Agent-Based Modeling and Genetic Algorithm Simulation for the Climate Game Problem

摘要

The cooperative game of global temperature lacks automaticity and emotional jamming. To solve this issue, an agent-based modelling method is developed based on Milinski's noncooperative game experiments. In addition, genetic algorithm is used to improve the investment strategy of each agent. Simulations are carried out by designing different coding schemes, mutation schemes, and fitness functions. It is demonstrated that the method can achieve maximum benefits under the premise of the agent non-cooperative game through encouraging optimal individuals. The results provide a sound basis for developing tools and methods to support the simulation of climate game strategy that involves multiple stakeholders.