Bootstrapping Knowledge About Social Phenomena Using Simulation Models

Edmonds, BM ORCID: https://orcid.org/0000-0002-3903-2507 (2010) Bootstrapping Knowledge About Social Phenomena Using Simulation Models. Journal of Artificial Societies and Social Simulation, 13 (1). p. 8. ISSN 1460-7425

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Abstract

There are considerable difficulties in the way of the development of useful and reliable simulation models of social phenomena, including that any simulation necessarily includes many assumptions that are not directly supported by evidence. Despite these difficulties, many still hope to develop quite general models of social phenomena. This paper argues that such hopes are ill-founded, in other words that there will be no short-cut to useful and reliable simulation models. However this paper argues that there is a way forward, that simulation modelling can be used to "boot-strap" useful knowledge about social phenomena. If each bit of simulation work can result in the rejection of some of the possible processes in observed social phenomena, even if this is about a very specific social context, then this can be used as part of a process of gradually refining our knowledge about such processes in the form of simulation models. Such a boot-strapping process will only be possible if simulation models are more carefully judged, that is a greater selective pressure is applied. In particular models which are just an analogy of social processes in computational form should be treated as "personal" rather than "scientific" knowledge. Such analogical models are useful for informing the intuition of its developers and users, but do not help the community of social simulators and social scientists to "boot-strap" reliable social knowledge. However, it is argued that both participatory modelling and evidence-based modelling can play a useful part in this process. Some kinds of simulation model are discussed with respect to their suitability for the boot-strapping of social knowledge. The knowledge that results is likely to be of a more context-specific, conditional and mundane nature than many social scientists hope for.