Advertisement

Networks in Agent-Based Social Simulation

  • Shah Jamal Alam
  • Armando GellerEmail author
Chapter

Abstract

Computational social science and in particular agent-based social simulation continue to gain momentum in the academic community. Social network analysis enjoys even more popularity. They both have much in common. In agent-based models, individual interactions are simulated to generate social patterns of all kinds, including relationships that can then be analyzed by social network analysis. This chapter describes and discusses the role of agent-based modeling in the generative-analytical part of this symbiosis. More precisely, we look at what concepts are used, how they are used (implemented), and what kind of validation procedures can be applied.

Keywords

Social Network Geographic Information System Random Graph Multiagent System Social Network Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Alam, S. J., & Meyer, R. (2010). Comparing two sexual mixing schemes for modelling the spread of HIV/AIDS. In K. Takadama, G. Deffuant, & C. Cioffi-Rivella (Eds.), Proceedings of the second world congress on social simulation (pp. 33–45). Berlin: Springer.Google Scholar
  2. Alam, S. J., Edmonds, B., & Meyer, R. (2009). Identifying structural changes in networks generated from agent-based social simulation models’. In A. Ghose, G. Governatori, & R. Sadananda (Eds.), Agent computing and multi-agent systems (pp. 298–307). Berlin: Springer.Google Scholar
  3. Alam, S. J., Geller, A., Meyer, R., & Werth, B. (2010). Modelling contextualized reasoning in complex societies with endorsements. Journal of Artificial Societies and Social Simulation 13(4), 6. http://jasss.soc.surrey.ac.uk/13/4/6.html
  4. Albert, R., & Barabási, A. (2002). Statistical mechanics of complex networks. Review of Modern Physics, 74(1), 47–97.CrossRefGoogle Scholar
  5. Amblard, F. (2002). Which ties to choose? A survey of social networks models for agent-based social simulations. In Proceedings of the 2002 SCS International Conference on Artificial Intelligence, Simulation and Planning in High Autonomy Systems (pp. 253–258), Lisbon, Portugal, April 2002.Google Scholar
  6. Axelrod, R. (1997). Advancing the art of simulation in the social science. In R. Conte, R. Hegselmann, & P. Terna (Eds.), Simulating social phenomena (pp. 21–40). Berlin: Springer.Google Scholar
  7. Bailey, T. C., & Gatrell, A. C. (1995). Interactive spatial data analysis. Burnt Mill, Essex: Longman Scientific & Technical.Google Scholar
  8. Barreteau, O., Bousquet F., & Attonaty J. M. (2001). Role-playing games for opening the black box of multi-agent systems: Method and lessons of its application to Senegal River Valley irrigated systems. Journal of Artificial Societies and Social Simulation, 4(2). http://jasss.soc.surrey.ac.uk/4/2/5.html
  9. Barreteau, O. et al. (2003). Our companion modelling approach. Journal of Artificial Societies and Social Simulation, 6(2). http://jasss.soc.surrey.ac.uk/6/2/1.html
  10. Barthélémy, O. T. (2006). Untangling scenario components with agent based modelling: An example of social simulations of water demand forecasts. Unpublished PhD thesis, Centre for Policy Modelling, Manchester Metropolitan University, Manchester.Google Scholar
  11. Bearman, P. S., Moody, J., & Stovel, K. (2004). Chains of affection: The structure of adolescent romantic and sexual networks. The American Journal of Sociology, 110(1), 44–91.CrossRefGoogle Scholar
  12. Becu, N., Perez, P., Walker, A., Barreteau, O., & Le Page, C. (2003). Agent based simulation of a small catchment water management in northern Thailand: Description of the CATCHSCAPE model. Ecological Modelling, 170(2–3), 319–331.CrossRefGoogle Scholar
  13. Boer, P., Huisman, M., Snijders, T. A. B., Steglich, C. H., Wichers, L. H. Y., & Zeggelink, E. P. H. (2006). StOCNET: An open software system for the advanced statistical analysis of social networks, version 1.7. Groningen: ICS SciencePlus.Google Scholar
  14. Borgatti, S., Everett, M., & Freeman, L. (2004). UCINET: Software for social network analysis. analytictech.com/ucinet
  15. Borgatti, S. P., Carley, K., & Krackhardt, D. (2006). On the robustness of centrality measures under conditions of imperfect data. Social Networks, 28(2), 124–136.CrossRefGoogle Scholar
  16. Boudon, R. (1998). Social mechanisms without black boxes. In P. Hedström & R. Swedberg (Eds.), Social mechanisms: An analytical approach to social theory. Cambridge: Cambridge University Press.Google Scholar
  17. Brandes, U., Eiglsperger, M., Kaufmann, M., & Lerner, J. (2004). The GraphML file format. graphml.graphdrawing.org
  18. Brown, D. G. (2006). Agent-based models. In H. Geist (Ed.), The earth’s changing land: An encyclopedia of land-use and land-cover change (pp. 7–13). Westport: Greenwood Publishing Group.Google Scholar
  19. Bruch, E. E., & Mare, R. D. (2006). Neighborhood choice and neighborhood change. The American Journal of Sociology, 112(4), 667–709.CrossRefGoogle Scholar
  20. Carley, K. (2003). Dynamic network analysis. In R. Breiger, K. Carley, & P. Pattison (Eds.), Dynamic social network modeling and analysis: Workshop summary and papers. Washington, DC: The National Academies Press.Google Scholar
  21. Carley, K. M., Columbus, D., Reno, M., Reminga, J., & Moon, I.-C. (2007). ORA user‘s guide 2007 (Technical Report, CMU-ISRI-07-115). Carnegie Mellon University, School of Computer Science, Institute for Software Research.Google Scholar
  22. Cederman, L.-E. (2001). Agent-based modeling in political science. The Political Methodologist, 10(1), 16–22.Google Scholar
  23. Cioffi-Revilla, C., & Osman, H. (2009). A theoretical model of individual radicalization: Understanding complexity in terrorism and insurgency’. Poster prepared for the 2009 annual European Social Simulation Association conference, University of Surry, Guildford.Google Scholar
  24. Crooks, A. T. (2010). Constructing and implementing an agent-based model of residential segregation through vector GIS. International Journal of Geographical Information Science, 24(5), 661–675.CrossRefGoogle Scholar
  25. Crooks, A. T., & Castle, C. (2012). The integration of agent-based modelling and geographical information for geospatial simulation. In A. J. Heppenstall, A. T. Crooks, L. M. See & M. Batty (Eds.), Agent-based models of geographical systems (pp. 219–252). Dordrecht: Springer.Google Scholar
  26. Crooks, A. T., Castle, C. J. E., & Batty, M. (2008). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6), 417–430.CrossRefGoogle Scholar
  27. De Nooy, W., Mrvar, A., & Batagelj, V. (2005). Exploratory social network analysis with Pajek. Cambridge: Cambridge University Press.Google Scholar
  28. Degenne, A., & Forsé, M. (1999). Introducing social networks (A. Borgess, Trans.). London: Sage.Google Scholar
  29. DiMaggio, P. (1997). Culture and cognition. Annual Review of Sociology, 23, 263–287.CrossRefGoogle Scholar
  30. Dunham, J. B. (2005). An agent-based spatially explicit epidemiological model in MASON’. Journal of Artificial Societies and Social Simulation, 9(1). http://jasss.soc.surrey.ac.uk/9/1/3.html
  31. Edmonds, B. (1999). Capturing social embeddedness: A constructivist approach, adaptive behavior (Vol. 7, pp. 323–348). London: Sage.Google Scholar
  32. Edmonds, B. (2006). How are physical and social spaces related? – Cognitive agents as the necessary “glue”. In F. C. Billari, T. Fent, A. Prskawetz, & J. Scheffran (Eds.), Agent-based computational modelling: Applications in demography, social, economic and environmental sciences (pp. 195–214). Berlin: Springer.Google Scholar
  33. Edmonds, B., & Chattoe, E. (2005). When simple measures fail: Characterising social networks using simulation. In Social Network Analysis: Advances and Empirical Applications Forum, Oxford. <http://cfpm.org/cpmrep158.html>
  34. Erdös, P., & Rényi, A. (1959). On random graphs I. Publicationes Mathematicae, 6, 290–297.Google Scholar
  35. Eubank, S., Guclu, H., Kumar, A., Marathe, M. V., Srinivasan, A., Toroczkai, Z., & Wang, N. (2004). Modelling disease outbreaks in realistic urban social networks. Nature, 429, 180–184.CrossRefGoogle Scholar
  36. Ferber, J. (1999). Multi-agent systems: An introduction to distributed artificial intelligence. London: Addison-Wesley.Google Scholar
  37. Flache, A., & Hegselmann, R. (2001). Do irregular grids make a difference? Relaxing the spatial regularity assumption in cellular models of social dynamics. Journal of Artificial Societies and Social Simulation, 4(4). http://jasss.soc.surrey.ac.uk/4/4/6/html
  38. Fortunato, S. (2009). Community detection in graphs, Physics Reports. http://dx.doi.org/10.1016/j.physrep.2009.11.002
  39. Geller, A., & Moss, S. (2008). Growing qawm: An evidence-driven declarative model of Afghan power structures. Advances in Complex Systems, 11(2), 321–335.CrossRefGoogle Scholar
  40. Granovetter, M. S. (1973). The strength of weak ties. The American Journal of Sociology, 78(6), 1360–1380.CrossRefGoogle Scholar
  41. Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. The American Journal of Sociology, 91(3), 481–510.CrossRefGoogle Scholar
  42. Hales, D., & Arteconi, S. (2008). Motifs in evolving cooperative networks look like protein structure networks. Networks and Heterogeneous Media, 3(2), 239–249.CrossRefGoogle Scholar
  43. Hedström, P. (2005). Dissecting the social: On the principles of analytical sociology. Cambridge: University of Cambridge Press.CrossRefGoogle Scholar
  44. Hoffer, L. (2006). Junkie business: The evolution and operation of a heroin dealing network. Belmont: Thomson Wadsworth Publishing.Google Scholar
  45. Huang, C.-Y., Sun, C.-T., Hsiehm J.-L., & Lin, H. (2004). Simulating SARS: Small-world epidemiological modeling and public health policy assessments. Journal of Artificial Societies and Social Simulation, 7(4). http://jasss.soc.surrey.ac.uk/7/4/2.html
  46. Huang, C.-Y., Tsai, Y.-S., & Wen, T.-H. (2010). Network-based simulation architecture for studying epidemic dynamics. Simulation, 86(5-6), 351–368. (May 2010) doi=10.1177/0037549709340733http://dx.doi.org/10.1177/0037549709340733. CrossRefGoogle Scholar
  47. Jensen, H. (1998). Self-organized criticality: Emergent complex behavior in physical and biological systems. Cambridge: University of Cambridge Press.Google Scholar
  48. Jin, E. M., Girvan, M., & Newman, M. E. J. (2001). Structure of growing social networks. Physical Review E, 64(4).Google Scholar
  49. Krebs, F., Elbers, M., & Ernst, A. (2007). Modelling social and economic influences on the decision making of farmers in the Odra Region. In F. Amblard (Ed.), Proceeding of the 2007 Annual European Social Simulation Association, Toulouse.Google Scholar
  50. Leskovec, J., Kleinberg, J., & Faloutsos, C. (2005). Graphs over time: Densification laws, shrinking diameters and possible explanations. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, Chicago.Google Scholar
  51. Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K., & Balan, G. (2005). MASON: A multi-agent simulation environment. Simulation, 81(7), 517–527.CrossRefGoogle Scholar
  52. Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G., & Gotts, N. M. (2007). Agent-based land-use models: A review of applications. Landscape Ecology, 22, 1447–1459.CrossRefGoogle Scholar
  53. McCulloh, I. (2009). Detecting changes in a dynamic social network. Unpublished PhD thesis, Carnegie Mellon University, Pittsburgh.Google Scholar
  54. Milo, R., Shen-Orr, S., Itzkovitz, S., et al. (2002). Network motifs: Simple building blocks of complex networks. Science, 298, 824–827.CrossRefGoogle Scholar
  55. Minar N., Burkhart R., Langton C., & Askenazi M. (1996). The Swarm simulation system: a toolkit for building multiagent simulations (Working paper 96-06-042). Santa Fe: Santa Fe Institute.Google Scholar
  56. Mitchell, C. (1989). Ethnography and network analysis. In T. Schweizer (Ed.), Netzwerkanalyse: Ethnologische Perspektiven. Berlin: Reimer.Google Scholar
  57. Moss, S. (2002). Policy analysis from first principles. Proceedings of the National Academy of Sciences, 99(suppl. 3), 7267–7274.CrossRefGoogle Scholar
  58. Moss, S. (2008). Simplicity, generality and truth in social modelling part 1: Epistemology. In T. Takama, G. Deffuant, & C. Cioffi-Rivella (Eds.), Proceedings of the second world congress on social simulation. Berlin: Springer.Google Scholar
  59. Moss, S., & Edmonds, B. (2005). Sociology and simulation: Statistical and qualitative cross-validation. The American Journal of Sociology, 110(4), 1095–1131.CrossRefGoogle Scholar
  60. Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks, 328(5980), 876–878. doi=10.1126/science.1184819 http://arxiv.org/abs/0911.1824
  61. Newman, M. E. J. (2004). The structure and function of complex networks. SIAM Review, 45, 167–256.CrossRefGoogle Scholar
  62. Nikolai, C., & Madey, G. (2009). Tools of the trade: A survey of various agent-based modeling platforms. Journal of Artificial Societies and Social Simulation, 12(2). http://jasss.soc.surrey.ac.uk/12/2/2.html http://www.sciencemag.org/content/328/5980/876
  63. North, M. J., & Macal, C. M. (2007). Managing business complexity: Discovering strategic solutions with agent-based modelling and simulation. New York: Oxford University Press.Google Scholar
  64. O’Madadhain, J., Fisher, D., Smyth, P., White, S., & Boey, Y.-B. (2005). Analysis and visualization of network data using JUNG. Journal of Statistical Software, VV(II), 1–35.Google Scholar
  65. Pahl-Wostl, C., & Hare, M. (2004). Processes of social learning in integrated resources management. Journal of Community & Applied Social Psychology, 14(3), 193–206.CrossRefGoogle Scholar
  66. Parker, D. C. (2005). Integration of geographic information systems and agent-based models of land use: Challenges and prospects. In D. J. Maguire, M. F. Goodchild, & M. Batty (Eds.), GIS, Spatial analysis and modeling (pp. 403–422). Redlands: ESRI Press.Google Scholar
  67. Polhill, J. G., Parker, D. C., & Gotts, N. M. (2008). Effects of land markets on competition between imitators and innovators: Results from FEARLUS-ELMM. In B. Edmonds, C. Hernandez, & K. Troitzsch (Eds.), Social simulation: Technologies, advances and new discoveries (pp. 81–97). Hershey: GI Global.Google Scholar
  68. Pujol, J. M., Flache, A., Delgado, J., & Sangüesa, R. (2005). How can social networks ever become complex? Modelling the emergence of complex networks from local social exchanges’. Journal of Artificial Societies and Social Simulation, 8(4). http://jasss.soc.surrey.ac.uk/8/4/12.html
  69. Rakowski, F., Gruziel, M., Krych, M., & Radomski, J. P. (2010). Large scale daily contacts and mobility model – An individual-based countrywide simulation study for Poland. Journal of Artificial Societies and Social Simulation, 13(1), 13. http://jasss.soc.surrey.ac.uk/13/1/13.html
  70. Schelling, T. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 143–186.CrossRefGoogle Scholar
  71. Schensul, J. J., LeCompte, M., Trotter, P., & Singer, M. (1999). Mapping social networks, spatial data & hidden populations. Thousand Oaks: Sage.Google Scholar
  72. Simon, H. (1955). On a class of skew distribution functions. Biometrika, 42(3–4), 425–440.Google Scholar
  73. Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32(1), 44–60.CrossRefGoogle Scholar
  74. Stroud, P., Del Valle, S., Sydoriak, S., Riese, J., & Mniszewski, S. (2007). Spatial dynamics of pandemic influenza in a massive artificial society. Journal of Artificial Societies and Social Simulation, 10(4). http://jasss.soc.surrey.ac.uk/10/4/9.html
  75. Tsvetovat, M. (2005). Social structure simulation and inference using artificial intelligence techniques. Unpublished PhD thesis, Carnegie Mellon University, Pittsburgh.Google Scholar
  76. Wasserman, S., & Faust, K. (1994). Social network analysis: methods and applications. Cambridge: Cambridge University Press.Google Scholar
  77. Watts, D. J. (1999). Networks, dynamics, and the small-world phenomenon. The American Journal of Sociology, 13(2), 493–527.CrossRefGoogle Scholar
  78. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440–442.CrossRefGoogle Scholar
  79. Werth, B., & Moss, S. (2007). Modelling migration in the Sahel: An alternative to cost-benefit analysis. In S. Takahashi, D. Sallach, & J. Rouchier (Eds.), Advancing social simulation: The first world congress (pp. 331–342). Berlin: Springer.Google Scholar
  80. Wilensky, U., & Rand, W. (in press). An introduction to agent-based modeling: Modeling natural, social and engineered complex systems with NetLogo. Cambridge, MA: MIT Press.Google Scholar
  81. Yang, Y., & Atkinson, P. M. (2008). Individual space – Time activity-based model: A model for the simulation of airborne infectious-disease transmission by activity-bundle simulation. Environment and Planning B, 35(1), 80–99.CrossRefGoogle Scholar
  82. Ziervogel, G., Bharwani, S., & Downing, T. E. (2006). Adapting to climate variability: Pumpkins, people and policy. Natural Resource Forum, 30(4), 294–305.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.School of Public HealthUniversity of MichiganAnn ArborUSA
  2. 2.Krasnow Institute for Advanced StudyGeorge Mason UniversityFairfaxUSA

Personalised recommendations