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Disagreement in discipline-building processes

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Abstract

Successful instances of interdisciplinary collaboration can eventually enter a process of disciplinarisation. This article analyses one of those instances: agent-based computational social science, an emerging disciplinary field articulated around the use of computational models to study social phenomena. The discussion centres on how, in knowledge transfer dynamics from traditional disciplinary areas, practitioners parsed several epistemic resources to produce new foundational disciplinary shared commitments, and how disagreements operated as a mechanism of differentiation in their production. Two parsing processes are examined to illustrate this claim. The first one is the parsing of the qualitative–quantitative dualism, arguably the most important methodological disagreement in social science. The second one is the parsing of prediction, a key value in contemporary science. The analysis evidences that disagreements have fostered both external and internal dynamics of differentiation in agent-based computational social science. The former have permitted a more efficient use of epistemic resources, whereas the latter have forced practitioners to modify the foundational narrative and the agenda of the field.

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Notes

  1. Disciplinarisation requires the fulfilment of some institutional (e.g., programmes and procedures of certification, events, academic positions) and intellectual (e.g., foundational narratives, a defined domain and agenda, a set of exemplars) conditions (Becher and Trowler 2001; D’Agostino 2012). While there are a few terms that explicitly denote disciplinary areas (e.g., complexity theory, cultural studies or decision sciences) or are used interchangeably with ‘discipline’ (e.g., ‘field’ or ‘specialty’), it could be argued that the term ‘discipline’, as such, is still mostly reserved for traditional disciplinary areas, for they have a wider scope and are more deeply institutionalised, especially in terms of their pedagogical (e.g., undergraduate and postgraduate programmes) and professional (e.g., well-defined labour market) components. The terms ‘field’ and ‘specialty’, however, are relatively flexible. They are not only used to refer to disciplines, but also to subdisciplines, relatively novel disciplinary areas or institutionalised scientific collaboration beyond disciplinary borders. This article refers to agent-based computational social science as a field, first, because it captures both the perceived sense of novelty and the interdisciplinary nature of everyday practices of social simulation and, second, because it is the term practitioners seems to favour when referring to agent-based computational social science as a disciplinary area.

  2. Being centred on a method is an obstacle for the articulation of a full structure of certification, since undergraduate programmes are, in general, still organised under traditional disciplinary lines.

  3. Programming languages not only permit to more easily incorporate nominal and ordinal data in the design, operation and validation of the model, but also provide some advantages for the model’s implementation. In practice, a production system allows to more easily incorporate complex decision making than typical probability-based heuristics.

  4. The view of agent-based modelling as a middle ground is also used to conflate additional dichotomies e.g., deductive–inductive, empirical research-formal theory, theory-data (Axelrod 1997; Conte et al. 2001; Epstein 2006; Squazzoni 2012). It is likely these other conflations also save resources by reducing the risk of epistemic tension and disagreement.

  5. The method could still be criticised for its ability to forecast, particularly in the form of point-prediction. However, given that, up to this point, empirically-calibrated agent-based modelling is not a fully-fledged area of research (mainly because of the lack of suitable data), it is not clear whether this truly is an inherent limitation of the method. While there will be always some challenges posed by elements such the stochastic nature of a computer simulation, there is still much to discuss about how the method instantiates prediction. Practitioners, for example, have yet to inquire about how the indirect nature of the knowledge produced by computational modelling affects prediction, especially given the increasing popularity of fictionalist theories of modelling and representation.

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    David Anzola

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Anzola, D. Disagreement in discipline-building processes. Synthese 198 (Suppl 25), 6201–6224 (2021). https://doi.org/10.1007/s11229-019-02438-9

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