Order this book
University of Salzburg
In her recent book Reconstructing Reality, Margaret Morrison, Professor of Philosophy at the University of Toronto, suggested to rethink the relationship between models, simulations and reality from different epistemological and methodological perspectives. Her main focus was on physics; this restriction, however, should not prevent social scientists from reading her book as her claims have general implications.
The book is structured in three parts: The first one is dedicated to the role of mathematics in approaching the physical target system (a phrase she used repeatedly as synonymous to reality). If we draw a distinction between ‘abstraction’ – Morrison defines it as “a process whereby we describe phenomena in ways that cannot possibly be realised in the physical world” (p. 20) – and ‘idealisation’, which “on the other hand involves a process of approximation whereby the system can become less idealised by adding correction factors” (ibid.), then mathematics is necessary to deal with abstraction in a sophisticated way.
Part II of the book addresses three problems in modelling with regard to an adequate interpretation of information contained in models: First, the role of fictional models in gaining a reliable understanding of the underlying principles nonetheless. The point Morrison makes here is that we should seriously consider the correct sequence of reasoning, which is from ‘law’ to ‘model’ to ‘reality’, i.e. reality is an instance of a model, and a model is an instance of a law. According to this structure, fictional models used deliberately as ‘wrong idealisations’ can provide information that can be used to further develop theories about the subject matter. Secondly, it is the meaning of the representational account that models are typically concerned with. Here, Morrison (p. 130ff) distinguished between theory-based ‘interpretive models’ and world-based ‘representative models’. One crucial question arises essentially: what comes first? Morrison referred to Cartwright (1999) who claimed that theory is necessarily prior to representation, but she argued for a reverse reading of this direction, “one needs a representative model before we can determine how the abstract concepts/theory are going to be applied in a specific situation” (p. 132). Thirdly, she considered the role of inconsistent models. Morrison advocated a perspectival realism approach, suggesting that inconsistency may help to explore the target system with an inappropriate practice. Perspectivism leaves, however, the problem of verification and validation mostly unsolved.
Part III of the book finally highlights the relevance of simulation as a significant technique in gaining new and different knowledge about the real system. The question of the impact taht simulation may yield in science has been put in a context of experimentation and measurement. Is simulation equivalent to experiments (in the sense of material laboratory-based investigations)? Or is it an appropriate tool for measuring facts that could not be measured otherwise?
Morrison took an in-between position here. On the one hand, she argued that “materiality in and of itself doesn’t really tell us much, if anything, in experiments like the Higgs search” (p. 211). On the other hand, she asked herself: “If simulations enjoy the same epistemic status as experiments then why spend money building large colliders designed to test the results produced by Monte Carlo simulations of particle interactions” (p. 213). One of her conclusions is to consider simulations as an integral part of the entire experimentation process.
Although it seems that much has been said about modelling and simulation, this book offers many opportunities for a critical – and self-critical – reconsideration of our ontological and epistemological attitudes towards models. What are the benefits of a model? What are its constraints? How can abstract models without empirical foundations be used to understand real systems? Why are simulation models an essential part of knowledge generation in experimental/empirical science?
What is missing in the book (and not only in this book) is a thorough consideration of the meaning of ‘reality’ or the ‘target system’. In an earlier volume, Morrison together with Morgan (1999) suggested to view models as ‘autonomous mediators’, that is, models have an independent status when describing and representing ‘reality’. Remarkably, Stachowiak 1973 in his definition of the nature of models didn’t refer to reality but to originals which could be natural or artificial. This interpretation of independence would then lead to an understanding of models as reality generators. Once we have created a model or a computer simulation, this model or computer simulation then represents reality in its own right. The title of Morrison’s book may confirm this understanding: reconstructing reality means constructing reality, that is, ‘reality’ or ‘truth’ or ‘fact’ do emerge and do not exist a priori and eo ipso. There is only one clear point (on page 209) where Morrison explicitly refers to this idea: “As we can see, it was largely the model that came to define and constrain reality, and not vice versa. What was physically realisable was what could be mechanically constructed, and what was mechanically constructed could be measured. This constituted the experimental basis for theoretical beliefs and ontological commitments, and in that sense the model-based unity of theory, method, and ontology created a very powerful web of belief”.
CARTWRIGHT, N. (1999). Models and the Limits of Theory: Quantum Hamiltonians and the BCS Models of Superconductivity. In: Morgan, M. and Morrison, M. (eds.): Models as Mediators: Perspectives on Natural and Social Science. Cambridge University Press, Cambridge, p. 241-281.
DERMAN, E. (2011). Models. Behaving. Badly. Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street an in Life. Free Press, New York.
EPSTEIN, J. M. (2006). Generative Social Science. Studies in Agent-Based Computational Modeling. Princeton University Press, Princeton and Oxford.
IHDE, D. (2006). Models, Models Everywhere. In: Lenhard, J., Küppers, G., Shinn, T. (eds.): Simulation. Pragmatic Construction of Reality. Springer, Dordrecht, p. 79-86.
KUEPPERS, G., Lenhard, J. and Shinn T. (2006). Computer Simulation: Practice, Epistemology, and Social Dynamics. In: Lenhard, J., Küppers, G., Shinn, T. (eds.): Simulation. Pragmatic Construction of Reality. Springer, Dordrecht.
MORGAN, M. and Morrison, M. (1999). Models as Mediators: Perspectives on Natural and Social Science. Cambridge University Press, Cambridge.
STACHOWIAK, H. (1973), Allgemeine Modelltheorie. Springer, Wien New York.
Return to Contents of this issue
© Copyright JASSS, 2015