Among theory theorists, it is commonly thought that folk psychological theory is tacitly known. However, folk psychological knowledge has none of the central features of tacit knowledge. But if it is ordinary knowledge, why is it that we have difficulties expressing anything but a handful of folk psychological generalisations? The reason is that our knowledge is of theoretical models and hypotheses, not of universal generalisations. Adopting this alternative view of (scientific) theories, we come to see that, given time and reflection, (...) we can say what we know. (shrink)

It used to be thought that folk psychology is the only game in town. Focusing merely on what people do will not allow you to predict what they are likely to do next. For that, you must consider their beliefs, desires, intentions, etc. Recent evidence from developmental psychology and fMRI studies indicates that this conclusion was premature. We parse motion in an environment as behavior of a particular type, and behavior thus construed can feature in systematizations that we know. Building (...) on the view that folk psychological knowledge is knowledge of theoretical models, I argue that social knowledge is best understood as lying on a continuum between behavioral and full-blown psychological models. Between the two extremes, we have what I call social models. Social models represent social structures in terms of their overall purpose and circumscribe individuals' roles within them. These models help us predict what others will do or plan what we should do without providing information about what agents think or want. Thinking about social knowledge this way gives us a more nuanced picture of what capacities are engaged in social planning and interaction, and gives us a better tool with which to think about the social knowledge of animals and young children. (shrink)

Naturalists seek continuity between epistemology and science. Critics argue this illegitimately expands science into epistemology and commits the fallacy of scientism. Must naturalists commit this fallacy? I defend a conception of naturalized epistemology which upholds the non-identity of epistemic ends, norms, and concepts with scientific evidential ends, norms, and concepts. I argue it enables naturalists to avoid three leading scientistic fallacies: dogmatism, one dimensionalism, and granting science an epistemic monopoly.

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools to (...) handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations. (shrink)

Regulatory science, which generates knowledge relevant for regulatory decision?making, is different from standard academic science in that it is oriented mainly towards the attainment of non?epistemic (practical) aims. The role of uncertainty and the limits to the relevance of academic science are being recognized more and more explicitly in regulatory decision?making. This has led to the introduction of regulation?specific scientific methodologies in order to generate decision?relevant data. However, recent practical experience with such non?standard methodologies indicates that they, too, may be (...) subject to important limitations. We argue that the attainment of non?epistemic values and aims (like the protection of human health and the environment) requires not only control of the quality of the data and the methodologies, but also the selection of the level of regulation deemed adequate in each specific case (including a decision about which of the two, under?regulation or over?regulation, would be more acceptable). (shrink)

The philosophy of information (PI) is a new area of research with its own field of investigation and methodology. This article, based on the Herbert A. Simon Lecture of Computing and Philosophy I gave at Carnegie Mellon University in 2001, analyses the eighteen principal open problems in PI. Section 1 introduces the analysis by outlining Herbert Simon's approach to PI. Section 2 discusses some methodological considerations about what counts as a good philosophical problem. The discussion centers on Hilbert's famous analysis (...) of the central problems in mathematics. The rest of the article is devoted to the eighteen problems. These are organized into five sections: problems in the analysis of the concept of information, in semantics, in the study of intelligence, in the relation between information and nature, and in the investigation of values. (shrink)

In this paper, we argue that symbols are conventional vehicles whose chief function is denotation, while models are epistemic vehicles, and their chief function is to show what their targets are like in the relevant aspects. And we explain why this is incompatible with the deflationary view on scientific modeling. Although the same object may serve both functions, the two vehicles are conceptually distinct and most models employ both elements. With the clarification of this point we offer an alternative account (...) to the deflationary view – the Hybrid Account; and we defend our account in contrast with deflationism. (shrink)

This paper is the second of a two-part series on models and theories, the first of which appeared in International Studies in the Philosophy of Science, Vol. 11, No. 2, 1997. It further explores some of themes of the first paper and examines applications, including: the relations between “similarity” and “isomorphism”, and between “model” and “interpretation”, and the notion of structural explanation.

The paper, as Part I of a two-part series, argues for a hybrid formulation of the semantic view of scientific theories. For stage-setting, it first reviews the elements of the model theory in mathematical logic (on whose foundation the semantic view rests), the syntactic and the semantic view, and the different notions of models used in the practice of science. The paper then argues for an integration of the notions into the semantic view, and thereby offers a hybrid semantic view, (...) which at once secures the view's logical foundations and enhances its applicability. The dilemma of either losing touch with the practice of science or yielding up the benefits of the model theory is thus avoided. (shrink)

This article develops an approach to modelling and models in science—the hybrid view—that is against model fictionalism of a recent stripe. It further argues that there is a version of fictionalism about models to which my approach is neutral and which makes sense only if one adopts a special sort of antirealism. Otherwise, my approach strongly suggests that one stay away from fictionalism and embrace realism directly.

In this paper, a criticism of the traditional theories of approximation and idealization is given as a summary of previous works. After identifying the real purpose and measure of idealization in the practice of science, it is argued that the best way to characterize idealization is not to formulate a logical model – something analogous to Hempel's D-N model for explanation – but to study its different guises in the praxis of science. A case study of it is then made (...) in thermostatistical physics. After a brief sketch of the theories for phase transitions and critical phenomena, I examine the various idealizations that go into the making of models at three difference levels. The intended result is to induce a deeper appreciation of the complexity and fruitfulness of idealization in the praxis of model-building, not to give an abstract theory of it. (shrink)

This paper explores the issue of epistemic injustice in research evaluation. Through an analysis of the disciplinary cultures of physics and humanities, we attempt to identify some aims and values specific to the disciplinary areas. We suggest that credibility is at stake when the cultural values and goals of a discipline contradict those presupposed by official evaluation standards. Disciplines that are better aligned with the epistemic assumptions of evaluation standards appear to produce more "scientific" findings. To restore epistemic justice in (...) research evaluation, we argue that the specificity of a discipline's epistemic aims, values, and cultural identities must be taken into account. (shrink)

Several, seemingly unrelated problems of empirical research in the 'sociology of scientific knowledge' can be analyzed as following from initial assumptions with respect to the status of the knowledge content of science. These problems involve: (1) the relation between the level of the scientific field and the group level; (2) the boundaries and the status of 'contexts', and (3) the emergence of so-called 'asymmetry' in discourse analysis. It is suggested that these problems can be clarified by allowing for cognitive factors (...) as independent ('heterogeneous') variables, in addition to and in interaction with (i.e., not only as attributes of) social factors. In the 'sociology of translation', 'heterogeneity' among scientists, cognitions and textual elements has been made a basic assumption. This heterogeneity is bound together in an 'actor network'. However, since the 'actor network' is an empirical category, the methodological problems remain unresolved. This has consequences for the relation between empirical data and theoretical inferences. (shrink)

Scientific models are often said to be more or less general depending on how many cases they cover. In this paper we argue that the cardinality of cases is insufficient as a metric of generality, and we present a novel account based on measure theory. This account overcomes several problems with the cardinality approach, and additionally provides some insight into the nature of assessments of generality. Specifically, measure theory affords a natural and quantitative way of describing local spaces of possibility. (...) The generality of models can be understood as the measure of possibilities to which the models apply. In order to illustrate our view, we consider the example of structural genomics, the ongoing project of building three-dimensional models of biological macromolecules like proteins. Using measure theory, we interpret the practice of homology modeling, where such models are understood to apply widely but imperfectly to the space of possible proteins. (shrink)

Modeling is an important scientific practice, yet it raises significant philosophical puzzles. Models are typically idealized, and they are often explored via imaginative engagement and at a certain “distance” from empirical reality. These features raise questions such as what models are and how they relate to the world. Recent years have seen a growing discussion of these issues, including a number of views that treat modeling in terms of indirect representation and analysis. Indirect views treat the model as a bona (...) fide object, specified by the modeler and used to represent and reason about some portion of the concrete empirical world. On some indirect views, model systems are abstract entities, such as mathematical structures, while on other views they are concrete hypothetical things. Here I assess these views and offer a novel account of models. I argue that regarding models as abstracta results in some significant tensions with the practice of modeling, especially in areas where non-mathematical models are common. Furthermore, viewing models as concrete hypotheticals raises difficult questions about model-world relations. The view I argue for treats models as direct, albeit simplified, representations of targets in the world. I close by suggesting a treatment of model-world relations that draws on a recent work by Stephen Yablo concerning the notion of partial truth. (shrink)

Some philosophers of science – the present author included – appeal to fiction as an interpretation of the practice of modeling. This raises the specter of an incompatibility with realism, since fiction-making is essentially non-truth-regulated. I argue that the prima facie conflict can be resolved in two ways, each involving a distinct notion of fiction and a corresponding formulation of realism. The main goal of the paper is to describe these two packages. Toward the end I comment on how to (...) choose among them. (shrink)

Idealization and abstraction are central concepts in the philosophy of science and in science itself. My goal in this paper is suggest an account of these concepts, building on and refining an existing view due to Jones Idealization XII: correcting the model. Idealization and abstraction in the sciences, vol 86. Rodopi, Amsterdam, pp 173–217, 2005) and Godfrey-Smith Mapping the future of biology: evolving concepts and theories. Springer, Berlin, 2009). On this line of thought, abstraction—which I call, for reasons to be (...) explained, abstractness—involves the omission of detail, whereas idealization consists in a deliberate mismatch between a description and the world. I will suggest that while the core idea underlying these authors’ view is correct, they make several assumptions and stipulations that are best avoided. For one thing, they tie abstractness too close to truth. For another, they do not allow sufficient room to the difference between idealization and error. Taking these points into account leads to a refined account of the distinction, in which abstractness is seen in terms of relative richness of detail, and idealization is seen as closely connected with the knowledge and intentions of idealizers. I lay out these accounts in turn, and then discuss the relationship between the two concepts, and several other upshots of the present way of construing the distinction. (shrink)

New accents in the philosophy of technology and philosophy of science amounting, e.g., to the so-called schools of the “New Experimentalism”, “New Instrumentalism” and, recently, “New Mechanism” emphasize the impact of instruments, experiments, and “mechanisms” of the respective technologies opened up by the progress of ever-improving measuring instruments, procedures etc. In addition, it would be necessary to accentuate the process- and action-orientation including practical responsibility problems and dynamic systems models from an epistemological perspective of the methodological scheme-interpretationist approach developed by (...) the author since the seventies of last century. The necessity of multi-disciplinary mutual influences was already time and again stressed regarding philosophy of science. But relatively new is not only the methodological turn involved but also the necessity to go to higher levels of abstraction, metatheories, action- and value-orientation. Therefore, we have to go more interdisciplinary, systems-oriented, and higher-level. That implies notable consequences for an ethics, or rather, meta-ethics for philosophy of science. Practice-oriented philosophers of science will have to go more methodological, multidisciplinary and even responsible. (shrink)

Speaking: from Intention to Articulation Willem J. M. Levelt, 1989 (1993 paperback) Cambridge, MA: MIT Press ISBN: 0–262–12137–9(hb), 0–262–62089–8(pb)Rules for Reasoning Richard E. Nisbett (Ed.), 1993 Hillsdale, NJ, Lawrence Erlbaum Associates ISBN: 0–8058–1256–3(hb), 0–8085–1257–1 (pb)Readings in Philosophy and Cognitive Science Alvin I. Goldman, 1993 Cambridge, MA, MIT Press ISBN: 0–262–07153–3(hb), 0–262–57100–5(pb)Language Comprehension in Ape and Child, Monographs of the Society for Research in Child Development, Serial No. 233, Vol. 58, Nos 3–4 Sue Savage‐Rumbaugh, Jeannine Murphy, Rose A. Sevcik, Karen E. (...) Brakke, Shelly L. Williams & Duane M. Rumbaugh, with commentary by Elizabeth Bates, and a reply by the authors, 1993 Chicago: University of Chicago Press ISBN: 0–226–73542–7Language, Music and Mind Diana Raffman, 1993 Cambridge, MIT Press/Bradford Books ISBN: 0–262–18150–9Psychology of Proof Lance J. Rips, 1994 Cambridge, MA, MIT Press ISBN: 0–262–18153–3Human and Machine Thinking P. Johnson‐Laird, 1993 Hillsdale, NJ, Erlbaum ISBN: 0–8058–0921‐XIntelligent Behavior in Animals and Robots David McFarland & Thomas Bosser, 1993 Cambridge, MA and London, UK: MIT Press/ Bradford Books ISBN: 0–262–13293–1The Eye and the Mind: Reflections on Perception and the Problem of Knowledge Charles Landesman, 1993 Dordrecht: Kluwer Academic Publishers ISBN: 0–7923–2586–9The Moral Self GilG. Noam & Thomas E. Wren (Eds), in cooperation with Gertud Nunner‐Winkler & Wolfgang Edelstein, 1993 Cambridge, MA: MIT Press ISBN: 0–262–14052–1The Logic of Discovery: A Theory of the Rationality of Scientific Research Scott A. Kleiner, 1993 Dordrecht: Kluwer Academic Publishers ISBN: 0–7923–2371–8Folk Psychology and the Philosophy of Mind S.M. Christenson & D.R. Turner (Eds), 1993 Hillsdale, NJ: Lawrence Erlbaum ISBN: 0–805–80931–7. (shrink)

In this paper, a modest version of the Semantic View is motivated as both tenable and potentially fruitful for philosophy of science. An analysis is proposed in which the Semantic View is characterized by three main claims. For each of these claims, a distinction is made between stronger and more modest interpretations. It is argued that the criticisms recently leveled against the Semantic View hold only under the stronger interpretations of these claims. However, if one only commits to the modest (...) interpretation for all the claims, then the view obtained, the Modest Semantic View, is tenable and fruitful for the philosophy of science. (shrink)

This article deals with the problematic concepts of the rational and the social, which have been typically seen as dichotomous in the history and philosophy of science literature. I argue that this view is mistaken and that the social can be seen as something that enables rationality in science, and further, that a scientific community as well as an individual can be taken as an epistemic subject. Furthermore, I consider how scientific communities could be seen as freely acting and choosing (...) agents. Fundamentally, this boils down to the question whether we accept the voluntarist conception of human beings, one consequence of which is that scientists possess, in principle, the capacity for deliberative reflection and choice. If this is accepted, we can talk about the degrees of autonomy that communities possess. I also examine what kinds of decisions an autonomous community should make in order to produce objective knowledge. My suggestion is that objectivity be understood as intersubjectivity: a view is objective when it has been exposed to critical reflection from various points of view, and due to this, transcends subjective idiosyncrasies. (shrink)

Robert Sugden argues that robustness analysis cannot play an epistemic role in grounding model-world relationships because the procedure is only a matter of comparing models with each other. We posit that this argument is based on a view of models as being surrogate systems in too literal a sense. In contrast, the epistemic importance of robustness analysis is easy to explicate if modelling is viewed as extended cognition, as inference from assumptions to conclusions. Robustness analysis is about assessing the reliability (...) of our extended inferences, and when our confidence in these inferences changes, so does our confidence in the results. Furthermore, we argue that Sugden’s inductive account relies tacitly on robustness considerations. (shrink)

I present a justification for the intution that more-varied data are more valuable than the same number of less-varied data by showing that the more-varied data help to improve the accuracy of our predictions.

This paper raises the question of the prominence and use of statistical graphs in science, and argues that their use in problem solving analysis can best be understood in an ‘interactionist’ frame of analysis, including bio-emotion, culture, social organization, and environment as elements. The frame contrasts both with philosophical realism and with social constructivism, which posit two variables and one way causal flows. We next posit basic differences between visual, verbal, and numerical media of perception and communication. Graphs are thus (...) seen as key interactive sites where different media are transformed into more interpretable forms. Examples are taken from Limnology where numbers are transformed into graphs to find patterns in them, and thus, by implication in the environmental materials from which the numerical measurements were taken. Their revisualization by passes a human cognitive limitation, for the direct analysis — interpretation of lists and tables of numbers, visual imaging being a cognitive strength. Sense of problem, conceptual repertoire, and social relations are seen to direct this pattern search and interpretive process. (shrink)

In this paper, we try to shed light on the ontological puzzle pertaining to models and to contribute to a better understanding of what models are. Our suggestion is that models should be regarded as a specific kind of signs according to the sign theory put forward by Charles S. Peirce, and, more precisely, as icons, i.e. as signs which are characterized by a similarity relation between sign (model) and object (original). We argue for this (1) by analyzing from a (...) semiotic point of view the representational relation which is characteristic of models. We then corroborate our hypothesis (2) by discussing the conceptual differences between icons, i.e. models, and indexical and symbolic signs and (3) by putting forward a general classification of all icons into three functional subclasses (images, diagrams, and metaphors). Subsequently, we (4) integratively refine our results by resorting to two influential and, as can be shown, complementary philosophy of science approaches to models. This yields the following result: models are determined by a semiotic structure in which a subject intentionally uses an object, i.e. the model, as a sign for another object, i.e. the original, in the context of a chosen theory or language in order to attain a specific end by instituting a representational relation in which the syntactic structure of the model, i.e. its attributes and relations, represents by way of a mapping the properties of the original, which hence are regarded as similar in a relevant manner. (shrink)

In this essay, I aim to motivate and defend a pluralistic view of epistemic rationality. At the core of the view is the notion that epistemic rationality is essentially a species of practical rationality put in the service of various epistemic goals. I begin by sketching some closely related views that have appeared in the literature. I then present my preferred version of the view and sketch some of its benefits. Thomas Kelly has raised challenging objections to a part of (...) the view, which I then present and address. I then raise some distinct worries that might linger and sketch some possible ways one might address them. (shrink)

Model-based learning has an established position within science education. It has been found to enhance conceptual understanding and provide a way for engaging students in authentic scientific activity. Despite ample research, few studies have examined the cognitive processes regarding learning scientific concepts within MBL. On the other hand, recent research within cognitive science has examined the learning of so-called relational categories. Relational categories are categories whose membership is determined on the basis of the common relational structure. In this theoretical paper, (...) I argue that viewing models as relational categories provides a well-motivated cognitive basis for MBL. I discuss the different roles of models and modeling within MBL and discern the related cognitive aspects brought forward by the reinterpretation of models as relational categories. I will argue that relational knowledge is vital in learning novel models and in the transfer of learning. Moreover, relational knowledge underlies the coherent, hierarchical knowledge of experts. Lastly, I will examine how the format of external representations may affect the learning of models and the relevant relations. The nature of the learning mechanisms underlying students’ mental representations of models is an interesting open question to be examined. Furthermore, the ways in which the expert-like knowledge develops and how to best support it is in need of more research. The discussion and conceptualization of models as relational categories allows discerning students’ mental representations of models in terms of evolving relational structures in greater detail than previously done. (shrink)

Our concern is in explaining how and why models give us useful knowledge. We argue that if we are to understand how models function in the actual scientific practice the representational approach to models proves either misleading or too minimal. We propose turning from the representational approach to the artefactual, which implies also a new unit of analysis: the activity of modelling. Modelling, we suggest, could be approached as a specific practice in which concrete artefacts, i.e., models, are constructed with (...) the help of specific representational means and used in various ways, for example, for the purposes of scientific reasoning, theory construction and design of experiments and other artefacts. Furthermore, in this activity of modelling the model construction is intertwined with the construction of new phenomena, theoretical principles and new scientific concepts. We will illustrate these claims by studying the construction of the ideal heat engine by Sadi Carnot. (shrink)

This paper presents an artifactual approach to models that also addresses their fictional features. It discusses first the imaginary accounts of models and fiction that set model descriptions apart from imagined-objects, concentrating on the latter :251–268, 2010; Frigg and Nguyen in The Monist 99:225–242, 2016; Godfrey-Smith in Biol Philos 21:725–740, 2006; Philos Stud 143:101–116, 2009). While the imaginary approaches accommodate surrogative reasoning as an important characteristic of scientific modeling, they simultaneously raise difficult questions concerning how the imagined entities are related (...) to actual representational tools, and coordinated among different scientists, and with real-world phenomena. The artifactual account focuses, in contrast, on the culturally established external representational tools that enable, embody, and extend scientific imagination and reasoning. While there are commonalities between models and fictions, it is argued that the focus should be on the fictional uses of models rather than considering models as fictions. (shrink)

Multiply realizable properties are those whose realizers are physically diverse. It is often argued that theories which contain them are ipso facto irreducible. These arguments assume that physical explanations are restricted to the most specific descriptions possible of physical entities. This assumption is descriptively false, and philosophically unmotivated. I argue that it is a holdover from the late positivist axiomatic view of theories. A semantic view of theories, by contrast, correctly allows scientific explanations to be couched in the most perspicuous, (...) powerful language available. On a semantic view, traditional notions of multiple realizability are thus very hard to motivate. At best, one must abandon either the idea that multiple realizability is an interesting scientific notion, or else admit that multiply realizable properties do not automatically block scientific reductions. (shrink)

Multiply realizable kinds are scientifically problematic, for it appears that we should not expect discoveries about them to hold of other members of that kind. As such, it looks like MR kinds should have no place in the ontology of the special sciences. Many resist this conclusion, however, because we lack a positive account of the role that certain realization-unrestricted terms play in special science explanations. I argue that many such terms actually pick out idealizing models. Idealizing explanation has many (...) of the features normally associated with explanation by MR kinds. As idealized models are usually mere possibilia, such explanations do not run afoul of the metaphysical problems that plague MR kinds. (shrink)