ABM: Simulating Roman Social Life and Civil Violence

My paper in a special edition of Digital Studies has just come out. The special issue was edited by Kevin Kee and John Bonnett from Brock University. John and Kevin write in their introduction,

It is a truism in the digital humanities, a constant one, and a good one, that it is always in a state of transition. Such an observation is not surprising since the instrument upon which it relies – the computer – is itself in a state of flux. For the moment, its computational power remains firmly in the grip of Moore’s Law, exponentially increasing its computational power as the decades pass. Scholars, whether they want it or not, are constantly being presented with new paradigms of computing — be it cloud computing, ubiquitous computing, or high performance computing — and new tools and markup schemes to express, treat and analyze content. In any publication devoted to the digital humanities, then, it would seem superfluous to mention that change is our constant condition and our constant preoccupation, a trite observation best left unsaid. We sympathize with this view. But when it comes to describing digital humanities scholarship generally, and computationally supported scholarship in Canada particularly, we think it is wrong. In Canada and abroad, a number of important developments have recently emerged that will impinge on the practice and future trajectory of our inter-discipline. They are new, important, and are reflected in the contributions to this issue. They are of sufficient moment and frequency that we feel justified in rendering this issue of Digital Studies with the thematic stamp it now bears: that of transition.

So I was quite honored to be included in this collection. The abstract from my contribution:

Behaviour Space: Simulating Roman Social Life and Civil Violence

Shawn Graham

Agent based modelling, also known as individual-based modelling, holds great promise for historians as a tool for formalizing and visualizing historians’ understandings of historical processes. It also provides a means to explore exploring the emergent consequences of such assumptions. Such models specify the possible behaviours for a single individual, and then enable individuals within the simulation to interact, with each applying the behaviours in a context-specific manner. Artificial societies begin to emerge from these interactions, allowing us to study their characteristics. Moreover, when these models produce behaviours that cohere with patterns embedded in historical or archaeological data, it becomes possible to interrogate aspects of past experience otherwise lost to us. This article presents PatronWorld, an agent based model of the ancient Roman daily ritual of salutatio, a ritual in which clients gathered to make a morning greeting to their patron. It explores the ritual’s role in the development and maintenance of patronage networks, and its relationship to the emergence of civil violence in the Roman world. The model is also based on a framework that describes the social network surrounding land holding (the foundations of wealth in antiquity) in the City of Rome from the late first – early second century AD. Civil violence was a constant feature of Roman society, frequently targeting the men upon whose social connections the political economy of the state depended. Results from the model suggest the social conditions that made the state vulnerable to periodic bouts of violence, and suggest new directions for further research.

I’ve recently been reading John Miller and Scott Page’s Complex Adaptive Systems: An introduction to computational models of social life (2007, Princeton UP) and there are some models and insights there which are making me rethink the complexity of the model that I created for Behaviorspace. But that’s a post for another day…

(For those interested in novel uses of GIS for historical situations, see in the same special issue,

‘Buried Beneath the Waves’: Using GIS to Examine the Physical and Social Impact of a Historical Flood Abstract HTML
Mathew Novak, Jason Gilliland )
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