Social Science
10.1177/0894439305282430
Kuznar
/ High-Fidelity
Computer
Computational
Review
Social Science
High-Fidelity Computational Social
Science in Anthropology
Social Science Computer Review
Volume 24 Number 1
Spring 2006 15-29
© 2006 Sage Publications
10.1177/0894439305282430
http://ssc.sagepub.com
hosted at
http://online.sagepub.com
Prospects for Developing a Comparative Framework
Lawrence A. Kuznar
Indiana University–Purdue University at Fort Wayne
Approaches to modeling social phenomena vary on a continuum from simple models, in which
causality is clear and parameters few, to realistic, high-fidelity models designed to capture the
most detailed system behavior possible in a specific setting. Anthropologists have produced
both simple and high-fidelity models. The focus of this article is on high-fidelity modeling in
anthropology and the special challenge its complexity presents for model comparison. Useful
model comparison requires docking, or rendering models comparable, and the author presents a
framework for docking based on the work of Axelrod, and Cioffi-Revilla and Gotts. Docking not
only renders models more comparable, allowing for more traditional theory testing, but it also
sharpens the discussion about the ontology of anthropological phenomena and how they are best
represented as theories and models.
Keywords: computational social science; agent-based modeling; anthropology; high fidelity;
docking
C
omputational social science (CSS) employs formal mathematics, and especially agentbased modeling (ABM) simulation techniques, for representing social theories (Bankes,
Lempert, & Popper, 2002; Berry, Kiel, & Elliott, 2002; Kohler, 2000; Sallach, 2003). These
models typically represent classes of agents who interact with one another and produce emergent phenomena such as migrations, social structures, and epidemics (Epstein & Axtell,
1996). Anthropologists are increasingly using CSS methods to model social dynamics, test
anthropological theory, and inform policy. Early examples date to the 1960s and 1970s, when
anthropologists employed simulations to model demography and economic decisionmaking (Gladwin, 1975; Kunstadter, Buhler, Stephen, & Westoff, 1963). Since then, anthropologists have used CSS to explore social evolutionary processes (Boyd & Richerson, 1985),
Balinese irrigation regulation (Lansing, 1993), the rise and fall of civilizations (Kohler,
Kresl, Van West, Carr, & Wilshusen, 2000; Reynolds, 2000), and sharing in pastoral societies
(Flannery, Marcus, & Reynolds, 1989), among other topics (see below).
Trends in the development of anthropological CSS parallel other fields, including a bifurcation between simple abstract models and complicated realistic models (see Sallach &
Macal, 2001, for a general discussion of the issue). Axelrod (1997) advocates the “Keep It
Simple Stupid” (KISS) approach in which researchers keep parameters at a minimum to
facilitate exploring relationships among a few abstract and general variables. Although no
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computer model can provide a complete description of a particular, real social situation,
some researchers favor relatively more detailed and realistic models (Carley, 2002). I propose using the term high-fidelity (Hi-Fi) models for simulations that model detailed and complicated phenomena focused on capturing the social dynamics of particular social settings.
The focus of this article is on such Hi-Fi simulations in the field of anthropology. This reflects
no prejudice regarding KISS versus Hi-Fi modeling; each has its place, and my colleagues
and I have done both (see Kuznar, Frederick, & Sedlmeyer, 2006; Kuznar, Sedlmeyer, &
Frederick, 2005, for examples of KISS; see below for Hi-Fi examples). However, Hi-Fi simulation has advantages when studying systems subject to nonlinear dynamics and for vetting
policy decisions.
Hi-Fi models are likely to be most necessary where small perturbations in minor variables
have multiplicative effects (the butterfly effect). If researchers suspect that such
nonlinearities influence the systems they study, then Hi-Fi approaches are appropriate. Hi-Fi
approaches are also appropriate for policy applications. If one hopes to use CSS for policy or
planning purposes and if one suspects that small variations in parameters may have disproportionate effects on a real social system, then the stakes are high on producing a simulation
with as much fidelity to social reality as possible. The ability to re-run and re-program CSS
models provides the capability to explore possibilities (“what if”–ing) in an experimental
manner. The ability to employ counterfactual reasoning (What if A does not occur? What if
we do not do B?) is an invaluable tool for policy analysis. Engaging in what-if exercises only
makes sense if the model is sufficiently detailed to account for unseen possibilities.
Anthropologists have kept pace with CSS developments in other fields, and they have
shared the limitations. One problem with the current state of CSS models in anthropology is
that they are seldom if ever compared to one another. This problem is exacerbated by the
development of increasingly detailed simulations tailored to specific cultural situations; the
models’ complexity makes straightforward comparison difficult. It is important to remember
that CSS models are a representation of anthropological theory (or collections of theories),
and if scientific progress is to be made, then anthropologists must have a means for comparing the relative efficacy of models as explanatory devices (Axelrod, 1997; Cioffi-Revilla &
Gotts, 2003).
The purpose of this article is to review recent examples of Hi-Fi CSS in anthropology and
to provide an assessment of the possibility for comparing these complicated and detailed
simulations. I will first review the issue of model comparison, known as docking, and employ
a framework for comparison suggested by Cioffi-Revilla and Gotts (2003). Then I will
describe several recent Hi-Fi simulations done by anthropologists and compare them using
the framework for model comparison. The article closes with a consideration of the benefits
of model comparison, which include theory testing and the improvement of ontologies for
anthropological phenomena.
Docking CSS Models
The flexibility of CSS modeling allows researchers freer reign for their intellectual creativity. However, as Axelrod (1997) noted,
If these wonderful new possibilities of computational modeling are to become intellectual tools
well harnessed to the requirements of advancing our understanding of social systems, then we
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Figure 1
Levels of Computational Social Science Model Comparison
must overcome the natural impulse for self-contained creation and carefully develop the methodology of using them to “Reason and Compare.” (p. 204)
Axelrod (1997) referred to the method of making CSS models comparable as “alignment
of computational models” or simply “docking” (p. 184). The docking experiment he and others conducted was challenging because they asked to what extent two different models with
different purposes could be rendered comparable. This exercise was even more challenging
because they bridged the gap between a simple KISS model (Axelrod’s) and a more complicated virtual history model (Epstein & Axtell’s [1996] Sugarscape). The researchers found
that by rendering agents’ vision, movement capabilities, and behavioral rules comparable,
they were able to produce statistically similar results and then use the more complicated
framework (Sugarscape) to extend Axelrod’s analysis. Axerod and others’ call for and
attempt at comparability was originally done in 1997. Such approaches remain novel, as
Cioffi-Revilla and Gotts’s (2003) recent plea for model comparison indicates. They focused
on four levels at which models could be analyzed and compared (implementation, structure,
target domain, and function; see Figure 1).
Implementation refers to the computer software and hardware tools used in a particular
CSS. Several standard tools have emerged for ABM programming, with the majority using
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modern object-oriented programming languages such as Java and C++. Early programming
frameworks used languages such as Fortran, Basic, Pascal, and C (Axelrod, 1997; Gladwin,
1975; Kuznar, 1990; Lustick, 2000; Plattner, 1975; Raczynski, 2004). The next generation of
ABM programming employs frameworks such as Sugarscape (originally Objective C based;
Epstein & Axtell, 1996), Swarm (originally C based; Terna, 1998), and RePast (Java-based;
Collier, 2002). Recently, easier to use but more graphically and programming limited frameworks such as Netlogo have emerged (Wilensky, 1999) and are particularly useful for initial
proofing and simpler models.
Structure refers to the abstractions (e.g., classes, objects, methods) coded in a CSS model.
Nearly all CSS modeling takes advantage of object-oriented programming, whereby users
define classes of object types (programs, routines, functions), in which individual objects
(e.g., an agent) can be instantiated by defining object properties (attributes) and methods
(things it does) (Epstein & Axtell, 1996; O’Sullivan, 2004, p. 289). CSS models often operate
in artificially created environment structures such as rings, simple grids, or toruses (grids
whose sides meet). The manner in which time is modeled (days, weeks, years, decades) is
another important structural element.
Target domain refers to how abstractions are interpreted as representations of real world
entities. For instance, does an agent represent an individual, a group, or a nation? Is the environment a spatial representation of a social network or of a physical landscape? Is an
exchange of information an economic trade or an act of violence?
Finally, function refers to the purposes and uses of CSS models. Some modelers suggest
that models should be externally valid representations of reality, useful for theory testing
(Lustick, Miodownik, & Eidelson, 2004). Others insist that models be used as devices to
explore the limits of theoretical possibilities, without empirical validation (Bankes et al.,
2002). Increasingly, CSS models are used to inform policy decisions (MacKerrow, 2003).
Cioffi-Revilla and Gotts (2003) noted that the implementation level is often the clearest
and most straightforward level for comparison. The implementations I review here are sufficiently similar so that I will not make explicit comparisons among them. However, the other
three levels require closer consideration, and I will compare anthropological CSS models
accordingly.
Hi-Fi CSS in Anthropology
CSS has attracted the interest of anthropological researchers. I do not intend to provide an
exhaustive review of all anthropological simulation studies, but the following provides an
overview of their history and select recent examples. Early applications focused on modeling
economic interactions in peasant societies and on demographic effects on social structure
(Coult, 1965, 1968; Gladwin, 1975; Kunstadter et al., 1963; Plattner, 1975). Many applications are closer to the KISS approach. For instance, Boyd and Richerson (1985, 1992, 2001)
focused on how simulations of cognitively simple agents can give rise to emergent behaviors
such as cultural adaptation or maladaptation. Their work has been followed with simulations
of bounded rationality and cultural transmission (Henrich, 2002; Henrich & Boyd, 1998) and
the emergence of ethnicity (McElreath, Boyd, & Richerson, 2003). Aoki, Wakano, and
Feldman (2005) used simple models to explore how competing paradigms of behavior
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(social learning, individual learning, and instinct) form a suite of abilities humans use for
context-dependent decision making. Optimal foraging theorists use simple simulations to
explore the consequences of foraging and sharing decisions in hunter-gatherer societies
(Winterhalder, 1986a, 1986b). Likewise, Brantingham (2003) employed a relatively simple
agent model to simulate how parameters such as distance to raw material source and raw
material quality influence stone tool assemblages.
Read’s (2002) and Read and LeBlanc’s (2003) simulation of how competition influences
social evolution is a more complicated approach to simulation because it explores the interactions among several dimensions of human life (birth spacing, competition, and decision making). Dahl and Hjort’s (1976) landmark simulation of herd demographics and off-take rates is
similar to Read’s (2002) in that they considered several aspects of herding systems (animal
demography, economic use) to estimate the value of herds to traditional pastoralists. Both of
these simulations are, however, designed as general abstract applications and do not provide
detailed representations of any particular cultural setting.
White (1999; 2004, pp. 314-315) and others used ABM in a novel manner for hypothesis
testing that uses both fidelity to specific cultural systems and a simplified approach to focus
on one aspect of a social system, that is, marriage. They began with network data on marriage
links in known social systems and then conducted random permutations of mates, providing
a baseline random mating pattern in marriages to which known patterns can be compared.
Their approach involves controlling for population demography as well as for different cultural marriage proscriptions and prescriptions. Consequently, their modeling efforts go
beyond Axelrod’s (1997) KISS approach, although their focus on one aspect of cultural life,
marriage, provides a focus that is not quite the Hi-Fi approach defined here. Anthropologists
have produced several Hi-Fi applications applied to very specific cultural settings.
Early Hi-Fi simulations in anthropology include simulation of the interactions among
environmental and climatic variables and social organization and labor in Oaxaca, Mexico
(Fischer, 1980), risk-reducing strategies among the Hopi (Hegman, 1989), herding systems
in the Andes (Flannery et al., 1989; Kuznar, 1990) and Africa (Mace, 1993; Mace & Houston, 1989), and irrigation regulation in Bali (Lansing, 1993; Lansing & Kremer, 1987). More
recent applications include the following: simulation of the impact of raiding on cultural evolution in Mexico (Lazar & Reynolds, 2002; Reynolds, 2000), the rise and fall of Anasazi civilization (Kohler et al., 2000), continued investigations into Balinese irrigation (Lansing &
Miller, 2005), Amazonian colonization and land development (Deadman, Robinson, Moran,
& Brondizio, 2004), the impact of tourism on Arctic communities (Berman, Nicolson,
Kofinas, Tetlichi, & Martin, 2004), drug use epidemics (Agar, 2005), the Creolization of languages (Satterfield, 2001), the dynamics of social and kin networks among Turkish nomads
(White & Johansen, 2004), and interactions between pastoral nomad and peasant communities (Kuznar, Sedlmeyer, & Kreft, in press). It is beyond the scope of this article to offer a
detailed account of each of these recent examples. I have chosen to focus on a few that represent a breadth of project goals, programming tools and applications along temporal and spatial dimensions. Consequently, I have chosen simulations of Anasazi civilization, Amazonian colonization, tourism in the Arctic, and simulations of pastoral nomad societies as
examples of how anthropologists have used the tools of simulation to explore
anthropological theory and practical application.
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Anasazi
Researchers have applied simulation techniques to examine the rise and fall of Anasazi
civilization in two areas of the southwestern United States: the four corners area of Utah/Colorado/New Mexico/Arizona (Kohler et al., 2000) and Long House Valley on Navajo Nation
lands within Arizona (Axtell et al., 2002; Dean et al., 2000; Gumerman, Swedlund, Dean, &
Epstein, 2003). The aim of all of these efforts is to explain the growth and the rapid decline
around A.D. 1250 of the Anasazi culture. All models pay close attention to environmental features of the landscape, ecological relationships between population growth and land use, the
effect of drought, and human demography. The similarities among these efforts warrant
grouping them under the general heading of Anasazi simulations.
Amazon
The Amazon rainforest is one of the most rapidly changing environments on earth due to
human colonization, and a team of researchers provide a simulation of that complicated process (Deadman et al., 2004). These researchers’ LUCITA model simulates the ecological and
economic effects of Brazilian colonization policies. Similar to Anasazi models, the Amazonian simulations hinge on ecological relationships between population growth and land use,
and human demography. They add considerations of agent choice among land use strategies
and cropping decisions for a market economy. Although the Amazon simulation can inform
general anthropological theory, these researchers are clearly more concerned with informing
ongoing policy decisions on Amazonian colonization. Their work is similar in purpose and
scope to other tropical forest simulations (Huigen, 2004).
Arctic
Similar in purpose to the Amazon research, Berman and others (2004) provided a simulation of the effects of different approaches to tourism on the culture and traditional activities of
the indigenous Caribou hunting Gwitchin of Old Crow, in the Canadian Yukon. As with other
simulations, modelers pay attention to environmental features, the ecological relationships
between population growth and land use, and demography. However, these researchers add
the consideration of how different policies not yet enacted would affect the system they simulate. This what-if approach illustrates one of the strengths of CSS; when repeatable experiments are not possible, simulations provide a means by which controlled exploration of
possible worlds can be explored.
Middle Eastern and East African Pastoralists
My colleagues and I use a simulation tool, called NOMAD, to model the dynamic trading
and raiding interactions between animal herding pastoralists and their agrarian peasant partners. We apply it to two scenarios: a generalized Middle Eastern small-stock (sheep and
goats) system (Kuznar et al., in press) and a model of the more volatile relations between cattle nomads and peasants in Darfur, Sudan, that includes the effects of drought in stimulating
the recent genocide (Kuznar & Sedlmeyer, 2005). As with all of the above scenarios, we pay
close attention to environmental features, the ecological relationships between population
growth and land use, and demography. We add special consideration of how terms of trade
would evolve between nomad and peasant agents and how such terms can tip relations from
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peaceful trading to violent raiding. In the Darfur case, we also examine how, under conditions of extreme drought, raiding patterns can become set, leading to outright enslavement or
genocide. Our initial aim and most general goal is the testing of anthropological and historical theories of pastoralism. However, our Darfur application moves us closer to informing
policy because we think that some of our findings suggest ways in which the Darfur crisis
might be mitigated.
Comparison of Hi-Fi Simulations
If each anthropological application were totally different in scope, purpose, data input,
object definition, methods, and so forth, then the possibility of comparing the models would
not exist. However, as a glance at Table 1 shows, there is substantial overlap in structure, target domain, and function in the anthropological ABM’s I reviewed.
Structure
Space is always defined on a grid. Most agent updating takes place within each time step or
iteration of the simulation. The one exception is Arctic, where there are major and minor time
steps, with different updating processes taking place at different levels. The object classes in
these models correspond to agent individuals, and agents typically have the same attributes
and methods, although the parameters of these definitions differ from agent to agent. Stronger differences emerge in the target domains of each simulation, although even at this level,
basic similarities exist, making model comparison possible.
Target Domain
The target domains of these simulations vary from ancient civilizations to pastoral nomads
to modern hunter-gatherers. However, strong similarities remain in the source of information, instantiation of space, time, and even individual agents. Even those simulations that
model the past are highly dependent on knowledge ethnographers gather on the present. This
dependence raises concerns about the use of ethnographic analogies in simulation, which are
unfortunately beyond the scope of this article (Kuznar, 2001; Stahl, 1993; Wylie, 1988). The
reliance of all simulations on ethnographic data also adds to the burden of field researchers to
gather valid and accurate data. Space is always conceived as a two-dimensional landscape
grid. Some simulations (NOMAD, Arctic) use idealized landscapes, whereas others create
more realistic and detailed landscapes from Geographic Information Systems (GIS) and
archaeological databases. The typical time step is 1 year. Arctic departs from this scheme by
using 5-year time steps as the major iteration, with some agent updating taking place on a
five-seasonal basis (early winter, late winter, spring, summer, and fall) within the larger time
step. NOMAD interprets iterations as separate seasons (spring, summer, fall, winter). The
agents in each model represent individuals in households, and basic attributes cover age, sex,
birth rates, nutritional requirements, labor rates, and marriage rates. These categories track
important dimensions of any individual’s life, and culturally specific information is captured
in the parameter values given to these dimensions (e.g., age at marriage or labor rates for
activities). There is also much overlap in agent rules; all agents are born, reproduce, eat, marry,
die, and often move. Amazon and NOMAD also contain rules for trading, and NOMAD
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Key agent/objects
Environment
Implementation
Programming
framework
Structure/Target domain
Space
Time step
Data inputs
Project
Households, individuals
Raster cell = 1 ha
1 year
Archaeological surveys,
topographic maps,
ethnographic data,
climate data
Soil productivity, vegetation, elevation, water resources, GIS
Raster cell = 4 ha
1 year
Archaeological surveys,
topographic maps,
ethnographic data,
climate data
Soil productivity, vegetation, elevation, water resources, GIS
Households, individuals
Sugarscape
Anasazi:
Long House Valley
Swarm
Anasazi:
Four Corners
Households, individuals,
crops
Raster cell = 1 ha
1 year
Idealized land distribution, crop yields,
prices, regression
equations for soils
Soil productivity, crop
yields, land cover
RePast
Amazon
Households, individuals
Hunting zones, climate
change
None, but implied
5 years/seasons
Demography, hunting
dynamics, environmental change
None specified
Arctic
Table 1
Comparison of Anthropological High-Fidelity Simulations
Pastoralist/peasant demography, landscape
type, productivity,
hazard rates, crops,
pastures
Households, individuals,
animals
Raster cell = 1 ha
Four seasons
Idealized landscape,
crop yields, productivity, drought
Swarm
NOMAD
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Note: GIS = Geographic Information Systems.
Theory testing
Historical comparison
to random distribution
Degradation, farm, eat,
weed, relocate, reproduce, die, marry, form
new households
Rules/method
Function
Purpose
Validation
Age, nutritional requirements, sex, food
stores, maize yield,
labor rates
Agent attributes
Theory testing
Historical comparison,
visual inspection
Degradation, farm, eat,
weed, relocate, reproduce, die, marry, form
new households,
migrate/die, move,
store food
Age, nutritional requirements, sex, food
stores, maize yield,
labor rates
Policy
Realism for validation
against historical data
Age, nutritional requirements, sex, food
stores, harvest variance, crop yields,
farming strategy, cash
needs, labor rates
Farm, eat, reproduce,
die, marry, form new
households, move,
store food, use labor
pool
Policy
None sought
Household formation,
employment, hunting,
gear sharing, hunting
location choice, harvest sharing, communal hunt, migrate
Age, household type,
education, wages,
transfers, taxes, nutritional need
Theory testing
Historical comparison
Birth, eat, marry, move,
trade, raid, die/
migrate, slaughter,
harvest,
Age, nutritional requirements, sex, food
stores, crop yield, vision, labor rates
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includes raiding as an option. Despite the very different cultural settings these simulations
represent, their agent configurations share many basic attributes and rules, making model
comparison very possible.
Function
Three purposes emerge at the level of function in these anthropological models. Modelers
design some simulations, such as Anasazi and NOMAD, for testing specific anthropological
theories regarding the rise and fall of civilizations, political cycles, and the influence of environmental factors on culture. Other CSS models, such as Amazon and Arctic, inform public
policy by generating possible histories based on alternative parameter settings or policy decisions. However, even at this level, the potential for overlap and comparison exists; for
instance, we altered NOMAD to produce a simulation of Darfur with policy implications.
Comparing Models
Figure 1 can be used as a guideline for comparing Cioffi-Revilla and Gott’s (2003) levels
between models, especially if models are to be rendered similar in function so they can be
docked. For instance, the spatial and temporal aspects of models need to be docked. In the
case of the Anasazi models, this is already evident: Space is a GIS representation of actual
landscapes, and iterations represent years. Docking with NOMAD would require
instantiating a master year step, with perhaps seasonal substeps or, alternatively, programming seasons into the Anasazi simulations.
Comparing agents in these models presents the larger issue of what anthropologists should
consider the fundamental unit of their analysis and what attributes those units should have.
The simulations reviewed above all take the individual as their fundamental units. Also, individuals in these models share many basic attribute variables (age, sex, fertility, etc.). However, pastoral agents will necessarily differ from Amazonian peasants, which in turn will differ in some ways from Anasazi horticulturalists. One means of comparing agents across
models is to develop an ontology of basic agents who can take on various attributes based on
their cultural settings. Developing an ontology will certainly prove to be challenging because
anthropologists are far from agreement on the substance of the discipline (Fischer, 2004;
Leaf, 2004). Nonetheless, until anthropologists seriously engage in the endeavor, it is
unlikely that they will make much progress. Although daunting, there should be some room
for encouragement, because some units seem to be rather self-evident. For instance, the livestock-owned variable will be null for the ancient Anasazi, activated for agents in NOMAD,
and possibly filled in for some Amazonian colonists. A similar approach to developing a
range of agent methods could accommodate the range of culturally appropriate behaviors;
hunting would be activated for Anasazi and Arctic, with appropriate parameters to model
mule deer versus caribou hunting costs, benefits, and success rates.
Finally, comparison of models with different functions may actually improve their relative
functions. Whether Amazon is used to have an impact on policy or test anthropological theory, the model still needs to cover the same basic agent attributes and methods and provide
some reasonable means of representing space, environment, and time. Different functions
may cause modelers to focus on certain model aspects but ignore other variables necessary
for a valid model. For instance, land and markets are the focus of Amazon, but kin networks
are neglected. Our primary focus with the NOMAD–Darfur simulation was on theory test-
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ing, and we simply eliminated families who were no longer viable (economically or because
of loss of husband). However, an explicit policy orientation would have caused us to account
for the displaced women and children who, in reality, now populate refugee camps and create
a whole new set of factors (food aid, international pressure) influencing conflict in that
region. Comparing models with different functions has the potential for enhancing the original functions of CSS models, and this is best achieved by attempting comparison across the
full range of CSS models developed by anthropologists.
Similarities Across Anthropology: Other Models
The similarities are evident in the small sample of applications I have reviewed, but these
similarities easily extend to other applications. The MameLuke simulation of Philippine
tropical forest colonization uses a GIS map of an actual region and takes farming households
as its primary agent but also monitors individuals (Huigen, 2004). MameLuke introduces
another anthropologically relevant agent attribute, ethnicity. Lake’s simulation of Mesolithic
hazel nut collecting on Islay takes individual foragers as its basic agents (Lake, 2000). Lansing’s (1993, 2000) well-known simulations of Balinese irrigation take a higher structural
unit, the subak or cooperative farmer’s association, as its basic decision-making agent. Similarly, the SHADOC simulation of Senegalese irrigation monitors both individual farmer
agents as well as water user federations (Barreteau, Bousquet, Millier, & Weber, 2004).
Even anthropological applications that do not involve land use can be compared at some
level. Agar’s (2004, 2005) study of drug use epidemics is based on agent individuals who
experience a drug and share that experience with others. Because many attributes specified in
land use models (labor rates, nutritional requirements) may not be relevant to Agar’s applications, it is not very comparable with more environmentally and materially based models such
as LUCITA or NOMAD. However, Agar paid attention to the nature of communication that
other models neglect, indicating that all simulations eventually need to incorporate methods
for communication if they are to be more complete and valid anthropological models.
Satterfield’s (2001) simulation of language Creolization focuses on agent individuals and
how their basic demographic profiles influence their linguistic behavior. Satterfield’s model
includes standard demographic attributes, such as age, sex, and health, but also ethnic identity (as in MameLuke) and social status. Once again, these are further attribute categories that
could be generally configured for agent individuals and compared across models.
Conclusions
The similarities across Hi-Fi anthropological CSS models in implementation, structure,
target domain, and function indicate the potential for docking or model comparison. Models
are representations of alternative anthropological theories of social life, and so, developing a
means for testing these theories against one another is necessary if researchers are to use CSS
for furthering anthropological science. Docking is clearly a necessary prerequisite for theory
testing. However, docking anthropological CSS models has further implications for the
development of CSS and anthropological theory in more fundamental ways.
Docking CSS models requires finding similarities and analogies between model agents,
objects, and methods. Docking therefore forces anthropologists to consider just what anthropological phenomena exist, in addition to how those phenomena are best represented as com-
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putational objects. In other words, docking attempts provide another tool anthropologists can
use in considering the ontology of the human condition. The very act of comparing agents
forces anthropologists to be more explicit about what agents they think exist and what attributes and methods agents should have. This need for rigor will sharpen anthropological
debates about the ontology of anthropological phenomena. Anthropologists need to consider
whether kinship systems operate, as their categories imply, to warrant modeling a kinship
unit as an agent (see White & Johansen, 2004, for some interesting perspectives). Anthropologists need to address the age-old question concerning whether social structures have causal
effects (see O’Meara, 1997). What we mean by “farmer in a peasant agricultural system”
may not be sufficiently similar to a farmer in a horticultural system to justify using the same
types of agents to model both (see discussion in Netting, 1986). Do our categories unnecessarily narrow the dimensionality of individuals and social structures? Forcing rigor in the
definition of agents may help to differentiate concepts that are lumped under the same category. For example, are New Guinean horticulturalists sufficiently similar to Amazonian horticulturalists to share the same label? Conversely, modelers may find that phenomena traditionally separated may be sufficiently similar to be modeled under the same agent category.
For example, could there be sufficient similarities between the political posturing between
states and the politics of raiding and trading among tribes that each is an instance of the same
general variable? Developing a sounder ontology of anthropological phenomena will focus
scientific researchers on those aspects that the field researchers have best defined and on
using these structures to advance theory.
References
Agar, M. (2004). An anthropological problem, a complex solution. Human Organization, 63(4), 411-418.
Agar, M. (2005). Agents in living color: Towards emic agent-based models. JASSS—Journal of Artificial
Societies and Social Simulation, 8(1),1-19.Retrieved from http://jass.soc.surrey.ac.uk/8/1/4.html
Aoki, K., Wakano, J. Y., & Feldman, M. W. (2005). The emergence of social learning in a temporally changing
environment: A theoretical model. Current Anthropology, 46(2), 334-340.
Axelrod, R. (1997). The complexity of cooperation: Agent-based models of conflict and cooperation. Princeton,
NJ: Princeton University Press.
Axtell, R. L., Epstein, J. M., Dean, J. S., Gumerman, G. J., Swelund, A. C., Harburger, J., et al. (2002). Population
growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley. Proceedings of the
National Academy of Sciences, 99(Suppl. 3), 7275-7279.
Bankes, S., Lempert, R., & Popper, S. (2002). Making computational social science effective. Social Science
Computer Review, 20(4), 377-388.
Barreteau, O., Bousquet, F., Millier, C., & Weber, J. (2004). Suitability of multi-agent simulations to study irrigated system viability: Application to case studies in the Senegal River Valley. Agricultural Systems, 80, 255275.
Berman, M., Nicolson, C., Kofinas, G., Tetlichi, J., & Martin, S. (2004). Adaptation and sustainability in a small
arctic community: Results of an agent-based simulation model. Arctic, 57(4), 401-414.
Berry, B. J. L., Kiel, L. D., & Elliott, E. (2002). Adaptive agents, intelligence, and emergent human organization:
Capturing complexity through agent-based modeling. PNAS–Proceedings of the National Academy of Sciences, 99(Suppl. 3), 7187-7188.
Boyd, R., & Richerson, P. J. (1985). Culture and the evolutionary process. Chicago: University of Chicago Press.
Boyd, R., & Richerson, P. J. (1992). Punishment allows the evolution of cooperation (or anything else) in sizable
groups. Ethology and Sociobiology, 13, 171-195.
Boyd, R., & Richerson, P. J. (2001). Norms and bounded rationality. In G. Gigerenzer & R. Selten (Eds.), Bounded
rationality: The adaptive toolbox (pp. 281-296). Cambridge, MA: MIT Press.
Downloaded from ssc.sagepub.com at PENNSYLVANIA STATE UNIV on September 11, 2016
Kuznar / High-Fidelity Computational Social Science
27
Brantingham, P. J. (2003). A neutral model of stone raw material procurement. American Antiquity, 68(3), 487509.
Carley, K. (2002). Computational organization science: A new frontier. PNAS–Proceedings of the National Academy of Sciences, 99(Suppl. 3), 7257-7262.
Cioffi-Revilla, C., & Gotts, N. (2003). Comparative analysis of agent-based social simulations: GeoSim and
FEARLUS models. JASSS–Journal of Artificial Societies and Social Simulation, 6(4), 1-23. Retrieved from
http://jass.soc.surrey.ac.uk/6/4/10.html
Collier, N. (2002). RePast: An extensible framework for agent simulation. Retrieved from http://
repast.sourceforge.net/projects.html
Coult, A. D. (1965). Computer methods for analyzing geneological space. American Anthropologist, 67, 21-29.
Coult, A. D. (1968). A computer analysis of Bedouin marriage. Southwestern Journal of Anthropology, 24, 83-99.
Dahl, G., & Hjort, A. (1976). Having herds. Stockholm: Stockholm Studies in Social Anthropology 2, Department of Social Anthropology, University of Stockholm.
Deadman, P., Robinson, D., Moran, E., & Brondizio, E. (2004). Colonist household decisionmaking and land-use
change in the Amazon rainforest: An agent-based simulation. Environment and Planning B: Planning and
Design, 31, 693-709.
Dean, J. S., Gumerman, G. J., Epstein, J. M., Axtell, R. L., Swedlund, A. C., Parker, M. T., et al. (2000). Understanding Anasazi culture change through agent-based modeling. In T. A. Kohler & G. J. Gumerman (Eds.),
Dynamics in human and primate societies: Agent-based modeling of social and spatial processes (pp. 179205). New York: Oxford University Press.
Epstein, J. M., & Axtell, R. L. (1996). Growing artificial societies: Social science from the bottom up. Cambridge,
MA: MIT Press.
Fischer, M. D. (1980). A generalized simulation of production and consumption in the valley of Oaxaca, Mexico.
Unpublished master’s thesis, University of Texas at Austin, Department of Anthropology.
Fischer, M.D. (2004). Integrating anthropological approaches to the study of culture: The “hard” and the “soft.”
Cybernetics and Systems, 35, 147-162.
Flannery, K., Marcus, J., & Reynolds, R. (1989). The flocks of the Wamani. Orlando, FL: Academic Press.
Gladwin, H. (1975). Looking for an aggregate additive model in data from a hierarchical decision process. In S.
Plattner (Ed.), Formal methods in economic anthropology (pp. 159-196). Washington, DC: American Anthropological Association.
Gumerman, G. J., Swedlund, A. C., Dean, J. S., & Epstein, J. M. (2003). The evolution of social behavior in the
prehistoric American Southwest. Artificial Life, 9, 435-444.
Hegman, M. (1989). Risk reduction and variation in agricultural economies: A computer simulation of Hopi agriculture. Economic Anthropology, 11, 89-121.
Henrich, J. (2002). Cultural transmission and the diffusion of innovations: Adoption dynamics indicate that
biased cultural transmission is the predominate force in behavioral change. American Anthropologist, 103(4),
992-1013.
Henrich, J., & Boyd, R. (1998). The evolution of conformist transmission and the emergence of between-group
differences. Evolution and Human Behavior, 19, 215-241.
Huigen, M. G. A. (2004). First principles of the MameLuke multi-actor modelling framework for land use change,
illustrated with a Philippine case study. Journal of Environmental Management, 72, 5-21.
Kohler, T. A. (2000). Putting social sciences together again. In T. A. Kohler & G. J. Gumerman (Eds.), Dynamics
in human and primate societies: Agent-based modeling of social and spatial processes (pp. 1-18). New York:
Oxford University Press.
Kohler, T. A., Kresl, J., Van West, C., Carr, E., & Wilshusen, R. H. (2000). Be there then: A modeling approach to
settlement determinants and spatial efficiency among late ancestral Pueblo populations of the Mesa Verde
Region, U.S. Southwest. In T. A. Kohler & G. J. Gumerman (Eds.), Dynamics in human and primate societies:
Agent-based modeling of social and spatial processes (pp. 145-178). New York: Oxford University Press.
Kunstadter, P., Buhler, R., Stephen, F., & Westoff, C. F. (1963). Demographic variability and preferential marriage
patterns. American Journal of Physical Anthropology, 21, 511-519.
Kuznar, L. A. (1990). Economic models, ethnoarchaeology, and early pastoralism in the High Sierra of the South
Central Andes. Unpublished Ph.D., Northwestern University, Evanston, IL.
Downloaded from ssc.sagepub.com at PENNSYLVANIA STATE UNIV on September 11, 2016
28
Social Science Computer Review
Kuznar, L. A. (2001). Introduction to Andean ethnoarchaeology. In L. A. Kuznar (Ed.), Ethnoarchaeology in
Andean South America: Contributions to archaeological method and theory (pp. 1-18). Ann Arbor, MI: International Monographs in Prehistory.
Kuznar, L. A., Frederick, W. G., & Sedlmeyer, R. L. (2006). The effect of nepotism on the evolution of social
inequality. In L. A. Kuznar & S. K. Sanderson (Eds.), Studying societies and cultures: Marvin Harris’s cultural materialism and its legacy. Elmsford, NY: Pergamon.
Kuznar, L. A., & Sedlmeyer, R. L. (2005). Collective violence in Darfur: An agent-based model of pastoral
nomad/sedentary peasant interaction. Mathematical Anthropology and Culture Theory.
Kuznar, L. A., Sedlmeyer, R. L., & Frederick, W. G. (2005, May 18-22). Agent based models of risk sensitivity:
Applications to social unrest, collective violence and terrorism. Paper Presented at the 3rd Lake Arrowhead
Conference on Human Complex Systems, Lake Arrowhead, CA.
Kuznar, L. A., Sedlmeyer, R. L., & Kreft, A. (in press). NOMAD: An agent-based model of nomadic pastoralist/
sedentary peasant interaction. In Nomadic cultures. Los Angeles: University of California, COTSEN Institute.
Lake, M. W. (2000). MAGICAL computer-simulation of Mesolithic foraging. In T. A. Kohler & G. J. Gumerman
(Eds.), Dynamics in human and primate societies: Agent-based modeling of social and spatial processes (pp.
107-144). New York: Oxford University Press.
Lansing, J. S. (1993). Priests and programmers: Technologies of power in the engineered landscape of Bali.
Princeton, NJ: Princeton University Press.
Lansing, J. S. (2000). Anti-chaos, common property, and the emergence of cooperation. In T. A. Kohler & G. J.
Gumerman (Eds.), Dynamics in human and primate societies: Agent-based modeling of social and spatial
processes (pp. 207-224). New York: Oxford University Press.
Lansing, J. S., & Kremer, J. N. (1987). Emergent properties of Balinese water temple networks. American Anthropologist, 95(1), 97-114.
Lansing, J. S., & Miller, J. H. (2005). Cooperation, games, and ecological feedback: Some insights from Bali.
Current Anthropology, 46(2), 328-333.
Lazar, A., & Reynolds, R. G. (2002, June 21). Computational framework for modeling the dynamic evolution of
large-scale multi-agent organizations. Paper presented at the Computational Analysis of Social and Organizational Systems Proceedings, Pittsburg.
Leaf, M. J. (2004). Cultural systems and Organizational Processes. Cybernetics and Systems, 35, 289-313.
Lustick, I. S. (2000). Agent-based modelling of collective identity: Testing constructivist theory. JASSS–Journal
of Artificial Societies and Social Simulation, 3(1), 1-29. Retrieved from http://www.soc.surrey
.ac.uk/JASSS/3/1/1.html
Lustick, I. S., Miodownik, D., & Eidelson, R. J. (2004). Secessionism in multicultural states: Does sharing power
prevent or encourage it? American Political Science Review, 98(2), 209-229.
Mace, R. (1993). Nomadic pastoralists adopt subsistence strategies that maximise household survival. Behavioral
Ecology and Sociobiology, 33, 329-334.
Mace, R., & Houston, A. (1989). Pastoralist strategies for survival in unpredictable environments: A model of
herd composition that maximises viability. Agricultural Systems, 31, 185-204.
MacKerrow, E. P. (2003). Understanding why—Dissecting radical Islamist terrorism with agent-based simulation. Los Alamos Science, 28, 184-191.
McElreath, R., Boyd, R., & Richerson, P. J. (2003). Shared norms and the evolution of ethnic markers. Current
Anthropology, 44(1), 122-129.
Netting, R. (1986). Cultural ecology (2nd. ed.). Prospect Heights, IL: Waveland Press.
O’Meara, J. T. (1997). Causation and the struggle for a science of culture. Current Anthropology, 38, 399-418.
O’Sullivan, D. (2004). Complexity science and human geography. Transactions of the Institute of British Geographers, 29(3), 282-295.
Plattner, S. (1975). Pedlar: A computer game in economic anthropology. In S. Plattner (Ed.), Formal methods in
economic anthropology (pp. 197-215). Washington, DC: American Anthropological Association.
Raczynski, S. (2004). Simulation of the dynamic interactions between terror and anti-terror organizational structures. JASSS—Journal of Artificial Societies and Social Simulation, 7(2). Retrieved from http://
jasss.soc.surrey.ac.uk/7/2/8.html
Read, D. W. (2002). A multitrajectory, competition model of emergent complexity in human social organization.
PNAS—Proceedings of the National Academy of Sciences, 99(Supplement 3), 7251-7256.
Downloaded from ssc.sagepub.com at PENNSYLVANIA STATE UNIV on September 11, 2016
Kuznar / High-Fidelity Computational Social Science
29
Read, D. W., & LeBlanc, S. A. (2003). Population growth, carrying capacity, and conflict. Current Anthropology,
44(1), 59-85.
Reynolds, R. G. (2000). The impact of raiding on settlement patterns in the northern valley of Oaxaca: An
approach using decision trees. In T. A. Kohler & G. J. Gumerman (Eds.), Dynamics in human and primate
societies: Agent-based modeling of social and spatial processes (pp. 251-274). New York: Oxford University
Press.
Sallach, D. L. (2003). Social theory and agent architectures: Prospective issues in rapid-discovery social science.
Social Science Computer Review, 21(2), 179-195.
Sallach, D. L., & Macal, C. M. (2001). Introduction: The simulation of social agents. Social Science Computer
Review, 19(3), 245-248.
Satterfield, T. (2001). Toward a sociogenetic solution: Examining language formation processes through
SWARM modeling. Social Science Computer Review, 19(3), 281-295.
Stahl, A. B. (1993). Concepts of time and approaches to analogical reasoning in historical perspective. American
Antiquity, 58(2), 235-260.
Terna, P. (1998). Simulation tools for social scientists: Building agent based models with SWARM. JASSS—Journal of Artificial Societies and Social Simulation, 1(2). Retrieved from http://www.soc.surrey
.ac.uk/JASSS/1/2/4.html
White, D. R. (1999). Controlled simulation of marriage systems. JASSS—Journal of Artificial Societies and
Social Simulation, 2(3). Retrieved from http://www.soc.surrey.ac.uk/JASSS2/3/5.html
White, D. R. (2004). Network analysis and social dynamics. Cybernetics and Systems, 35, 173-192.
White, D. R., & Johansen, U. C. (2004). Network analysis and ethnographic problems: Process models of a Turkish nomad clan. Lanham, MD: Lexington Books.
Wilensky, U. (1999). NetLogo. Evanston, IL: Northwestern University, Center for Connected Learning and
Computer-Based Modeling. Retrieved from http://ccl.northwestern.edu/netlogo/
Winterhalder, B. (1986a). Diet choice, risk, and food sharing in a stochastic environment. Journal of Anthropological Archaeology, 5, 369-392.
Winterhalder, B. (1986b). Optimal foraging: Simulation studies of diet choice in a stochastic environment. Journal of Ethnobiology, 6, 205-223.
Wylie, A. (1988). “Simple” analogy and the role of relevance assumptions: Implications of archaeological practice. International Studies in the Philosophy of Science, 2, 134-150.
Lawrence A. Kuznar is a professor of anthropology at Indiana University–Purdue University at Fort Wayne. He
received his Ph.D. in anthropology and a master’s degree in mathematical methods in the social sciences from
Northwestern University in 1990. His research interests include the economics and ecology of traditional herding
societies, the evolution of human behavior, conflict and terrorism, and computational social science. He has conducted field research among the Aymara of Andean Peru and the Navajo of the U.S. Southwest. He has published
articles in American Anthropologist, Current Anthropology, Ecological Economics, and others. He is author of
Reclaiming a Scientific Anthropology (Altamira Press, 1997) and Awatimarka: Ethnoarchaeolopgy of an Andean
Herding Community (Harcourt Brace, 1995).
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