Online research in philosophy

Are conscious states conscious in virtue of representing themselves? Content Type Journal Article Pages 1-8 DOI 10.1007/s11098-011-9762-x Authors Berit Brogaard, Department of Philosophy, University of Missouri, St. Louis, 599 Lucas Hall, One University Blvd., St. Louis, MO 63121-4400, USA Journal Philosophical Studies Online ISSN 1573-0883 Print ISSN 0031-8116.

Mathematical models are potentially as useful for culture as for evolution, but cultural models must have different designs from genetic models. Social sciences must borrow from biology the idea of modeling, rather than the structure of models, because copying the product is fundamentally different from copying the design. Transfer of most cultural information from brains to artificial media increases the differences between cultural and biological information. (Published Online November 9 2006).

In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I demonstrate an important advantage my account has over other theories of scientific representation. All existing theories analyse scientific representation in terms of relations, such as similarity or denotation. By contrast, my account does not take representation in modelling to be essentially relational. For this reason, it can accommodate a group of models often ignored in discussions of scientific representation, namely models which are representational but which represent no actual object.

I. Introduction. Philosophical discussions of models and modeling in the biological sciences have exploded in the last few decades. Given that there are three-dimensional models of DNA in molecular genetics, individual-based computer simulations in population ecology, statistical models in paleontology, diffusion models in population genetics, and remnant models in taxonomy, we clearly should have a philosophical account of such models and their relation to the world. In this essay, I provide a critical survey of the accounts of models provided by philosophers of science and philosophers of biology including models as analogies, relational structures, partially independent representations, and material objects. However, there is much, much more work to be done.

Models that fail to satisfy the Markov condition are unstable because changes in state variable values may cause changes in the values of background variables, and these changes in background lead to predictive error. Such error arises because non‐Markovian models fail to track the causal relations generating the values of response variables. This has implications for discussions of the level of selection: under certain plausible conditoins most standard models of group selection will not satisfy the Markov condition when fit to data from real populations. These models neither correctly represent the causal structure generating nor correctly explain the phenomena of interest. †To contact the author, please write to: Bruce Glymour, Department of Philosophy, 201 Dickens Hall, Kansas State University, Manhattan KS, 66506; e‐mail: glymour@ksu.edu.

Most models of generational succession in sexually reproducing populations necessarily move back and forth between genic and genotypic spaces. We show that transitions between and within these spaces are usually hidden by unstated assumptions about processes in these spaces. We also examine a widely endorsed claim regarding the mathematical equivalence of kin-, group-, individual-, and allelic-selection models made by Lee Dugatkin and Kern Reeve. We show that the claimed mathematical equivalence of the models does not hold. *Received January 2007; revised April 2008. †To contact the authors, please write to: Elisabeth Lloyd, Department of History and Philosophy of Science, 130 Goodbody Hall, Indiana University, Bloomington, IN 47405; e-mail: ealloyd@indiana.edu; Richard Lewontin, Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138; Marcus Feldman, Department of Biological Sciences, Stanford University, Stanford, CA 94305; e-mail: marc@charles.stanford.edu.

Scientific models invariably involve some degree of idealization, abstraction, or fictionalization of their target system. Nonetheless, I argue that there are circumstances under which such false models can offer genuine scientific explanations. After reviewing three different proposals in the literature for how models can explain, I shall introduce a more general account of what I call model explanations , which specify the conditions under which models can be counted as explanatory. I shall illustrate this new framework by applying it to the case of Bohr’s model of the atom, and conclude by drawing some distinctions between phenomenological models, explanatory models, and fictional models.

Can social phenomena be understood by analyzing their parts? Contemporary economic theory often assumes that they can. The methodology of constructing models which trace the behavior of perfectly rational agents in idealized environments rests on the premise that such models, while restricted, help us isolate tendencies, that is, the stable separate effects of economic causes that can be used to explain and predict economic phenomena. In this paper, I question both the claim that models in economics supply claims about tendencies and also the view that economics, when successful, necessarily follows this method. When economics licenses successful policy interventions, as it did in the case of the Federal Communications Commission spectrum auctions, its method is not to study tendencies but rather to study the phenomenon as a whole. Key Words: economic models • tendencies • economic experiments • policy making • John Stuart Mill.

Most scientific models are not physical objects, and this raises important questions. What sort of entity are models, what is truth in a model, and how do we learn about models? In this paper I argue that models share important aspects in common with literary fiction, and that therefore theories of fiction can be brought to bear on these questions. In particular, I argue that the pretence theory as developed by Walton (1990, Mimesis as make-believe: on the foundations of the representational arts. Harvard University Press, Cambridge/MA) has the resources to answer these questions. I introduce this account, outline the answers that it offers, and develop a general picture of scientific modelling based on it.

Denis Walsh has written a striking new defense in this journal of the statisticalist (i.e., noncausalist) position regarding the forces of evolution. I defend the causalist view against his new objections. I argue that the heart of the issue lies in the nature of nonadditive causation. Detailed consideration of that turns out to defuse Walsh’s ‘description‐dependence’ critique of causalism. Nevertheless, the critique does suggest a basis for reconciliation between the two competing views. *Received December 2009; revised December 2009. †To contact the author, please write to: Department of Philosophy, 599 Lucas Hall, One University Boulevard, University of Missouri, St. Louis, MO 63121; e‐mail: northcottr@umsl.edu.