université Libre de Bruxelles, Boulevard du Triomphe CP212, Bruxelles, Belgium