Description of Activities
Over the past decade our methods for analysing ancient societies have been dramatically transformed by the application of ideas and methods from Social Network Analysis and network science. Studies range considerably in scope, from the quantitative analysis of large archaeological datasets, to the qualitative evaluation of complex inter-regional phenomena.
One of the attractions of network approaches is this considerable range between the quantitative and the qualitative. Another is that network methods can be relatively forgiving of open-ended datasets and of incomplete or idiosyncratic data. So when, for example, we seek to understand interaction and mobility in the ancient Mediterranean, we certainly do not know all the settlements that existed, nor all the trade routes between known and unknown settlements; and yet through various proxies we seek to piece together a partial picture. Network models seem suited to this kind of enterprise.
However, the considerable success and popularity of network approaches in such studies has created a lot of diversity in the kinds of approaches that are adopted. It is clear that in borrowing from sociology on the one hand, and social physics and network science on the other, scholars are finding quite distinct pathways into network theories and methods, sometimes without much formal guidance. Collaborative efforts have mitigated the associated risks, but there is a bewildering array of routes into the subject, and a plethora of models and approaches from which to choose. The Covid-19 pandemic has put into sharp relief the choices that are made when statistical models are brought to bear on idiosyncratic data.
Our group has two main goals:
- To create a dialogue within the University of Toronto setting between those exploring network ideas in the context of ancient societies and those working on networks in different disciplines from across the social, biological and data sciences. This will enable increased understanding of how different disciplines work with idiosyncratic data, and how models are constructed.
- To think about network thinking as applied to ancient societies within the broader setting of humanities research. There is a lot of discussion within the university currently around how we might extend data science more effectively into humanities scholarship and teaching. One of the key intersections for data science and the humanities has been Digital Humanities (DH). Yet, DH has not seen very much uptake of network methods, although they can be powerful exploratory tools for data visualisation. We would like, therefore, to discuss the role of network approaches within the humanities more broadly, and the ways in which network science of various forms might be more fruitfully applied.
- John Kloppenborg, FAS Study of Religion
- Carl Knappett, FAS Art History
Faculty, University of Toronto
- Christian Abizaid, FAS Geography & Planning
- Gary Bader, FAS Molecular Genetics
- Katherine Blouin, UTSC Historical & Cultural Studies
- Bonnie Erickson, FAS Sociology
- David Fisman, Della Lana School of Public Health, Epidemiology
- Marie-Josée Fortin, FAS Ecology & Evolutionary Biology
- Marney Isaac, UTSC Physical & Environmental Sciences
- Peter Marbach, FAS Computer Science
- Markus Schafer, FAS Sociology
- Chris Smith, FAS Sociology
- Ashley Tuite, Della Lana School of Public Health, Epidemiology
Postdoctoral Fellow, University of Toronto
- Gisli Palsson, FAS Art History
Graduate Students, University of Toronto
- Katerina Apokatinidis, Classics
- Paola Gheorghiade, Art History
- Elizabeth Gibbon, Anthropology
- Christina Gousolopoulos, Study of Religion
- Rebecca Runesson, Study of Religion