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Cluster Computing

, Volume 19, Issue 1, pp 183–195 | Cite as

A grid based simulation environment for agent-based models with vast parameter spaces

  • Chao Yang
  • Bin Jiang
  • Isao Ono
  • Setsuya Kurahashi
  • Takao Terano
Article
  • 232 Downloads

Abstract

Agent-based simulation models with large experiments for a precise and robust result over a vast parameter space are becoming a common practice, where enormous runs intrinsically require highly intensive computational resources. This paper proposes a grid based simulation environment, named Social Macro Scope (SOMAS) to support parallel exploration on agent-based models with vast parameter space. We focus on three types of simulation methods for agent-based models with various objectives (1) forward simulation to conduct experiments in a straightforward way by simply operating sets of parameter values to perform sensitivity analysis; (2) inverse simulation to search for solutions that reduce the error between simulated results and actual data by means of solving “inverse problem”, which executes the simulation steps in a reverse order and employs optimization algorithms to fit the simulation results to the desired objectives; and (3) model selection to find an optimal model structure with subset of parameters and procedures, which conducts two-layer optimization to obtain a simple and more accurate simulation result. We have confirmed the practical scalability and efficiency of SOMAS by one case study in history simulation domain.

Keywords

Agent-based simulation Grid computing Forward simulation Inverse simulation Model selection 

References

  1. 1.
    Takadama, K., Cioffi-Revilla, C., Deffuant, G.: Simulating Interacting Agents and Social Phenomena. Springer, New York (2010)CrossRefGoogle Scholar
  2. 2.
    Chen, S.-H., Terano, T., Yamamoto, R., Tai, C.C.: Advances in Computational Social Science. Springer, New York (2014)CrossRefGoogle Scholar
  3. 3.
    Gilbert, N.: Agent-Based Models. Thousand Oaks, Sage Publications (2007)Google Scholar
  4. 4.
    van Dam, K.H., Nikolic, I., Lukszo, Z.: Agent-Based Modelling of Socio-Technical Systems. Springer, New York (2013)CrossRefGoogle Scholar
  5. 5.
    Nakai, Y., Koyama, Y., Terano, T.: Agent-Based Approaches in Economic and Social Complex Systems VIII. Springer, New York (2015)CrossRefGoogle Scholar
  6. 6.
    Terano, T.: Exploring the vast parameter space of multi-agent based simulation. In: Antunes L., Takadama K. (eds.) Proceedings of the Seventh International Workshop on Multi-Agent-Based Simulation (MABS’06), LNAI 4442, Springer, pp. 1–14 (2007)Google Scholar
  7. 7.
    Yang, C., Kurahashi, S., Kurahashi, K., Ono, I., Terano, T.: Agent-based simulation on womens role in a family line on civil service examination in Chinese history. J. Artif. Soc. Soc. Simul. 12(25) (2009). http://jasss.soc.surrey.ac.uk/12/2/5.html
  8. 8.
    Yang, C., Kurahashi, S., Kurahashi, K., Ono, I., Terano, T.: Pattern-oriented inverse simulation for analyzing social problems: family strategies in civil service examination in imperial China. Adv. Complex Syst. 15(7), 1250038 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hou, B.N., Yao, Y.P., Wang, B.: Modeling and simulation of large-scale social networks using parallel discrete event simulation. Simulation 89(10), 1173–1183 (2013)CrossRefGoogle Scholar
  10. 10.
    Chen, D., Theodoropoulos, K.G., Turner, T.S., Cai, W.T., Minson, R., Zhang, Y.: Large scale agent-based simulation on the grid. Future Gener. Comput. Syst. 24(7), 658–671 (2008)CrossRefGoogle Scholar
  11. 11.
    Blanchart, E., Cambier, C., Canape, C., et al.: EPIS: a grid platform to ease and optimize multi-agent simulators running. In: Demazeau, Y., Pechoucek, M., Corchado, JM., Bajo, J. (eds.) The 9th international conference on practical applications of agents and multi-agent systems, advances on practical applications of agents and multi-agent systems, pp. 129–134, Salamanca, Spain (2011)Google Scholar
  12. 12.
    Yamamoto, G., Mizuta, H., Tai, H.: A platform for massive agent-based simulation and its evaluation. In: The first international workshop on coordination and control in massively multi-agent systems (2007)Google Scholar
  13. 13.
    Pignotti, J.G., et al.: A semantic grid service for experimentation with an agent-based model of land-use change. J. Artif. Soc. Soc. Simul. 10(2), 2 (2007)Google Scholar
  14. 14.
    Pignotti, E., et al.: A semantic workflow mechanism to realise experimental goals and constraints. In: Proceedings of the 3rd workshop on workflows in support of large-scale science, Works-08, Austin, Texas (2008)Google Scholar
  15. 15.
  16. 16.
    Imade, H., Morishita, R., Ono, I., Ono, N.: A grid-oriented genetic algorithm framework for bioinformatics. New Gener. Comput. 22(2), 177–186 (2004)CrossRefMATHGoogle Scholar
  17. 17.
    Ono, I.: Grid-oriented genetic algorithms for large-scale optimization. J. Soc. Instrum. Control Eng. 47(6), 473–479 (2008)Google Scholar
  18. 18.
    Axelrod, R.: The dissemination of culture: a model with local convergence and global polarization. J. Confl. Resolut. 41(2), 203–226 (1997)CrossRefGoogle Scholar
  19. 19.
    Axelrod, R.: The Complexity of Cooperation. Princeton University, New Jersey (1999)Google Scholar
  20. 20.
    Deffuant, G., Amblard, F., Weisbuch, G., Faure, T.: How can extremism prevail? A study based on the relative agreement interaction model. J. Artif. Soc. Soc. Simul. 5(4) (2002)Google Scholar
  21. 21.
    Huet, S., Edwards, M., Deffuant, G.: Taking into account the variations of neighbourhood sizes in the mean-field approximation of the threshold model on a random network. J. Artif. Soc. Soc. Simul. 10(1) (2007). http://jasss.soc.surrey.ac.uk/10/1/10/10.pdf
  22. 22.
    Kurahashi, S., Minami U., Terano, T.: Why not multiple solutions: agent-based social interaction analysis via inverse simulation. In: IEEE International Conference on System, Man, and Cybernetics, (SMC99), 2048 (1999)Google Scholar
  23. 23.
    Terano, T., Kurahashi, S. Inverse simulation: genetic-algorithm based approach to analyzing emergent phenomena. In: Proceedings of the International Workshop on Emergent Synthesis (IWES’99), pp. 271–276 (1999)Google Scholar
  24. 24.
    Kurahashi, S., Terano, T.: Historical simulation: a study of civil service examinations, family line, and cultural capital in China. In: Proceeding of the 4th Conference of the European Social Simulation Association (ESSA07), pp. 139–150 (2007)Google Scholar
  25. 25.
    Liu, H., Motoda, H.: Computational Methods of Feature Selection. Data Mining and Knowledge Discovery Series. Chapman & Hall/CRC, Baco Raton (2007)MATHGoogle Scholar
  26. 26.
    Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (2004)Google Scholar
  27. 27.
    Sato, H., Ono, I., Kobayashi, S.: A new generation alternation model of genetic algorithms and its assessment. J. Jpn. Soc. Artif. Intell. 12(5), 734–735 (1997)Google Scholar
  28. 28.
    Ono, I., Kita, H., Kobayashi, S.: A real-coded genetic algorithm using the unimodal normal distribution crossover. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computing. Springer, NewYork (2002)Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Chao Yang
    • 1
    • 3
  • Bin Jiang
    • 2
    • 3
  • Isao Ono
    • 2
  • Setsuya Kurahashi
    • 4
  • Takao Terano
    • 3
  1. 1.Business SchoolHunan UniversityChangshaChina
  2. 2.College of Computer Science and Electronic EngineeringHunan UniversityChangshaChina
  3. 3.Department of Computational Intelligence and Systems ScienceTokyo Institute of TechnologyTokyoJapan
  4. 4.Graduate School of Business SciencesUniversity of TsukubaTsukubaJapan

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