These are the slides of the talk I gave at the Land and Natural Resources Conference last week at the Free University in Brussels. That talk was a bit more free form than the slides would suggest, so I’m not quite ready to share the written version (mostly because it’s still in a process of becoming…)
But, I think I’m fairly safe to share at least the opening bit….
Could the extractive economy of Rome (such as mining, logging and forestry) promote structural growth? What would be the archaeological signs of structural growth?
So much in the ancient world appears to rely on connectivity and mobility, either literally in terms of things like road-building (Laurence, 1999) or more abstractly, in terms of social connections, bonds of friendship, amicitia, and patronage. Horden and Purcell (2000) make the argument for multiple connectivities, both physical and social, that bound up the Mediterranean world. In my own work, I have argued for social connections becoming ‘real’ in the physical landscape, as a mechanism for the creation of notions of territory and landscape (Graham and Steiner 2008). Sometimes these social connections can be read from the archaeometry of material culture; other times from epigraphy; sometimes from history. In this paper, I want to explore how patronage intersects with natural resource extraction, and whether or not these intersections could promote structural growth. As Mokyer points out, “There is a qualitative difference between an economy in which GDP per capita grows at 1.5 percent and one in which it grows at 0.2 percent” (2005:286). Understanding which possible conditions could promote growth (and to what degree) therefore is a useful exercise.
I use an agent based modeling framework (ABM) for this exploration because I am interested not in simulating the past, but in understanding how different understandings about the past combine. ABM allows me to systematically test the ways these ideas combine and to generate a landscape of possibilities against which I may then lay archaeological or historical evidence.
Agent Based Modelling
Agent based modelling is an approach to simulation that focuses on the individual (indeed, it is sometimes known as individual based modeling). In an agent based model, the agents or individuals are autonomous computing objects – they are their own programme. They are allowed to interact within an environment (which frequently represents some real-world physical environment). Every agent has the same suite of variables (if it were a model of basketball, ever agent would have a ‘height’ variable, and an ‘ability’ variable), but each agent’s individual combination of variables is 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 model. What is important to note, especially when we are interested in the past, is that we are not trying to simulate the past; rather, the model 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). When we simulate, we are interrogating our own understandings and beliefs. What is particularly valuable then is that we can build a model, 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’s more, in order to code a particular behavior, we have to be clear about how we think about that behavior. It forces us to make our assumptions explicit. A second investigator then can examine the code, critique these assumptions and biases (or indeed, errors) and modify the model towards a ‘better’ state. In this way, the model is both a laboratory and a crowdsourced argument about the past. In that spirit, I offer the code for this model at http://graeworks.net, and encourage the reader to download, adapt, critique and improve the argument. The model is built using the Netlogo modeling environment and language (Wilensky 1999). Simulations make their argument in computer processes, and like all forms of expression, they carry their own rhetoric which must be analysed (Bogost 2007).
Did Rome experience growth? If so, in what ways did that growth occur? What was ‘modern’ about the Roman economy? Why did not Rome make the leap that Europe did? If it is any consolation, historians of the Industrial Revolution are as puzzled by how it happened, as we are by why Rome’s didn’t. An important consideration though comes from Mokyr’s analysis of the intellectual foundations of the Industrial Revolution (2005). For Mokyer, it comes down to the idea not just that ‘useful’ knowledge available had increased, but that the social setting for this knowledge had expanded (2005: 287). For Mokyer, useful knowledge relates directly to the physical world, and how it works.
This is not a view that would’ve been foreign to the bien-pensants of antiquity. Columella, Varo, Pliny all set about to catalogue and categorize the world around them. The difference though is one of quality; an Enlightment description of a phenomenon attempted a degree of accuracy and thoroughness that was alien to the Roman mind.
For Mokyer, the easier it becomes for individuals to access that knowledge, the more likely technological change was to happen, thus resulting in sustained economic growth (Mokyer 2005: 295-6). Though Mokyer doesn’t say it explicitly, he is talking about how information passes through social networks. As Mokyer points out, this useful knowledge did not have to percolate down to the many (301). It simply had to reach those in a position to act on it (a figure he reckons to be at most a few tens of thousands in all of Europe, 301). In Roman terms, to talk about social networks of influence is to talk about patronage.
Once we have created a model that encodes our understanding of the phenomena under question, it remains to interpret the results. A framework for understanding our model of resource extraction in the Roman world is provided by the Canadian economic historian and media theorist Harold Innis (Innis was the mentor of Marshall McLuhan).