TRURL: artificial world for social interaction studies
ALIFE Proceedings of the sixth international conference on Artificial life
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Agent-Based Modeling for Competing Firms: From Balanced-Scorecards to Multi-Objective Strategies
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 3 - Volume 3
Analyzing norm emergence in communal sharing via agent-based simulation
Systems and Computers in Japan
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A Quantitative Method for Comparing Multi-Agent-Based Simulations in Feature Space
Multi-Agent-Based Simulation IX
Agent-Based In-Store Simulator for Analyzing Customer Behaviors in a Super-Market
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Multi-agent model of hepatitis C virus infection
Artificial Intelligence in Medicine
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This paper addresses the problem regarding the parameter exploration of Multi-Agent Based Simulation for social systems. We focus on the principles of Inverse Simulation and Genetics-Based Validation. In conventional artificial society models, the simulation is executed straightforwardly: Initially, many micro-level parameters and initial conditions are set, then, the simulation steps are executed, and finally the macro-level results are observed. Unlike this, Inverse Simulation executes these steps in the reverse order: set a macro-level objective function, evolve the worlds to fit to the objectives, then observe the micro-level agent characteristics. Another unique point of our approach is that, using Genetic Algorithms with the functionalities of multi-modal and multi-objective function optimization, we are able to validate the sensitivity of the solutions. This means that, from the same initial conditions and the same objective function, we can evolve different results, which we often observe in real world phenomena. This is the principle of Genetics-Based Validation.