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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Takadama, K., Cioffi-Revilla, C., Deffuant, G.: Simulating Interacting Agents and Social Phenomena. Springer, New York (2010)
Chen, S.-H., Terano, T., Yamamoto, R., Tai, C.C.: Advances in Computational Social Science. Springer, New York (2014)
Gilbert, N.: Agent-Based Models. Thousand Oaks, Sage Publications (2007)
van Dam, K.H., Nikolic, I., Lukszo, Z.: Agent-Based Modelling of Socio-Technical Systems. Springer, New York (2013)
Nakai, Y., Koyama, Y., Terano, T.: Agent-Based Approaches in Economic and Social Complex Systems VIII. Springer, New York (2015)
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)
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
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)
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)
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)
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)
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)
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)
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)
MEME. http://modelexploration.aitia.ai/ (2014)
Imade, H., Morishita, R., Ono, I., Ono, N.: A grid-oriented genetic algorithm framework for bioinformatics. New Gener. Comput. 22(2), 177–186 (2004)
Ono, I.: Grid-oriented genetic algorithms for large-scale optimization. J. Soc. Instrum. Control Eng. 47(6), 473–479 (2008)
Axelrod, R.: The dissemination of culture: a model with local convergence and global polarization. J. Confl. Resolut. 41(2), 203–226 (1997)
Axelrod, R.: The Complexity of Cooperation. Princeton University, New Jersey (1999)
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)
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
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)
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)
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)
Liu, H., Motoda, H.: Computational Methods of Feature Selection. Data Mining and Knowledge Discovery Series. Chapman & Hall/CRC, Baco Raton (2007)
Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (2004)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, C., Jiang, B., Ono, I. et al. A grid based simulation environment for agent-based models with vast parameter spaces. Cluster Comput 19, 183–195 (2016). https://doi.org/10.1007/s10586-015-0500-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-015-0500-6