This article looks at how fisheries biologists of the early twentieth century conceptualized and measured overfishing and attempted to make it a scientific object. Considering both theorizing and physical practices, the essay shows that categories and understandings of both the fishing industry and fisheries science were deeply and, at times, inextricably interwoven. Fish were both scientific and economic objects. The various models fisheries science used to understand the world reflected amalgamations of biological, physical, economic, and political factors. As a result, (...) scientists had great difficulty stabilizing the concept of overfishing and many influential scholars into the 1930s even doubted the coherence of the concept. In light of recent literature in history of fisheries and environmental social sciences that critiques the infiltration of political and economic imperatives into fisheries and environmental sciences more generally, this essay highlights both how early fisheries scientists understood their field of study as the entire combination of interactions between political, economic, biological and physical factors and the work that was necessary to separate them. (shrink)

I argue that philosophical issues concerning reductive explanations help constrain the construction of quantum theories with appropriate state spaces. I illustrate this general proposal with two examples of restricting attention to physical states in quantum theories: regular states and symmetry-invariant states. 1Introduction2Background2.1 Physical states2.2 Reductive explanations3The Proposed ‘Correspondence Principle’4Example: Regularity5Example: Symmetry-Invariance6Conclusion: Heuristics and Discovery.

Metaphor has been considered as a cognitive process, independent of the verbal versus visual mode, through which an unknown conceptual domain is understood in terms of another known conceptual domain. Metaphor might instead be viewed as a cognitive process, dependent on the mode, which leads to genuinely new knowledge via ignorance. First, I argue that there are two main senses of ignorance at stake when we understand a metaphor: we ignore some existing properties of the known domain in the sense (...) that we disregard or neglect them; we ignore some “non-existing” properties of the known domain in the sense that they are not a piece of information belonging to the known domain, but emerge in metaphor interpretation. Secondly, I consider a metaphor as a reasoning device, guiding the interpreters along a path of inferences to a conclusion, which attributes to the target some properties of the source. In this path, interpreters might discover the ignored existing properties of the known domain and/or recover the “non-existing” properties, inferring or imagining the missing piece of information. Finally, I argue that, especially in visual metaphors, this process is guided by a “sentiment of rationality”, tracking a disruption of existing familiar conceptualisations of objects and/or actions and a recovery of ignored properties. (shrink)

Mathematicians and physical scientists depend heavily on the formal symbolism of mathematics in order to express and develop their theories. For this and other reasons the last hundred years has seen a growing interest in the nature of formal language and the way it expresses meaning; particularly the objective, shared aspect of meaning as opposed to subjective, personal aspects. This dichotomy suggests the question: do the objective philosophical theories of meaning offer concepts which can be applied in psychological theories of (...) meaning? In recent years cognitive scientists such as Chomsky [1980], Fodor [1981] and MacNamara [1982] have used philosophical approaches to the meaning of formal language expressions as the basis for their psychological theories. Following this lead it seems appropriate to review some of the main treatments of meaning with a view to their transferability. (shrink)

http://dx.doi.org/10.5007/1808-1711.2008v12n1p73 This paper aims at discussing from the point of view of a pragmatic stance the concept of model as an abstract replica. According to this view, scientific models are abstract structures different from set-theoretic models. The view of models argued for here stems from the conceptions of some important philosophers of science who elaborated on the notion of model, such as Suppe, Cartwright, Hempel, and Nagel. Differently from all those authors, however, the conception of model argued for here is (...) typically pragmatic, not semantic, i.e. it has not to do with the interpretation of scientific theories, but with the explanation and construction of given circumstances (both abstract and concrete), from the point of view of the theory. (shrink)

Ist der Unterschied zwischen wissenschaftlichen Gesetzen und Theorien ein qualitativer oder lediglich von quantitativer Art? Der Autor versucht zu zeigen, daß Gesetze und Theorien fundamental verschieden sind und daß die Kenntnis ihrer verschiedenen Natur notwendig für ein richtiges Wissenschaftsverständnis ist. Aus seiner Sicht sind Theorien geistige Konstruktionen mit dem Ziel, kausale Erklärungen von empirischen Gesetzen zu geben, während diese Gesetze auf der Grundlage von Messungen entdeckt werden und die Tatsachen der Wissenschaft konstituieren. Erkenntnistheoretisch sind daher Theorien und Gesetze auf verschiedenen (...) Ebenen anzusiedeln: während Gesetze Wissen liefern, liefern Theorien Verstehen. Der Kern der Theorien besteht aus Modellen, die idealisierte Abstraktionen aus Zustandsarten darstellen. Theoretische Modelle konstituieren Ontologien, die kausale Mechanismen aufzeigen. Solche Ontologien betreffen den Bedeutungsaspekt des Gegenstandsbereichs, auf den die Theorie angewendet wird, während empirische Gesetze den Ausdrucksaspekt des gleichen Gegenstandsbereichs betreffen. Theorien erklären Gesetze, indem sie zeigen, wie der Ausdrucksaspekt lediglich der natürliche Ausdruck der Auswirkungen der Kausalmechanismen in Bedeutungsaspekt sind. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

Peter Ludlow shows how word meanings are much more dynamic than we might have supposed, and explores how they are modulated even during everyday conversation. The resulting view is radical, and has far-reaching consequences for our political and legal discourse, and for enduring puzzles in the foundations of semantics, epistemology, and logic.

This inaugural handbook documents the distinctive research field that utilizes history and philosophy in investigation of theoretical, curricular and pedagogical issues in the teaching of science and mathematics. It is contributed to by 130 researchers from 30 countries; it provides a logically structured, fully referenced guide to the ways in which science and mathematics education is, informed by the history and philosophy of these disciplines, as well as by the philosophy of education more generally. The first handbook to cover the (...) field, it lays down a much-needed marker of progress to date and provides a platform for informed and coherent future analysis and research of the subject. -/- The publication comes at a time of heightened worldwide concern over the standard of science and mathematics education, attended by fierce debate over how best to reform curricula and enliven student engagement in the subjects There is a growing recognition among educators and policy makers that the learning of science must dovetail with learning about science; this handbook is uniquely positioned as a locus for the discussion. -/- The handbook features sections on pedagogical, theoretical, national, and biographical research, setting the literature of each tradition in its historical context. Each chapter engages in an assessment of the strengths and weakness of the research addressed, and suggests potentially fruitful avenues of future research. A key element of the handbook’s broader analytical framework is its identification and examination of unnoticed philosophical assumptions in science and mathematics research. It reminds readers at a crucial juncture that there has been a long and rich tradition of historical and philosophical engagements with science and mathematics teaching, and that lessons can be learnt from these engagements for the resolution of current theoretical, curricular and pedagogical questions that face teachers and administrators. (shrink)

Written by experts in the field, this volume presents a comprehensive investigation into the relationship between argumentation theory and the philosophy of mathematical practice. Argumentation theory studies reasoning and argument, and especially those aspects not addressed, or not addressed well, by formal deduction. The philosophy of mathematical practice diverges from mainstream philosophy of mathematics in the emphasis it places on what the majority of working mathematicians actually do, rather than on mathematical foundations. -/- The book begins by first challenging the (...) assumption that there is no role for informal logic in mathematics. Next, it details the usefulness of argumentation theory in the understanding of mathematical practice, offering an impressively diverse set of examples, covering the history of mathematics, mathematics education and, perhaps surprisingly, formal proof verification. From there, the book demonstrates that mathematics also offers a valuable testbed for argumentation theory. Coverage concludes by defending attention to mathematical argumentation as the basis for new perspectives on the philosophy of mathematics. ​. (shrink)

Deterrence is the backbone of military strategy. Dissuading an opponent from taking a specific action by threat of violence is the definition of deterrence. From the outset of the Cold War there has been a strong link between the study of deterrence and game theoretic analysis. There are compelling epistemic reasons for studying deterrence as a game. By doing so, the strategic interaction between actors is placed at the centre of the analysis, mapping the possible outcomes and revealing the strategies (...) available to the actors. Discussions about various models’ appropriateness and model comparison therefore play a central role in deterrence research; from underlying assumptions and deterrence representation to theory and evidence. This dissertation treats aspects of all of these topics. Article I, “Prospects of Deterrence – Deterrence Theory – Representation and Evidence”, analyses the relationship between model and theory and what happens to a deterrence theory when the rationality assumption is switched to a prospect theoretical utility function. Article II, “A Misfit Model – Bounded Rationality and Deterrence Representation”, defends and remodels Schelling’s idea of irrational threats for effective deterrence. Article III, “Comparable Deterrence – Target, Criteria and Purpose”, treats the issue of how one can compare game theoretic models with one another and proposes a meta-model for how this can be done. Article IV, “Deterring the Dauntless – Appraising the Effects of Naval Deterrence against the Somali Piracy”, estimates whether and to what extent Somali piracy was deterred by the naval intervention. (shrink)

Entropy is ubiquitous in physics, and it plays important roles in numerous other disciplines ranging from logic and statistics to biology and economics. However, a closer look reveals a complicated picture: entropy is defined differently in different contexts, and even within the same domain different notions of entropy are at work. Some of these are defined in terms of probabilities, others are not. The aim of this chapter is to arrive at an understanding of some of the most important notions (...) of entropy and to clarify the relations between them, After setting the stage by introducing the thermodynamic entropy, we discuss notions of entropy in information theory, statistical mechanics, dynamical systems theory and fractal geometry. (shrink)

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...) the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly. (shrink)

Der Begriff ‘Modell’ leitet sich vom Lateinischen ‘modulus’ (das Maß) ab, im Italienischen existiert seit dem 16. Jh. ‘modello’ und R. Descartes verwendet im 17. Jh. ‘modèlle’. Während der Begriff in Architektur und Kunst schon seit der Renaissance gängig ist, wird er in den Naturwissenschaften erst im 19. Jh. verwendet.1 Dort greifen wissenschaftliche Modelle die für eine gegebene Problemstellung als wesentlich erachteten Charakteristika (Eigenschaften, Beziehungen, etc.) eines Untersuchungsgegenstandes heraus und machen diesen so einem Verständnis bzw. einer weiterführenden Untersuchung zugänglich. Es (...) ist üblich, zwischen Skalenmodellen, Analogmodellen und theoretischen Modellen zu unterscheiden. (shrink)

By the early part of the twentieth century, academia in the English-speaking world had stabilized (or ossified!) into a set of scientific and humanistic disciplines that still survives at the century’s end. The natural sciences have such disciplines as physics, chemistry, and biology, and the social sciences include economics, psychology, and sociology. These disciplines provide a convenient organizing principle for university departments and professional organizations, but they often bear little relation to cuttingedge research, which can concern topics that cut across (...) or occur at the boundaries of two or more of the established disciplines. When this happens, productive research and teaching must be interdisciplinary. Cognitive science is the interdisciplinary study of mind, embracing psychology, artificial intelligence, philosophy, neuroscience, linguistics, and anthropology. It is undoubtedly one of the major interdisciplinary successes of the twentieth century, with its own society, journal, and textbooks, and with more than sixty cognitive science programs established at universities in North American and Europe. This paper is an attempt to answer the question: What are the factors contributing to the success of the interdisciplinary field of cognitive science? My discussion is organized around the metaphor of the trading zone, a novel and fertile analogy that Gallison (1997) developed for his rich and detailed discussion of the practices of twentieth-century physics. To understand the diverse groups of December 1, 2006 experimenters and theoreticians, Gallison presents their interactions in terms of the trading zones described by anthropologists: Subcultures trade. Anthropologists have extensively studied how different groups, with radically different ways of dividing up the world and symbolically organizing its parts, can not only exchange goods but also depend essentially on those trades.. (shrink)

In this chapter, we discuss a specific kind of progress that occurs in most branches of economics today: progress involving the repeated use of mathematical models. We adopt a functional account of progress to argue that progress in economics occurs through the use of what we call “common recipes” and model templates for defining and solving problems of relevance for economists. We support our argument by discussing the case of 20th century business cycle research. By presenting this case study in (...) detail, we show how model templates are not only reapplied to different phenomena. We also show how scientists first develop them and how, once they are considered less useful, they are replaced with new ones. Finally, our case also illustrates that it is not only the mathematical structure that is reused but that such reuse also requires a shared conceptual vision of the core properties of the phenomenon to be studied. If that vision is no longer shared among economists, a model template can become useless and has to be replaced, sometimes against resistance, with a different one. (shrink)

Concerns over epistemic opacity abound in contemporary debates on Artificial Intelligence (AI). However, it is not always clear to what extent these concerns refer to the same set of problems. We can observe, first, that the terms 'transparency' and 'opacity' are used either in reference to the computational elements of an AI model or to the models to which they pertain. Second, opacity and transparency might either be understood to refer to the properties of AI systems or to the epistemic (...) situation of human agents with respect to these systems. While these diagnoses are independently discussed in the literature, juxtaposing them and exploring possible interrelations will help to get a view of the relevant distinctions between conceptions of opacity and their empirical bearing. In pursuit of this aim, two pertinent conditions affecting computer models in general and contemporary AI in particular are outlined and discussed: opacity as a problem of computational tractability and opacity as a problem of the universality of the computational method. (shrink)

Ordinary Language Philosophy has largely fallen out of favour, and with it the belief in the primary importance of analyses of ordinary language for philosophical purposes. Still, in their various endeavours, philosophers not only from analytic but also from other backgrounds refer to the use and meaning of terms of interest in ordinary parlance. In doing so, they most commonly appeal to their own linguistic intuitions. Often, the appeal to individual intuitions is supplemented by reference to dictionaries. In recent times, (...) Internet search engine queries for expressions of interest have become quite popular. Apparently, philosophers attempt to surpass the limits of their own linguistic intuitions by appealing to experts or to factual uses of language. I argue that this attempt is commendable but that its execution is wanting. Instead of appealing to dictionaries or Internet queries, philosophers should employ computer-based linguistic corpora in order to confirm or falsify hypotheses about the factual use of language. This approach also has some advantages over methods employed by experimental philosophers. If the importance of ordinary language is stressed, the use of linguistic corpora is hardly avoidable. (shrink)

Distributive egalitarians believe that distributive justice is to be explained by the idea of distributive equality (DE) and that DE is of intrinsic value. The socio-relational critique argues that distributive egalitarianism does not account for the “true” value of equality, which rather lies in the idea of “equality as a substantive social value” (ESV). This paper examines the socio-relational critique and argues that it fails because – contrary to what the critique presupposes –, first, ESV is not conceptually distinct from (...) DE, and second, the idea of ESV cannot serve as a “foundation” or “root” of distributive egalitarianism. (shrink)

“Teleosemantic” or “biosemantic” theories form a strong naturalistic programme in the philosophy of mind and language. They seek to explain the nature of mind and language by recourse to a natural history of “proper functions” as selected-for effects of language- and thought-producing mechanisms. However, they remain vague with respect to the nature of the proposed analogy between selected-for effects on the biological level and phenomena that are not strictly biological, such as reproducible linguistic and cultural forms. This essay critically explores (...) various interpretations of this analogy. It suggests that these interpretations can be explicated by contrasting adaptationist with pluralist readings of the evolutionary concept of adaptation. Among the possible interpretations of the relations between biological adaptations and their analogues in language and culture, the two most relevant are a linear, hierarchical, signalling-based model that takes its cues from the evolution of co-operation and joint intentionality and a mutualistic, pluralist model that takes its cues from mimesis and symbolism in the evolution of human communication. Arguing for the merits of the mutualistic model, the present analysis indicates a path towards an evolutionary pluralist version of biosemantics that will align with theories of cognition as being environmentally “scaffolded”. Language and other cultural forms are partly independent reproducible structures that acquire proper functions of their own while being integrated with organism-based cognitive traits in co-evolutionary fashion. (shrink)

Mathematicians often speak of conjectures, yet unproved, as probable or well-confirmed by evidence. The Riemann Hypothesis, for example, is widely believed to be almost certainly true. There seems no initial reason to distinguish such probability from the same notion in empirical science. Yet it is hard to see how there could be probabilistic relations between the necessary truths of pure mathematics. The existence of such logical relations, short of certainty, is defended using the theory of logical probability (or objective Bayesianism (...) or non-deductive logic), and some detailed examples of its use in mathematics surveyed. Examples of inductive reasoning in experimental mathematics are given and it is argued that the problem of induction is best appreciated in the mathematical case. (shrink)

At the close of Philosophy and the Mirror of Nature Richard Rorty lays out a contrast between what he calls 'systematic' and 'edifying' philosophical anthropologies. Whereas the systematic philosopher aims to speak for the ages, the edifying philosopher addresses herself to issues of her day, often by way of shattering conventional idols. Rorty sees these two approaches as mutually exclusive. The aim of this paper is to defend a conception of philosophy as both systematic and edifying in the relevant senses. (...) I first respond to Rorty's argument that Wilfrid Sellars' account of picturing, as an isomorphic relation between the world and states of the central nervous system, involves an illicit 'mind as mirror' metaphor. I then lay out some of the philosophical anthropology that motivates Sellars' account of picturing, and I connect this anthropology to philosophical and scientific work undertaken in Europe and America in the nineteenth and early twentieth centuries. On this basis I argue that we are left with a (systematic) project handed down to us by earlier generations, and that in taking up this project we are in the (edifying) business of creating new ways of thinking about ourselves as part of the natural world. (shrink)

This paper assesses the proposal to transform the monetary system into an Ecology of money, that is, into a system made of a large diversity of complementary currencies. Its central aim is to examine whether this proposal could provide a systemic solution to both the ecological and financial crises, as several authors, most notably Lietaer and Douthwaite, have argued. To this end, it analyses the two main arguments in favour of this proposal. First, it focuses on the claim that an (...) Ecology of money would be more resilient and less prone to crisis than the present monetary system. It shows that this argument suffers from several conceptual flaws and argues that it fails to provide sufficient normative reasons to favour an Ecology of money. Second, this paper analyses the claim that an Ecology of money could help defeat the monetary growth imperative that supposedly plagues our economies. The paper raises serious doubts about the existence of such an imperative and questions the claim that an Ecology of money could contribute to weaken it in a significant way. (shrink)

The concept of natural kind is center stage in the debates about scientific realism. Champions of scientific realism such as Richard Boyd hold that our most developed scientific theories allow us to “cut the world at its joints” (Boyd, 1981, 1984, 1991). In the long run we can disclose natural kinds as nature made them, though as science progresses improvements in theory allow us to revise the extension of natural kind terms.

This paper is a supplement to, and provides a proof of principle of, Kuhn vs. Popper on Criticism and Dogmatism in Science: A Resolution at the Group Level. It illustrates how calculations may be performed in order to determine how the balance between different functions in science—such as imaginative, critical, and dogmatic—should be struck, with respect to confirmation functions and rules of scientific method.

Laboratory experimentation was once considered impossible or irrelevant in economics. Recently, however, economic science has gone through a real ‘laboratory revolution’, and experimental economics is now a most lively subfield of the discipline. The methodological advantages and disadvantages of controlled experimentation constitute the main subject of this thesis. After a survey of the literature on experiments in philosophy and economics, the problem of testing normative theories of rationality is tackled. This philosophical issue was at the centre of a famous controversy (...) in decision theory, during which a methodology of normative falsification was first articulated and used to assess experimental results. In the third chapter, the methodological advantages of controlled experimentation are illustrated and discussed with examples taken from the experiments on the so-called ‘preference reversal’ phenomenon. Laboratory testing allows to establish with a high degree of certainty that certain phenomena lie behind the experimental data, by means of independent testing, elimination of alternative hypotheses, and the use of different instruments of observation. The fourth chapter is devoted to a conceptual analysis of the problem of ‘parallelism’. This is the problem of inferring from the occurrence of a phenomenon in the laboratory, to its instantiation also in non-laboratory environments. Experimental economists have discussed parallelism at length, and their views are presented and criticised. Eventually, it is argued that parallelism is a factual matter and as such can only be established on empirical grounds. The fifth chapter provides an example of how one can argue for parallelism, focusing on the case of experimentation on the ‘winner’s curse’ phenomenon. The role of experiments as ‘mediators’ between theoretical models and their target domain of application is illustrated, and the structure of parallelism arguments analysed in detail. Finally, in the last chapter, economic experiments are compared to simulations, in order to highlight their specific characteristics. (shrink)

At the interface of developmental biology and evolutionary biology, the very criteria of scientific knowledge are up for grabs. A central issue is the status of evolutionary genetics models, which some argue cannot coherently be used with complex gene regulatory network (GRN) models to explain the same evolutionary phenomena. Despite those claims, many researchers use evolutionary genetics models jointly with GRN models to study evolutionary phenomena. This dissertation compares two recent research projects in which researchers jointly use the two kinds (...) of models. I use methods from the empirical social sciences, such as digital corpus analysis, content analysis, and structured case analysis. I infer three primary conclusions about projects of the kind studied. First, they employ an implicit concept of gene that enables the joint use of both kinds of models. Second, they pursue more epistemic aims besides mechanistic explanation of phenomena. Third, they don’t work to create and export broad synthesized theories. Rather, they focus on phenomena too complex to be understood by a common general theory, they distinguish parts of the phenomena, and they apply models from different theories to the different parts. For such projects, seemingly incompatible models are synthesized largely through mediated representations of complex phenomena. (shrink)

This is a thesis about interdisciplinarity, scientific integration, and problem solving in sustainability science. Sustainability science is an emerging and highly interdisciplinary field that seeks to integrate vastly differentiated bodies of knowledge in addressing the challenge of transitioning contemporary societies towards sustainability. Interdisciplinarity is paramount. Interdisciplinarity in general, and in the context of sustainability science in particular, has often been associated with solving particular problems and problem solving is one important theme in this thesis. A central idea that is developed (...) is that of problem-feeding. Sometimes problems arise within one discipline that can only be solved with the help of another. This concept, that has predecessors in e.g. the work of Lindley Darden and Nancy Maull, is explored considerably. It is argued that in interdisciplinary contexts—such as sustainability science—where collaboration is sought it is important to maintain cross-boundary problem stability. That is to say, as the problem is transferred from one discipline to another transformations will often be necessary. These transformations then, need to be acceptable to all involved parties to maintain an active interdisciplinary connection. Another topic that is discussed both in the introductory essay and some of the papers included is that of scientific imperialism. Scientific imperialism—the infringement of one discipline upon the domain of another—is here suggested to be primarily a threat to in-terdisciplinary collaborations. A distinction is introduced between imperialist failures of expansionism and failures of replacement. These are labelled type-I and type-II imperialism respectively. Particular attention is devoted to the latter form. Type-II imperialism concerns cases where imperialist infringements fail as the imperial- izing framework replaces viable, or compatible alternatives. Such an error of replacement does, importantly, not imply that the framework or theory should be disregarded completely. This type of imperialist error can both be quite subtle, and damaging. For one, if one directs the attention to specific contexts knowledge is actually lost in the process. This is particularly serious in fields such as sustainability science that are, to such an large extent, aimed at influencing concrete policy. Finally, interdisciplinarity is difficult to achieve and in many cases represents a grand challenge in itself. There are however many different ways in which interdisciplinarity may be accomplished and different forms are suitable in different contexts. In a field such as sustainability science where complexity is such a prevalent feature, an inclusive, pluralist, approach is likely to be appropriate. (shrink)

The article develops and justifies, on the basis of the epistemological argumentation theory, two central pieces of the theory of evaluative argumentation interpretation: 1. criteria for recognizing argument types and 2. rules for adding reasons to create ideal arguments. Ad 1: The criteria for identifying argument types are a selection of essential elements from the definitions of the respective argument types. Ad 2: After presenting the general principles for adding reasons (benevolence, authenticity, immanence, optimization), heuristics are proposed for finding missing (...) reasons, for deductive arguments, e.g., semantic tableaux are suggested. (shrink)

The main thrust of the argument of this thesis is to show the possibility of articulating a method of construction or of synthesis--as against the most common method of analysis or division--which has always been (so we shall argue) a necessary component of scientific theorization. This method will be shown to be based on a fundamental synthetic logical relation of thought, that we shall call inversion--to be understood as a species of logical opposition, and as one of the basic monadic (...) logical operators. Thus the major objective of this thesis is to This thesis can be viewed as a response to Larry Laudan's challenge, which is based on the claim that ``the case has yet to be made that the rules governing the techniques whereby theories are invented (if any such rules there be) are the sorts of things that philosophers should claim any interest in or competence at.'' The challenge itself would be to show that the logic of discovery (if at all formulatable) performs the epistemological role of the justification of scientific theories. We propose to meet this challenge head on: a) by suggesting precisely how such a logic would be formulated; b) by demonstrating its epistemological relevance (in the context of justification) and c) by showing that a) and b) can be carried out without sacrificing the fallibilist view of scientific knowledge. OBJECTIVES: We have set three successive objectives: one general, one specific, and one sub-specific, each one related to the other in that very order. (A) The general objective is to indicate the clear possibility of renovating the traditional analytico-synthetic epistemology. By realizing this objective, we attempt to widen the scope of scientific reason or rationality, which for some time now has perniciously been dominated by pure analytic reason alone. In order to achieve this end we need to show specifically that there exists the possibility of articulating a synthetic (constructive) logic/reason, which has been considered by most mainstream thinkers either as not articulatable, or simply non-existent. (B) The second (specific) task is to respond to the challenge of Larry Laudan by demonstrating the possibility of an epistemologically significant generativism. In this context we will argue that this generativism, which is our suggested alternative, and the simplified structuralist and semantic view of scientific theories, mutually reinforce each other to form a single coherent foundation for the renovated analytico-synthetic methodological framework. (C) The third (sub-specific) objective, accordingly, is to show the possibility of articulating a synthetic logic that could guide us in understanding the process of theorization. This is realized by proposing the foundations for developing a logic of inversion, which represents the pattern of synthetic reason in the process of constructing scientific definitions. (shrink)

In the paper entitled “Scientific Explanation and Metaphor” Jerzy Kmita divided all metaphors on reporting and explicative ones. He assumed that the explicative metaphors could play a cognitive function in science, and also characterized them according to Max Black’s interactive theory of metaphor. The main purpose of my paper is to analyse Kmita’s explicative conception of metaphor in the view of Lakoff & Johnson’s cognitive theory of metaphor. I attempt to show that metaphors play an important role in a process (...) of making knowledge, especially in a conceptualization of domain being studied. In spite of an interactive account of metaphor I claim that making use of a metaphor is a process, which proceeds only in the one direction. In the last section of the paper I briefly analyse a few examples of metaphors used in natural sciences. (shrink)

A difference-making account of causality is proposed that is based on a counterfactual definition, but differs from traditional counterfactual approaches to causation in a number of crucial respects: it introduces a notion of causal irrelevance; it evaluates the truth-value of counterfactual statements in terms of difference-making; it renders causal statements background-dependent. On the basis of the fundamental notions 'causal relevance' and 'causal irrelevance', further causal concepts are defined including causal factors, alternative causes, and importantly inus-conditions. Problems and advantages of the (...) proposed account are discussed. Finally, it is shown how the account can shed new light on three classic problems in epistemology, the problem of induction, the logic of analogy, and the framing of eliminative induction. (shrink)

Scientific models are important, if not the sole, units of science. This thesis addresses the following question: in virtue of what do scientific models represent their target systems? In Part i I motivate the question, and lay out some important desiderata that any successful answer must meet. This provides a novel conceptual framework in which to think about the question of scientific representation. I then argue against Callender and Cohen’s attempt to diffuse the question. In Part ii I investigate the (...) ideas that scientific models are ‘similar’, or structurally morphic, to their target systems. I argue that these approaches are misguided, and that at best these relationships concern the accuracy of a pre-existing representational relationship. I also pay particular attention to the sense in which target systems can be appropriately taken to exhibit a ‘structure’, and van Fraassen’s recent argument concerning the pragmatic equivalence between representing phenomena and data. My next target is the idea that models should not be seen as objects in their own right, but rather what look like descriptions of them are actually direct descriptions of target systems, albeit not ones that should be understood literally. I argue that these approaches fail to do justice to the practice of scientific modelling. Finally I turn to the idea that how models represent is grounded, in some sense, in their inferential capacity. I compare this approach to anti-representationalism in the philosophy of language and argue that analogous issues arise in the context of scientific representation. Part iii contains my positive proposal. I provide an account of scientific representation based on Goodman and Elgin’s notion of representation-as. The result is a highly conventional account which is the appropriate level of generality to capture all of its instances, whilst remaining informative about the notion. I illustrate it with reference to the Phillips-Newlyn machine, models of proteins, and the Lotka-Volterra model of predator-prey systems. These examples demonstrate how the account must be understood, and how it sheds light on our understanding of how models are used. I finally demonstrate how the account meets the desiderata laid out at the beginning of the thesis, and outline its implications for further questions from the philosophy of science; not limited to issues surrounding the applicability of mathematics, idealisation, and what it takes for a model to be ‘true’. (shrink)

This paper is a conceptual analysis of the theory debate in psychology, as carried out by cognitivists and radical behaviorists. The debate has focused on the necessity of theories in psychology. However, the logically primary issue is the nature of theories, or what theories are. This claim stems from the fact that cognitivists and radical behaviorists adopt disparate accounts of the nature of theories. The cognitivists' account is closely akin to the received view from logical positivism, where theories are collections (...) of statements and theoretical terms are restricted to designate unobservable entities. Radical behaviorists also conceive theories as collections of statements, but restrict theoretical terms in psychology to designate observable entities. If each side approaches the necessity issue with its preferred account, both agree that theories are necessary in psychology. Hence, there is no disagreement over the necessity of theories in psychology per se, only over the nature of theories. The received view, however, has been shown to suffer from irreparable difficulties that have led to its total rejection in post-positivistic philosophy of science. Alternative accounts, like the structuralist and semantic views, do not raise the issue of whether theories are necessary in psychology. (shrink)

The aim of this paper is to argue that simulation is the activity of inferring future states. I argue that simulations instantiate models and that models are complexes of representations, so the inference in question makes use of the relations between the representations in a simulation's associated model. It follows that simulations should not be properly considered to be models in general, despite it being the case that they are commonly treated, or referred to, as being models, or even models (...) of a special type, namely dynamic models. Further consequences of this position are also discussed. (shrink)

Understanding scientific modelling can be divided into two sub-projects: analysing what model-systems are, and understanding how they are used to represent something beyond themselves. The first is a prerequisite for the second: we can only start analysing how representation works once we understand the intrinsic character of the vehicle that does the representing. Coming to terms with this issue is the project of the first half of this chapter. My central contention is that models are akin to places and characters (...) of literary fictions, and that therefore theories of fiction play an essential role in explaining the nature of model-systems. This sets the agenda. Section 2 provides a statement of this view, which I label the fiction view of model-systems, and argues for its prima facie plausibility. Section 3 presents a defence of this view against its main rival, the structuralist conception of models. In Section 4 I develop an account of model-systems as imagined objects on the basis of the so-called pretence theory of fiction. This theory needs to be discussed in great detail for two reasons. First, developing an acceptable account of imagined objects is mandatory to make the fiction view acceptable, and I will show that the pretence theory has the resources to achieve this goal. Second, the term ‘representation’ is ambiguous; in fact, there are two very different relations that are commonly called ‘representation’ and a conflation between the two is the root of some of the problems that beset scientific representation. Pretence theory provides us with the conceptual resources to articulate these two different forms of representation, which I call p-representation and t-representation respectively. Putting these elements together provides us with a coherent overall picture of scientific modelling, which I develop in Section 5. (shrink)

I argue that everyday folk-psychological skill might best be explained in terms of the deployment of something like a model, in a specific sense drawn from recent philosophy of science. Theoretical models in this sense do not make definite commitments about the systems they are used to understand; they are employed with a particular kind of flexibility. This analysis is used to dissolve the eliminativism debate of the 1980s, and to transform a number of other questions about the status and (...) role of folk psychology. (shrink)

The dissertation criticizes two analogical applications of Darwinism to the spheres of mind and culture: the Darwinian approach to creativity and memetics. These theories rely on three basic analogies: the ontological analogy states that the basic ontological units of culture are so-called memes, which are replicators like genes; the origination analogy states that novelty in human creativity emerges in a "blind" Darwinian manner; and the explanatory units of selection analogy states that memes are "egoistic" and that they can spread independently (...) of our mind. The detailed philosophical analysis offered in the dissertation shows that these three analogies rely on either wrong or trivial statements: they provide either wrong or no new descriptions or explanations of the phenomena at hand. In Chapter One, I introduce the diverse ways, in which contemporary Darwinism is used today outside of evolutionary biology. Chapter Two explains what it means to claim that something evolves in a Darwinian manner. Chapter Three to Five address each of the three analogies separately. Darwinism applied to culture and mind, at least in the way discussed in this study, is not a dangerous idea. It leads either to wrong claims or to a re-telling in Darwinian terms of what we have already known. (shrink)