Strategy evaluation and optimization with an artificial society toward a Pareto optimum.

Area of Science:

• Computational Social Science
• Urban Planning
• Public Health Policy

Background:

• Urban issues are complex, exacerbated by social uncertainty and unreliable predictions, hindering effective strategy evaluation and decision-making.
• Existing models often lack the granularity to assess diverse strategy combinations and their real-world impacts.
• The need for robust frameworks to optimize interventions for urban challenges is critical.

Purpose of the Study:

• To propose a universal computational experiment framework integrating fine-grained artificial societies and data-based models.
• To evaluate the consequences of various strategy combinations for achieving a Pareto optimum between efficacy and cost.
• To provide a method for optimizing national intervention strategies through large-scale computational experiments.

Main Methods:

• Development of a computational experiment framework with a fine-grained artificial society.
• Integration of data-based models for simulating urban scenarios.
• Modeling of coronavirus 2019 mitigation strategies using real-world data.
• Analysis of strategy combinations to identify Pareto frontiers for efficacy versus cost.

Main Results:

• Demonstrated improved economic growth and more effective epidemic control for Pareto frontier nations in coronavirus 2019 mitigation.
• Identified optimal intervention strategies by analyzing measures adopted by Pareto frontier nations.
• Validated the framework's effectiveness for epidemic control.

Conclusions:

• The proposed framework offers a robust method for evaluating and optimizing urban intervention strategies.
• Computational experiments with artificial societies can guide policy decisions towards better outcomes in complex urban environments.
• The framework's generalizability extends beyond epidemic control to other pressing urban issues.