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Juan A. Barcelo
Departament de Prehistòria, Universitat Autonoma de Barcelona
Archaeologists and historians have begun to convert social theories to computer programs, intending to simulate social processes and historical trajectories of known societies. We can mention two kinds of approaches: implementing a social theory of human action in the past to test the internal coherence of such a theory or explaining historical/archaeological data with some hypothesis from ethnographic research.
A good example of the first kind of historical simulation was Jim Doran's EOS system (Doran and Palmer 1995). Doran and colleagues explored a computational interpretation of growth of social complexity in the Upper Paleolithic period (around the time of the last glacial maximum) relating changing features of the natural environment to the emergence in the prehistoric past of centralized decision making, hierarchy and related social phenomena. This application opened a new way of doing historical research, and its long-term consequences merit still a detailed examination.
More or less in the same years, Doran and collaborators built the very first artificial prehistoric societies, Epstein and Axtell associated with archaeologists built the Virtual Anasazi artificial society, whose objective was explaining historical/archaeological data with some hypothesis from ethnographic research. The book edited by Timothy Kohler and Mark D. Varien takes this second approach, and it is an impressive improvement on any early effort.
The book is an example of agent-based modeling designed to investigate where prehistoric people of the American Southwest would have situated their households based on both the natural and social environments in which they lived. The idea has been to define nuclear families (households, the smallest social unit consistently definable in the archaeological record) as agents, and loosed them on landscapes, which have been archaeologically studied for different historical periods, and plenty of paleo-productivity data exist. The model is used to predict individual household responses to changes in agricultural productivity in annual increments based on reconstructions of yearly climatic conditions, as well as long-term hydrologic trends, cycles of erosion and deposition, and demographic change. The simulation imitates the target data by computing the individual agents' behavior in response to some input environmental data, by computing the effects of the individual behaviors on the environment, and by computing the repercussions these environmental effects have on individual agents. The performance of the model is evaluated against actual population, settlement, and organizational parameters. By manipulating numbers and attributes of households, climate patterns, and other environmental variables, it is possible to evaluate the roles of these factors in prehistoric culture change. Here the household is a theoretical construct, but it moves on a historically defined environment, which is the most precise available archaeological data allow.
Written by 16 authors, all of them collaborators of a research team that has been working on the subject for more than 12 years, the book illustrates in detail how paleo-environmental data have been investigated, how the climatic processes can be reconstructed and the productivity of landscape estimated in order to understand evolving social actions along a historical trajectory of changes and transformations, both technological, economic, social, cultural and political. The authors have also tried to include the natural production and human degradation of what they consider Critical Natural Resources into the agent-based simulation modeling of household settlement patterns. Archaeological data show how agricultural yields varied greatly from year to year, and the simulated model suggests how prehistoric farmers would have needed to adapt mechanisms to reduce their uncertainty of future yields. One such mechanism thought to be important is reciprocity between households. After a reasonable model of agent planting, authors explain how agents may be endowed with balanced reciprocity behaviors and adaptive encodings of exchange, placing the households into a social and an economic network or other (related and unrelated) households. This model is flexible enough to evolve according to agent interactions and changes in the world environment. By demonstrating the ease with which populations could have depleted natural resources in this environment, the simulation builds a context in which changes in farm land, agriculture, craft production, architecture, frequency of axes, and so forth, can be more plausibly interpreted.
More than the particular results of this prehistoric research, the book shows what does it mean to simulate historical processes. By implementing social events as computational agents and their mutual influences as interactions, an "historical" simulation assumes that collective action is accentuated by continuous transitions and transformations between subjects. The explanatory model also takes into account needs, motivations, goals, behavior, signs, tools, rules, community, division of labor, and the embedded hierarchical levels of collective motivation-driven activity and individual goal-driven action. The advantages of such an approach are obvious. Historical agents are not static entities, with a precise position, nor a fixed impulse. They have always different possibilities for action, according to the characteristics of the context and circumstances in which the action or actions takes place. Social action is the joint result of the modalities of the action, the other social agents who act in their spatiotemporal neighborhood, the forms of collaboration or lack of collaboration between social agents, the power relations which prevent to conduct certain actions or force to execute others, etc.
The book edited by Kohler and Varien is a very good effort in this direction. There are many publications that insist on the necessity of simulation approaches to understand history (e.g., Epstein 2006; Christiansen and Altaweel 2006; Kohler and van der Leeuw 2007; Barceló 2009; Costopoulos and Lake 2010). Although not unique, it is one of the best ones available, specially for its detailed account of all elements concerned with the simulation. Maybe some of the chapters can appear too technical for non-archaeologists, but in general this is a compulsory reading for anyone interested in a revolutionary change in the current paradigm of historical research.
To conclude, I share the same view as the authors of this fundamental book: archaeology is not limited to the reproduction of stones, walls, buildings, pottery sherds, animal carcasses, but is an in-depth study of chronologically ordered sequence of changes and modifications acting over the consequence of former changes and modifications. Thanks to distributed simulations like that presented in the book, the Past will be seen inside a computer not as it once was, but as potentialities for explanation.
CHRISTIANSEN , J., Altaweel, M. (2006) Simulation of Natural and Social Process Interactions: An example from Bronze Age Mesopotamia. Social Science Computer Review, 24(2), pp. 209-226
COSTOPOULOS , A., Lake, M., (2010) (eds.) Simulating Change. Archaeologists into the 21st century. Salt lake City (UT):The University of Utah Press
DORAN , J. E., Palmer, M. (1995) "The EOS Project: Integrating Two Models of Palaeolithic Social Change". In Gilbert N. and Conte R. (eds.), Artificial Societies: the Computer Simulation of Social Life. London: UCL Press, pp. 103-125
EPSTEIN, J-M. (2006) Generative social science. Studies in agent-based computational modeling. Princeton, NJ: Princeton University Press
KOHLER, T.A., Van Der Leeuw, S.E. (eds.) (2007) Model-Based Archaeology of Socionatural Systems. Santa Fe, New Mexico: SAR Press
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