Translating Agents ' Actions to Strategic Measures: Agent-Based Modeling with Genetic Algorithms to Analyze Competing Companies

Abstract. Agent-based simulation has become one of the promising gears for computational social sciences including business management strategy studies. This paper addresses a novel technology of agent-based modeling with genetic algorithms. Our method is characterized by (1) Decision making agents or competing companies with strategic parameters to be optimized; (2) A multiob-jective optimization framework to evolve the artificial simulated society; (3) Grounding the simulation conditions with marketing survey data in the real world, and (4) Validating the strategic parameters of the agents after simulation via statistical analysis of the individual genes. The proposed method enables us to investigate the strategic measures or balanced scorecards of competing com-panies from the agents ’ actions in the simulator.