By Edmund Chattoe-Brown
“Scientists tend not to ask themselves questions until they can see the rudiments of an answer in their minds. Embarrassing questions tend to remain unasked or, if asked, to be asked rudely” -Peter Medawar.
Disciplines and research methods are often arbitrarily divided by the assumptions they make about the social world. Economics is based almost exclusively on its own definition of rationality which is a minority interest (and widely regarded with scepticism) in almost all other social sciences. Statisticians focus on finding “big picture” patterns in “usual suspects” variables while qualitative researchers emphasise the role of agency, interaction, and context. While this state of affairs may be adequate under the normal academic divisions of labour, it creates a particular problem for interdisciplinary research and research intended for policy. In interdisciplinary research, different (and often entrenched) assumptions must somehow be reconciled so that the outcome really is collective insight rather than simply a ragbag of disconnected “business as usual” sub-projects. In policy research, we need to be confident of all the things that actually seem to reduce crime, not just the subset that criminologists (or economists or sociologists or statisticians or ethnographers) decide that their field should attend to.
But if we want to address this problem scientifically, we need an approach that can represent different organising beliefs about the social world fairly and effectively (which quantitative and qualitative approaches, for example, notoriously cannot do with each other’s insights). If we can represent two different views of the social world using the same framework, we can then examine how much difference it makes if we assume one thing rather than another. The argument of my article (after laying out the nature of this problem) is that a form of computer simulation known as Agent-Based Modelling (Chattoe-Brown 2019) can be developed to offer such an approach. Agent-Based Modelling is increasingly recognised as a technique that offers distinctive advantages to social science in representing process and fundamental heterogeneity (not just in “variables” but also in behaviour) and in analysing systems where simple individual interactions can lead to counter-intuitive aggregates, so-called complex systems displaying emergence (Chattoe-Brown 2013). This representational richness, based on describing social processes explicitly, allows the technique to avoid “technical” assumptions (made purely on analytical grounds) and to focus instead on the effective use of different sorts of data to justify building models in one way rather than another. (It is thus not only the technology that is distinctive but its associated methodology and relationship with different sorts of data.)
Therefore, most of the article is devoted to laying out and analysing a “worked example” concerning the social aspects of disease transmission, illustrating how Agent-Based Models operate in general and how they can be designed to answer the kind of questions that separate different fields of research. For example, does the presence or absence of social networks “matter” to the behaviour of systems? Some areas (like Social Network Analysis) take it for granted that networks do matter while others like large scale statistical analysis (with no less empirical success) analyse social behaviours without reference to network variables. To address this question, then, we can design an Agent-Based Model In which the social network can be “switched off” while all other aspects of the social process described remain the same. Any differences in the resulting behaviour of the system, therefore, necessarily arise from the presence (or absence) of social networks alone. We are effectively controlling for model assumptions independently. The result obtained from analysing this example is that static social networks matter considerably to the dynamics of disease transmission while evolving social networks make little additional difference. (Like a lot of social science, these results might be considered unsurprising with hindsight but that tells us more about hindsight than it does about the social world!)
Although the article uses the single example of networks as an aspect of social process, another aim of the article is to point out that many important social science debates tend to hinge on mere assertions endorsed by different disciplines which this approach could make a constructive contribution to addressing. For example, is decision behaviour rational, adaptive, habitual, or imitative as different disciplines assert? This debate is unlikely to progress scientifically without a technique for exploring how different kinds of decision making may give rise to distinctive patterns in data that we could discover. The same applies to the opposition between the statistical quest for “big patterns” and the qualitative emphasis on detail. Can suitably designed variants of Agent-Based Models show when “detail matters” and when it may “wash out” to leave big patterns? This sort of approach would be particularly valuable in analysing educational attainment, for example, where individual, interactional, and structural elements are all clearly in play. Being able to move these different positions forward from a “is, isn’t, is too” style of argument should be a major contribution to interdisciplinarity and more effective policy.
Of course, since writing this article, the importance of being able to draw on the best evidence from all relevant disciplines and methods has been made hugely more topical by the COVID pandemic. To tackle a real problem (which in this case is literally a matter of life and death), we need ways of understanding how geography, networks and social behaviour interact with diseases, the physics of PPE and surface contamination and many other aspects of the social process (like who cares for children when schools are closed). Simply biting off parts of the problem using existing approaches and studying them in isolation will almost certainly not be enough to produce effective policy. This article thus shows yet another way in which Agent-Based Modelling can make a distinctive contribution to advancing social science.
See full IJSRM article here.
Chattoe-Brown, Edmund (2013) ‘Why Sociology Should Use Agent Based Modelling’, Sociological Research Online, 18(3), article 3, August. doi:10.5153/sro.3055
Chattoe-Brown, Edmund (2019) ‘Agent Based Models’, in Atkinson, Paul, Delamont, Sara, Cernat, Alexandru, Sakshaug, Joseph W. and Williams, Richard A. (eds.) SAGE Research Methods. doi:10.4135/9781526421036836969