Online research in philosophy

In this paper, I consider how different versions of the similarity account of scientific representation might apply to a simple case of scientific representation, in which a model is used to predict the behaviour of a system. I will argue that the similarity account is potentially susceptible to the problem of accidental similarities between the model and the system and that, if it is to avoid this problem, one has to specify which similarities have to hold between a model and a system for the model to be a faithful representation of that system. The sort of similarity that needs to hold between the model and the system, I argue, is a “second-order” similarity rather than simply a “first-order” similarity. This will not only avoid the problem but hopefully will contribute to dispelling the impression that an account of representation based on similarity is hopelessly vague.

In this paper, I provide an account of scientific representation that makes sense of the notion both at the nanoscale and at the quantum level: the partial mappings account. The account offers an extension of a proposal developed by R. I. G. Hughes in terms of denotation, demonstration, and interpretation (DDI). I first argue that the DDI account needs some amendments to accommodate representation of nano and quantum phenomena. I then introduce a generalized framework with the notions of unsharp denotation, unsharp interpretation, and partial mappings. I conclude by arguing that the resulting view not only accommodates nano and quantum representations but also makes sense of representation by imaging in (scanning tunneling) microscopy.

In this essay, I shall show that the so-called inferential (Suárez 2003 and 2004 ) and interpretational (Contessa 2007 ) accounts of scientific representation are respectively unsatisfactory and too weak to account for scientific representation ( pars destruens ). Along the way, I shall also argue that the pragmatic similarity (Giere 2004 and Giere 2010 ) and the partial isomorphism (da Costa and French 2003 and French 2003 ) accounts are unable to single out scientific representation. In the pars construens I spell out a limiting case account which has explanatory surplus vis à vis the approaches which I have previously reviewed. My account offers an adequate treatment of scientific representation, or so I shall try to argue. Central to my account is the notion of a pragmatic limiting case, which will be characterized in due course.

It is argued that a number of important, and seemingly disparate, types of representation are species of a single relation, here called structural representation, that can be described in detail and studied in a way that is of considerable philosophical interest. A structural representation depends on the existence of a common structure between a representation and that which it represents, and it is important because it allows us to reason directly about the representation in order to draw conclusions about the phenomenon that it depicts. The present goal is to give a general and precise account of structural representation, then to use that account to illuminate several problems of current philosophical interest — including some that do not initially seem to involve representation at all. In particular, it is argued that ontological reductions (like that of the natural numbers to sets), compositional accounts of semantics, several important sorts of mental representation, and (perhaps) possible worlds semantics for intensional logics are all species of structural representation and are fruitfully studied in the framework developed here.

This paper defends an inferential conception of scientific representation. It approaches the notion of representation in a deflationary spirit, and minimally characterizes the concept as it appears in science by means of two necessary conditions: its essential directionality and its capacity to allow surrogate reasoning and inference. The conception is defended by showing that it successfully meets the objections that make its competitors, such as isomorphism and similarity, untenable. In addition the inferential conception captures the objectivity of the cognitive representations used by science, it sheds light on their truth and completeness, and it explains the source of the analogy between scientific and artistic modes of representation.

In this paper I will defend a new account of scientific representation. I will begin by looking at the benefits and drawbacks of two recent accounts on scientific representation: Hughes’ DDI account and Suárez’ inferential account. Next I use some of Galileo’s models in the Discorsi as a heuristic tool for a better account of scientific representation. Next I will present my model. The main idea of my account, which I refer to as the pragmatic model of shared characteristics (PMSC), is that a model represents, if and only if, (1) a person accepts that there is a set of shared characteristics between the model and its target; (2) this set has the inferential power to generate results which can be tested empirically; (3) and the corresponding test(s) of these results is/are in agreement with our data and the specific cognitive goals we have in mind.

The main aim of this paper is to disentangle three senses in which we can say that a model represents a system—denotation epistemic representation, and successful epistemic representation--and to individuate what questions arise from each sense of the notion of representation as used in this context. Also, I argue that a model is an epistemic representation of a system only if a user adopts a general interpretation of the model in terms of a system. In the process, I hope to clarify where those who, following Craig Callander and Jonathan Cohen, claim that there is no special problem about scientific representation go wrong. In the terminology adopted here, even if scientific representation is only an instance of epistemic representation, scientific representation should not be confounded with denotation.

In this paper, I show how some of the fundamental problems that face a structuralist conception of representation can be solved by (a) distinguishing between three relevant senses of ‘representation’ (i.e. denotation, epistemic representation, and faithful epistemic representation), (b) claiming that, properly understood, the structural conception of representation aims at providing us with an account of faithful epistemic representation not of epistemic representation simpliciter, and (c) adopting an interpretational conception of epistemic representation.

Today most philosophers of science believe that models play a central role in science and that one of the main functions of scientific models is to represent systems in the world. Despite much talk of models and representation, however, it is not yet clear what representation in this context amounts to nor what conditions a certain model needs to meet in order to be a representation of a certain system. In this thesis, I address these two questions. First, I will distinguish three senses in which something, a vehicle, can be said to be a representation of something else, a target—which I will call respectively denotation, epistemic representation, and faithful epistemic representation—and I will argue that the last two senses are the most important in this context. I will then outline a general account of what makes a vehicle an epistemic representation of a certain target for a certain user—which, according to the account I defend, is the fact that a user adopts what I call an interpretation of the vehicle in terms of the target—and of what makes an epistemic representation of a certain target a faithful epistemic representation of it—which, according to the account I defend, is a specific sort of structural similarity between the vehicle and the target.