Complex Adaptive Systems and Agent-Based Modelling

  • Alexander Tarvid
Part of the SpringerBriefs in Complexity book series (BRIEFSCOMPLEXITY)


In a labour–education market system, there are many individuals and firms with adaptive behaviour. As we have seen in the previous chapter, networks are prevalent in LEMS and play an important role in many decisions of its actors. Thus, LEMS can be analysed as a complex adaptive system (CAS). Agent-based modelling (ABM) is typically used for such purposes, and the next chapter will dig into details of various ways of applying ABM in modelling LEMS. To be ready for it, we first have to understand the motivation behind and the details of this method. This is what will be discussed here.


Cellular Automaton System Dynamic Model Complex Adaptive System Neoclassical Economic Microsimulation Model 
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.


  1. 6.
    Akgün, A.E., Keskin, H., Byrne, J.C., Ilhan, Z.: Complex adaptive system mechanisms, adaptive management practices, and firm product innovativeness. R&D Manag. 44(1), 18–41 (2014). doi: 10.1111/radm.12036 CrossRefGoogle Scholar
  2. 14.
    Axtell, R.L.: Why agents? On the varied motivations for agent computing in the social sciences. Working paper 17, Center on Social and Economic Dynamics, The Brookings Institution (2000)Google Scholar
  3. 16.
    Axtell, R.L.: What economic agents do: how cognition and interaction lead to emergence and complexity. Rev. Austrian Econ. 20(2–3), 105–122 (2007). doi:  10.1007/s11138-007-0021-5 CrossRefGoogle Scholar
  4. 22.
    Bankes, S.C.: Agent-based modeling: a revolution? Proc. Natl. Acad. Sci. 99(Suppl. 3), 7199–7200 (2002). doi:  10.1073/pnas.072081299 CrossRefGoogle Scholar
  5. 25.
    Batty, M.: Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. MIT Press, Cambridge (2005)Google Scholar
  6. 32.
    Beinhocker, E.D.: The Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economics. Harvard Business School Press, Boston (2006)Google Scholar
  7. 35.
    Bianchi, C., Cirillo, P., Gallegati, M., Vagliasindi, P.A.: Validating and calibrating agent-based models: a case study. Comput. Econ. 30(3), 245–264 (2007). doi:  10.1007/s10614-007-9097-z CrossRefGoogle Scholar
  8. 36.
    Birkin, M., Wu, B.: A review of microsimulation and hybrid agent-based approaches. In: Heppenstall, A.J., Crooks, A.T., See, L.M., Batty, M. (eds.) Agent-Based Models of Geographical Systems, pp. 51–68. Springer, Dordrecht (2012). doi:  10.1007/978-90-481-8927-4_3 CrossRefGoogle Scholar
  9. 49.
    Brenner, T., Werker, C.: A taxonomy of inference in simulation models. Comput. Econ. 30(3), 227–244 (2007). doi:  10.1007/s10614-007-9102-6 CrossRefGoogle Scholar
  10. 86.
    Deckert, A., Klein, R.: Agentenbasierte Simulation zur Analyse und Lösung betriebswirtschaftlicher Entscheidungsprobleme. J. Betriebswirt. 60(2), 89–125 (2010). doi:  10.1007/s11301-010-0058-6 CrossRefGoogle Scholar
  11. 101.
    Ellis, B.: An overview of complexity theory: understanding primary care as a complex adaptive system. In: Sturmberg, J.P., Martin, C.M. (eds.) Handbook of Systems and Complexity in Health, pp. 485–494. Springer, New York (2013). doi:  10.1007/978-1-4614-4998-0_29 CrossRefGoogle Scholar
  12. 102.
    Ellis, B., Herbert, S.I.: Complex adaptive systems (cas): an overview of key elements, characteristics and application to management theory. Inform. Prim. Care 19(1), 33–37 (2011)Google Scholar
  13. 103.
    Epstein, J.M., Axtell, R.: Growing Artificial Societies: Social Science from the Bottom Up. The Brookings Institution, Washington (1996)Google Scholar
  14. 106.
    Fagiolo, G., Moneta, A., Windrum, P.: A critical guide to empirical validation of agent-based models in economics: methodologies, procedures, and open problems. Comput. Econ. 30(3), 195–226 (2007). doi:  10.1007/s10614-007-9104-4 CrossRefGoogle Scholar
  15. 109.
    Figari, F., Paulus, A., Sutherland, H.: Microsimulation and policy analysis. In: Atkinson, A.B., Bourguignon, F. (eds.) Handbook of Income Distribution. Handbook of Income Distribution, Chap. 24, vol. 2, pp. 2141–2221. Elsevier, Amsterdam (2015). doi:  10.1016/B978-0-444-59429-7.00025-X Google Scholar
  16. 134.
    Hegselmann, R., Flache, A.: Understanding complex social dynamics: a plea for cellular automata based modelling. J. Artif. Soc. Soc. Simul. 1(3), 1 (1998)Google Scholar
  17. 138.
    Hoff, K., Stiglitz, J.E.: Modern economic theory and development. In: Meier, G.M., Stiglitz, J.E. (eds.) Frontiers of Development Economics: The Future in Perspective, pp. 389–459. Oxford University Press, New York (2001)Google Scholar
  18. 139.
    Holland, J.H.: Studying complex adaptive systems. J. Syst. Sci. Complex. 19(1), 1–8 (2006). doi:  10.1007/s11424-006-0001-z CrossRefGoogle Scholar
  19. 140.
    Holland, J.H., Miller, J.H.: Artificial adaptive agents in economic theory. Am. Econ. Rev. 81(2), 365–370 (1991)Google Scholar
  20. 155.
    Kennedy, M.: A review of system dynamics models of educational policy issues. In: 27th International Conference of the System Dynamics Society 2009, vol. 3, pp. 1661–1683 (2009)Google Scholar
  21. 157.
    Kirman, A.P.: The intrinsic limits of modern economic theory: the emperor has no clothes. Econ. J. 99(395), 126–139 (1989)CrossRefGoogle Scholar
  22. 158.
    Kirman, A.P.: Whom or what does the representative individual represent? J. Econ. Perspect. 6(2), 117–136 (1992)CrossRefGoogle Scholar
  23. 166.
    Leijonhufvud, A.: Limits to the equilibrating capabilities of market systems. J. Econ. Interac. Coord. 4(2), 173–182 (2009). doi:  10.1007/s11403-009-0052-z CrossRefGoogle Scholar
  24. 171.
    Levin, S.A.: Ecosystems and the biosphere as complex adaptive systems. Ecosystems 1(5), 431–436 (1998). doi:  10.1007/s100219900037 CrossRefGoogle Scholar
  25. 172.
    Levin, S., Xepapadeas, T., Crépin, A.S., Norberg, J., de Zeeuw, A., Folke, C., Hughes, T., Arrow, K., Barrett, S., Daily, G., Ehrlich, P., Kautsky, N., Mäler, K.G., Polasky, S., Troell, M., Vincent, J.R., Walker, B.: Social-ecological systems as complex adaptive systems: modeling and policy implications. Environ. Dev. Econ. 18, 111–132 (2013). doi:  10.1017/S1355770X12000460 CrossRefGoogle Scholar
  26. 174.
    Li, J., O’Donoghue, C.: A survey of dynamic microsimulation models: uses, model structure and methodology. Int. J. Microsimul. 6(2), 3–55 (2013)Google Scholar
  27. 176.
    Lis, G.A., Hanson, P., Burgermeister, D., Banfield, B.: Transforming graduate nursing education in the context of complex adaptive systems: implications for master’s and {DNP} curricula. J. Prof. Nurs. 30(6), 456–462 (2014). doi:  10.1016/j.profnurs.2014.05.003 CrossRefGoogle Scholar
  28. 178.
    Lorscheid, I., Meyer, M., Hocke, S.: Simulation model and data analysis: where are we and where should we go? In: ESSA 2013: 9th Conference of the European Social Simulation Association (2013)Google Scholar
  29. 179.
    Luuger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Pearson Education, Boston (2009)Google Scholar
  30. 182.
    Mason, M. (ed.): Complexity Theory and the Philosophy of Education. Wiley, Chichester (2008)Google Scholar
  31. 185.
    McGee, S., Edson, R.: Challenges of governance in complex adaptive systems: a case study of U.S. public education. Procedia Comput. Sci. 36, 131–139 (2014). doi:  10.1016/j.procs.2014.09.049
  32. 203.
    Nikolai, C., Madey, G.: Tools of the trade: a survey of various agent based modeling platforms. J. Artif. Soc. Soc. Simul. 12(2), 2 (2009)Google Scholar
  33. 204.
    North, M.J., Macal, C.M.: Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation. Oxford University Press, Oxford (2007)CrossRefGoogle Scholar
  34. 214.
    Railsback, S.F., Lytinen, S.L., Jackson, S.K.: Agent-based simulation platforms: review and development recommendations. Simulation 82(9), 609–623 (2006). doi:  10.1177/0037549706073695 CrossRefGoogle Scholar
  35. 221.
    Richiardi, M., Leombruni, R., Saam, N., Sonnessa, M.: A common protocol for agent-based social simulation. J. Artif. Soc. Soc. Simul. 9(1), 15 (2006)Google Scholar
  36. 230.
    Sakoda, J.M.: The checkerboard model of social interaction. J. Math. Sociol. 1(1), 119–132 (1971). doi:  10.1080/0022250X.1971.9989791 CrossRefGoogle Scholar
  37. 231.
    Santé, I., García, A.M., Miranda, D., Crecente, R.: Cellular automata models for the simulation of real-world urban processes: a review and analysis. Landsc. Urban Plan. 96(2), 108–122 (2010). doi:  10.1016/j.landurbplan.2010.03.001 CrossRefGoogle Scholar
  38. 233.
    Schelling, T.C.: Dynamic models of segregation. J. Math. Sociol. 1(2), 143–186 (1971). doi: 10.1080/0022250X.1971.9989794 CrossRefGoogle Scholar
  39. 243.
    Simon, H.A.: The Sciences of the Artificial, 3rd edn. The MIT Press, Cambridge (1996)Google Scholar
  40. 254.
    Strauss, L.M., Borenstein, D.: A system dynamics model for long-term planning of the undergraduate education in Brazil. High. Educ. 69(3), 375–397 (2015). doi: 10.1007/s10734-014-9781-6 CrossRefGoogle Scholar
  41. 269.
    The Economist: Agents of change. The Economist. (July 2010)
  42. 275.
    Torrens, P.M., O’Sullivan, D.: Cellular automata and urban simulation: where do we fo from here? Environ. Plan. B 28(2), 163–168 (2001). doi: 10.1068/b2802ed CrossRefGoogle Scholar
  43. 278.
    Uluhan, E., Aydin, M.N.: Complex adaptive systems theory in the context of business process management. In: Zehbold, C. (ed.) S-BPM ONE - Application Studies and Work in Progress. Communications in Computer and Information Science, vol. 422, pp. 147–156. Springer, Cham (2014). doi: 10.1007/978-3-319-06191-7_10 Google Scholar
  44. 298.
    Zheng, H., Son, Y.J., Chiu, Y.C., Head, L., Feng, Y., Xi, H., Kim, S., Hickman, M.: A Primer for Agent-Based Simulation and Modeling in Transportation Applications, Chap. 3 U.S. Department of Transportation (2013)Google Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Alexander Tarvid
    • 1
    • 2
  1. 1.Faculty of Economics and ManagementUniversity of LatviaRigaLatvia
  2. 2.Riga Business SchoolRiga Technical UniversityRigaLatvia

Personalised recommendations