Simulating the Dynamics of Socio-Economic Systems



To the two traditional modes of doing science, in vivo (observation) and in vitro (experimentation), has been added “in silico”: computer simulation. It has become routine in the natural sciences, as well as in systems planning and business process management (Baines et al. 2004; Laguna and Marklund 2013; Paul et al. 1999) to recreate the dynamics of physical systems in computer code. The code is then executed to give outputs that describe how a system evolves from given inputs. Simulation models of simple physical processes, like boiling water or materials rupturing, give precise outputs that reliably match the outcomes of the actual physical system. However, as Winsberg (2010, p. 71) argues, scientists who rely on simulations do so because they “assume as background knowledge that we already know a great deal about how to build good models of the very features of the target system that we are interested in learning about.”


Betweenness Centrality Business Process Management Real World System Network Governance Computerize Thought 
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  1. Abbott, A. (1988). Transcending general linear reality. Sociol Theory, 6(2), 169. doi: 10.2307/202114.CrossRefGoogle Scholar
  2. Alexander, J. C., Giesen, B., Münch, R., & Smelser, N. J. (Eds.). (1987). The micro-macro link. Berkeley, CA: University of California Press.Google Scholar
  3. Anthonisse, J. M. (1971). The rush in a directed graph (Technical report no. BN 9/71). Amsterdam: Stichting Mathematisch Centrum.Google Scholar
  4. Arceneaux, K., Gerber, A. S., & Green, D. P. (2010). A cautionary note on the use of matching to estimate causal effects: An empirical example comparing matching estimates to an experimental benchmark. Sociological Methods and Research, 39(2), 256–282. doi: 10.1177/0049124110378098.CrossRefGoogle Scholar
  5. Baines, T., Mason, S., Siebers, P.-O., & Ladbrook, J. (2004). Humans: The missing link in manufacturing simulation? Simulation Modelling Practice and Theory, 12(7–8), 515–526. doi: 10.1016/S1569-190X(03)00094-7.CrossRefGoogle Scholar
  6. Barabási, A.-L. (2011). The network takeover. Nature Physics, 8(1), 14–16. doi: 10.1038/nphys2188.CrossRefGoogle Scholar
  7. Bharathy, G. K., & Silverman, B. (2013). Holistically evaluating agent-based social systems models: A case study. Simulation, 89(1), 102–135. doi: 10.1177/0037549712446854.CrossRefGoogle Scholar
  8. Borgatti, S. P., & Everett, M. G. (1999). Models of core/periphery structures. Social Networks, 21(4), 375–395. doi: 10.1016/S0378-8733(99)00019-2.CrossRefGoogle Scholar
  9. Burton, R. M., & Obel, B. (1995). The validity of computational models in organization science: From model realism to purpose of the model. Computational and Mathematical Organization Theory, 1(1), 57–71. doi: 10.1007/BF01307828.CrossRefGoogle Scholar
  10. Carley, K. M., Morgan, G., Lanham, M., & Pfeffer, J. (2013). Multi-modeling and socio-cultural complexity: Reuse and validation. In D. D. Schmorrow & D. Nicholson (Eds.), Advances in design for cross-cultural activities (pp. 265–274). Boca Raton, FL: Taylor & Francis.Google Scholar
  11. Centola, D. (2010). The spread of behavior in an online social network experiment. Science, 329(5996), 1194–1197. doi: 10.1126/science.1185231.CrossRefGoogle Scholar
  12. Chattoe-Brown, E. (2012). How do we convince agent-based modeling agnostics? Religion, Brain and Behavior, 2(3), 201–203. doi: 10.1080/2153599X.2012.703448.CrossRefGoogle Scholar
  13. Davis, G. F., Yoo, M., & Baker, W. E. (2003). The small world of the American corporate elite, 1982-2001. Strategic Organization, 1(3), 301–326. doi: 10.1177/14761270030013002.CrossRefGoogle Scholar
  14. Di Paolo, E. A., Noble, J., & Bullock, S. (2000). Simulation models as opaque thought experiments. In M. A. Bedau, J. S. McCaskill, N. H. Packard, & S. Rasmussen (Eds.), Artificial life VII: proceedings of the seventh international conference on the simulation and synthesis of living systems (pp. 497–506). Cambridge, MA: MIT Press.Google Scholar
  15. Dow, M. M., Burton, M. L., & White, D. R. (1982). Network autocorrelation: A simulation study of a foundational problem in regression and survey research. Social Networks, 4(2), 169–200. doi: 10.1016/0378-8733(82)90031-4.CrossRefGoogle Scholar
  16. Epstein, B. (2011). Agent-based modeling and the fallacies of individualism. In P. Humphreys & C. Imbert (Eds.), Models, simulations, and representations (pp. 115–144). New York: Routledge.Google Scholar
  17. Epstein, B. (2015). The ant trap: Rebuilding the foundations of the social sciences. Oxford, UK: Oxford University Press.CrossRefGoogle Scholar
  18. Erikson, E. (2013). Formalist and relationalist theory in social network analysis. Sociol Theory, 31(3), 219–242. doi: 10.1177/0735275113501998.CrossRefGoogle Scholar
  19. Faust, K. (1997). Centrality in affiliation networks. Social Networks, 19(3), 157–191.CrossRefGoogle Scholar
  20. Feder, S. A. (2002). Forecasting for policy making in the post-Cold War period. Annual Review of Political Science, 5(1), 111–125. doi: 10.1146/annurev.polisci.5.102601.115116.CrossRefGoogle Scholar
  21. Forrester, J. W. (1961). Industrial dynamics. Cambridge, MA: MIT Press.Google Scholar
  22. Forrester, J. W. (1969). Urban dynamics. Cambridge, MA: MIT Press.Google Scholar
  23. Forrester, J. W. (1971). World dynamics. Waltham, MA: Pegasus Communications.Google Scholar
  24. Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. doi: 10.2307/3033543.CrossRefGoogle Scholar
  25. Gilbert, G. N. (2008). Agent-based models. Los Angeles, CA: SAGE Publications Ltd..CrossRefGoogle Scholar
  26. Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist (2nd ed.). Maidenhead, UK: Open University Press.Google Scholar
  27. Gordon, D. (1999). Ants at work: How an insect society is organized. New York, NY: Free Press.Google Scholar
  28. Hayes, R., & Hayes, R. (2014). Agent-based simulation of mass shootings: Determining how to limit the scale of a tragedy. Journal of Artificial Societies and Social Simulation, 17(2), 5.CrossRefGoogle Scholar
  29. Hennig, M., Brandes, U., Pfeffer, J., & Mergel, I. (2012). Studying social networks: A guide to empirical research. Frankfurt, Germany: Campus Verlag.Google Scholar
  30. Johnson, S. B. (2001). Emergence: The connected lives of ants, brains, cities, and software. New York, NY: Scribner.Google Scholar
  31. Jucca, L. (2012, March 15). Italy aims to break up clubby bank boards. Milan, Italy: Reuters. Retrieved from
  32. Kamiński, B. (2012, April 29). Animating Schelling’s segregation model [blog post]. R-bloggers.
  33. Kickert, W. J. M., Klijn, E.-H., & Koppenjan, J. F. M. (1997). Introduction. In W. J. M. Kickert, E.-H. Klijn, & J. F. M. Koppenjan (Eds.), Managing complex networks: Strategies for the public sector (pp. 1–13). London, UK: SAGE Publications Ltd..CrossRefGoogle Scholar
  34. Klijn, E.-H., & Teisman, G. R. (1997). Strategies and games in networks. In W. J. M. Kickert, E.-H. Klijn, & J. F. M. Koppenjan (Eds.), Managing complex networks: Strategies for the public sector (pp. 98–118). London, UK: SAGE Publications Ltd..CrossRefGoogle Scholar
  35. Kovacic, A., & Pecek, B. (2007). Use of simulation in a public administration process. Simulation, 83(12), 851–861. doi: 10.1177/0037549707087249.CrossRefGoogle Scholar
  36. Laguna, M., & Marklund, J. (2013). Business process modeling, simulation and design (2nd ed.). Boca Raton, FL: CRC Press.Google Scholar
  37. Lieberman, S. (2012). Extensible software for whole of society modeling: Framework and preliminary results. Simulation, 88(5), 557–564. doi: 10.1177/0037549711404918.CrossRefGoogle Scholar
  38. Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28(1), 143–166. doi: 10.1146/annurev.soc.28.110601.141117.CrossRefGoogle Scholar
  39. Maroulis, S., Bakshy, E., Gomez, L., & Wilensky, U. (2014). Modeling the transition to public school choice. Journal of Artificial Societies and Social Simulation, 17(2), 3.CrossRefGoogle Scholar
  40. Meadows, D. H., Meadows, D. L., & Randers, J. (1972). Limits to growth. New York, NY: Universe Books.Google Scholar
  41. Meadows, D. H., Randers, J., & Meadows, D. L. (2004). Limits to growth: The 30-year update (3rd ed.). White River Junction, VT: Chelsea Green Publishing.Google Scholar
  42. Medina, F. J. L., Quesada, F. J. M., & Lozano, V. A. (2014). The production of step-level public goods in structured social networks: An agent-based simulation. Journal of Artificial Societies and Social Simulation, 17(1), 4.CrossRefGoogle Scholar
  43. Paul, R. J., Giaglis, G. M., & Hlupic, V. (1999). Simulation of business processes. American Behavioral Scientist, 42(10), 1551–1576. doi: 10.1177/0002764299042010006.CrossRefGoogle Scholar
  44. Pfeffer, J., & Carley, K. M. (2013). The importance of local clusters for the diffusion of opinions and beliefs in interpersonal communication networks. International Journal of Innovation and Technology Management, 10(5). doi: 10.1142/S0219877013400221.
  45. Resnick, M. (2001). Turtles, termites, and traffic jams (7th ed.). Cambridge, MA: The MIT Press.Google Scholar
  46. Robins, G. L. (2015). Doing social network research: Network-based research design for social scientists. London, UK: SAGE Publications Ltd..Google Scholar
  47. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York, NY: Free Press.Google Scholar
  48. Rosenbaum, P. R. (2007). Interference between units in randomized experiments. Journal of the American Statistical Association, 102(477), 191–200. doi: 10.1198/016214506000001112.CrossRefGoogle Scholar
  49. Schelling, T. C. (1971). Dynamic models of segregation. The Journal of Mathematical Sociology, 1(2), 143–186. doi: 10.1080/0022250X.1971.9989794.CrossRefGoogle Scholar
  50. Schwaninger, M., Neuhofer, S., & Kittel, B. (2017). Contributions of experimental research to networked governance. In B. Hollstein, W. Matiaske, & K.-U. Schnapp (Eds.), Networked governance. New research perspectives. Heidelberg: Springer.Google Scholar
  51. Scott, J. (2011). Social physics and social networks. In J. Scott & P. J. Carrington (Eds.), The SAGE handbook of social network analysis (pp. 55–66). London, UK: SAGE Publications Ltd..Google Scholar
  52. Shalizi, C. R., & Thomas, A. C. (2011). Homophily and contagion are generically confounded in observational social network studies. Sociological Methods and Research, 40(2), 211–239. doi: 10.1177/0049124111404820.CrossRefGoogle Scholar
  53. Simon, H. A. (1972). Theories of bounded rationality. In C. B. McGuire & R. Radner (Eds.), Decision and organization (pp. 161–176). Amsterdam, Netherlands: North-Holland Publishing Company.Google Scholar
  54. Smith, R. D. (2000). Simulation. In A. Ralston, D. Hemmendinger, & E. D. Reilly (Eds.), Encyclopedia of computer science (4th ed.). New York, NY: Grove’s Dictionaries.Google Scholar
  55. Stachowiak, H. (1973). Allgemeine Modelltheorie. Vienna, Austria: Springer-Verlag.CrossRefGoogle Scholar
  56. Streit, R. E., & Borenstein, D. (2009). An agent-based simulation model for analyzing the governance of the Brazilian financial system. Expert Systems with Applications, 36(9), 11489–11501. doi: 10.1016/j.eswa.2009.03.043.CrossRefGoogle Scholar
  57. Valkering, P., Rotmans, J., Krywkow, J., & van der Veen, A. (2005). Simulating stakeholder support in a policy process: An application to river management. Simulation, 81(10), 701–718. doi: 10.1177/0037549705060793.CrossRefGoogle Scholar
  58. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
  59. Watts, D. J. (2004). The “new” science of networks. Annual Review of Sociology, 30(1), 243–270. doi: 10.1146/annurev.soc.30.020404.104342.CrossRefGoogle Scholar
  60. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393(6684), 440–442. doi: 10.1038/30918.CrossRefGoogle Scholar
  61. Windrum, P., Fagiolo, G., & Moneta, A. (2007). Empirical validation of agent-based models: Alternatives and prospects. Journal of Artificial Societies and Social Simulation, 10(2), 8.Google Scholar
  62. Winsberg, E. B. (2010). Science in the age of computer simulation. Chicago, IL: The University of Chicago Press.CrossRefGoogle Scholar
  63. Zhao, K., Yen, J., Ngamassi, L.-M., Maitland, C., & Tapia, A. H. (2012). Simulating inter-organizational collaboration network: A multi-relational and event-based approach. Simulation, 88(5), 617–633. doi: 10.1177/0037549711421942.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Bavarian School of Public PolicyMünchenGermany
  2. 2.Carnegie Mellon School of Computer SciencePittsburghUSA

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