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Position: Home > issue > September 13, 2022 Volume 3, Issue 5
Strategy evaluation and optimization with an artificial society toward a Pareto optimum
Category:   Perspective   Download:  PDF  Figure  Endnote
Author: Zhengqiu Zhu, Bin Chen, Hailiang Chen, Sihang Qiu, Changjun Fan, Yong Zhao, Runkang Guo, Chuan Ai, Zhong Liu, Zhiming Zhao, Liqun Fang, Xin Lu

a2.jpg

Overview of the proposed universal computational experiment framework for strategy evaluation and optimization in a fine-grained artificial society


Strategy evaluation and optimization in response to troubling urban issues has become a challenging issue due to increasing social uncertainty, unreliable predictions, and poor decision-making. To address this problem, we propose a universal computational experiment framework with a fine-grained artificial society that is integrated with data-based models. The purpose of the framework is to evaluate the consequences of various combinations of strategies geared towards reaching a Pareto optimum with regards to efficacy versus costs.





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