Abstract
This article makes a dual contribution to scholarship in science and technology studies (STS) on simulation-building. It both documents a specific simulation-building project, and demonstrates a concrete contribution of STS insights to interdisciplinary work. The article analyses the struggles that arise in the course of determining what counts as theory, as model and even as a simulation. Such debates are especially decisive when working across disciplinary boundaries, and their resolution is an important part of the work involved in building simulations. In particular, we show how ontological arguments about the value of simulations tend to determine the direction of simulation-building. This dynamic makes it difficult to maintain an interest in the heterogeneity of simulations and a view of simulations as unfolding scientific objects. As an outcome of our analysis of the process and reflections about interdisciplinary work around simulations, we propose a chart, as a tool to facilitate discussions about simulations. This chart can be a means to create common ground among actors in a simulation-building project, and a support for discussions that address other features of simulations besides their ontological status. Rather than foregrounding the chart’s classificatory potential, we stress its (past and potential) role in discussing and reflecting on simulation-building as interdisciplinary endeavor. This chart is a concrete instance of the kinds of contributions that STS can make to better, more reflexive practice of simulation-building.
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Notes
In assessing the usefulness of simulations for social science, it is precisely the possibility that simulations can be ‘more’ than mathematical formulations that makes them appealing (Moretti 2002).
Uncertainty is important for two reasons. First, it makes explicit that learning often occurs in situations where the goal is shaped during the process (Erpenbeck and Heyse 1999). Second, uncertainty is an implicit part of the ‘real world’ context in which competence is exercised, where, for example, the problem of unemployment and the need for flexible work forces remain major policy issues.
However, it is important to make explicit that even in the case of the swarm simulation, some ‘massaging’ of the equations was required in order to make them work within the swarm software framework. This type of ‘articulation work’ (Strauss 1988; Fujimura 1987), often referred to as ‘tuning’, has been documented in other examples of mathematical modelling and simulation. (e.g., Kueppers and Lenhard 2005; Winsberg 2006).
Actually, Evolino has turned out not to be particularly suitable for the kinds of model-testing (e.g. the statistical analysis of high numbers of individual simulation ‘runs’) that physicists and other simulation users often require for legitimacy. The computer language (Shockwave Flash) requires quite a lot of memory and as a web program does not allow the storage of results. The simulation therefore tends to crash if it runs for too long or too often.
This and other insights are detailed in another article (Beaulieu et al. 2007).
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Acknowledgments
The simulation-building work described in this manuscript was funded by the Federal Ministry for Education and Research (Germany) and the European Social Fund, while the work of Matt Ratto was funded by the Dutch Organisation for Scientific Research (NWO) through the project Dissimilar Simulation: the Epistemics of Simulation in the Humanities, in the framework program ‘Culturele vernieuwing en de grondslagen van de geesteswetenschappen’. We are grateful to the other members of the simulation project for discussions on the issues analyzed in this article, and to John Erpenbeck who encouraged us to work on the interactive aspect of the simulations. We would also like to thank the editors and reviewers of this special issue, many careful and critical readers including Marcel Boumans, Guenter Kueppers, Sabina Leonelli, and Kyriaki Papageorgiou, as well as the members of the Virtual Knowledge Studio for their detailed comments on earlier versions of this article.
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Beaulieu, A., Ratto, M. & Scharnhorst, A. Learning in a landscape: simulation-building as reflexive intervention. Mind Soc 12, 91–112 (2013). https://doi.org/10.1007/s11299-013-0117-5
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DOI: https://doi.org/10.1007/s11299-013-0117-5