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The first time you teach a course, you have to expect some rough edges as things you wanted to try don’t quite work out, some topics aren’t as engaging or don’t tie together how you thought… it’s always a bit of a work in progress. As I design the course, I try to think in blocks of ideas, and arrange them sequentially so that some kind of thread will eventually emerge. This is where I am right now; ask me tomorrow and it’ll probably have changed.
The aim of this course is to explore ways of expressing historical narratives through interactive digital media and simulations.
This does not mean that you have to be a programmer. There are many roles that need to be filled when we enter into this realm. The final project is the creation of a detailed game/simulation design document, working in groups of five.
These documents will be posted online and brought to the attention of the history & games community. The three that generate the most interest (as measured by retweets, likes, or other social media metrics) will receive an XP bonus. I intend to ask PlaythePast.org if they will publish these ones.
Anything you produce above and beyond that (like a working prototype) is also an XP bonus.
XP may be earned by performing any of the tasks listed in the ‘Level Up!’ folder for a given week. They may only be performed during that week. These might involved doing programming tutorials, modifying scenarios or simulations, trying out things in Codeacademy, and yes, playing games. XP can be traded in for a bye on your blogging duties for a given week, or for an extension on certain assignments, or, for those of you in the top third by XP, a small bonus on your final grade.
Required Text: Watrall, E. et al, Play the Past
1 – Getting started. How this course works, how assessment works; Intellectual foundations for the study of history via/of games/simulations
2- Historical Consciousness and the video game industry
3- Deep history of games and simulations, from Lascaux to M.A.D. to Monopoly to the Serious Games movement (and probably some formal game theory)
4- Practical Necromancy, or Simulations: how, why, what, when.
5- Meaningful Play and Digital History (looking at playful approaches to the past fostered by digital history, so not necessarily games; I’m thinking here of the conversations about ‘deformative humanities’)
6- ‘Educational Games’ – how games foster learning; why ‘educational’ games are awful…
7- Interactive Fiction – beautiful simulations, and the literary affinities with how we normally write history…
8- ARGs, AR, and blurring the boundaries
9- Civilization and its discontents: on modding
10- Game design presentations 1
11- Game design presentations 2. I have it in mind that it’d be good to live stream these, if I can get permission…
12- Gaming and Simulation for History: A powerful way of writing immersive, engaging history (wrap up).
- In-Class Participation: 15%
- Blogging: 20%
- Critical Analysis of a game: 25%
- Group Game Design Document: 30% (Missing the checkpoints results in -2% each time). Each group will present their game/simulation during the last two weeks of class. Each member of the group is expected to contribute to the presentation. Missing the presentation, or standing silent/idle during your group’s presentation, will result in -5% for you as an individual. I reserve the right to grade group work on an individual basis.
- Individual reflection: 10%
(crossposted at Play the Past)
There are things you can do, and can’t do, to undergraduate students, I’ve discovered. Recently heard in class:
Math? You want us to do math? But… but… we’re history students!
This of course is my continuing digital antiquity class, ‘Cities and Countryside in the Ancient World’. I have them playing right now with maps and spatial data, trying to do some basic spatial analysis. Earlier in the year, to accustom folks to trying to think about ancient spaces with a suitably ancient mindset, I had the students do some readings, play ‘Stranger in These Parts‘ interactive fiction, and then explore the same territory using the ORBIS simulation of geographic space.
I had broken this assignment into three pieces. The first was a basic seminar discussion of the two articles, R. Ling’s 1990 article, Stranger in Town, and Tim Ingold’s Temporality of Landscape. Then, play the IF. I ask the students to pay close attention as they played to the way they moved through the game, the things that were easy to do, the things that were difficult to do, and to reflect on their ignorance of the world as they played. The next week, ORBIS. After a few panicky emails, I sent around an email which read in part,
Look at the course objectives. Read Ingold and Ling from week 1. Play through the interactive fiction, paying attention to how you navigate space, and how space is represented. Play with Orbis, looking at the ways the connectivity of places – or the perception of closeness/farness – can change with the seasons, the mode of travel and so on (and note that mode of travel will correlate often with social class!). Reflect on all of this. How is space socially constructed?
Now, I had modeled in class how to interact with both the game and the simulation. I figured this would be a bit of an easy way into some of the more substantive issues of the course. I should’ve known better. This is what happened next. It sounded a bit like this:
Play a game? A game? But… but… we’re history students! We don’t know what you want us to write!
There was great resistance to the idea that playing the game could have some sort of valid pedagogical outcome, which came down to a very instrumental view of what education is about. Write the standard historical essay. Write the midterm. Write the final. Get grade. Repeat. The sheer fear of doing something other than writing a research essay meant that I had to throw my lesson plans out the window. To calm nerves, we had to play the game together, as a class, me running the computer, them suggesting things to try. By turning it into a collaborative game, it seemed to take some of the danger away – what if I play the game wrong? Students still had to write their own reflection pieces, but I discovered that I couldn’t push them to do the playing on their own, at least at first.
So was it worthwhile? The best results looked similar to what student A wrote:
[...]The ‘PlayFic’ interactive fiction (Graham: 2012) further emphasizes the fragmentary nature of travel and reminds the reader of the social interactions that would have been necessary for the ancient traveller in order to properly find their way amidst an absence of public transport, urban or international, and of regular signposting. This immersive fiction gives a practical experience of ancient travel and space to modern readers, and also attempts to impart the sense of noise, movement and business of cities and urban hubs. Far from the neat remove of ‘Orbis’, the IF conveys the messiness and overwhelming frustration of packed city-living and uncertain directionality in travel. No clear route may be chosen, but must instead be gleaned through socializing with others. Directions are had on an ad hoc basis. Travel on foot or by ox-cart are cross-over option features in both ‘Orbis’’ and ‘PlayFic’s’ journeys, highlighting popular means of transit in antiquity.
Ingold’s article, ‘The Temporality of Landscape’ (1993) gives a philosophical explanation between the concepts of landscape and environment, cityscape and taskscape, seeking to intelligize cityscape and landmarks through cultural/temporal perception. At the same time, Ingold echoes the blueprint for ancient travel as laid out in ‘Orbis’ and the IF: “In the landscape, the distance between two places, A and B, is experienced as a journey made, a bodily movement from one place to the other, and the gradually changing vistas along the route” (Ingold, 154:1993). As well, the connective importance of networks and crossed pathways is given consideration: “…the landscape is the world as it is known to those who dwell therein, who inhabit its places and journey along the paths connecting them” (Ingold, 156:1993).
And sometimes, people got very much into the details. B, who was concerned more with ORBIS than the Interactive Fiction, wrote:
[...]The Roman world in the first half of the fifth century A.D. was plagued with invasions both before and during the reign of Attila the Hun, the scourge of God. The greatest problem that the Romans had with the Huns was that, even when they were not organized under Attila, they moved so quickly, in a time when long range communication moved only as fast as a messenger on a horse, that The Romans could not respond quickly enough. By the time they arrived, the Huns had already sacked and burned the countryside after simply riding past all Roman fortified locations. In the year 443, the Huns sacked the city of Philippopolis and Margus faster than the Romans could respond. If we use ORBIS, and place in the start city of, say, Apulum which is well into Hunnic territory in the 5th century, and place the destination in ORBIS as Philippopolis and place the speed at which the Huns would’ve ridden, ‘horse relay’, we can get an approximate duration of travel; in this case, 2.6 days in the month of July on the fastest route possible. Also, ORBIS shows the route taken by means of primary roads; this is also important because the Huns, who would’ve known very little of Roman Geography, would just have followed the roads straight from Apulum to Philippopolis. If we assume that a messanger from Philippopolis races to Constantinople on ‘horse relay’, it would take him 1.7 days to get there and then another 7.1 days for the army to March immediately from there to Philippopolis in order to save the city. Thus it would take approximately 9 days in order for the Romans to support the semi-defenseless city!
So did we learn anything? The majority of students came away with at least an idea that how we imagine space is at least as important as how space actually lays out, geographically speaking. The best students did what A & B did here, making far deeper connections. I certainly learned that the only way I’m going to get any traction for my playful approaches to history in these parts is to break everything into very small pieces, and to do as much of it collaboratively, in class room time, as possible. I need to ‘flip’ my classroom, leaving lectures to video and the hands-on stuff when I’m right there to guide, to reassure, to cajole, and to encourage.
It’s sad, in a way, that we as educators have beaten so much of the playfulness out of students that when encouraged to go play, the first instinct is to run back to the box.
Yesterday I described how I was going to do an unconference in my Historian’s Craft class. I’ve storified the results here:
I teach HIST2809A, The Historian’s Craft. Each week we have 2 hours of lecture and 1 hour of tutorial with a TA. This term, I have 102 students in the class. Over 12 weeks, I try to instill a measure of reflexivity in my students, in their approach to primary documents. In my assignments, I have them transcribe primary documents, analyze visual evidence (photos, paintings, etc) and even some material culture. As a final assignment, I do a variation of the Forgery Game and we try to approach the same problems from 180 degrees the other direction.
At the midway point, I like to stop and ask for feedback on my teaching, and the course. I hand out 3×5 index cards, and have students write on one side the things that are working for them, and on the other, the things that’d be even better if…
That exercise takes about 20 minutes. What do I do with the other 100 minutes? Given the theme of reflexivity and community, I think an unconference is appropriate. Watch for #hist2809 on your twitter stream tomorrow. So here’s what I’m going to do.
8.35- 8.55 – mid-term feedback on my teaching.
8.55-9.15 – unconference explanation & scheduling. They already know what an unconference is, as I’m hosting THATCamp Accessibility on Saturday. Some of them are even coming! I’ll ask for suggestions, and put them up on the board. I’ll direct them to think about the things we’ve already covered.
9.15-9.20- voting. Simple show of hands. We’ll have four breakout areas, in the different corners of the room, in 15-20 minute ish sessions.
9.25-9.45. Session 1.
9.45-10.10. Session 2.
10.10-10.25. My concluding remarks. Class over.
I’ll encourage students to tweet while this is all going on. A handful are already on twitter; I’ll have a live stream displaying on the classroom projector. Maybe some of you will buzz in with comments on how you approach The Historian’s Craft. #hist2809, October 26th, 9 til 10 ish.
See you there!
What follows is a draft of an article I’ve submitted to a journal. If two blind reviews are good, more must be better, right? Keep in mind this could get rejected or substantially altered from its final version.
This paper outlines how agent-based modeling can be used as a laboratory for exploring aspects of ancient economic life. Bang (2006; 2008) has put forth a model of the Roman economy developed from the insights of Clifford Geertz (1979) as an ‘imperial bazaar’. A significant portion of Bang’s model hinges on social networks. Particular network topologies have implications for the flows of materials or ideas through them, and so knowing the kind of network shapes that the ‘bazaar’ might generate should be explored. We can develop an agent based simulation of Bang’s model which as a by-product of its functioning generates social networks. We can then look at under what conditions the generated social network matches social networks known archaeologically from the extractive economy of Roman brick and tile. The simulation thus represents a way of bridging economic theory with the archaeological evidence. Suggestions for extending the model to explore multiple kinds of products and adapting it are presented.
Agent based modeling; Roman economic history; simulation; trade, natural resources
This paper explores what the economist Lea Tesfatsion calls ‘agent-based computational economics’ (2012). It uses the Netlogo (Wilensky, 1999) agent based modeling platform to implement a (necessarily) simplified Roman economy. The model generates social networks which can then be measured against known archaeological networks; where there is a degree of congruence, I argue that the model has generated new knowledge. In this regard, what I am building is a ‘computational laboratory’ that takes place in an explicitly spatial environment (Dibble, 2006). In the spirit of open access, I make the model and its code available for experimentation and extension and so the results presented here should be seen as necessarily preliminary.
According to Peter Bang, the Roman economic system is best understood as a complex, agrarian tributary empire, of a kind similar to the Ottoman or Mughal (Bang 2006; 2008). In such states, trade and markets remained locally and regionally fragmented (though there could be overlapping ‘regions’ of different sizes, depending on the product; cf Horden and Purcell 2000: 123-72 on ‘connectivity of microregions’). This was a ‘stable’ economic state, with its own characteristics and patterns. Bang (2006: 72-9) draws attention to the concept of the bazaar. The bazaar was a complete social system that incorporated the small peddler with larger merchants, long distance trade, with a smearing of categories of role and scale. “The bazaar was notorious as a place of high risk and uncertainty where bottlenecks, asymmetries and imbalances were endemic… Bazaar can be used to denote a stable and complex business environment characterised by uncertainty, unpredictability and local segmentation of markets.” (79)
The bazaar emerged from the interplay of instability and fragmentation. The mechanisms developed to cope with these reproduced that same instability and fragmentation. Bang identifies four key mechanisms that did this: small parcels of capital (to combat risk, cf Skydsgaard 1976); little homogenization of products (agricultural output and quality varied year by year, and region by region as Pliny discusses in Naturalis Historia 12 and 18); opportunism; and social networks (80-4). As Bang demonstrates, these characteristics correspond well with the archaeology of the Roman economy and the picture we know from legal and other text. Bang concludes, “…the set of strategies outlined here provided a complex and resistant foundation for trade. But the result was to consolidate tendencies towards market segmentation where economic flows seem to run in separate, compartmentalised channels and networks… The bazaar, to conclude, is the model of agrarian markets we have been looking for.” (84).
It is this last component on which I focus. ‘Networks’ as both a metaphor and a statistical, measurable feature of ancient society have recently begun to invade the literature. Brughmans 2012 gives an overview of the historiography of network approaches to archaeology. He draws attention to the work of Wasserman and Faust (1994: 4) that illustrates the social assumptions of network analysis:
-Actors and their actions are viewed as interdependent rather than independent, autonomous units.
- Relational ties (linkages) between actors are channels for transfer or “flow” of resources (either material or nonmaterial).
- Network models focusing on individuals view the network structural environment as providing opportunities for or constraints on individual action.
- Network models conceptualize structure (social, economic, political, and so forth) as lasting patterns of relations among actors. (Brughamns 2012: An Introduction to SNA).
In a recent application of network theory to the problems of Greek colonization, Malkin (2011: 18-19) argues that since we can never know the exact, perfect ‘wiring’ of any ancient network, it is better to use ‘network analysis’ and its concepts metaphorically. This would miss an opportunity. A network approach should properly not stop at ‘network’ as metaphor. It should outline as fully as possible the connections relevant to the question we are asking, to understand the implications of the topology (pattern of connections) for what they might mean for history. A small-world, in network terms, is one where most connections are local and short, while a handful of individuals have connections that connect otherwise disparate parts of the network. This allows whatever flows through the network to reach all its parts quite quickly (Buchanan 2002; Milgram 1967; Watts and Strogatz 1998; Watts 1999). Consequently, as Brughmans points out, a social network with this pattern of connections would have implications for our understanding of the processes underlying its formation, such as the spread of religious ideas or the establishment of social norms or political power (2010).
Bang’s model of the bazaar (2008; 2006), and the role of social networks within that model, can be simulated computationally. What follows is a speculative attempt to do so, and should be couched in all appropriate caveats and warnings. Networks can be discerned and drawn out from archaeology, prosopography, and historical sources (e.g., Brughmans 2010; Manning 2010; Ruffini 2008; Graham and Ruffini 2007; Graham 2006a). If the networks we see in the ancient evidence correspond to networks generated from the computational simulation of our models for the ancient economy, we have a powerful tool for exploring antiquity, for playing with different ideas about how the ancient world worked (cf. Dibble 2006). Computation might be able to bridge our models and our evidence. In this paper, I develop a simple simulation that represents a starting point for bringing this agenda about, and explore some of its consequences.
The Bazaar and the New Institutional Economics
The idea of the Roman economy as a kind of bazaar puts the emphasis on the trading institutions of the Roman world. This is a trend that fits into the ‘New Institutional Economics’ (NIE) of Douglass North (1990) and his followers (especially for antiquity as formulated by Frier and Kehoe’s 2007 chapter in the Cambridge Economic History of the Greco-Roman World; cf also Bang’s 2009 review). What North proposed in his NIE was not a rejection of neoclassical economics, but rather a re-assessment of what rationality could mean, especially over time. Over time, what is most costly in any transaction is information. Working out these costs and measuring them are the roots of institutions (Lo Cascio 2006: 219, citing North 1990: 27).
NIE assumes that knowledge is costly to acquire, which limits actors in their ability (or their will) to act. Thus, individuals tend to make ‘good enough’ decisions, that is, ones that are ‘satisficing’. They account for incomplete knowledge through guess work, reliance on social relationships, values and judgements that are necessarily incomplete (Frier and Kehoe 2007: 121-122). Once a particular choice is made, further choices in the same direction are easier (less immediately costly) than perhaps superior alternatives. This is called ‘path dependence’ (Fier and Kehoe 2007: 137, citing North 1990). The development of the slave-based villa economy is an example, where the immediate profits from the system forestalled the development of a more sustainable system that promoted longer-term growth.
Institutions help to regularize and promote the flow of knowledge; it was a strategy to cope with uncertainty. In the ancient world, the institution of the market or fair helped to overcome the problems of asymmetrical knowledge not through some ‘invisible hand’ setting a ‘correct’ price, but through the formation of personal networks. “[more important than bringing buyers and sellers together is] the network of long-term personal relationships that arise within regular markets: patterns of trust and reliance based upon prior experience… cultivating these long-term “relational” contracts is often of more importance than obtaining the lowest price” (Frier and Kehoe 2007: 119)
According to Bang, the key characteristics of a bazaar-type economy lie in poor information, fragmented organization, and little standardisation. Personal connections were used to obtain information and bridge the gaps (2008: 198). The participants in a bazaar market actively sought to minimize uncertainty by establishing a clientele. By trading with a preferred partner, the marketer is sheltered from some risk and uncertainty, but only by fostering particular relationships. Other possibilities might exist, but are not open to the market trader, since he is limited to what can be known through his own network of contacts (5). More generally, Bang finds it telling for instance that transport amphorae never lost their regional characteristics, that despite entering trading networks of long or mid range, they never evolved to a standard type or size. He argues that the world of Roman trade should therefore be modelled not as a ‘generalized market sphere’, but as a patchy, weakly integrated space where trading ‘circuits’ are segmented at different scales: “It was a high-risk, high transaction-cost environment” (194-5).
Despite market irregularities, imperfect knowledge, and differences in social power, the trader should not be seen as passive. Rather, what the NIE suggests is that despite high transaction costs creating so-called ‘market imperfections’, they also create different approaches, different strategies to cope with uncertainty. For Bang, the key is to explore social differences between actors in a market. “[The differences] show the existence of a hierarchy and thus of a particular social system replete with institutionalised forms of behaviour and specialisation of functions. The markets of traditional trade should not be seen in terms only of one of its players, the pedlar, they constituted an entre social universe – the bazaar’. For Bang, ‘the bazaar’ is not the quaint tourist trap of labyrinthine shops and traders, but rather a ‘system and hierarchy’ that includes fairs and markets, stretching from rural hinterlands to inter-urban exchange networks (Bang 2008: 197).
Euergetism or other investments in ‘social capital’ by merchants was ‘sound business’. These investments helped to maintain or improve the fides of the individual, thus proving or signalling the honour and thus credit-worthiness of the individual (260). This idea accords well with the ideas of Shennan (2002: 224-7) on costly signalling, an evolutionary idea where one gains prestige and status, by putting energy into conspicuous display. Game theory experiments suggest that individuals are prepared to accept lower returns from individuals with a perceived higher status (Shennan 225, citing Boone and Kessler 1999:271); the experiments also seem to suggest that individuals who invest in costly signalling also end up at the head of the queue for resources in times of crisis (Shennan 225, citing Boone and Kessler 1999: 262-5). Shennan also draws attention to the fact that individuals who engage in costly signalling also must be able to ‘back up’ the display by providing benefits to the larger social group as a whole. He argues that their display achieves this end also by making it too expensive for other individuals to compete in the same game, thus cementing their role (Shennan 2002: 225). Thus, market activities of merchants taken as a communal whole turn the bazaar into a social system; for Bang, a market was also a “social universe fostering a sense of hierarchy and promoting norms of proper conduct between individual traders” (Bang 2008: 260).
The Piazzale delle Corporazione in Ostia, the collegiae of traders in Lugdunum, the Palmyrene merchants in Palmyra – all of these are evidence, for Bang, of the ways traders banded together into social groups in attempts to mitigate the imperfect knowledge and regional vagaries of the Roman world (Bang 2008: 251-3). Information uncertainty and unpredictability of supply and demand are the ‘ideal-typical’ characteristics of the bazaar (Bang 2008: 4). Building networks was one response to this situation. These then are the economic characteristics and social behaviours that we will seek to model in our computational simulation of the bazaar. Can such a simulation generate social differences between actors? What kinds of hierarchies can emerge? Under what conditions does this computational world resemble the one we see in the archaeology – and what can that tell us about the Roman economy?
Agent Based Modelling
Agent based modelling is an approach to simulation that focuses on the individual. In an agent based model, the agents or individuals are autonomous computing objects. They are their own programmes. They are allowed to interact within an environment (which frequently represents some real-world physical environment). Every agent has the same suite of variables but each agent’s individual combination of variables is unique (if it was a simulation of an ice-hockey game, every agent would have a ‘speed’ variable, and an ‘ability’ variable, and so the nature of every game would be unique). Agents can be aware of each other and the state of the world (or their location within it), depending on the needs of the simulation. It is a tool to simulate how we believe a particular phenomenon worked in the past (cf Gilbert and Troitzsch 2005:17 on the logic of simulation; Macal and North 2010). When we simulate, we are interrogating our own understandings and beliefs. What is particularly valuable then is that we can build a simulation, and when the agents begin to interact along the patterns of behavior that we have specified (drawn from our understanding of how various processes worked), we have a way of exploring the non-linear, non-intuitive, emergent consequences of those beliefs.
What is more, in order to code a particular behavior, we have to be clear about what we think about that behavior. We have to make our assumptions explicit in order to translate an historical argument into code. A second investigator then can examine the code, critique these assumptions and biases (or indeed, errors) and modify the simulation towards a ‘better’ state. The model is built using the open-source Netlogo modeling environment and language (Wilensky 1999). Examples of agent based models used for archaeological questions include Wilkinson et al.(2007); Graham (2009, 2006b); Graham and Steiner (2008); Kohler et al.(2005).
The Model Setup and Rules
This model imagines a ‘world’ (‘gameboard’ would not be an inappropriate term) in which help is necessary to find and consume resources. The agents do not know when or where resources will appear or become exhausted. By accumulating resources, and ‘investing’ in improvements to make extraction easier, agents can accrue prestige. When agents get into ‘trouble’ (they run out of resources) they can examine their local area and become a ‘client’ of someone with more prestige than themselves. It is an exceedingly simple simulation, but one that exhibits subtle complexity in its results. (It is an extension of Wilensky’s Wealth Distribution model, 1998). It is always better to start with a simple simulation, even at the expense of fidelity to the phenomenon under consideration, on the grounds that it is easier to understand and interpret outputs. A simple model can always be made more complex when we understand what it is doing and why; a complex model is rather the inverse, its outcomes difficult to isolate and understand.
In this simple world, only one kind of resource is simulated at a time – forest, coppicing, clay, and mining/quarrying. These resources are chosen because of the probable use of broadly similar kinds of strategies to organize the trade (Graham 2005: 111-115, the use of stamps on the product to organize extraction and distribution). There was of course a great deal of difference between these resources in terms of the scale of the organization by the Imperial period (cf Meiggs, 1982:325-370; Hirt 2010: 357-369; for an agent-based simulation of mining in Bronze Age Halstatt see Kowarik et al. 2012). The model takes account of scale and variability by giving each point in the world a chance of holding a certain amount of whatever resource is being simulated (with mines being the least likely, and forests the most). In the model, forest and coppicing regenerate after a set amount of time, while clay pits and mines do not. Each location keeps track of how often it has been ‘harvested’, allowing for exhaustion or depletion of the resource and thus taking it out of play.
Each individual agent represents a single individual who works in this world. Their sole task is to locate and ‘harvest’ the resource. Each agent consumes a portion of whatever is harvested in order to remain active. The amount consumed is set randomly per agent, to a user preset maximum; every individual is different in their abilities. The original simulation calls this ‘metabolism’; we can think of it as representing ‘transaction cost’. Individual agents also have knowledge of the world (which is called ‘vision’ in the model, representing how ‘far’ they can ‘see’ within the environment), to differing degrees (again, to a user preset maximum). We can think of this as representing Bang’s informational uncertainty. These two variables allow the user to simulate worlds of differing economic conditions.
An agent searches the environment within its field of vision, looking for a resource. If it finds some, it may harvest it. If its costs to move are greater than the amount of the resource it has on hand, it is removed from the simulation. A new agent takes its place, representing a generational change-over. If the agent has now consumed all of its resources, it may ask for help (and thus stave off removal). Each agent keeps track of who has given it help and to whom it has given help in turn, which generates a network structure that we may analyse at the conclusion of the simulation.
When an agent asks for help, it examines its local neighbourhood (within its range of vision, ie, knowledge-of-the-world) for a possible patron. A possible patron is one whose prestige is equal to or greater than its own (initially, all agents have the same prestige value; exact values are not as important as having appropriately conceived processes, cf Agar 2003). If a potential patron can be found, and the potential patron accepts the other agent as client (determined by a roll of the die), then the patron gives the client some of its resource. This gift increases the patron’s prestige, and puts the client in its debt. Patrons, in the model, invest some of their resources in improving the yield from a location, thus representing the investment in fides.
At the end of each cycle, the agents compare their resource amount against others whom they ‘know’ (who may be found within the agent’s ‘vision’). The simulation makes the same comparison for the population of all agents as a whole at the same time. The agents set their ‘prestige’ to reflect their local status into top middle, and bottom thirds. Each ‘patron’ (an agent with at least two other agents in its debt) selects another patron to compete against at the same rank (thus local elites compete against other local). Elites compare both the quality and number of their followers against each other. A patron with a few wealthy clients might beat a patron with several poor ones. Winning the game increases prestige, losing reduces prestige. The winner then calls on its clients to support it through gifts of resources (while simplistic, this modeling of ‘patronage’ does not stray from the broad outlines suggested by scholars collected in Wallace-Hadrill, 1989a).
At the end of the simulation, each agent writes its patrons and clients into a single file for network analysis. The network analysis is performed using the Gephi network analysis program (Bastian et al.2009; Kuchar 2011). Data on the state of the model at each time step is written to a spreadsheet, counting the number of agents who are patrons, clients, their degree of prestige, and their classification into high-middle-low status both locally and globally. [Insert Endnote 1 here] What goes in, and what comes out, of an agent based model
A criticism of computational simulation is that one only gets out of it what one puts in; that its results are tautological. This is to misunderstand what an agent based simulation does. As Box and Draper put it, ‘all models are wrong, but some are useful’ (1987: 424). In the model developed here, I put no information into the model about the ‘real world’, the archaeological information against which I measure the results. The model is meant to simulate my understanding of key elements of Bang’s formulation of the ‘Imperial Bazaar’. We measure whether or not this formulation is correct by matching its results against archaeological information which was never incorporated into the agents’ rules, procedures, or starting points. I never pre-specify the shape of the social networks that the agents will employ; rather, I allow them to generate their own social networks which I then measure against those known from archaeology. Social networks can be discerned in archaeological materials since artefacts are the direct result of social relationships (Knappett 2011; Coward 2010; Graham and Ruffini 2007: 325-331).
Our archaeological base line for the networks comes primarily from epigraphy. Clay and timber carried explicit messages on them, in the form of stamps (Graham 2005; Graham 2006a). Other classes of raw materials in the Roman world similarly carried explicit messages on them (such as masons’ marks on marble in the quarries). These messages ranged from the simple name of the maker, to the name of the estate whence they came, to the year in which they were made/cut down/quarried. In essence, the right to extract or use the resource was arranged through locatio-conductio contracts, whether or not the landowner took an active stake in production, which the language of the stamps reflects (Steinby 1993; Setälä 1977; Helen 1975; Aubert 1994). Meiggs supposes that the same system used for letting out public contracts was used in managing the public forests (1982: 329). Hirt discusses the differences between private and public mines/quarries, in much the same terms (2010: 84-93).
It is possible to reconstruct something of the social networks surrounding the exploitation of materials from this epigraphic material. (Additionally, we can see these networks in the archeometry of brick, tile, and other objects, Graham 2006a: 92-113; Malkin et al.2007; for criticism of the approach see Brughmans 2010). The named individuals in brick stamps can be knitted together into a social network. I use network statistics generated from a study of the patterning of co-occurrence of names of individuals, estates, and workshops, as well as patterns of co-exploitation of clay bodies as the control in this study. I set the simulation to run through all combinations of its variables, and then match the shape of the resulting generated social networks against the archaeological ones to identify simulation runs of interest.
If the simulations processes match our understandings of the phenomenon under consideration, then its outputs (the emergent, unpredictable outcomes) must have some validity. If they do not match, it may mean that our understanding we sought to model is incorrect. It is worth noting that this too is an important result.
We sweep the ‘parameter space’ to understand how the simulation behaves; ie, the simulation is set to run multiple times with different variable settings. In this case, there are only two agent variables that we are interested in (having already pre-set the environment to reflect different kinds of resources). Because we are ultimately interested in comparing the social networks produced by the model against a known network, the number of agents is set at 235, a number that reflects the networks known from archaeometric and epigraphic analysis of the South Etruria Collection of stamped Roman bricks (Graham 2006a).
Nine different combinations of variables are used to sweep the entire space, twice for each of the four resources, making for 72 different runs over nearly 60 000 iterations of the model. The simulation is set to stop at the arbitrary point of 50 generations (the question of when to halt an experiment is not at all obvious when it comes to simulation. The reader may wish to experiment with changing this; cf Weingart 2012).
|Transaction costs (“metabolism”)||knowledge of the world (“vision”)|
Table 1. Combinations of variables in the ‘sweep’ of the model’s behaviour space.
We should expect some basic trends to emerge. We could expect that:
- increasing the transaction costs should make it much more difficult for agents to survive (i.e., make the simulation reach its arbitrary end-conditions, a point where the average number of generations per agent equals 50) relatively quickly).
- increasing ‘vision’ (we reduce information uncertainty by increasing the amount of the world it is possible to ‘know’) should make it much easier for agents to survive; that the simulation takes a relatively long time to reach its arbitrary end point.
This is in fact what we see (Figure 1). A world where fifty generations are reached quickly might be characterised as unstable, while one that takes some time could be called stable. Combinations 3, 6, and 9 lead to the greatest peaks in stability. If one were to map these against the economic development of the Roman world, one could argue that combinations 9 and 6 would agree with a situation or area where many transport or communications links have still to be developed, though the general lay of the land is well known. Combination 3 would agree with a situation where the transportation or communications networks were at the most developed and secure. Either way, these combinations point to a degree of integration (and the differing circumstances under which integration could be produced, in the simulation).
Combinations 4 and 7, the greatest dips in the graph (and thus worlds of instability), are suggestive of a situation where transaction costs are high and communications are poor. The question is, which of these situations corresponds with the ‘real’ world? We turn to the generated social networks and their comparison with archaeological data.
Social Networks Analysis
|Equivalent on a random graph||0.008||0.005||0.02||0.08|
|Average Path Length||1.16||1||3.88||2|
|Equivalent on a random graph||6.76||4||4.3||5|
Table 2. Network characteristics of stamped Tiber Valley bricks, based on the epigraphy of the stamps. Shading suggests that small-world conditions might be in evidence (same average path length as in a random graph although the clustering is of an order of magnitude or more higher, Watts: 1999:114). After Graham 2006: 102, Table 6.1
The way individuals are connected carries implications for the ways in which information or other materials flow through that network. Network structure carries implication for the ability to act, and the ways individuals embedded in a network can leverage the information/material that flows through that network. Individuals and their positioning matters. In a network, individual’s local situations give rise to a global network whose dynamics emerge from this local interplay (see for instance Brughmans 2012; Coward 2010; Mitchell 2009: 227-290; Christakis and Fowler 2009; Ruffini 2008; Graham 2006 whose in charge; Barabasi 2002; Watts 1999).
One can compute metrics to understand the implications of an individual’s positioning (such as the number of connections, or the number of paths through the network between every pair of individuals on which this particular individual sits). I use the Gephi network visualization suite (Bastian et al. 2009). Here, since we are not interested in actual individual historic actors, we are more concerned with global metrics. Two in particular allow us to compare the behavior of the generated networks with the ones known from archaeology and epigraphy (Brughmans 2012 gives an excellent overview of the statistical properties of networks and their archaeological implications).
- Clustering coefficient. This is a measurement that looks at how dense the connections are amongst the neighbors of each individual in the network (neighbors are those to whom the individual is connected at ‘one degree’ remove). The coefficient is the average of all the ‘neighborhoods’ (Hanneman and Riddle 2005 Chapter 8).
- Average Path Length. This is a measurement that takes the mean of the number of links between every pair of actor.
Watts (1999) formally identified a network structure that appears in social (and other) systems of all kinds, which he called the ‘small-world’ phenomena. In a small-world, most individuals are tightly connected in small groups or neighborhoods; it is highly ordered. Normally, this means that it takes many links to get from one side of the network to the other. Yet, in a small-world occasionally some individuals have links that connect otherwise disparate parts of the network, a kind of short-circuit (thus an element of randomness). This has the effect of making the entire network much ‘closer’ than its clustering would suggest – it looks ordered but behaves randomly. We can tentatively identify a small-world then as one with a short average path length and a strong clustering coefficient, compared to a randomly connected network with the same number of actors and connections. Watts suggests that a small-world exists when the path lengths are similar but the clustering coefficient is an order of magnitude greater than in the equivalent random network (Watts 1999: 114).
In Roman economic history, discussions of the degree of market integration within and across the regions of the Empire could usefully be recast as a discussion of small-worlds. If small-worlds could be identified in the archaeology (or emerge as a consequence of a simulation of the economy), then we would have a powerful tool for exploring flows of power, information, and materials. Perhaps Rome’s structural growth – or lack thereof – could be understood in terms of the degree to which the imperial economy resembles a small-world (cf the papers in Manning and Morris 2005)? Small-worlds also seem to be a pre-requisite for self-organization, where complex phenomena emerge from the interaction of the constituent parts (Granovetter 2002; Cilliers 1998).
The networks generated from the study of brick stamps are of course a proxy indicator at best. Not everyone (presumably) who made brick stamped it. That said, there are two particular combination of settings that produce results similar to those observed in stamp networks, in terms of their internal structure and the average path length between any two agents. In run 21, the variable settings are those for combination 3, which corresponds to a world where transactions costs are significant (M = 10) and knowledge of the world is deep (V = 20); the resource is ‘forest’. The clustering coefficient and average path length observed for stamped bricks during the second century (Antonines) fall just outside the range of results for multiple runs with these settings (from 0 to 0.018 for the clustering coefficient; and 2 to 8 for the average path length). Small-world conditions do not seem to be fulfilled in the real-world network, which perhaps means that we are dealing with a form of economic activity that does not correspond to the bazaar, that is not emergent: some form of outside control is imposed.
In the second run which produces network statistics very similar to observed real-world brick networks (run 13), the variable settings are those for combination 4, a world where transaction costs are significant (but not prohibitive; M = 10), and knowledge of the world is limited (V = 2); the resource is ‘forest’. The clustering coefficient and average path length observed for stamped bricks during the second century fall within the range of results for multiple runs with these settings (from 0 to 0.034 for the clustering coefficient; and 2 to 14 for the average path length). In the simulation, the rate at which individuals linked together into a network suggests that there was a constant demand for help and support.
There were a number of runs that did not produce any clustering at all (and very little social network growth). Most of those runs occurred when the resource being simulated was coppiced woodland. This would suggest that the nature of the resource is such that social networks do not need to emerge to any great degree (for the most part, they are all dyadic pairs, as small groups of agents exploit the same patch of land over and over again). The implication is that some kinds of resources do not need to be tied into social networks to any great degree in order for them to be exploited successfully (these were also some of the longest model runs, another indicator of stability).
The strongest network cohesion occurred in runs 5 (forest), 28 (mine), and 23 (clay) (table 3). Run 5 occurred in a combination implying a world where transaction costs were low, and knowledge of the world was middling. Run 28 occurred in a combination implying a world where the transactions costs were great, and the knowledge of the world was limited. Run 23 shows a world where the transactions costs were middling, and knowledge of the world was deep. In each case, it is the nature of the resource that makes the difference, rather than the variable settings. The social network which emerges depends on the kind of resource or product involved.
|# of Nodes||
|# of Links||
|Average path length||
|% participation in the social network||
Table 3. Network statistics for the greatest participation rates (number of agents tied into the networks)
What are some of the implications of thinking of the Roman economy as a kind of bazaar? If, despite its flaws, this model correctly encapsulates something of the way the Roman economy worked, we have an idea of, and the ability to explore, some of the circumstances that promoted economic stability. It depends on the nature of the resource and the interplay with the degree of transaction costs and the agents’ knowledge of the world. In some situations, ‘patronage’ (as instantiated in the model) serves as a system for enabling continual extraction; in other situations, patronage does not seem to be a factor.
However, with that said, none of the model runs produced networks that had the characteristics of a small-world. The equivalent random graph in every case had similar clustering coefficients and similar path lengths. This is rather interesting. If we have correctly modeled the way patronage works in the Roman world, and patronage is the key to understanding Rome (cf Verboven 2002), we should have expected that small-worlds would naturally emerge. This suggests that something is missing from the model – or our thinking about patronage is incorrect. We can begin to explore the conundrum by examining the argument made in the code of the simulation, especially in the way agents search for patrons. In the model, it is a local search. There is no way of creating those occasionally long-distance ties. I had initially imagined that the differences in the individual agents’ ‘vision’ would allow some agents to have a greater ability to know more about the world and thus choose from a wider range. In practice, those with greater ‘vision’ were able to find the best patches of resources, indeed, the variability in the distribution of resources allowed these individuals to squat on what was locally best. My ‘competition’ and prestige mechanisms seem to have promoted a kind of path dependence. Perhaps I should have instead included something like a ‘salutatio’, a way for the agents to assess patrons’ fitness or change patrons (cf Graham 2009; Garnsey and Woolf 1989: 154; Drummond 1989: 101; Wallace-Hadrill 1989b: 72-3). Even when models fail, their failures still throw useful light. This failure of my model suggests that we should focus on markets and fairs as not just economic mechanisms, but as social mechanisms that allow individuals to make the long distance links. A subsequent iteration of the model will include just this.
Finally, the model runs on a torus-shaped world (that is, the left and right sides of the environment are connected, as are the top and bottom. If an agent wanders off the top of the screen, it re-appears at the bottom). A more useful model would be instantiated on top of a GIS with the real locations of various resources (and associated infrastructure) known. Perhaps it could be made to work with the transport economics modeled by Scheidel and Meeks and the ORBIS project (this interactive mapping project allows the user to explore the differences in the economic geography of the Roman world depending on time of year, mode of transport, and routes through the Roman world).
This model will come into its own once there is more and better network data drawn from archaeological, epigraphic, historical sources. This will allow the refining of both the set-up of the model and comparanda for the results. The model presented here is a very simple model, with obvious faults and limitations. Nevertheless, it does have the virtue of forcing us to think about how patronage, resource extraction, and social networks intersected in the Roman economy. It produces output that can be directly measured against archaeological data, unlike most models of the Roman economy. When one finds fault with the model (since every model is a simplification), and with the assumptions coded therein, he or she is invited to download the model and to modify it to better reflect his or her understandings. In this way, we develop a laboratory, a petri-dish, to test our beliefs about the Roman economy. I offer this model in that spirit.
The nucleus of this paper was presented at the Land and Natural Resources in the Roman World conference in Brussels, May 2011. I would like to thank Paul Erdkamp and Koen Verboven for inviting me to participate in that conference, and also the participants of that conference for their insight and criticism of these ideas. Various drafts have been seen by various people at various stages, and I thank them for their comments and patience, especially Mark Lawall. Errors of logic or understanding are of course my own.
- For the full model code and the details of its routines, please download at http://figshare.com/authors/Shawn%20Graham/97736 , open with Netlogo 5, and click on the ‘information’ tab. The code itself is annotated with comments explaining what is happening in each procedure, and may be reviewed by clicking on the ‘Code’ button.
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A longer post will follow with details, but I’m so pleased with the results I’m putting some stuff up right now. In my first year seminar class on digital antiquity which just ended, we’ve been experimenting with 123D Catch to make models of materials conserved at the Canadian Museum of Civilization (thanks Terry & Matt!). Our end of term project was to take these models, and think through ways of using them to open up the hidden museum to a wider public. We wondered if we could get these models onto people’s smartphones, as a kind of augmented reality (we settled on Junaio).
The students researched the artefacts, wrote up a booklet, and had it printed. They made the models, taking the photos, cleaning up in Meshlab, making videos and all the other sundry tasks necessary to the project. We ran out of time though with regard to the augmented reality part. By the end of term, we only had one model that was viewable on a smartphone. Today I added the rest of the materials to our ‘channel’ on Junaio, and tested it on the booklet.
It was magic. I was so excited, I ran around campus, trying to find people who I could show it to, who would appreciate it (nothing kills a buzz like showing off work to people who don’t really appreciate it, yet smile politely as you trail off…)
More about our workflow and the tacit knowledge necessary to make this all work will follow. In the image below, a model sits on the booklet on my desk. Handsome devil, eh?
Shawn Graham (@electricarchaeo) April 26, 2012
[below is the draft text of a talk I will be delivering on March 16th, 2012, at Dumbarton Oaks. Usual caveats apply. If you spot anything odd, please let me know.]
Networks from Artefacts
Trevor Hodge, a distinguished professor of classical archaeology and a former professor at Carleton University, passed away recently.[slide 2] I was supposed to attend a lecture of his in early February, but due to the regular rhythms of life at a university, meetings and duties conspired against me and I never heard him speak; and now I’ll never have the chance. I deeply regret this, because I wanted to tell him what an impact his book on Roman aqueducts and water supply had on me as a young graduate student.
Through Hodge’s book, I had a connection to Prof. Hodge that always felt personal. We understand that kind of social connection that can exist through the network of author – book – reader. That kind of relationship has influence, and matters: otherwise we would never publish anything [slide 3]. As a student, Hodge and the aqueducts led me to the construction industry of Rome, and thence to the brick industry more specifically. At first, it seemed a dry and sterile field. But then I encountered formal social network analysis, and suddenly, I had real people on my hands again, real people whose actions in the past I could dimly perceive.
In the same way that books can connect us, artefacts can become nodes in a network. [slide 4] Artefacts are the result of human, individual, decisions. They influenced other individuals at the time through their complex resonances of thing and place and object life-history. By considering artefacts and their relationships explicitly in terms of social network analysis, we reconnect with the individual in the past, and we obtain a perspective that allows us to see what kinds of actions were possible in the past, patterns of agency and structure, that those actors themselves could not see.
Anytime one can discern a relationship, it becomes possible to draw a network. In which case, theories of evolving networks & social network analysis should concern us all. Tom Brughmans recently argued (2010) that the ‘social’ should be taken out of the analysis, that we should just be concerned with the networks themselves. I’m not sure I entirely agree. Fiona Coward (2010) wrote,
” … the archaeological record is not a passive by-product of social relationships: rather, it is social relationships (Gamble 1999, 2007; Barrett, 2000; Knappett 2005). The patterning of material culture is a direct result of the social relationships between individuals and groups in which these objects were caught up. A network perspective provides a much more realistic picture, not only of objective sociality, but also potentially of individuals’ subjective experience of their worlds”
But as Scott Weingart warns us, we also have to take into account the dangers of methodology appropriation. Network analysis comes to us from graph theory, from statistics, from computer science. The methods, philosophies and concerns of those disciplines are not necessarily congruent with archaeology:
“Methodology appropriation is dangerous. Even when the people designing a methodology for some specific purpose get it right – and they rarely do – there is often a score of theoretical and philosophical caveats that get lost when the methodology gets translated. In the more frequent case, when those caveats are not known to begin with, “borrowing” the methodology becomes even more dangerous.”
In which case, let me show you how I draw networks out of the urban fabric of Rome and Constantinople trying to navigate the shoals of this dangerous method, and let us consider what these networks might mean for understanding the way Constantinople and her people worked. And then let’s push it a step further by reanimating those networks with agent based models. Let’s raise the dead.
We’ll start with Rome. This is a typical second century stamped brick from the industry centred on Rome. Other major cities all had their own industries (and occasionally, loads of stamped brick from places like Rome turn up in places like North Africa or Sardinia). The interpretation of stamped brick throughout the Roman world ultimately comes down to recasting the local version in light of what scholars believe was happening around the City of Rome.
Brick stamping at Rome began in the first century and ran, with some interruptions, until the 6th century. A typical second century brick looks like this [CIL XV.1 861][slide 5]
EX FIG ASINIAE QVADRATILLAE O D C NVN
NIDI FORTUNAT LVCIO
Signum: pine nut.
The consensus is that these elements represents an abridged version of the contract between the officinator and the dominus. Locatio-conductio contracts were one of the usual means of letting out building contracts. In this context there are two varieties. Locatio rei refers to the plant and property used, while locatio operis refers to the finished product itself. Both types firmly involve the dominus in production. If the stamp is an abridged locatio operis contract, then the dominus paid the officinator to make a certain amount of bricks. If on the other hand, the stamp refers to locatio rei, then the entrepreneur is the officinator, contracting with the landowner to use his land for the officinator’s own profit.
These are, of course, social relationships, whatever kind of contract. Brick and brick stamps are ideal things for archaeological network analysis. To produce brick means having the ability to command resources, to control land, and to be tied into the webwork of patronage that physically creates humanity’s machine for living, the city.
In terms of how the stamps actually functioned in day-to-day life, one can imagine them serving multiple roles: distinguishing the output of different officinatores working side by side; for compensation or verification that the work has been carried out; to indicate the products of different figlinae belonging to one dominus; or different domini who used the same warehouse. From legal texts and other notices in the ancient literature, it seems likely that in Rome and in Constantinople, a certain proportion of bricks were levied annually for maintenance of public works. Brickstamps from Constantinople sometimes bear the phrase, ‘indiktionos’, ‘of the indiction’ without referring to the year in the cycle, which Bardill takes to mean that they were stamped to indicate that tax liabilities imposed by the annual indictio on the owners of clay lands had met their obligations.
So that’s a potted history of brick stamp ology. The practice of stamping bricks at Rome continues into the reigns of the Gothic kings, and so is contemporaneous with stamping practice at Constantinople. A typical stamped brick from Constantinople is more difficult to interpret, but again can contain names and years and other signs in various combination. The question is, what can we do with this information? What might it all actually mean, when put into perspective?
There is a lot of information in a typical stamped brick, even if we’re not always clear what it might mean, or what function, precisely, the stamps served. The most typical, and basic, use of brick stamp data by scholars is to help with dating built structures. However, it is also possible to do as Janet DeLaine has done, looking for patterning in the names on bricks from particular buildings, looking for interconnections and tying what she knows to the local prosopography. She is able to identify social patterns of patronage behind the provisioning of materials. In a sense, what she has done is a kind of network analysis without the formal network.
The strong version of Delaine’s approach is advocated by Irad Malkin. Malkin writes,
“Two-dimensional representations of connectivity mostly turn out to be messy “spaghetti monsters” with very long verbal explanations that are needed to accompany them”.
For Malkin, because we can’t have total knowledge of a network it is better to not try to draw out a network. It is as if he views the whole point of networks as the visualization of the network, and any accompanying statistics as suspect. This rhetorical dodge allows Malkin to avoid having to deal with the formal problems of methodology from the outset. Visualizations are simply maps, and maps necessarily simplify. We _necessarily_ put a boundary on a network when we focus on it. Nevertheless, it is better to have some knowledge that can be formally outlined, than to have a hand-waving description that turns out to be infinitely elastic. We can’t know every parameter of an ancient social network. But with brick, we can come pretty darn close.
When we start thinking in formal network terms, we can see that brick is a potentially very rich source. The relationships between the various kinds of data preserved in stamped brick make the stamped brick a rich multi-dimensional fossil of past social relationships. Let’s draw these out and that’s just the epigraphy! [slide 6] Formal network analysis is the most appropriate means to tease out what all these relationships might mean for individuals in the past.
In my PhD work, I began knitting these various dimensions together into various kinds of networks. I performed archaeometric analysis of the fabric of both stamped and unstamped brick, collected from across the Tiber Valley. I was able to cluster bricks into groups sharing the same clay sources, detecting patterns of usage underneath the epigraphic data. Because I had chronological information, I was able to observe how this network changed over time. Similarly, I could knit stamped bricks together on the basis of intersecting names, of estates, workshops, brick makers, and landowners, observing how these changed over time. I was able to calculate various statistics for particular periods, seeing that in some periods, these networks resembled small-worlds and hence, I argued, were self-organizing and emergent: no government control required (there’s a venerable argument that all brick making around Rome was directed by the state). At other times they appeared exceedingly fragile. It was a picture of constant flux and dynamism.
I built a beautiful and elegant argument which I recognize now could be fatally flawed. Is a network connecting landowners and brickmakers a one mode or a two mode network? That is, are all the nodes the same kind of thing, or are they different kinds of thing? If we imagine it to be a one mode network – these are all humans, after all – then all is well. But if it is a two mode network, if landowners and brickmakers are fundamentally different kinds of actors, then some of the statistics I calculated are flawed because the methodology behind the many of the various statistical algorithms I ran assume one-mode. Network analysis can be dangerous.
So, for today’s paper, I went back to the drawing board. I reformed a network of stamp types to findspots in the hinterland of Rome in the Tiber Valley. I have not tried to decompose the epigraphic multi-dimensionality to any great degree (though I probably should do that some day). I then reshaped this network into two one-mode networks: stamps connected to other stamps by virtue of being found at the same location, and locations to locations by virtue of using the same stamps. Ideally, I would do this for the city of Rome itself, but that is a job of epic proportions; for my purposes today, the Tiber Valley is sufficient.
And this is what you find [slide 7 locations to locations]. What does it mean? There are any number of metrics which could be computed, but the right one depends, I think, on the nature of the data, of what human process could result in these physical traces, these fossils. That’s why we cannot divorce the social from archaeological network analysis.
Let’s begin by assuming that whatever we’re seeing in a network of locations connected by similar stamped brick, is related to the consumption of building materials and thus to ideologies of construction. In the Roman world, construction is a kind of show, a kind of costly signalling that the person having the structure built has access to resources. None of the structures that these bricks came from, if the accompanying assemblages are any indication (polychrome marble fragments, for instance), were simple basic farmsteads; they all appear to have been housing a notch above the ordinary.
Evidence from stamped brick at Rome, and from Cassiodorus, attest to the existence of brick depots along the Tiber – Portus Licini, portus Corneli(i), portus Neapolitanus, and portus Parrae. Evidence from for example the Baths of Caracalla in Rome point strongly to the warehousing of brick; the prosopography of individuals named in the brick seems to suggest a strong patronage element to what gets warehoused where. In which case, an appropriate metric to analyse a network based on locations joined by shared brick types would be community detection, which is also known as modularity.
Modularity, as implemented in the Gephi network visualization suite, depends on finding localized patterns of similar linkages or sub-networks which can then be aggregated at ever larger scales; when it can’t be scaled up it defines that collection of nodes and links as a ’module’ or ‘community’. This particular algorithm was developed looking at cellphone data from tens of thousands of European customers, and seems to work well when tested against networks with known subnetwork structures.
Modularity is a property of the network that no one person within the network could possibly perceive. We can imagine though that communities would tend to have access to the same kinds and amounts of information, or be subjected to the same influences, due to these particular network linkages. The visualization is coloured according to community. There are about X communities here, which perhaps can be interpreted as brick depots serving the Tiber valley (since some of these types are known in Rome too, these depots could well correspond to the named depots known from other evidence).
Some brick types appear to be part of two or more communities, suggesting that domini or officinators have some choice or option in where to ship their bricks. These bricks tend to have higher betweeness scores, too. Betweeness centrality is a measure to which a particular node sits atop the most shortest paths between every pair of nodes in a network. When we resize nodes (brick types) according to betweeness scores, we see that these are at the intersection of communities. Betweeness would seem to imply some sort of social relationship between the warehouses.
The Tiber Valley network shows results that make sense, both from what we know about the structure and organization of the Roman brick industr. Let us turn to the catalogue of stamped brick of Constantinople collected by Jonathan Bardill.[slide 8]
There are approximately 2100 individual stamp types recorded in the immediate environs of Constantinople. I sent an undergrad on a fruitless errand to see if any are recorded at Byzantine sites around the Eastern Mediterranean, but so far we’ve drawn a blank. Instead, I had her create a list of type according to findspot, which I then imported into Gephi. I collapsed this two-mode network [slide 9] into two one-mode networks. Let’s look at the location to location by brick type network
If we turn this network inside out, and consider brick types connected to brick types by virtue of being found at the same site,
Modularity at first glance doesn’t seem to be as useful since there are a large number of unconnected bits and pieces; which perhaps indicates a combination of brick makers shipping to depots and directly to the site. (Total #=71). If we take the giant component,
There is a chronological question of resolution that needs to be addressed. These bricks range from the middle 5th century to the middle 6th century. Obviously, this network should be decomposed into sensible chronological chunks; brick demands it, I suppose. But, at the level of resolution we’re looking at here, I think that broad trends are preserved: modularity works, but betweeness should not be leaned on too much, other than implying a social connection between warehouses.
When you plot this network against the real-world geography of Constantinople
Obviously, this entire analysis represents a first pass on the data, highlighting some interesting trends that will need to be refined. But even at this rough level, when we reconsider these networks in light of my initial arguments for thinking of archaeological networks as social networks, we have the substrate for exploring ancient society in new, powerful ways. The static network analysis, which focuses on only two metrics, shows the brick industry of both Rome and Constantinople composed of communities and individuals balanced between multiple tensions. It holds the promise of a route into exploring the dynamic interplace between structure and agency.
I would like to conclude this piece by showing how we could reanimate these fossils of individual choices made in the past, preserved for us in brick, within the confines of a digital laboratory for simulation. Archaeologists and historians have had great success with this method explaining such complicated moments as the collapse of Anasazi settlement, the emergence of Bali water-temple networks, or the outcome of the Battle of Trafalgar. As far as I know, I’m the only Romanist playing this game at the moment, so please bear in mind the extremely tentative and provisional nature of this step!
The laboratory is the agent based (or individual based) modeling environment Netlogo
Let us create a population of artificial Romans or Byzantines, where each brick type stands for an individual person, and give that person the suite of connections preserved in the archaeology. [image slide 15, 16, 17 import routine] Let us then give them rules for behavior that are based on our understanding of how some phenomenon in the past worked – I would suggest patronage as the best choice. [slide 18] Each individual it should be noted is an individual: they might all have a capacity for remembering who they’ve interacted with, but some will have long memories, some will have short. It is a heterogeneous population. Then, we let these artificial individuals interact with each other according to their pattern of connections. I run this simulation in a world where we can imagine that the economic environment, an individual’s aversion to risk, and patterns of gift-giving matter in cementing patterns of patronage.
I haven’t done this yet for the Byzantine patterns [slide 19 represents a developing model], so my comments are limited to Rome.
At least, in my simulation. It’s entirely possible that my understanding of how patronage works is ill-founded. But that’s the nice thing about doing these kinds of simulations. My simulation, my code, is completely available online for perusal, critic, and extension. I would be most pleased if you did so.
I’ve made the argument that archaeological networks are social networks. I’ve shown how I draw networks from archaeological materials, and discussed some of the appropriate metrics for understanding what these network topologies might imply for our understanding of the past. Modularity determines groupings of sites or artefacts that share some common social links. Betweeness can identify key sites or artefact types that act as linch pins for the entire system. I can see patterns of provisioning that seem to respond to social, rather than economic factors; in Constantinople, there looks like a clear civil, military split in the communities suggested by brick use. Then, I went out on a limb to show that once we’ve drawn these networks, the possibility exists that we can reanimate these fossils, and use them to explore questions about the past that previously only existed as thought experiments. They are a very particular kind of thought experiment, one that can be rigorously specified, shared, critiqued, and built upon.
In the same way I felt connected to Prof. Hodge through his book, these artefacts connected all levels of ancient society. When I look at stamped brick, I see individuals, hard working folks enmeshed in a web of legal and social obligations. Computation & formal network analysis let me begin to untangle it.
I scraped 400 items tagged as Roman antiquities from eBay.ca today. One was going for 1.9$ million; consensus on twitter was that it was a fake. Of the rest, the combined valued was ~ $50 000. Only 188 items had more than one bid on them. That subset had a combined value of $4000, so an interesting discrepancy there. The location of the sellers was as you’d expect – USA, UK were the top two. Next up: Germany and Slovenia. So I’d keep an eye peeled on Slovenia as a conduit for illicit materials from the Balkans.
In my mailbox:
Taking Archaeology Digital
A Conference on the Use of New Technologies in Archaeology
University of Puget Sound, Oct. 25-28, 2012
Technology is changing our world in ways that previous centuries could not have imagined, and it is a constant struggle for us to keep up with these frequent changes and innovations. While archaeology is a very old practice, only in the later 20th century was it given serious methodological consideration, and now, in the 21st century, this explosion in the availability of technological tools offers the potential to transform the practice of archaeology. But the mere existence of a new tool, no matter how fun and exciting it might seem, does not necessarily translate into good use of that tool. This is the theme we hope to address in the upcoming Redford Conference in Archaeology at the University of Puget Sound, October 25-28, 2012.
We invite proposals for papers and presentations that explore the question of how archaeologists can best make use of the vast range of possibilities that technology opens up. We are particularly interested in presentations from people who may have already had some experiences in trying to fit new technologies into archaeological practice. Often those who study the past have had difficulty adapting their practice to the existence of new tools, and one goal is to help us learn from the experiences of others.
Some issues we hope to address include:
- How do technological tools allow archaeologists not only to do their work differently, but better?
- What kinds of new questions do these tools allow us to ask, and why are those questions useful to a broader understanding of the ancient world?
- How is the processing of archaeological material after an excavation affected – from archiving data through to publication?
- How can we maximize the possibilities offered by the new digital technology?
While all areas relating to the question of how to make technology work best for archaeologists are open, we anticipate focusing our discussions on three areas and especially encourage submissions that relate directly to them:
- Fieldwork: How do traditional archaeological methods intersect with digital technologies? What problems can technology help us solve in the field? And just as important, perhaps, how might the limitations of these technologies hinder us or, at the very least, not help us in our fieldwork?
- Archiving: If technology increases the amount of information we gain from the field, how can this information be stored so that it can be efficiently accessed again in the future? How can we account for future changes in technology that might make current storage techniques obsolete? How can we avoid the loss of data when that happens, and mitigate any problems that the technological change-over might present?
- Publication: What possibilities for publication are opened up by digital technology? How can we make these new electronic publications more valuable, and increase the quality and not just the quantity of the published material? Is peer review still important, and how will it be connected to the new publication possibilities?
The conference will include both demonstrations of technological innovations as well as critical discussion of the value of such innovations. Confirmed speakers include:
- Nick Eiteljorg II, Center for the Study of Architecture
- Sebastian Heath, Institute for the Study of the Ancient World
- Norbert Zimmerman, Vienna Academy of Sciences
Proposals for papers should be sent to Eric Orlin at firstname.lastname@example.org. The deadline for receipt of proposals is April 1, 2012. Some subsidies may be available to help offset travel costs for speakers.
For ongoing updates of Conference news, please check out the Redford Conference in Archaeology link at http://archaeology.pugetsound.edu.
Let’s get meta!
Known as N+7, this mode of procedural writing was popularized by the Oulipo group, an attempt to divest creative expression of two hobgoblins that haunt the modern age: the myth of the muse-touched creative genius, and the equally debilitating reductivism of the Freudian unconscious. N+7 circumvents these fictions, these stories, these—what “stories” under N+7 become—straightjackets. N+7 celebrates the random, valorizing the lack of what artists, writers, and intellectuals often seek in their work: control. It is a yielding, a submission—not to the muses, not to genius, not to dreams and desires, but to the world beyond us. It is pure engagement with the outside world.
The results of N+7 would seem absolutely nonsensical, if not for the disruptive juxtapositions, startling evocations, and unexpected revelations that ruthless application of the algorithm draws out from the original work. Consider the opening substitution of Hacking the Academy, sustained throughout the entire book: every instance of academy is literally an accident.
I’m no literary scholar. But what I am interested in are the ways deeper patterns can sometimes be seen through machine lenses (and in particular, topic modeling), and I think that the Oulipo group might’ve been on to something (just ahead of their time). So, I scraped every paragraph from the last 10 posts of Play the Past, via its RSS feed (which took me back to November 29 2011). I searched for 15 topics, and then mapped the interconnections between paragraphs by topics:
So: it’s time to choose your own adventure. If you click on http://j.mp/AADmUS you can follow the tangled web of ideas and text. What happens when you playfully read Play the Past?