Abstract
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.
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Notes
swidden farming v.2 can be downloaded from the Computational Modeling Library of CoMSES Net (the Network for Computational Modeling in the Social and Ecological Sciences): http://www.openabm.org/model/3826.
References
Acemoglu, D., Aghion, P., Bursztyn, L., Hemous, D. (2009). The Environment and Directed Technical Change. National Bureau of Economic Research.
Adams, R. M. (2001). Complexity in Archaic States. Journal of Anthropological Archaeology, 20, 345–360. doi:10.1006/jaar.2000.0377.
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.
Altaweel, M.R., Wu, Y. (2010). Route Selection and Pedestrian Traffic: Applying an Integrated Modeling Approach to Understanding Movement. Structure and Dynamics, 4.
Bankes, S. C., Lempert, R., & Popper, S. (2002). Making Computational Social Science Effective: Epistemology, Methodology, and Technology. Social Science Computer Review, 20, 377–388.
Barabasi, A.-L. (2012). The network takeover. Nature Physics, 8, 14–16. doi:10.1038/nphys2188.
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.
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.
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.
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.
Bentley, R. A., & Maschner, H. D. G. (2003). Complex Systems and Archaeology: Empirical and Theoretical Applications. Salt Lake City: University of Utah Press.
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.
Boyd, R., & Richerson, P. J. (1985). Culture and the evolutionary process. Chicago: The University of Chicago Press.
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.
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.
Cowan, G. A., Pines, D., & Meltzer, D. (1994). Complexity: metaphors, models, and reality. Reading, MA: Addison-Wesley.
Cowgill, G. L. (2004). Origins and Development of Urbanism: Archaeological Perspectives. Annual Review of Anthropology, 33, 525–549.
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.
Feinman, G. M. (2011). Size, Complexity, and Organizational Variation: A Comparative Approach. Cross-Cultural Research, 45, 37–58. doi:10.1177/1069397110383658.
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.
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.
Flannery, K. (1986). Guilá Naquitz: Archaic Foraging & Early Agriculture in Oaxaca. New York: Academic Press.
Gaines, S. W., & Gaines, W. M. (1997). Simulating Success or Failure: Another Look at Small-Population Dynamics. American Antiquity, 62, 683–697.
Hegmon, M. (1989). Risk Reduction and Variation in Agricultural Economics: A Computer Simulation of Hopi Agriculture. Research in Economic Anthropology, 11, 89–121.
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.
Holland, J. D. (1992). Genetic algorithms. Scientific American, 267, 44–50.
Holland, J. H. (1996). Hidden order: How adaptation builds complexity. Basic Books.
Holland, J. D. (2000). Emergence: From Chaos to Order. Oxford: Oxford University Press.
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.
Johnson, G. (1982). Organizational structure and scalar stress. Theory and Explanation in Archaeology.
Kohler, T. A., van der Leeuw, S. E. (2007). The model-based archaeology of socionatural systems. School for Advanced Research Press.
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.
Kvamme, K. (2007). Integrating Multiple Geophysical Datasets. Remote Sensing in Archaeology, pp 345–374.
Lansing, J.S. (2003). Complex Adaptive Systems. Annual Review of Anthropology, 32.
Martin, R., & Simmie, J. (2008). Path dependence and local innovation systems in city-regions. Innovation: Management, Policy & Practice, 10, 183–196.
Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: an introduction to computational models of social life. Princeton, N.J.: Princeton University Press.
Mitchell, M. (1998). An introduction to genetic algorithms, 1st MIT Press pbk. Cambridge, Mass: MIT Press.
Mitchell, M. (2006). Complex systems: Network thinking. Artificial Intelligence, 170, 1194–1212. doi:10.1016/j.artint.2006.10.002.
Mitchell, M. (2009). Complexity: A guided tour. USA: Oxford University Press.
Moran, E. F. (1991). The ecosystem approach in anthropology: from concept to practice. University of Michigan Press.
O’Brien, M. J., & Holland, T. D. (1992). The role of adaptation in archaeological explanation. American Antiquity, 57, 36–59.
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.
Peeples, M., Barton, C M., Schmich, S. (2006). Resilience lost: intersecting landuse and landscape dynamics in the upland southwest. Ecology and Society, 12.
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.
Rappaport, R. A. (1971). Pigs for the ancestors: Ritual in the ecology of a New Guinea people. Yale University Press.
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.
Shennan, S. (2002). Genes, Memes, and Human History: Darwinian Archaeology and Human Evolution. London: Thames and Hudson.
Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106, 467–482.
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.
Strogatz, S. H. (2001). Exploring complex networks. Nature, 410, 268–276. doi:10.1038/35065725.
Turchin, P. (2003). Historical dynamics: why states rise and fall. Princeton: Princeton University Press.
Van der Leeuw, S. E. (2004). Why model? Cybernetics and Systems: An International Journal, 35, 117–128. doi:10.1080/01969720490426803.
Van der Leeuw, S. E., & Redman, C. L. (2002). Placing archaeology at the center of socio-natural studies. American Antiquity, 67, 597–605.
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.
Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer-Based Modeling. Northwestern University.
Winterhalder, B., & Smith, E. A. (2000). Analyzing adaptive strategies: human behavioral ecology at twenty-five. Evolutionary Anthropology, 9, 51–72.
Wobst, H. M. (1974). Boundary conditions for paleolithic social systems: a simulation approach. American Antiquity, 39, 147–178.
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Barton, C.M. Complexity, Social Complexity, and Modeling. J Archaeol Method Theory 21, 306–324 (2014). https://doi.org/10.1007/s10816-013-9187-2
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DOI: https://doi.org/10.1007/s10816-013-9187-2