This paper aims to evaluate notions of two crucial studies on the Roman economy by Bang and Temin based on the study of distribution patterns of ceramic tablewares in the Roman East in the period 150BCE-200CE. It presents an agentbased network model simulating the social networks which represent the flow of information and goods between traders. Results of the simulation are subsequently compared to the tableware data collected in the ICRATES database. Preliminary results suggest, contrary to Bang's hypothesis, that limited availability of reliable commercial information from different markets is unlikely to give rise to the large differences in the wideness of product's distributions observed in the archaeological record

Brughmans, Tom

Poblome, Jeroen

Diposit Digital de Documents de la UAB

Abstract—This paper aims to evaluate notions of two crucialstudies on the Roman economy by Bang and Temin based onthe study of distribution patterns of ceramic tablewares in theRoman East in the period 150BCE-200CE. It presents an agent-based  network  model  simulating  the  social  networks  whichrepresent the flow of information and goods between traders.Results  of  the  simulation  are  subsequently  compared  to  thetableware  data  collected  in  the  ICRATES  database.Preliminary results suggest, contrary to Bang’s hypothesis, thatlimited  availability  of  reliable  commercial  information  fromdifferent markets is unlikely to give rise to the large differencesin  the  wideness  of  product’s  distributions  observed  in  thearchaeological record.I. INTRODUCTIONeramic tableware is one of the most common find cate-gories on Roman sites in the Eastern Mediterranean andtherefore lends itself particularly well to quantification. It of-fers archaeologists a key tool for approaching aspects of theancient economy. One of the most robust patterns observedin the collected tableware data is the variability of distribu-tion  patterns  of  different  tablewares  at  consumption  sites(products  characterised  by a  distinct  clay fabric  and  pro-duced in different centres). Some wares such as Eastern Sig-illata A (ESA) were distributed on a supra-regional scale forcenturies, others were more of regional importance (EasternSigillatas B, C, and D), whilst yet other wares were producedfor local consumption. What were the mechanisms that led todifferences  in  the  wideness  of  products’  distributionpatterns? A number of hypotheses have been published iden-tifying,  coupling and balancing particular  combinations ofpossible contributing factors, such as state involvement, re-distributive centres, consumption “pulling forces”, commer-cial “piggy-back” trade, closeness to large-scale agriculturalproduction, connectivity, etc. [1]-[4]. Most scholars seem toagree that a complex mix of mechanisms working on multi-CThe  ICRATES  project  was  supported  by  the  Belgian  Programme  onInteruniversity Poles of Attraction (IAP 07/09),  the Research Fund of theUniversity  of  Leuven  (BOF-GOA  13/04),  Project  G.0562.11  of  theResearch  Council-Flanders  (FWO)  and  the  Hercules  Foundation(AKUL/09/16).ple levels was responsible for the considerable differences intableware distribution patterns.  Since there is evidently nolack of hypotheses, the main challenge becomes to recognisethe mix of contributing factors that is best supported by theavailable evidence, and finding a more quantitative approachthat  allows one  to  distinguish  between the  archaeological“signatures” of different hypothetical scenarios. The devel-opment of such approaches has so far been limited in Romanstudies [5]-[6].This paper aims to contribute to this on-going discussionby evaluating two hypotheses of the role of social networksin tableware trade and distribution. It uses a combination ofan exploratory analysis of the collected tableware data fromthe  Roman East  between  150BCE  and  200CE  (using  theICRATES database of tablewares) with computational mod-elling of hypothetical trade mechanisms.In this paper we will focus our efforts on exploring the po-tential role of social networks as a driving force of the Ro-man trade  system. The concept  of social  networks is  hereused as  an abstraction  of  the commercial  opportunities  oftraders, acting as a medium for the flow of information andproducts. We aim to formalise and evaluate aspects of twomodels of the Roman trade system in which such social net-works play a key role: Peter Bang’s The Roman Bazaar [7]and Peter Temin’s  The Roman Market Economy [8]. Bangconsiders three factors as crucial to understanding trade andmarkets in Roman Imperial times: bazaar-style markets, thetributary nature of the Roman Empire,  and the agrarianatenature of ancient societies. The engine of the model, howev-er, is clearly the concept of the bazaar: the existence of localmarkets characterised by a high uncertainty of information,and relative unpredictability of supply and demand. This lim-ited availability of commercial information led to poorly in-tegrated  markets  throughout  the  empire.  Bang argues  thatdifferent trajectories for the flow of goods could emerge asthe result of different trajectories for the flow of information.In other words, the observed distribution patterns of table-wares and different workshops’ products (when these can beidentified) were at least in part a reflection of the structureand functioning of  past  social  networks as  defined  above.Roman Bazaar or Market Economy? An Agent-based NetworkModel of Tableware Trade and Distribution in the Roman EastTom BrughmansSagalassos Archaeological Research Project,University of LeuvenDepartment of Computer and Information Science,Universität KonstanzEmail: Tom.Brughmans@uni-konstanz.deJeroen PoblomeSagalassos Archaeological Research Project,University of LeuvenEmail: Jeroen.Poblome@arts.kuleuven.beMiguel, Amblard, Barceló & Madella (eds.) Advances in Computational Social Science and Social SimulationBarcelona: Autònoma University of Barcelona, 2014, DDD repository <http://ddd.uab.cat/record/125597>Temin agrees with Bang that the information available to in-dividuals was limited and that local markets were structuringfactors. However, contrary to Bang he believes that the Ro-man  economy  was  a  well-functioning  integrated  marketwhere  prices  were  determined  by  supply and  demand.  InTemin’s model the structure of social  networks allows forcommercial  information  to  be  more  reliably  and  widelyavailable between markets than in Bang’s model, resulting inmore integrated markets across the Roman Empire.II. THE MODELAn agent-based network model was designed to test theabove two hypotheses. In the model 2000 traders are locatedat  100  sites  and  are  connected  in  a  social  network.  Fourproducts  are  produced  at  four  different  ‘production  sites’,and are subsequently distributed through commercial trans-actions between pairs of traders that are connected in the so-cial network.Setup procedures:  the model  is  initialised  by arrangingthe sites along a circular layout and distributing the tradersamong the sites following an exponential frequency distribu-tion,  which  represents  observed  differences  in  settlementsizes and the consumption demands of their populations, i.e.the existence of large urban centres with high demand. Foursites which are equally spaced along the circular layout arethen selected as production sites of four different products(i.e. four different tablewares).Traders are subsequently connected to each other to forma social network in three steps which results under differentvariable settings in the networks shown in Fig. 1:1. Traders  on  the  same site  are  connected  following asmall-world structure, with a high clustering coefficientand a low average shortest-path-length [9]. This is a suit-able  representation  of  the  communities  of  traders  inBang’s model who are more likely to only share commer-cial information to community members, since a featureof ‘small-world’ networks is the efficient spread of infor-mation  within  clusters  whilst  few intermediary  traderswill allow information to flow between clusters. The pro-cedure to create a network with a small-world structure isinspired by the model for the growth of social networksby [10], previously applied in an archaeological model ofexchange by [11].2. We ensure that at least one pair of traders is connectedbetween every pair of adjacent sites, and that the networkconsists of one connected component (the latter is doneat the very end of the setup procedures), which guaran-tees that each trader can, in theory, obtain an item of eachproduct. This step results in a high average shortest-path-length between traders at different sites.3. A  variable  proportion  (0%  -  0.1%)  of  all  pairs  oftraders are connected if they are located at different sites.The proportion is determined by the variable proportion-inter-site-links.Distribution procedures: in every time step traders per-form the following tasks in sequence:• traders determine their demand;• traders discard a small proportion of their stock (due toitems going out of fashion or breaking). This proportion isFig 1: Example of the network structure generated in the setup procedure of the ABM for different values of the proportion-inter-site-links variable. Atthe top sites are layed out along a circle and traders are positioned at sites. At the botton sites are no longer included and the traders’ social network islayed out using a force-directed layout algorithm (yFiles Organic layout in Cytoscape) to display its structure. Note the existence of clusters of traderson sites connected to few other clusters, a pattern which gradually disappears as traders receive more inter-site links and the sites become more inte-grated.Miguel, Amblard, Barceló & Madella (eds.) Advances in Computational Social Science and Social SimulationBarcelona: Autònoma University of Barcelona, 2014, DDD repository <http://ddd.uab.cat/record/125597>set to 14% following the model of the use-life of table-ware by [12];• traders on tableware production sites obtain new itemsif their current possession is below their demand;• traders  obtain  commercial  information  from  theirneighbours in the network;• traders determine what they believe to be the currentprice of an item using this commercial information;• finally, all items owned by all traders in that turn areconsidered for trade.Every time step each trader will only have commercial in-formation available from a proportion (10% - 100%) of itslink neighbours. This proportion is determined by the vari-able local-knowledge. The trader then calculates the averagedemand and average supply of this proportion of neighbours,including his own supply and demand. Using this informa-tion he calculates the price of a product as the average de-mand divided by the average supply (normalised so that thevalues fall between 0-1). When each product is considered ina transaction a seller will only agree to sell an item if thebuyer’s price is equal to or higher than this price.Each item is considered for trade once per time step. Anitem is put in a trader’s stock if he cannot make a profit or ifnone of his neighbours in the network requires an item (i.e.their demand equals 0). Items in stock can be redistributed inthe next time step. An item is sold to a buyer if the buyer’sprice promises a profit or break-even for the seller. The buy-er either places the obtained item in stock for redistributionif the average-demand is higher than his demand (i.e. redis-tribution holds the promise of a higher profit), or he sells itto a consumer. In the latter scenario the buyer’s demand isdecreased by 1, the item is taken out of the trade system, andit is deposited on the buyer’s site.III. PRELIMINARY RESULTSA number  of  experiments  were  run  to  test  Bang’s  andTemin’s  models  by  varying  two  variables:  proportion-in-ter-site-links and local-knowledge. The experiments present-ed here were run 10 times each, with 10.000 time steps perexperiment,  and with parameter  settings of  the social  net-work adopted from the model by [10]. When proportion-in-ter-site-links is increased  traders  will have a more diverseavailability  of  information.  Moreover,  the  average  short-est-path-length will become shorter, which enables productsto spread throughout the network in a lower number of steps,but the clustering coefficient will also decrease, reducing thecommunity structure that is so prominent in Bang’s model.When  local-knowledge is increased traders will have morecommercial information. High settings of either of these twovariables  therefore  reflect  Temin’s  hypothesis  of  a  higheravailability,  diversity and reliability of information that al-lows for integrated markets across the Roman Empire.The proportion-inter-site-links variable: setting this vari-able to 0 results in different products being deposited on asmall but more or less equal number of sites, whilst high val-ues result in strong differences between the number of sitesdifferent products are deposited on. A few examples are giv-en: a proportion of 0% (0 random inter-site links) results inproducts being only deposited to the two neighbouring sitesof the production centre, products have an equally wide dis-tribution,  and  volumes  of  products  deposited  on  sites  de-crease with distance away from the production centre. How-ever,  increasing this  to  a  proportion of  only 0.01% of alltrader  pairs  (200 random inter-site  links)  will  give rise tostrong differences in the number of sites a given product isdeposited on (the largest number of sites a given product isdeposited on = 22 out of 100 sites; the maximum differencebetween product’s distributions = 16 out of 100). Further in-creasing the proportion to 0.1% of all trader pairs (1999 ran-dom inter-site links) makes these differences even stronger (asingle product is invariably distributed to up to 90 out of 100sites;  the  maximum difference  between products’  distribu-tions = 84 sites).The  local-knowledge  variable: increasing  this  variabledid not lead to differences in the wideness of products’ dis-tributions, but did have a strong impact on the proportion offailed  and  successful  transactions.  This  is  not  surprisingsince in this model sellers aim to optimise their profits, andwill not agree to lower a price which they know to be thecorrect price in their part of the network.IV. CONCLUSIONPreliminary results suggest that the local-knowledge vari-able has a limited effect on the wideness of tableware distri-bution, whilst the  proportion-inter-site-links variable has astrong effect. Limited commercial knowledge can still giverise to wide differences in distributions, but only in systemswith  highly  integrated  markets.  This  means  that  the  lo-cal-knowledge variable is not instrumental in giving rise tothe pattern of interest, whilst the  proportion-inter-site-linksvariable is. Limited availability and high uncertainty of infor-mation,  and a  weak integration of  different  markets in aneconomy governed by supply and demand, is unlikely to giverise to large differences in the distribution patterns of table-ware.  Preliminary  results  of  this  model  therefore  rejectBang’s claim that limited market integration, availability andreliability of commercial information can give rise to differ-ences in the wideness of products’ distributions. However,more  experiments  and  a  detailed  comparison  with the  ar-chaeological record are required to support these results andto evaluate other aspects of Bang’s and Temin’s interestingmodels.  Indeed,  the  main  contribution  of  this  preliminarymodel is to illustrate how aspects of the many hypothesessurrounding the study of the Roman economy can be for-malised and tested, an approach which we believe to be alltoo rare but valuable and necessary for Roman studies as awhole.In future work a number of other experiments will be per-formed, including: a higher number of iterations per experi-Miguel, Amblard, Barceló & Madella (eds.) Advances in Computational Social Science and Social SimulationBarcelona: Autònoma University of Barcelona, 2014, DDD repository <http://ddd.uab.cat/record/125597>ment proportional to the stochasticity in the model; experi-ments that add a transport cost for transactions between sites;experiments in which the traders  are  distributed uniformlyand normally; and a comparison with random network struc-tures rather than the  small-world.  Furthermore, a more de-tailed goodness of fit between experiments’ results and theactual  tableware record  of the ICRATES database  will beevaluated.REFERENCES[1] C. Abadie-Reynal, “Céramique et commerce dans le bassin Egéen duIVe au VIIe siecle”.  In: Kravari  V,  Lefort  J,  Morrisson  C,  editors.Hommes et richesses dans l’empire byzantin Tome I, IVe-VIIe siècle .Paris; 1989. p. 143–59.[2] P. Bes, “A geographical and chronological study of the distributionand  consumption  of  tablewares  in  the  Roman  East”.  PhD  thesis.Leuven: K.U.Leuven; 2007. [3] T. Lewit, “Dynamics of fineware production and trade: the puzzle ofsupra-regional exporters”. J Rom Archaeol. 2011;24:313–32. [4] P. Reynolds,  Trade in the Western Mediterranean, AD 400-700: theceramic  evidence. BAR  international  series 604.  Oxford:Archaeopress; 1995. [5] J.  K.  Davies,  “Linear  and  nonlinear  flow  models  for  ancienteconomies”. In: Manning JG, Morris I, editors. The ancient economy:evidence and models. Stanford; 2005. p. 127–56. [6] I. Morris, R. P. Saller, W. Scheidel, “Introduction”. In: Scheidel W,Morris I, Saller RP, editors.  The Cambridge economic history of theGreco-Roman world. Cambridge: Cambridge University Press; 2007.p. 1–12. [7] P. F. Bang,  The Roman bazaar,  a comparative  study  of  trade  andmarkets  in  a  tributary  empire.  Cambridge:  Cambridge  universitypress; 2008. [8] P.  Temin,  The  Roman  Market  Economy.  Princeton:  PrincetonUniversity Press; 2013.[9] D. J.  Watts,  S. H. Strogatz,  “Collective dynamics of “small-world”networks”. Nature. 1998 Jun;393(6684):440–2.[10] E. M. Jin, M. Girvan, M. E. Newman, “Structure of growing socialnetworks”.  Phys Rev E Stat Nonlin Soft Matter Phys. 2001 Oct;64(4Pt 2):046132.[11] A.  Bentley,  M.  Lake,  S.  Shennan,  “Specialisation  and  wealthinequality in a model of a clustered economic network”.  J ArchaeolSci. 2005 Sep;32(9):1346–56.[12] T. Peña,  Roman pottery  in  the  archaeological  record.  Cambridge:Cambridge University Press; 2007.Miguel, Amblard, Barceló & Madella (eds.) Advances in Computational Social Science and Social SimulationBarcelona: Autònoma University of Barcelona, 2014, DDD repository <http://ddd.uab.cat/record/125597>

Roman bazaar or market economy? : An agent-based network model of tableware trade and distribution in the roman east

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Roman bazaar or market economy? : An agent-based network model of tableware trade and distribution in the roman east

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