Evaluating the Potential of Computational Modelling for Informing Debates on Roman Economic Integration
This chapter argues that existing approaches to the study of the Roman economy need to be complemented with formal computational tools that enable comparisons of complex theories. It illustrates this by means of a case study simulating correlations betwee
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Evaluating the Potential of Computational Modelling for Informing Debates on Roman Economic Integration Tom Brughmans
1 Introduction The study of the Roman economy is a thriving discipline in need of methodological innovations. A large number of complex theories exist that aim to explain aspects of the functioning and performance of the Roman economy.1 Yet their sheer complexity, often involving a large number of explanatory factors that are argued to affect each other, means that they cannot be compared or tested through the traditional and current practice of qualitative argumentation and comparison with selected small sets of written and material sources alone. This has led to complex theories often being debated as conflicting even though their proponents admit they 1 Scheidel, Morris, and Saller, The Cambridge Economic History of the Greco-Roman World; Scheidel, The Cambridge Companion to the Roman Economy.
T. Brughmans (*) Centre for Urban Network Evolutions (UrbNet) and Classical Archaeology, Aarhus University, Aarhus, Denmark e-mail: [email protected] © The Author(s) 2021 K. Verboven (ed.), Complexity Economics, Palgrave Studies in Ancient Economies, https://doi.org/10.1007/978-3-030-47898-8_4
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must to some extent be overlapping and compatible.2 The inability to formally study this overlap hampers comparison and evaluation of hypotheses and the development of new theories on solid foundations. The existing approaches to the study of the Roman economy therefore need to be complemented with formal computational tools and approaches to enable comparison of complex theories.3 Most scholars of the Roman economy recognise this need but have so far addressed it only to a very limited extent.4 We need to know what data patterns we would expect to see if a certain hypothesis was true, what happens when we change the importance of an explanatory factor, how these expected patterns differ from those of other hypotheses, and how the available written and material data can be used in formal tests to determine the probability of each hypothesis. I will argue here that this need can be addressed by adding computational modelling to the list of tools used by scholars of the Roman economy. Complex theories of the functioning and performance of the Roman economy need to be broken down into the hypothesised mechanisms and explanatory factors they consist of. Computational modelling allows for the behaviour of these components to be studied in isolation, and enables the simulation of the data patterns one would expect to see as their outcomes. Only when individual mechanisms are understood on their own terms can they be combined into more complex models. I argue that findings from computational models can constructively contribute to ongoing debates of complex theories by providing reproducible expectations for suggested processes, and creating solid foundations for more data-driven contextual discussions of these theories. Moreover, they hold the potential of assessing the probability of hypotheses
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