David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Mind and Society 3 (1):81-91 (2002)
The paper studies the nature of understanding in condensed matter physics (CMP), mediated by the successful employment of its models. I first consider two obvious candidates for the criteria of model-based understanding, Van Fraassen's sense of empirical adequacy and Hacking's instrumental utility , and conclude that both are unsatisfactory. Inspired by Hasok Chang's recent proposal to reformulate realism as the pursuit of ontological plausibility in our system of knowledge, we may require the model under consideration to be understood (or intelligible) before claiming model-based understanding. Here the understanding of a model typically consists of the following: figuring out at least one plausible (preferably realistic) physical mechanism for the model, determining the theoretical consequences of the model by mathematically probing it and developing our physical intuitions about the model. I consider the q-state Potts model to illustrate. After having understood a model, we may employ the model to understand its target phenomena in the world. This is done by matching one of the interpretative models of the model with the central features of the phenomena. The matching should be motivated (ideally both theoretically and empirically) in the sense that we have good reason to believe that the central features of the phenomena can be thought of as having more or less the same structure as postulated by the interpretative model. In conclusion, I propose a two-stage account of model-based understanding in CMP: (1) understanding of a model and (2) matching a target phenomenon with a well-motivated interpretative model of the model. Empirical success and instrumental utility both play their roles in the evaluation of how successful the model is, but are not the essential part of model-based understanding
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
|Through your library||Configure|
References found in this work BETA
No references found.
Citations of this work BETA
Axel Gelfert (2013). Strategies of Model-Building in Condensed Matter Physics: Trade-Offs as a Demarcation Criterion Between Physics and Biology? Synthese 190 (2):253-272.
Axel Gelfert (2009). Rigorous Results, Cross-Model Justification, and the Transfer of Empirical Warrant: The Case of Many-Body Models in Physics. Synthese 169 (3):497 - 519.
Similar books and articles
L. Taper Mark, F. Staples David & B. Shepard Bradley (2008). Model Structure Adequacy Analysis: Selecting Models on the Basis of Their Ability to Answer Scientific Questions. Synthese 163 (3).
Mark L. Taper, David F. Staples & Bradley B. Shepard (2008). Model Structure Adequacy Analysis: Selecting Models on the Basis of Their Ability to Answer Scientific Questions. Synthese 163 (3):357 - 370.
Daniela M. Bailer-Jones (2003). When Scientific Models Represent. International Studies in the Philosophy of Science 17 (1):59 – 74.
Jason Scott Robert (2008). The Comparative Biology of Human Nature. Philosophical Psychology 21 (3):425 – 436.
John R. Pani (2001). The Mathematics of Symmetry Does Not Provide an Appropriate Model for the Human Understanding of Elementary Motions. Behavioral and Brain Sciences 24 (4):696-697.
Victor J. Stenger (2006). A Scenario for a Natural Origin of Our Universe Using a Mathematical Model Based on Established Physics and Cosmology. Philo 9 (2):93-102.
Roman Frigg & Stephan Hartmann (2005). Scientific Models. In Sahotra Sarkar et al (ed.), The Philosophy of Science: An Encyclopedia, Vol. 2. Routledge.
Karlis Podnieks (2009). Is Scientific Modeling an Indirect Methodology? The Reasoner 3 (1):4-5.
Patricia H. Miller (2001). Developmental Issues in Model-Based Reasoning During Childhood. Mind and Society 2 (2):49-58.
Added to index2010-08-10
Total downloads49 ( #27,954 of 1,088,907 )
Recent downloads (6 months)8 ( #13,565 of 1,088,907 )
How can I increase my downloads?