Abstract
Consumer’s behavior can be modeled using a utility function that allows for measuring the success of an individual’s decision, which consists of a tuple of goods an individual would like to buy and the hours of work necessary to pay for this purchase and consumption. The success of such a decision is measured by a utility function which incorporates not only the purchase and consumption of goods, but also leisure, which additionally increases the utility of an individual. In this paper, we present a new agent based social simulation in which the decision finding process of consumers is performed by Particle Swarm Optimization (PSO), a well-known swarm intelligence method.
PSO appears to be suitable for the underlying problem as it is based on previous and current information, but also contains a stochastic part which allows for modeling the uncertainty usually involved in the human decision making process. We investigate the adequacy of different bounding strategies that map particles violating the underlying budget constraints to a feasible region. Experiments indicate that one of these bounding strategies is able to achieve very fast and stable convergence for the given optimization problem. However, an even more interesting question refers to adequacy of these bounding strategies for the underlying social simulation task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gilbert, N.: Computer simulation of social processes. Social Research Update (6) (1994)
Kennedy, J.: Swarm intelligence. In: Handbook of Nature-inspired and Innovative Computing, pp. 187–219 (2006)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Holland, J.L.: Adaption in natural and artificial systems. MIT Press (1992)
Schelling, T.C.: Models of segregation. The American Economic Review 59(2), 488–493 (1969)
Deffuant, G., Amblard, F., Weisbuch, G.: How can extremism prevail? a study based on the relative agreement interaction model. Journal of Artificial Societies and Social Simulation 5(4) (2002)
Janssen, M.A., Jager, W.: An integrated approach to simulating behavioural processes: A case study of the lock-in of consumption patterns. Journal of Artificial Societies and Social Simulation 2(2) (1999)
Davidsson, P.: Agent based social simulation: a computer science view. Journal of Artificial Societies and Social Simulation 5 (2002)
Barbosa, M., de Lima Neto, F.: Distributed agent-based social simulations: An architecture to simulate complex social phenomena on highly parallel computational environments. In: IEEE Symposium on Intelligent Agent, pp. 1–8 (2011)
Barbosa Filho, H.S., Lima Neto, F.B., Fusco, W.: Migration, communication and social networks – an agent-based social simulation. In: Menezes, R., Evsukoff, A., González, M.C. (eds.) Complex Networks. SCI, vol. 424, pp. 67–74. Springer, Heidelberg (2013)
Meyer, M., Lorscheid, I., Troitzsch, K.G.: The development of social simulation as reflected in the first ten years of jasss: a citation and co-citation analysis. J. Artificial Societies and Social Simulation 12(4) (2009)
Colander, D., Howitt, P., Kirman, A., Leijonhufvud, A., Mehrling, P.: Beyond DSGE models: Toward an empirically based macroeconomics. The American Economic Review 98(2), 236–240 (2008)
Testfatsion, L.: Agent-based computational economics: Growing economies from the bottom up. Artificial Life 8(1), 55–82 (2002)
Delli-Gatti, D., Desiderio, S., Gaffeo, E., Cirillo, P., Gallegati, M.: Macroeconomics from the bottom-up (2011)
Bonabeau, E., Meyer, C.: Swarm intelligence: a whole new way to think about business. Harvard Business Review (5) (2001)
Nenortaite, J., Butleris, R.: Application of particle swarm optimization algorithm to decision making model incorporating cluster analysis. In: IEEE Conference on Human System Interactions, pp. 88–93 (2008)
Devi, S., Panigrahi, S.K.: Intelligent decision making using particle swarm optimization for optimizing product-mix model. J. Comp. Sc. & Inf. 1 (2011)
Kampouridis, M.: Computational intelligence in financial forecasting and agent-based modeling: Applications of genetic programming and self-organizing maps. Technical report, PhD Thesis. University of Essex (2011)
Xue, Z.: A particle swarm optimization based behavioral and probabilistic fire evacuation model incorporating fire hazards and human behaviors. M.S. Thesis, State University of New York at Buffalo (2006)
Cui, X., Potok, T.: A particle swarm social model for multi-agent based insurgency warfare simulation. In: 5th ACIS International Conference on Software Engineering Research, Management Applications, SERA 2007, pp. 177–183 (2007)
Bettman, J., Johnson, E., Payne, J.: Consumer decision making. In: Handbook of Consumer Behavior, pp. 50–79 (1991)
Meeusen, W., van Den Broeck, J.: Efficiency estimation from cobb-douglas production functions with composed error. Int. Economic Review, 435–444 (1977)
Potter, M.A., De Jong, K.A.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Janecek, A., Jordan, T., de Lima-Neto, F.B. (2013). Agent-Based Social Simulation and PSO. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-38715-9_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38714-2
Online ISBN: 978-3-642-38715-9
eBook Packages: Computer ScienceComputer Science (R0)