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Journal of Archaeological Science 38 (2011) 1583e1590

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Journal of Archaeological Science

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Specialization and social inequality in Bronze Age SE Arabia: analyzing thedevelopment of production strategies and economic networks using agent-basedmodeling

Lynne M. Rouse a,*, Lloyd Weeks b

aDepartment of Anthropology, Washington University in St. Louis, St. Louis, MO, USAbDepartment of Archaeology, University of Nottingham, Nottingham, United Kingdom

a r t i c l e i n f o

Article history:Received 12 November 2010Received in revised form17 February 2011Accepted 18 February 2011

Keywords:Production specializationWealth inequalityAgent-based modelingSocio-economic networksBronze AgeSouth-eastern Arabia

* Corresponding author.E-mail address: lmrouse@wustl.edu (L.M. Rouse).

0305-4403/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.jas.2011.02.023

a b s t r a c t

This paper investigates the role of specialized production strategies in the development of socio-economic inequalities in Bronze Age south-eastern (SE) Arabia, and particularly, the ways in whicha localized, internal exchange economy may have produced stress and instability in the SE Arabiansocio-economic system. While archaeological research has established that the communities of SE Arabiaparticipated in a widespread Bronze Age exchange system that included areas of the ancient Near East,South Asia, and Central Asia, it is unclear to what degree this interaction fostered the broad-scale socio-economic changes seen in the Early Bronze Age of SE Arabia. Here we present the results of anagent-based model that suggest the nature of the internal exchange economy in SE Arabia itself mayhave precipitated the social conditions necessary for change by allowing individuals to profit dispro-portionately. We thus emphasize the importance of local production strategies in generating socio-economic change, in addition to the well-established economic and cultural contacts with the widerBronze Age world.

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1. Introduction: evaluating internal and external factors inthe rise of social complexity in Bronze Age SE Arabia

Many studies of the Bronze Age societies of the Persian Gulfregion have emphasized broad scale political and economicprocesses in the explanation of local culture change. Forexample, Crawford (1996, 1998), Laursen (2009) and Højlund(2007) have highlighted the effects of variations in the scaleand structure of long-distance Bronze Age exchange in thegreater Persian Gulf region on socio-economic developments inDilmun (Bahrain island and the adjacent coast of Saudi Arabia)and neighboring lands. Others have modeled the societies of thePersian Gulf region as peripheries to a Bronze Age economic“world system” centered on Mesopotamia (Edens, 1992; Edensand Kohl, 1993).

For SE Arabia, the significance archaeologists have placed onparticipation in the Persian Gulf exchange system is neithersurprising nor unjustified. During the Hafit period (c. 3100e2700BC) and particularly during the Umm an-Nar period (c. 2700e2000

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BC), communities in SE Arabia were inextricably tied to the widerBronze Age world (Fig. 1). Demand from the urban centers ofMesopotamia, the Indus, Bahrain and Iran for the products of SEArabia (most notably copper but also a range of stones, woods,marine products and finished artifacts; e.g. Glassner, 1989: pp.187e189; Cleuziou and Tosi, 2007: pp. 186e187) influencedproduction systems in SE Arabia, whilst a variety of foreign luxuriesand staples (textiles, grain, pottery, precious and base metals, semiprecious stones, ivory, etc.) penetrated and no doubt partly con-toured all corners of the SE Arabian economy. Alongside thesematerial exchanges, direct contacts between SE Arabians and theirurban counterparts would also have provided a critical mechanismfor the transmission of knowledge and ideas.

However, more recent studies have emphasized the uniquenature of local economic and subsistence adaptations and socialstructures in Bronze Age SE Arabia (e.g. Al-Jahwari, 2009; Cleuziouand Tosi, 2007) and it is clear that a proper understanding ofcultural development in the region must also consider the roleof internal production and exchange systems in the generation ofinequality. Weeks (2003: p. 52) following Shennan (1999) haspreviously presented models of production based on Ricardianeconomic principles, particularly that of “comparative advantage”(see below), to discuss internal production and exchange systems

Fig. 1. Map of SE Arabia as area of interest and neighboring regions. Modern names are shown in black, with ancient names (from Bronze Age Mesopotamian historical sources)shown in white italics.

L.M. Rouse, L. Weeks / Journal of Archaeological Science 38 (2011) 1583e15901584

in Bronze Age SE Arabia. That study suggested that local variationsin the scale and nature of production in Bronze Age SE Arabia hadthe potential to increase socio-economic inequality rather thanmitigate it.

In this paper, we take this investigation further by using anagent-based model operating on Ricardian principles to investigatethe role of specialization in the rise of inequality. This modelincorporates approximations to economic situations that can berecognized in Bronze Age SE Arabia, and provides insights into theways in which internal economic specialization and specificproduction systems could have been managed by individuals (andgroups) to advance their wealth and status at the expense of others.The use of agent-based modeling to enhance archaeologicalunderstanding is not new (see, Bentley et al., 2005; Graham, 2006;Griffin and Stanish, 2007; Kohler et al., 1996; Low et al., 2005), butits application towards understanding the interplay of specializa-tion and wealth inequality and the effect on socio-economicnetworks presents a new approach in the study of Bronze Age SEArabia.

2. Archaeological context

2.1. Social complexity and Bronze Age SE Arabia’s “exchangeeconomy”

With few exceptions, archaeologists have characterized thecommunities of Bronze Age SE Arabia as approaching e but neverachieving e “state-level” complexity (Cleuziou, 2003: p. 140;Crawford, 1998: p. 149; Potts, 2008; Tosi, 1989: p. 157). Rather,the surviving archaeological and textual evidence has generallybeen interpreted as attesting a “decentralized” political structure(e.g. Potts, 2008). This fact sets the societies of Bronze Age SE

Arabia apart from their urbanized and deeply socio-politicallystratified contemporaries in Bahrain, Mesopotamia, Iran and theIndus.

It hasbeenargued that thedevelopmentof social complexity inSEArabia followed a fundamentally different trajectory than seen inneighboring areas of ancient SW Asia, incorporating social forma-tions that were the progenitors of the later “Arabian tribal system”

(Cleuziou, 2002, 2003: p. 226,140 and145). In essence, it is suggestedthat Bronze Age societies in SE Arabia adhered to a tribal “ethos” thatconferred very limited powers of coercion upon tribal rulers, cur-tailed individual efforts towards economic accumulation, anddid notsupport the development of redistributive or tributary economiesthat underpinned complex political structures in neighboringregions (Cleuziou, 1998, 2002, 2003; Cleuziou and Tosi, 2000, 2007:pp. 95e96; Tosi, 1986: p. 480; cf. Lancaster and Lancaster, 1992).

Whilst Cleuziou and Tosi regard the putative tribal structure ofUmm an-Nar period society in SE Arabia as a fundamental factor inthe region’s development, they also postulate an additional criticalfactor in the unique trajectory of Bronze Age Arabian society: theformation of an internal “exchange economy” (Cleuziou and Tosi,2007: p. 66, 168). This exchange economy developed due to themosaic distribution of resources in the region that necessitated theexchange of the products (e.g. copper, pottery, soft-stone, agricul-tural goods, marine resources, etc.) of regionally specializedproduction groups in order to maintain a stable subsistence base. Inthe marginal environments that characterized much of Bronze AgeSE Arabia, it is argued that the only viable adaptive strategy waseconomic inter-dependence (Cleuziou and Tosi, 2000: p. 26). In theabsence of the political integration of the region and of elite-controlled redistributive exchange mechanisms, the exchangeeconomy acted to facilitate economic and cultural integrationacross 3rd millennium SE Arabia.

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2.2. Ricardian principles, comparative advantage, and SE Arabiannetworks

Under the economic premise of comparative advantage,emphasis is placed on exchange value relative to production, ratherthan direct economic benefit (Ricardo, 1817). Comparative advan-tage follows the basic notion that if an item can be obtainedthrough trade for less than the cost of producing it oneself, it isbetter to trade for that item and spend labor time on other tasks.Another important feature of comparative advantage is the disas-sociation of ubiquitous benefit from equal benefit, so that self-guided action may produce more gains for some than others, and,as discussed elsewhere (Shennan, 1999), may even result ineconomic loss at times. All of these principles of comparativeadvantage rest on the premise that groups or individuals partici-pate in the system because of a perceived socio-cultural oreconomic necessity, which outweighs the benefit of total economicself-sufficiency. Such assumptions are consistent with the notion ofthe “exchange economy” and “tribal ethos” of Bronze Age SE Arabiadiscussed above, which values loyalties and alliances and whereparticipants continue to participate even in the face of hardshipsbecause of a perceived social necessity in doing so.

2.3. Specialization, complexity, and agent-based approaches

Specialized production is a fundamental component of the“exchange economy”model developed to explain aspects of culturalchange in Early Bronze Age SE Arabia. Archaeologically, we mightrecognize the variation in absolute scale of copper smelting residues(Weeks, 2003:pp. 45e53), or the differing distribution patterns ofUmman-Nar common ceramicwares (produced andusedwidely onlocal scales) andfinewares (produced in a small number of locationsand distributed across the region) (Cleuziou and Méry, 2002; Méry,2000) asevidence for co-existenceofproductive strategieswithverydifferent degrees of specialization. However, it can often be difficultin archaeological research to link small-scale observations withbroader-scale patterns, or vice versa. The archaeological studiesalone can be limited in their ability to recognize how large-scalepatterns such as these were precipitated or affected by actions ona very small scale. Agent-based modeling, as a complimentaryresearch tool to archaeology, allows researchers to explore thehypothesized links of cause and effect between various scales. Likeallmodels, agent-basedmodels are simplifications of the real world.Significantly, though, agents in agent-based models may choose toalter their interactive behavior given their understanding of thesystem around them, and it is these individual actions, througha series of iterated time steps, that precipitate change in the systemas a whole. While agents are limited in their range of behavioralchoices by the programming (as, it could be argued, people arelimited in their choices by cultural norms), the autonomy of agentchoice is what allows agent-based modeling to generate behaviorrather than impose it. The accumulationof individual agent behavioralters the structure of the interactive network through time, andreveals how multiple independent choices are linked to the emer-gence of global, system-wide trends.

The model presented here is not intended to establish the linkbetween specializedproduction and socio-economic complexity, forthat connection is already widely recognized in archaeological (andnon-archaeological) literature (Adams, 2005; Boone, 1992; Joffe,1991; Levy, 1983; Stanish, 2004). Rather, the goal is to developprevious observations and guide current understanding as to howseemingly diverse production strategies in distinct technologicalscenarios all converge in the development of unequal social andeconomic relationships. Thismodel is particularly relevant to placessuch as Bronze Age SE Arabia, where localized specialization and

wealth disparity may have contributed to regional developments insocio-economic interaction and complexity. This model exploresthese intertwined processes in three different techno-culturalenvironments or scenarios, which can be summarized as (1) anestablished base of technical knowledge, with productionmaterialsand techniques readily available, (2) introduction of a single newproduct or technology into an established system, thereby creatinga ‘niche’ market, and (3) the flooding of an established systemthrough the introduction of a suite of new products and/ortechnologies.

3. An agent-based model of specialized production

Our model is based upon the so-called JinGirNew model (Jinet al., 2001), which successfully approximates human socialnetworks through a system of nodes (the agents) and edges (linesconnecting interacting agents and representing relationshipsbetween them). Using a few basic and system-wide rules of inter-action, the JinGirNew model accurately simulates many of theproperties of real human social networks, such as a tendency forclusters of individuals to form, but with a few cross-networkconnections that join every agent to every other agent within a fewsteps (known as the small-world effect, see Bentley, 2003 andWatts and Strogatz, 1998). Adapting the JinGirNew model, we havecreated a new model that incorporates economic production andexchange, which is unique in the way it examines the economicinteractions that take place in socially structured networks.

3.1. Overview of model parameters

In the model we present, agents produce and trade threeproducts, labeled A, B, and C. The rate at which each agent produceseach product is determined by howmuch labor time they devote toits production (a value called Effort, which is effectively the agent’srate of specialization), and how efficient they are at producing saidproduct (a value called Efficiency, which may represent access toraw materials, technical skill, or quality of production equipment).The model runs through a series of iterations, or time steps, andeach time step an agent trades products with another agent whomit “knows” through already-established social connections. Theagent will give priority to a profitable trade e one where it cantrade away a product it can easily produce, and trade for a productthat is more costly to produce. There are two important aspects tothe exchange relationship. First, though each trading partner maybenefit, trade may not be equally profitable for both, depending onthe values of each agents’ Effort and Efficiency rates for eachproduct. Second, agents must trade through one of their socialconnections, even if it results ultimately in an unprofitableexchange. In these scenarios, agents seek to minimize their losses.At the end of each time step, agents evaluate their trade, and adjusttheir Effort values towards production of the product they under-stand to be most valuable (their Efficiency value for production ofeach product is fixed throughout the model). This arrangementallows an agent to ‘predict’ a profitable course of action for itself,but its adjustment is constrained by local experience and imperfectknowledge of product values within the system as a whole. Keyvariables and some basic operations of the model are laid out inTables 1 and 2, while more detailed explanations of modelparameters and the programming code can be found online atwww.saie.wustl.edu.

The parameters of our model follow very generally the Ricardianprinciples noted in Section 2.2. The necessity of group affinity isapproximated by the model operating as a closed system, wherethe number of participants does not fluctuate, only their labordevotion. The model also follows the principle of comparative

Table 1The default parameters of the specialized production model.

Parameter Description Default value

Agent variablesProduct (A, B, C) Amount of A, B, or C product owned by each agent 0< Product> 100Production effort (A, B, C) Value multiplied by production efficiency rate to determine an agent’s production

each time step; sum of efforts A, B, and C must equal 10� Effort> 1

Production efficiency (A, B, C) Value multiplied by production effort rate to determine an agent’s production eachtime step; must be whole integers greater than 0

0< Efficiency> 10

Strategy/specialization The product that gives the node the greatest return for labor investment; theproduct with the lowest price

Variable

Wanted trade item The least return for investment product; the opposite of strategy/specialization VariableWealth Sum of three products owned by the agent Wealth> 0Degree Number of connections to other nodes 0<Degree> 5Price The value assigned to each product during trade encounters; based on Effort,

Efficiency, and WealthVariable

Global parametersr0 Probability that random agents are connected each time step 0.0005r1 Used in calculating the probability two agents with mutual connection will meet 2Y Probability of a lost connection each time step 0.005Product cap (A, B, C) Maximum amount of product an agent can start with 100Effort cap Maximum Effort initially assigned to any Product 1Efficiency cap Maximum Efficiency assigned to each product 10NumNodes Number of nodes in the model 250MaxDegree Maximum number of connections allowed per node 5

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advantage through a bartered exchange system, and allowsunequal benefit for trading partners. The inequality of tradingpartnerships is an important and intentional feature of the model,because it simulates participation in a socio-economic systemenforced by cultural continuity among agents (as discussed byShennan, 1999, and representative of Cleuziou and Tosi’s, 2007:p. 168 hypothesized tribal configuration of Early Bronze Age SEArabia).

3.2. Variation in model modes: approximating scenarios

The model operates under three main “modes” which aredesigned to approximate past socio-economic systems. The first or‘default’ mode is an established regime of technical knowledge,where the materials and techniques for producing culturallysignificant objects are readily available, even if they are onlyutilized by a few individuals. The results of the default model runwere used in order to provide a baseline against which to comparethe results of the other twomodes. The parameters associated withthe default mode can be found in Table 1. In all modes, there was nolimit to how much of any product an agent was allowed to accu-mulate during the course of the model run.

The second mode is one where a single new product or tech-nology is introduced into the default system, so that the ability tosupply the product is ‘capped’ by limited supply of workablematerial and/or restricted knowledge of production techniques,thus creating a ‘niche’ market. The ‘one-product cap’ mode allowsone product to be regulated at the outset of the model (in thisinstance Product A, though the same results would have beenproduced if Products B or C were altered, as there is no inherentdifference between the Products as modeled). By limiting the

Table 2Various calculations employed by agents in the model.

Calculation For Formula

Production X Effort X$Efficiency XPrice X 1

Effort X$Efficiency X$�Product XWealth

�

Amount to buy WealthðownÞPriceXðtrading partnerÞ

availability of one product at the outset, scarcity of this productmakes its production a ‘niche’ market (Bentley et al., 2005), avail-able to those agents able or willing to adjust their productionstrategy to fill the demand for a rare product.

The third mode explored in this model is the infiltration of a suiteof new products and/or technologies into a default system. Archae-ologically, this can be equated to the often abrupt geographicalmovement and physical convergence of distinct cultural groups,whereby a whole range of new material culture and productionpractices are suddenly encountered. During ‘two-product cap’mode,corresponding to a suite of new technical skills penetratinga production system, the availability of two products was limited atthe outset (in this case Product A and Product C), while all otherparameters operated at default values (Table 1). Again, the choice ofProduct Caps A and C was arbitrary, and the model could have beenrun with any two-product caps varied. By creating scarcity ofmultiple products, agents were presented with an opportunity toadjust production towards emerging market demand, or remainproducers of an already ubiquitous, though not necessarily de-valued product.

3.3. The model’s applicability to Bronze Age SE Arabia

While a number of new technologies were established in SEArabia between c. 3100 and 2500 BC, it is difficult to evaluatewhether we should consider this a specific example of a one-product or two-product cap mode. As in many regions of the world,limitations in the available archaeological evidence and in chro-nological resolution result in uncertainties over the timing of thenew technological introductions into Early Bronze Age SE Arabia(including oasis agriculture, copper metallurgy, pottery production,baked steatite bead production, etc.), and thus whether suchinnovations were introduced into the system singly or as suites.Although Cleuziou and Tosi (2007: p. 91) state that “by ca. 3100 BC,the Great Transformation was over”, there is disagreement aboutthe earliest dates for a number of its components. Oasis agriculture,for example, is suggested to have flourished from the late 4thmillennium BC, although archaeological survey data from bothinterior and coastal regions (Al-Jahwari, 2009: pp.130e131; Giraud,2010) would seem to place its origins at the start of the Umm an-Nar period c. 2700 BC. Indigenous ceramic production, likewise,

Table 3P-value results of statistical testing for significant wealth inequality, broken down bygeneralist and specialist groups and run type. Italicized values indicate significantresults, and thus significant difference in wealth. These results demonstrate thesame significance as ANOVA and TukeyeKramer results (not shown).

A-Specialists B-Specialists C-Specialists Generalists

Default A-Specialists e 0.406 0.488 0.121B-Specialists 0.406 e 0.926 0.536C-Specialists 0.488 0.926 e 0.499Generalists 0.121 0.536 0.499 e

One-product

A-Specialists e 0.005 0.0001 0.014B-Specialists 0.005 e 0.375 0.298C-Specialists 0.0001 0.375 e 0.028Generalists 0.014 0.298 0.028 e

Two-product

A-Specialists e 1.87E�06 0.727 0.857B-Specialists 1.87E�-06 e 6.75E�07 6.48E�09C-Specialists 0.727 6.75E�07 e 0.814Generalists 0.857 6.48E�09 0.814 e

L.M. Rouse, L. Weeks / Journal of Archaeological Science 38 (2011) 1583e1590 1587

seems not to have begun until the early 3rd millennium BC (Méry,2000) and, despite the presence of copper-base artifacts in Hafitperiod sites (e.g. Cleuziou and Tosi, 2007: p. 115 and Fig. 101), thereis as yet no primary evidence for local copper smelting before themid-3rd millennium BC (Weeks, 2003).

Although uncertainties will persist in making direct linksbetween our model and archaeological observations, we believethe general trends revealed in our modeling are relevant forunderstanding social complexity in Early Bronze Age SE Arabia. Themodel can apply to social networks of various scales in SE Arabia(for example, incorporating a wider geographical extent or morelocalized communities), which may have experienced differentscenarios of technological and/or productive change. Thus,attempting to identify the Hafit or Umm an-Nar periods as a wholewith any of the model’s modes may be a less fruitful activity thanrecognizing that these modeled scenarios may have been experi-enced simultaneously on many different levels throughout BronzeAge SE Arabia. More important are the general trends andconvergences revealed in one-product and two-product capmodes,and how these relate to our current archaeological understandingof social developments.

4. Results of the model

The results of three distinct model run modes are presentedbelow. These are default mode (where all parameters are set to thedefaults presented in Table 1), one-product cap, and two-productcap, chosen for the simulation of different situations of expandingtechnical knowledge. In every instance, the results presented arebased on the average over ten runs of themodel to 5000 time steps.

Owing to the fact that the model addresses specialization, it islogical to break the results into groups of agents who are eitherspecialists or generalists. Specialists are defined as agents whoseproduction effort is 1 (the highest possible) for one product and0 for the other two products; similarly, any agent who has a positiveproduction effort for more than one product is considereda generalist, even if their three Efforts break down as 0.999, 0.001,and 0.000. This strict imposition reduced the bias of creatingarbitrary ‘specialist cut-off’ values. After dividing the agents intothese four groups (generalists and three types of specialists), weexamined the results for wealth differentiation and rate ofspecialist development.

4.1. Wealth inequality

In order to assess whether any significant differences in thedistribution of wealth existed between groups, we performeda one-way analysis of variance (ANOVA) test, and followed signif-icant results by applying the TukeyeKramer method to the data.Further ANOVA and t-tests with different combinations of dataallowed us to narrow down the significantly different group(s), andTable 3 summarizes these findings with the significance values fort-tests between groups. In the default mode, there was no indica-tion that any one group was significantly richer or poorer than anyother group. However, in both the one-product cap and two-product cap modes, a significant difference is evident (italicizedvalues, Table 3). When possession of Product A was capped at theoutset of the model, specialist producers of A were able to accu-mulate wealth more effectively than any other agent group. Asimilar, though reciprocal, inequality emerged in the two-productcap mode, when both Product A and Product C were initially cap-ped. In this case, the agents specializing in Product B lacked thecapacity to accumulate wealth in the same way as other agentgroups, and over time became significantly poorer than otheragents.

4.2. Product possession

The results of statistical testing on the distribution in productpossession between groups returned extremely small p-values,indicating that there is almost no chance the clearly skeweddistribution of product possession observed in the model wouldoccur by chance. Fig. 2 depicts the break down of product posses-sion graphically. As can be seen, there are many consistencies overthe three modes, for example, that generalists always owned thelargest proportion of product, at nearly half of the total supply. Inthe default mode, specialists owned little of their least-cost product(consistently 2%), while the other two specialist groups sharedpossession of the remaining products circulating the system. Insome ways, the results of the one-product cap mode also followedthis pattern. The difference in this mode was that the wealthiestagent group, A-Specialists, took their increased wealth equally fromthe possessions of B- and C-Specialists, and owned just over one-third of the total amounts of Products B and C in the system. Theresults of the default mode and one-product cap mode werepredictable in two ways. First, the low ownership of specialists isa function of the trade provisions built into the model, wherebypossession of an agent’s own specialty product is reduced as it issold to another agent whose production cost for the same item ishigher. Secondly, recalling that wealth is simply the total amount ofproduct owned by an agent, wealth disparity can only come aboutwhen an agent group over-compensates for the loss of its ownspecialty product with the possessions of other agent groups.

The most unexpected results came from the two-product capmode where the specialist producers of Product B, by far thepoorest agent group, owned nearly as much of Product C as thegeneralist agent group. This contradicts the suggested symmetry ofproduct possession seen in the other two modes. B-Specialists,rather than owning a small but equal amount of each product,instead invested almost all their wealth in one product. This mayindicate an otherwise unobserved ranking of production specialists,whereby Products A and Cwere not equally valuable in themajorityof local trading situations (though this conclusion warrants futureexamination). Another striking feature of product possession seenin Fig. 2 is the evident difference in total amount of productscirculating in the system between the three run types. The defaultrun allowed roughly 40% more product to enter the system thaneither of the capped product runs.

4.3. Specialist development

Although the final break down of labor division betweengeneralists and the three specialist groups does not exhibit

Fig. 3. Visual depiction of data in Table 4, showing the number of specialists at varioustime steps.

Fig. 2. Distribution of product possession by run type and product. Individual columnsdepict the break down of product ownership between generalists and specialistgroups, with the column’s height indicating the total amount of product in the system(averaged over 10 runs).

L.M. Rouse, L. Weeks / Journal of Archaeological Science 38 (2011) 1583e15901588

a significant degree of variation between modes, the processes bywhich specialists developed through time do vary by mode, asdepicted numerically in Table 4 and visually in Fig. 3. In defaultmode (Fig. 3, top panel), the number of specialists grew sharplybetween time steps 10 and 300, then grew more slowly until timestep 1000, at which point the number of specialist agents remainedsteady until the end of the model run. The growth rate of all threespecialist types (A, B, and C) followed the same pattern, ultimatelyreaching approximately 40e50 agents (Table 4). In one-product capmode (Fig. 3, middle panel), B- and C-Specialist numbers grewsteadily from time step 10 onwards until reaching a stable rate attime step 3000 with roughly 40 specialists of both types (Table 4).A-Specialist growth rate was much sharper between time steps 10and 300, and again sharper through growth to a peak of nearly 60specialists at time step 3000 (Table 4). The growth rate in two-product cap mode was even more pronounced (Fig. 3, bottompanel), with A-Specialists reaching by time step 1000 the samenumber of agents as seen in other modes at time step 3000. Growth

Table 4Average number of each type of specialist agent at various time steps, broken downby run type. Total number of specialist agents and proportion of total node-agents(out of 250 possible) also shown.

Time step Average number of specialists Total % of Nodes

A-Specialists B-Specialists C-Specialists

Default 2 0 0 0 0 0.010 4 4 3 11 4.6

300 32 31 33 96 38.41000 44 40 44 128 51.23000 47 39 41 127 51.05000 44 47 41 131 52.6

One-product 2 0 0 0 0 0.010 5 3 3 11 4.5

300 39 18 21 78 31.31000 45 32 34 111 44.53000 57 41 43 142 56.65000 52 37 44 134 53.5

Two-product 2 0 0 0 0 0.010 8 4 10 21 8.6

300 44 22 52 117 46.91000 57 25 58 139 55.63000 54 34 56 144 57.55000 59 32 53 145 57.9

rate then slowed but continued to rise through time step 3000,with A- and C-Specialists averaging 50e60 individuals each(Table 4). B-Specialists averaged approximately 30 individualagents, the lowest final average for any group in anymode (Table 4).

5. Discussion: general observations and archaeologicalimplications

5.1. General observations

The model demonstrates that diverse production strategies canconverge to support the emergence of economic inequality. Evenmore, we find this observation relevant when economic relation-ships function within the boundaries of normal human interactionnetworks, and are not simply the result of attempts to maximizeeconomic profit above all else. The model also demonstrates thatwhen small adjustments in supply and demand are made ina previously well-established and functioning production regime,a disparity inwealth quickly emerges.When a large-scale change toproduction strategy is introduced, those who are capable of makingthe adjustment are able to ‘keep up’ economically, while those whodo not quickly fall behind. These conditions may translate intoopportunities for individuals, groups, and sometimes entirecommunities to move towards a hierarchical social order.

Interestingly, the observations generated by this model supportrecent findings that transmittable wealth is closely associated withpersistent inequality (Borgerhoff Mulder et al., 2010; Shenk et al.,2010) and more complex social organization (Gurven et al., 2010).Our model can account for Smith et al.’s (2010b) notions of bothmaterial wealth (livestock, land, tools, goods) and embodiedwealth

L.M. Rouse, L. Weeks / Journal of Archaeological Science 38 (2011) 1583e1590 1589

(skill, knowledge) in the Efficiency factor granted to each agent. Ifwe view the Efficiency levels of agents in our model as represen-tative of the wealth (material or embodied) transmitted from theprevious generation, the differential accumulation and inequality ofagents produced in the one- and two-product cap modes clearlydemonstrate that inequality can be heritable. Importantly, ourmodel also supports the recurring caveat to these recent studies:that high levels of inequality are unlikely to emerge withoutdifferential access to non-ubiquitous, storable or defendableresources such as land, technology, or animals (Smith et al., 2010a).

5.2. Implications for Early Bronze Age SE Arabia

The production and exchange system that characterized the SEArabia in the 3rd millennium BC incorporated geographical varia-tions in resource availability, uneven distribution of technologicalknowledge, and a substantial degree of specialization, all withina tribally configured exchange economy. The overall model resultssupport Weeks’ (2003) assertion that such a system can indeedfoster wealth inequality, even as the majority of individuals benefit.Specialization and economic disparity were intimately linked andwere intrinsic to the internal socio-economic system of EarlyBronze Age SE Arabia. They were not simply a response to, orproduct of, the incorporation of SE Arabia into the exchangesystems of wider Bronze Age SWAsia and the related introductioninto the SE Arabian exchange system of foreign luxury/prestigegoods. In fact, the nature of exchange economy itself may haveprecipitated the social conditions for change by allowing individ-uals to profit disproportionately.

Moreover, the model clearly suggests that the appearance ofspecialist producers and a concomitant wealth disparity emergesmost quickly and to the highest degree precisely in scenarios oftechnological change, such as the Early Bronze Age of SE Arabia.Despite the difficulties in attributing one “mode” to Early BronzeAge SE Arabia, it is clear that this was a period of technological flux.Thus, as niche markets appeared or older production strategiesbecame obsolete, not only did specialist producers rapidly appear,but also differences in the ability to accumulate wealth developed,probably very quickly. Such differences in wealth could have rep-resented an internally generated force for instability and change ina regionwhere, as argued by Cleuziou and Tosi from the evidence ofcollective burials, social formations were underpinned by anideology that promoted group identity, affiliation, and equalityahead of personal acquisition and status (Cleuziou, 2003: p. 141;Tosi, 1989: pp. 155e156). The “consolidation” of material cultureand the increasingly elaborate collective burial traditions of thelater 3rd millennium BC in SE Arabia may be seen, in sucha reconstruction, as an ideological response to the destabilizingforces of increased wealth disparity (e.g. Cleuziou, 2002: p. 209;Méry, 2010).

In this context, it is significant that in Cleuziou and Tosi’s“Arabian tribal” model, wealth and power are dissociated; poweris situated in the kinship system and tribal relations whereas theaccumulation of wealth e although not necessarily an explicitaim of the individual or group (Lancaster and Lancaster, 1992: p.154) e can occur irrespective of one’s position within the kinshipsystem. This allows for egalitarian power systems to coexistalongside socio-economic disparity, and implies that a “tribalethos” does not necessarily require equality in all measures. Inthe modern Arabian peninsula, Lancaster and Lancaster (1992: p.154, 156) have stressed that wealth does not directly buy power,but is filtered through building a reputation of generosity and ofbeing a “good man”; reputation is the currency that is capable ofbuying influence, and may be regarded as a proxy for status andpower. Depending on their position in existing socio-political

networks, aggrandizing individuals and groups in Bronze Age SEArabia could have operated to either strengthen or challengeexisting tribal structures, i.e. as agents either of continuity or ofchange.

A final implication of our agent-based modeling that isimportant for our understanding of Bronze Age SE Arabia relatesto the impetus for intercultural contact and long-distanceexchange that is so characteristic of the 3rd millennium BC. SEArabia’s Bronze Age “exchange economy” produced individualsand groups with greater wealth whowould have been ideal agentsto promote exchange with neighboring regions seeking localresources such as copper and marine products. The prestigeassociated with such external contacts has beenmentioned above,and Lancaster and Lancaster (1992: p. 157), moreover, have notedthe importance of external contacts (including trade) in sup-porting the actions of more influential or elite members of modernArabian tribes. Rather than seeing the economic incorporation ofSE Arabia into the wider Bronze Age world as a locally passiveprocess of cultural reception driven by external (predominantlyMesopotamian) stimulus and demand, consideration of theeconomic and political motivations of SE Arabians affords thema much more active role in such developments. The specializationand wealth disparity generated by the internal SE Arabianexchange economy may have been as much of an influence onArabia’s interaction with the wider Bronze Age world as theMesopotamian search for metals, stone, and timber.

6. Conclusion

In studies of Bronze Age SE Arabia, traditional models explainlocal developments as stemming from the broad regional contactand exchange networks that characterized the greater Persian Gulfat this time. More recent studies emphasize the need to understandlocal systems of exchange and production as the possible progen-itors e or at least facilitators e of the rapid social and technologicalchanges observed archaeologically during the Hafit (c. 3100e2700BC) and the Umm an-Nar (c. 2700e2000 BC) periods. We haveutilized an agent-based model that approximates social and tech-nological conditions in Bronze Age SE Arabia to explore internaldevelopments of production specialization and wealth inequality.The results of our model clearly demonstrate that small, localizedvariations in supply, demand, and production strategies have theoverwhelming tendency to promote wealth inequality, even as theexchange system operates on social as well as economic rules.These results suggest that the localized diversity in resources,production, and exchange that characterized Bronze Age SE Arabiawere factors as influential as external contacts in sparking socialand economic changes during this period. The use of agent-basedmodeling in archaeology has demonstrable benefits towardsenhancing our understanding of how localized actions fomentbroader-scale changes, and is particularly useful in contexts ofmulti-layered social and economic networks, such as that found inBronze Age SE Arabia and neighboring regions.

Acknowledgements

Themodeling component of this workwas undertaken by one ofthe authors (LMR) at the Institute of Archaeology, UniversityCollege London, as part of the requirement for a Masters of Sciencedegree. Thanks are due to Dr. Stephen Shennan for many conver-sations about the practical and conceptual approaches to modelingarchaeological phenomena, and similarly, to Professor Clive Ortonfor his patient help in the statistical analysis of results. Severalreviewers, both anonymous and known, are also greatly thankedfor thoughtful and extremely helpful comments. As always, any

L.M. Rouse, L. Weeks / Journal of Archaeological Science 38 (2011) 1583e15901590

errors, including those in logic and interpretation, are unques-tionably our own. Finally, we would like to dedicate this paper tothe memory of Professor Serge Cleuziou, one of the pioneeringresearchers of Arabia’s prehistoric past, whose theories are socentral to the present work. His recent passing represents a greatloss to Arabian archaeology.

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