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CALL. 31.10.2017: The Uncertain Past: Probability and Other Approaches to Historical Uncertainty - St Andrews (Scotland)

03.10.2017

 

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As historians we are constantly beset by uncertainty, especially in our attempts at quantification. Yet as a field we have spent relatively little time reflecting on the nature of uncertainty and our strategies for managing it. We have all had recourse to the language of likelihood, but our use of it tends to be largely rhetorical rather than being grounded in any quantification of uncertainty. Most scholarly debate focuses on disputing the best estimate of the actual value, rather than trying to assess the degree of uncertainty, i.e. the margin of error, in our estimates.vAlthough it is easy for ancient historians to believe that we face unique difficulties in our attempts to base estimates on subjective assessments of what is likely, we are in fact far from alone in the challenges we face. Many other fields in the social and natural sciences have wrestled with analogous problems for decades and have developed more sophisticated ways of working with subjective estimates. There is a whole field of study devoted to the elicitation of expert opinion, i.e. quantifying subjective beliefs. Meanwhile developments in the philosophy of probability and statistics have collapsed the apparently obvious contrast between the ‘subjective’ estimates of the historian and the ‘objective’ estimates of the scientist. In the increasingly influential ‘Bayesian’ interpretation of probability, probability is always a property of our information about the world rather than an objective property of the world.

 

FECHA LÍMITE/DEADLINE/SCADENZA: 31/10/2017

 

FECHA CONGRESO/CONGRESS DATE/DATA CONGRESSO: 04-05-06/07/2018

 

LUGAR/LOCATION/LUOGO: University of St Andrews (St Andrews, Scotland)


ORGANIZADOR/ORGANIZER/ORGANIZZATORE: Myles Lavan

 

INFO: web - mpl2@st-andrews.ac.uk 

 

CALL:

This conference will explore what pre-modern historians might learn from other fields about handling uncertainty in quantitative analysis. The goal is to reflect honestly on the limits of existing approaches and to identify methods that might allow for greater analytical rigour while still producing useful results. The conference is part of a larger AHRC-funded project on the potential value of probabilistic reasoning in historical analysis. Rather than a formal call for papers, this is a request for expressions of interest from potential participants.

 

One particularly promising approach involves the representation of epistemic uncertainties as probability distributions and the use of Monte Carlo simulation to compute the implied probability distribution over the quantity of interest. It has already proven fruitful in a number of areas. In archaeology (where Bayesian reasoning has had a foothold since Buck et al 1996), scholars such as Crema 2012 and Roberts et al 2012 have recently illustrated its value in managing chronological uncertainty in the analysis of archaeological data sets (while Brughmans & Polome 2016 and Rubio-Campillo et al 2017 use similar probabilistic reasoning to test the explanatory power of competing hypotheses in economic history). The SESHAT project routinely uses probability to manage uncertainty in their Global History Databank. In ancient history, Lavan 2016 used a probabilistic approach to quantify the spread of Roman citizenship; Jew (in preparation) is employing it to produce a new estimate of the carrying capacity of Attica; and several other researchers already affiliated with this project are applying the method to a range of other problems in Greek and Roman history. Yet probability-based approaches remain the rare exception and many historians are unaware of them. Even among those who do employ probabilistic reasoning, there has been little discussion of the theoretical underpinnings of the approach, the range of possible applications or potential pitfalls.

 

This conference aims to address this deficit by bringing together historians and experts from other fields in order to reflect on the nature of uncertainty about the past and the tools available to conceptualise and represent it. There will be a core of papers focusing on the representation of beliefs as probability distributions, but also scope to discuss other approaches. The goal is to produce an edited volume that will foreground the challenges that uncertainty poses for quantitative history, show-case the potential of probabilistic and other rigorous approaches to uncertainty, and provide a useful methodological introduction for other scholars.

 

The conference will take place in St Andrews on 4-6 July 2018. The project has funding to cover the cost of speakers’ accommodation and meals at St Andrews and to make a contribution towards travel costs.

 

If you are interested in contributing, please get in touch with Myles Lavan (mpl2@st-andrews.ac.uk) by October 31. We can discuss how your interests might fit into the conference.

 

 

Works cited

Brughmans, T. and J. Poblome (2016), ‘Roman bazaar or market economy? Explaining tableware distributions through computational modelling’,Antiquity 90.350: 393-408.

Buck, C. E., W. G. Cavanagh and C. Litton (1996), Bayesian Approach to Interpreting Archaeological Data, Chichester.

Crema, E. R. (2012), ‘Modelling Temporal Uncertainty in Archaeological Analysis’, Journal of Archaeological Method and Theory 19: 440-61.

Jew, D. (in preparation), Agriculture and Carrying Capacity in Classical Athens: Modelling Historical Uncertainty, in preparation for Cambridge University Press

Lavan, M. (2016), ‘The spread of Roman citizenship, 14-212 CE: Quantification in the face of high uncertainty’, Past & Present 230: 3-46.

Roberts, J. M., B. J. Mills, J. J. Clark, W. R. Haas, D. L. Huntley and M. A. Trowbridge (2012), ‘A method for chronological apportioning of ceramic assemblages’, Journal of Archaeological Science 39: 1513-1520.

Rubio-Campillo, X., M. Coto-Sarmiento, J. Pérez-Gonzalez and J. Remesal Rodríguez (2017), ‘Bayesian analysis and free market trade within the Roman Empire’, Antiquity 91.351: 1241-52.

 

 

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