Journal of Archaeological Method and Theory

, Volume 21, Issue 2, pp 306–324 | Cite as

Complexity, Social Complexity, and Modeling

  • C. Michael BartonEmail author


Social complexity has long been a subject of considerable interest and study among archaeologists; it is generally taken to refer to human societies consisting of large numbers of people, many social and economic roles, large permanent settlements, along with a variety of other marker criteria. When viewed from a more general complex systems perspective, however, all human societies are complex systems regardless of size or organizational structure. Complex adaptive systems (CAS) represent systems which are dynamic in space, time, organization, and membership and which are characterized by information transmission and processing that allow them to adjust to changing external and internal conditions. Complex systems approaches offer the potential for new insights into processes of social change, linkages between the actions of individual human agents and societal-level characteristics, interactions between societies and their environment, and allometric relationships between size and organizational complexity. While complex systems approaches have not yet coalesced into a comprehensive theoretical framework, they have identified important isomorphic properties of organization and behavior across diverse phenomena. However, it is difficult to operationalize complex systems concepts in archaeology using the descriptive/confirmatory statistics that dominate quantitative aspects of modern archaeological practice. These are not designed to deal with complex interactions and multilevel feedbacks that vary across space and time. Nor do narratives that simply state that societies are characterized by interacting agent/actors who share cultural knowledge, and whose interacting practices create emergent social-level phenomena add much to our understanding. New analytical tools are needed to make effective use of the conceptual tools of complex systems approaches to human social dynamics. Computational and systems dynamics modeling offer the first generation of such analytical protocols especially oriented towards the systematic study of CAS. A computational model of small-scale society with subsistence agriculture is used to illustrate the complexity of even “simple” societies and the potential for new modeling methods to assist archaeologists in their study.


Complexity Complex adaptive systems Computational model Subsistence agriculture Emergence Adaptation Tipping points 


  1. Acemoglu, D., Aghion, P., Bursztyn, L., Hemous, D. (2009). The Environment and Directed Technical Change. National Bureau of Economic Research.Google Scholar
  2. Adams, R. M. (2001). Complexity in Archaic States. Journal of Anthropological Archaeology, 20, 345–360. doi: 10.1006/jaar.2000.0377.CrossRefGoogle Scholar
  3. Aktipis, C. A. (2006). Recognition Memory and the Evolution of Cooperation: How Simple Strategies Succeed in an Agent-Based World. Adaptive Behavior, 14, 239–247. doi: 10.1177/105971230601400301.CrossRefGoogle Scholar
  4. Altaweel, M.R., Wu, Y. (2010). Route Selection and Pedestrian Traffic: Applying an Integrated Modeling Approach to Understanding Movement. Structure and Dynamics, 4.Google Scholar
  5. Bankes, S. C., Lempert, R., & Popper, S. (2002). Making Computational Social Science Effective: Epistemology, Methodology, and Technology. Social Science Computer Review, 20, 377–388.CrossRefGoogle Scholar
  6. Barabasi, A.-L. (2012). The network takeover. Nature Physics, 8, 14–16. doi: 10.1038/nphys2188.CrossRefGoogle Scholar
  7. Barton, C. M. (2013). Stories of the past or science of the future? archaeology and computational social science. In A. Bevan & M. W. Lake (Eds.), Computational Approaches to Archaeological Spaces (pp. 151–178). Walnut Creek: Left Coast Press. in press.Google Scholar
  8. Barton, C. M., & Riel-Salvatore, J. (2012). Agents of change: modeling biocultural evolution in Upper Pleistocene western Eurasia. Advances in Complex Systems, 15, 1150003–1–1150003–24. doi: 10.1142/S0219525911003359.CrossRefGoogle Scholar
  9. Barton, C. M., Ullah, I. I. T., & Mitasova, H. (2010). Computational modeling and Neolithic socioecological dynamics: a case study from southwest Asia. American Antiquity, 75, 364–386.CrossRefGoogle Scholar
  10. Barton, C. M., Ullah, I. I. T., Bergin, S. M., et al. (2012). Looking for the future in the past: long-term change in socioecological systems. Ecological Modelling, 241, 42–53. doi: 10.1016/j.ecolmodel.2012.02.010.CrossRefGoogle Scholar
  11. Bentley, R. A., & Maschner, H. D. G. (2003). Complex Systems and Archaeology: Empirical and Theoretical Applications. Salt Lake City: University of Utah Press.Google Scholar
  12. Bernabeu Auban, J., Moreno Martín, A., & Barton, C. M. (2012). Complex systems, social networks and the evolution of social complexity. In M. Berrocal, L. García Sanjuán, & A. Gilman (Eds.), The Prehistory of Iberia: Debating Early Social Stratification and the State (pp. 23–37). New York: Routledge.Google Scholar
  13. Boyd, R., & Richerson, P. J. (1985). Culture and the evolutionary process. Chicago: The University of Chicago Press.Google Scholar
  14. Carballo, D.M, Roscoe, P., Feinman, G.M. (2013). Cooperation and Collective Action in the Cultural Evolution of Complex Societies. Journal of Archaeological Method and Theory. in press, online: 1–36. doi:  10.1007/s10816-012-9147-2.
  15. Chase, A. F., Chase, D. Z., Fisher, C. T., et al. (2012). Geospatial revolution and remote sensing LiDAR in Mesoamerican archaeology. Proceedings of the National Academy of Sciences, 109, 12916–12921. doi: 10.1073/pnas.1205198109.CrossRefGoogle Scholar
  16. Cowan, G. A., Pines, D., & Meltzer, D. (1994). Complexity: metaphors, models, and reality. Reading, MA: Addison-Wesley.Google Scholar
  17. Cowgill, G. L. (2004). Origins and Development of Urbanism: Archaeological Perspectives. Annual Review of Anthropology, 33, 525–549.CrossRefGoogle Scholar
  18. Feinman, G. M. (1998). Scale and social organization: Perspectives on the archaic state. In G. M. Feinman & J. Marcus (Eds.), Archaic states (pp. 95–133). Santa Fe, NM: School of American Research Press.Google Scholar
  19. Feinman, G. M. (2011). Size, Complexity, and Organizational Variation: A Comparative Approach. Cross-Cultural Research, 45, 37–58. doi: 10.1177/1069397110383658.CrossRefGoogle Scholar
  20. Feinman, G. M. (2013). The Emergence of Social Complexity: Why More than Population Size Matters. In D. M. Carballo (Ed.), Cooperation & Collective Action: Archaeological Perspectives (pp. 35–56). Boulder: University Press of Colorado.Google Scholar
  21. Fewell, J. H., Schmidt, S., & Taylor, T. (2009). Division of labor in the context of complexity. In J. Gadau & J. H. Fewell (Eds.), Organization of Insect Societies: From Genome to Sociocomplexity (pp. 584–610). Cambridge: Harvard University Press.Google Scholar
  22. Flannery, K. (1986). Guilá Naquitz: Archaic Foraging & Early Agriculture in Oaxaca. New York: Academic Press.Google Scholar
  23. Gaines, S. W., & Gaines, W. M. (1997). Simulating Success or Failure: Another Look at Small-Population Dynamics. American Antiquity, 62, 683–697.CrossRefGoogle Scholar
  24. Hegmon, M. (1989). Risk Reduction and Variation in Agricultural Economics: A Computer Simulation of Hopi Agriculture. Research in Economic Anthropology, 11, 89–121.Google Scholar
  25. Henrickson, L., & McKelvey, B. (2002). Foundations of “new” social science: Institutional legitimacy from philosophy, complexity science, postmodernism, and agent-based modeling. Proceedings of the National Academy of Sciences, 99, 7288–7295.CrossRefGoogle Scholar
  26. Holland, J. D. (1992). Genetic algorithms. Scientific American, 267, 44–50.CrossRefGoogle Scholar
  27. Holland, J. H. (1996). Hidden order: How adaptation builds complexity. Basic Books.Google Scholar
  28. Holland, J. D. (2000). Emergence: From Chaos to Order. Oxford: Oxford University Press.Google Scholar
  29. Hooper, P. L., Kaplan, H. S., & Boone, J. L. (2010). A theory of leadership in human cooperative groups. Journal of Theoretical Biology, 265, 633–646. doi: 10.1016/j.jtbi.2010.05.034.CrossRefGoogle Scholar
  30. Johnson, G. (1982). Organizational structure and scalar stress. Theory and Explanation in Archaeology.Google Scholar
  31. Kohler, T. A., van der Leeuw, S. E. (2007). The model-based archaeology of socionatural systems. School for Advanced Research Press.Google Scholar
  32. Kohler, T. A., & Varian, M. D. (2010). A Scale Model of Seven Hundred Years of Farming Settlements in Southwestern Colorado. In M. S. Bandy & J. R. Fox (Eds.), Becoming Villagers: Comparing Early Village Societies (pp. 37–61). Tucson: University of Arizona Press.Google Scholar
  33. Kvamme, K. (2007). Integrating Multiple Geophysical Datasets. Remote Sensing in Archaeology, pp 345–374.Google Scholar
  34. Lansing, J.S. (2003). Complex Adaptive Systems. Annual Review of Anthropology, 32.Google Scholar
  35. Martin, R., & Simmie, J. (2008). Path dependence and local innovation systems in city-regions. Innovation: Management, Policy & Practice, 10, 183–196.Google Scholar
  36. Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: an introduction to computational models of social life. Princeton, N.J.: Princeton University Press.Google Scholar
  37. Mitchell, M. (1998). An introduction to genetic algorithms, 1st MIT Press pbk. Cambridge, Mass: MIT Press.Google Scholar
  38. Mitchell, M. (2006). Complex systems: Network thinking. Artificial Intelligence, 170, 1194–1212. doi: 10.1016/j.artint.2006.10.002.CrossRefGoogle Scholar
  39. Mitchell, M. (2009). Complexity: A guided tour. USA: Oxford University Press.Google Scholar
  40. Moran, E. F. (1991). The ecosystem approach in anthropology: from concept to practice. University of Michigan Press.Google Scholar
  41. O’Brien, M. J., & Holland, T. D. (1992). The role of adaptation in archaeological explanation. American Antiquity, 57, 36–59.CrossRefGoogle Scholar
  42. Park, T. K. (1997). Indirass and the political ecology of flood recession agriculture. In A. E. Nyerges (Ed.), The Ecology of Practice: Studies of Food Crop Production in Sub-Saharan West Africa (pp. 77–95). Amsterdam: Gorden and Breach.Google Scholar
  43. Peeples, M., Barton, C M., Schmich, S. (2006). Resilience lost: intersecting landuse and landscape dynamics in the upland southwest. Ecology and Society, 12.Google Scholar
  44. Peter, I. S., & Davidson, E. H. (2009). Modularity and design principles in the sea urchin embryo gene regulatory network. FEBS Letters, 583, 3948–3958. doi: 10.1016/j.febslet.2009.11.060.CrossRefGoogle Scholar
  45. Rappaport, R. A. (1971). Pigs for the ancestors: Ritual in the ecology of a New Guinea people. Yale University Press.Google Scholar
  46. Shennan, S. (2001). Demography and Cultural Innovation: A Model and Its Implications for the Emergence of Modern Human Culture. Cambridge Archaeological Journal, 11, 5–16. doi: 10.1017/S0959774301000014.CrossRefGoogle Scholar
  47. Shennan, S. (2002). Genes, Memes, and Human History: Darwinian Archaeology and Human Evolution. London: Thames and Hudson.Google Scholar
  48. Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106, 467–482.Google Scholar
  49. Smith, M. E. (2009). V. Gordon Childe and the urban revolution: an historical perspective on a revolution in urban studies. Town Planning Review, 80, 2–29.CrossRefGoogle Scholar
  50. Strogatz, S. H. (2001). Exploring complex networks. Nature, 410, 268–276. doi: 10.1038/35065725.CrossRefGoogle Scholar
  51. Turchin, P. (2003). Historical dynamics: why states rise and fall. Princeton: Princeton University Press.Google Scholar
  52. Van der Leeuw, S. E. (2004). Why model? Cybernetics and Systems: An International Journal, 35, 117–128. doi: 10.1080/01969720490426803.CrossRefGoogle Scholar
  53. Van der Leeuw, S. E., & Redman, C. L. (2002). Placing archaeology at the center of socio-natural studies. American Antiquity, 67, 597–605.CrossRefGoogle Scholar
  54. Wandsnider, L. (1992). The spatial dimension of time. In J. Rossignol & L. Wandsnider (Eds.), Space, Time, and Archaeological Landscapes (pp. 257–282). New York: Plenum.CrossRefGoogle Scholar
  55. Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer-Based Modeling. Northwestern University.Google Scholar
  56. Winterhalder, B., & Smith, E. A. (2000). Analyzing adaptive strategies: human behavioral ecology at twenty-five. Evolutionary Anthropology, 9, 51–72.CrossRefGoogle Scholar
  57. Wobst, H. M. (1974). Boundary conditions for paleolithic social systems: a simulation approach. American Antiquity, 39, 147–178.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Center for Social Dynamics & Complexity, School of Human Evolution & Social ChangeArizona State UniversityTempeUSA

Personalised recommendations