The X-MAS SYSTEM: Toward Simulation Systems for Cross-model-validation in Multiagent-Based Simulations
- 109 Downloads
The huge number of simulation models available in different scientific communities shows the prominent role that simulations are playing in the study of complex social systems. However, model validation is not an established practice among communities, but it is indispensable for assuring reliable models and results when studying a certain social phenomenon. In order to help researchers in validation processes, this paper proposes the Cross-model validation for MultiAgent-based Simulation (X-MAS) System, a toolkit developed for supporting validation of models and facilitating the implementation of complex social system simulations by addressing the following three aspects: (1) rich object-oriented library for cross-model validation, simultaneously providing X-MAS with verification and validation capabilities. For validation purpose, X-MAS supplies with an agent structure embedding several kind of elements, such as different learning mechanisms and knowledge representation schemes, (2) high-level programming skills are not required for rapid prototyping, and (3) framework facilities for the promotion of an effective cumulative scientific process, making it possible to evaluate and verify different models, permitting their exchange from different scientific communities, and stimulating the replication of results and their further verification and validation. The effectiveness of X-MAS is shown by investigating a bargaining model, a well-study model in game theory.
Key wordsMultiagent-based systems cross-model validation object-oriented programming learning mechanisms knowledge representation schemes
Unable to display preview. Download preview PDF.
- Agent Sheets. http://agentsheets.com/, AgentSheets, Inc.
- Axelrod, R. M. (1997) The complexity of cooperation: Agent-based models of competition and collaboration. Princeton University PressGoogle Scholar
- Axelrod, R. M. and Cohen M. D. (2000) Harnessing complexity: Organizational implications of a scientific frontier. The Free PressGoogle Scholar
- Back, T., Hoffmeister, F. and Schwefel, H. (1991) A survey of evolution strategies. In Proceedings of the Forth International Conference on Genetic Algorithms (ICGA ed.). Morgan Kaufmann, pp. 2–9Google Scholar
- Carley, K. M. (1996) Validating computational models. Carnegie-Mellon University, Working PaperGoogle Scholar
- Gasser, L., Braganza, C. and Herman, N. (1987) Implementing distributed AI systems using MACE. In Proceedings of the 3rd IEEE Conference on Artificial Intelligence Applications 1987. IEEE, pp. 315–320Google Scholar
- Golberg, D. E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-WesleyGoogle Scholar
- Holland, J. H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan PressGoogle Scholar
- Holland, J. H., Holyoak, K. J., Nisbett, R.E., and Thagard, P. R. (1986) Induction. The MIT PressGoogle Scholar
- Minar N., Burkhart R., Langton C, and Askenazi, M. (1996) The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations. http://www.swarm.org, Swarm Development Group
- Moss, S. and Davidsson, P. (2001) Multi-Agent-Based Simulation. Lecture Notes in Artificial Intelligence. Springer-Verlag, Vol. 1979Google Scholar
- Muthoo, A. (2000) A Non-Technical Introduction to Bargaining Theory. World Economics, pp. 145–166Google Scholar
- Osborne, M. J. and Rubinstein, A. (1994) A Course in Game Theory. The MIT PressGoogle Scholar
- Parker, M. (1998) Ascape. http://www.brook.edu/es/dynamics/models/ascape, The Brooking Institution.
- Parker, M. (2001) What is Ascape and why should you care?. Journal of Artificial Societies and Social Simulation. Vol. 4, No. 1Google Scholar
- Ståhl, I. (1972) Bargaining Theory. Economics Research Institute at the Stockholm School of EconomicsGoogle Scholar
- StarLogo. http://el.www.media.mit.edu/projects/macstarlogo/, MIT Media Lab
- Takadama, K., L. Suematsu, Y., Nawa, E. and Shimohara, K. (2002) Cross-Validation in Multiagent-based Simulation: Analyzing evolutionary bargaining agents. In Proceedings of the 2002 Genetic and Evolutionary Computation Conference (GECCO′2002), pp. 121–128Google Scholar