Constrained Modified Genetic Algorithm for Optimizing RICE Climate Change Model Policy

Osman, Haitham

doi:10.3844/ajassp.2017.945.954

Cited by 1 publication

(1 citation statement)

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“…In this case, to ensure Pareto optimality, g should be maximized only over the set of Pareto-optimal outcomes, i.e., over the Pareto front. In practice, it is rarely possible to describe the Pareto front analytically, but it can be efficiently mapped using multi-objective optimization techniques or genetic algorithms [46,52]. Once the Pareto front is mapped by a sufficiently dense set of Pareto-optimal outcomes, g can be evaluated for each of them, and the outcome with the highest value of g can be picked as a Pareto-optimal solution to the problem max d t , 1≤t≤T g(U 1 , .…”

Section: Discussionmentioning

confidence: 99%

Sharing the Burdens of Climate Mitigation and Adaptation: Incorporating Fairness Perspectives into Policy Optimization Models

et al. 2022

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Mitigation of, and adaptation to, climate change can be addressed only through the collective action of multiple agents. The engagement of involved agents critically depends on their perception that the burdens and benefits of collective action are distributed fairly. Integrated Assessment Models (IAMs), which inform climate policies, focus on the minimization of costs and the maximization of overall utility, but they rarely pay sufficient attention to how costs and benefits are distributed among agents. Consequently, some agents may perceive the resultant model-based policy recommendations as unfair. In this paper, we propose how to adjust the objectives optimized within IAMs so as to derive policy recommendations that can plausibly be presented to agents as fair. We review approaches to aggregating the utilities of multiple agents into fairness-relevant social rankings of outcomes, analyze features of these rankings, and associate with them collections of properties that a model’s objective function must have to operationalize each of these rankings within the model. Moreover, for each considered ranking, we propose a selection of specific objective functions that can conveniently be used for generating this ranking in a model. Maximizing these objective functions within existing IAMs allows exploring and identifying climate polices to which multiple agents may be willing to commit.

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Section: Discussionmentioning

confidence: 99%