Historical analysis and policy making often require counterfactual thought experiments that isolate hypothesized causes from a vast array of historical possibilities. However, a core precept of Jervis's “systems thinking” is that causes are so interconnected that the historian can only with great difficulty imagine causation by subtracting all variables but one. Prediction, according to Jervis, is even more problematic: The more sensitive an event is to initial conditions (e.g., butterfly effects), the harder it is to derive accurate forecasts. Nevertheless, if (...) awareness of system effects can help forecasters better calibrate their probability estimates of whether or not certain events will come to pass, systems thinkers who are pessimistic about prediction are diluting their confidence too much. The challenge is a meta-cognitive one: thinking systematically about when to engage in systems thinking; and weighing the costs and benefits of using simple or complex heuristics in policy environments that can shift suddenly from quiescence to turbulence. (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)

This article presents moral jurisprudence theory as a systematic approach to business ethics that analogizes core problems of the field to related problems in law. Adapting theoretical approaches from contemporary philosophy of law, the article develops a decision-making method for business ethics.

Everything you always wanted to know about structural realism but were afraid to ask Content Type Journal Article Pages 227-276 DOI 10.1007/s13194-011-0025-7 Authors Roman Frigg, Department of Philosophy, Logic and Scientific Method, London School of Economics and Political Science, Houghton Street, London, WC2A 2AE UK Ioannis Votsis, Philosophisches Institut, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, Geb. 23.21/04.86, 40225 Düsseldorf, Germany Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912 Journal Volume Volume 1 Journal Issue Volume 1, Number 2.

In his late years, Thomas Kuhn became interested in the process of scientific specialization, which does not seem to possess the destructive element that is characteristic of scientific revolutions. It therefore makes sense to investigate whether and how Kuhn’s insights about specialization are consistent with, and actually fit, his model of scientific progress through revolutions. In this paper, I argue that the transition toward a new specialty corresponds to a revolutionary change for the group of scientists involved in such a (...) transition. I will clarify the role of the scientific community in revolutionary changes and characterize the incommensurability across specialties as possessing both semantic and methodological aspects. The discussion of the discovery of the structure of DNA will serve both as an illustration of my main argument and as reply to one criticism raised against Kuhn—namely, that his model cannot capture cases of revolutionary yet non-disruptive episodes of scientific progress. Revisiting Kuhn’s ideas on specialization will shed new light on some often overlooked features of scientific change. (shrink)

Scientific discourse is rife with passages that appear to be ordinary descriptions of systems of interest in a particular discipline. Equally, the pages of textbooks and journals are filled with discussions of the properties and the behavior of those systems. Students of mechanics investigate at length the dynamical properties of a system consisting of two or three spinning spheres with homogenous mass distributions gravitationally interacting only with each other. Population biologists study the evolution of one species procreating at a constant (...) rate in an isolated ecosystem. And when studying the exchange of goods, economists consider a situation in which there are only two goods, two perfectly rational agents, no restrictions on available information, no transaction costs, no money, and dealings are done immediately. Their surface structure notwithstanding, no competent scientist would mistake descriptions of such systems as descriptions of an actual system: we know very well that there are no such systems. These descriptions are descriptions of a model-system, and scientists use model-systems to represent parts or aspects of the world they are interested in. Following common practice, I refer to those parts or aspects as target-systems. What are we to make of this? Is discourse about such models merely a picturesque and ultimately dispensable façon de parler? This was the view of some early twentieth century philosophers. Duhem (1906) famously guarded against confusing model building with scientific theorizing and argued that model building has no real place in science, beyond a minor heuristic role. The aim of science was, instead, to construct theories, with theories understood as classificatory or representative structures systematically presented and formulated in precise symbolic.. (shrink)

In the present article, working from within the framework of critical rationalism and focusing mostly on the views developed by some Iranian writers, I argue that the programmes of producing ‘Islamic Science’ and ‘Islamisation of Science/Knowledge’ are doomed to failure. I develop my arguments in three parts. I start by explaining that the advocates of the programmes of producing cIS or IoK subscribe to mistaken images of science that are shaped by either a positivist or outmoded culturalist/interpretivist theories of science. (...) I shall then focus on the similarities and differences of ‘science’ and ‘technology’, arguing that despite close interconnection between the two it is of utmost importance, for analytical purposes, to keep these two socially constructed entities apart. Drawing on the above distinction, I argue that while creating ‘Islamic’ or ‘indigenous’ sciences is impossible, constructing ‘Islamic’ or ‘indigenous’ technologies is, in principle, feasible. Lastly, I turn to some of the... (shrink)

Proponents of the semantic approach to scientific theories cite a number of critical publications as the origins of their positions. While the semantic view experienced widespread adoption by philosophers of science in the decades leading up to the 1990s, over the last two decades opposition to the view has increased demonstrably. This growing disaffection suggests a two-part question: What exactly are the objections to the semantic view of scientific theories, and does the view have the conceptual resources to combat its (...) opposition? This essay seeks to answer this question by performing a careful analysis of the positions of both advocates and adversaries of the semantic view. In addition, it is argued that to save the semantic view it is necessary to locate the source of the position’s problems and to retool its conceptual foundations. To ensure that the semantic approach has the resources to meet objections to it, exegetical analysis is performed, which demonstrates that the source of the view’s present-day woes lies in a subtle conflation contained in one seminal articulation of the view, van Fraassen’s The Scientific Image. It is argued that supplanting central aspects of that work with ideas from Suppes is the remedy needed to provide the semantic view with the necessary resources for becoming wholly defensible against its oppositions. (shrink)

Halvorson argues that the semantic view of theories leads to absurdities. Glymour shows how to inoculate the semantic view against Halvorson's criticisms, namely by making it into a syntactic view of theories. I argue that this modified semantic-syntactic view cannot do the philosophical work that the original "language-free" semantic view was supposed to do.

Syntactic approaches in the philosophy of science, which are based on formalizations in predicate logic, are often considered in principle inferior to semantic approaches, which are based on formalizations with the help of structures. To compare the two kinds of approach, I identify some ambiguities in common semantic accounts and explicate the concept of a structure in a way that avoids hidden references to a specific vocabulary. From there, I argue that contrary to common opinion (i) unintended models do not (...) pose a significant problem for syntactic approaches to scientific theories, (ii) syntactic approaches can be at least as language-independent as semantic ones, and (iii) in syntactic approaches, scientific theories can be as well connected to the world as in semantic ones. Based on these results, I argue that syntactic and semantic approaches fare equally well when it comes to (iv) ease of application, (v) accommodating the use of models in the sciences, and (vi) capturing the theory-observation relation. (shrink)

This paper provides an account of mid-level models, which calibrate highly theoretical agent-based models of scientific communities by incorporating empirical information from real-world systems. As a result, these models more closely correspond with real-world communities, and are better suited for informing policy decisions than extant how-possibly models. I provide an exemplar of a mid-level model of science funding allocation that incorporates bibliometric data from scientific publications and data generated from empirical studies of peer review into an epistemic landscape model. The (...) results of my model show that on a dynamic epistemic landscape, allocating funding by modified and pure lottery strategies performs comparably to a perfect selection funding allocation strategy. These results support the idea that introducing randomness into a funding allocation process may be a tractable policy worth exploring further through pilot studies. My exemplar shows that agent-based models need not be restricted to the abstract and the a-priori; they can also be informed by real empirical data. (shrink)

Computational modeling plays an increasingly important explanatory role in cases where we investigate systems or problems that exceed our native epistemic capacities. One clear case where technological enhancement is indispensable involves the study of complex systems.1 However, even in contexts where the number of parameters and interactions that define a problem is small, simple systems sometimes exhibit non-linear features which computational models can illustrate and track. In recent decades, computational models have been proposed as a way to assist us in (...) understanding emergent phenomena. (shrink)

Philosophical interests of Joseph Życiński in the domain of the philosophy of science were focused on the debate concerning the nature of science and philosophy of science that followed the Einstein-Planck revolution in science. The unexpected discovery of the philosophical, extra-scientific presuppositions in science, as well as of the extra-rational factors determining the way these presuppositions are accepted in science were to be explained within the meta-scientific framework. It is the aim of this paper to present ˙ Życiński’s diagnosis of (...) this post-revolutionary situation in the philosophy of science as well as his critique of the metascientific answers to this challenge. The reasons will be given why all those answers are put under two dichotomous rubrics of _internalism_ and _externalism_. It will be also explained how Życiński intends to supersede this false in his opinion opposition with a new concept of the doxatic rationality. However, the details of the metascientific proposal of Życiński will be given only in the subsequent paper. In order to perform the aim of the paper the metatheoretic tools set out by Popper will be used. (shrink)

We have some justified beliefs about modal matters. A modal epistemology should explain what’s involved in our having that justification. Given that we’re realists about modality, how should we expect that explanation to go? In the first part of this essay, I suggest an answer to this question based on an analogy with games. Then, I outline a modal epistemology that fits with that answer. According to a theory-based epistemology of modality, you justifiably believe that p if you justifiably believe (...) a theory that says that p and you believe p on the basis of that theory. (shrink)

Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information-processing mechanisms, cognitive science needs an account of (...) how the representations invoked in cognitive mechanisms carry information about contents, and I suggest that control theory offers the needed perspective on the relation of representations to contents. Second, I argue that cognitive science requires, but is still in search of, a catalog of cognitive operations that researchers can draw upon in explaining cognitive mechanisms. Last, I provide a new perspective on the relation of cognitive science to brain sciences, one which embraces both reductive research on neural components that figure in cognitive mechanisms and a concern with recomposing higher-level mechanisms from their components and situating them in their environments. (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)

The focus of this paper is the recent revival of interest in structuralist approaches to science and, in particular, the structural realist position in philosophy of science . The challenge facing scientific structuralists is three-fold: i) to characterize scientific theories in ‘structural’ terms, and to use this characterization ii) to establish a theory-world connection (including an explanation of applicability) and iii) to address the relationship of ‘structural continuity’ between predecessor and successor theories. Our aim is to appeal to the notion (...) of shared structure between models to reconsider all of these challenges, and, in so doing, to classify the varieties of scientific structuralism and to offer a ‘minimal’ construal that is best viewed from a methodological stance. (shrink)

How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven dimensions on which (...) simulation models can differ are defined and distinctions between them discussed: 1. Relevance: whether the model tests and generates hypotheses applicable to biology. 2. Level: the elemental units of the model in the hierarchy from atoms to societies. 3. Generality: the range of biological systems the model can represent. 4. Abstraction: the complexity, relative to the target, or amount of detail included in the model. 5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour. 6. Performance match: to what extent the model behaviour matches the target behaviour. 7. Medium: the physical basis by which the model is implemented. No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments. Key Words: animal behaviour; levels; models; neuroethology; realism; robotics; simulation. (shrink)

We have two main aims: to construct mathematical models for analysing determinism, causality and supervenience; and then to use these to demonstrate the possibility of constructing an ontic construal of the operation of free will - one requiring both the presentation of genuine alternatives to an agent and their selecting between them in a manner that permits the attribution of responsibility. Determinism is modelled using trans-temporal ontic links between discrete juxtaposed universe states and shown to be distinct from predictability. Causality (...) is defined on a temporal sequence of δ-algebras and quantified using a measure. The measure leads to definitions of causal overdetermination and epiphenomena. Proofs are constructed to demonstrate deterministic universes must carry their properties essentially but not necessarily locally. We argue determinism and causality are separate doctrines. These models and results are marshalled to put the case that a counterfactual construal of ontic choice cannot work. In response we propose ‘immanence’ - a modified form of indeterminism whereby a universe can present choices to its denizens. We prove that beings subsumed within a universe cannot pilot their own actions. We then argue these beings can exercise free will only when selecting between choices inhering within immanent relata. A being is responsible for its selections if and only if it is constituted of a temporally evolving deterministic substructure. Our proposal is novel: it avoids injecting indeterminism into the decision process. Topological models for property supervenience are developed and used to reconstruct standard definitions from the literature. These are then used to demonstrate considerations of supervenience do not affect our arguments. We have demonstrated that a model of the exercise of free will involving both genuine choices and responsibility is possible but can only operate within a non-deterministic universe possessing specific traits. (shrink)

Unlike the sciences (physics), psychology has not developed in any of its areas (such as perception, learning, cognition) a top-theory like Newtonian theory, the theory of relativity, or quantum theory in physics. This difference is explained by a methodological discrepancy between the sciences and psychology, which centers on the measurement procedure: in psychology, measurement units similar to those in physics have not been discovered. Based on the arguments supporting this claim, a methodological distinction is made between the sciences and psychology (...) as an associational science. It is suggested that that these two kinds of science generate two different classes of technologies. The possibility that in psychology there is a connection between the issue of measurement and the unsolved consciousness/brain problem is discussed. (shrink)

On the basis of a wide range of historical examples various features of axioms are discussed in relation to their use in mathematical practice. A very general framework for this discussion is provided, and it is argued that axioms can play many roles in mathematics and that viewing them as self-evident truths does not do justice to the ways in which mathematicians employ axioms. Possible origins of axioms and criteria for choosing axioms are also examined. The distinctions introduced aim at (...) clarifying discussions in philosophy of mathematics and contributing towards a more refined view of mathematical practice. (shrink)

La parution de la monographic de Sneed,The Logical Structure of Mathematical Physics a suscité un renouveau d'intérêt en philosophie contemporaine des sciences. Cet ouvrage arrivait à un moment où l'épistémologie des sciences, telle que développée dans les milieux germaniques et anglo-saxons, accusait de graves insuffisances dans la reconstruction rationnelle du développement historique des théories physiques. Mis sur la défensive par les thèses et arguments historiques de Kuhn et de Feyerabend, ces milieux « orthodoxes » devaient reconnaitre l'état embryonnaire de ce (...) qui devait être uneépistémologie diachroniquedes sciences, une épistémologie du changement scientifique où les méthodes d'analyse formelle puissent être aussi mises á contribution. A cela s'ajoutait un certain malaise, une certaine stagnation de la problématique, plus synchronique, de l'approche traditionnelle issue du mouvement empiriste logique. Le probleme de la dichotomielangage théorique—langage observationnel, ainsi que son fidèle compagnon, le problème destermes théoriques, le statut épistémologique desrègies de correspondance, la dèfinition du concept d'analyticitéen science, autant de questions qui avaient été discutées et critiquées, à maintes reprises, pour aboutir à des résultats peu concluants aux yeux des philosophes, comme à ceux des physiciens préoccupés des fondements de leur discipline et à qui la teneur de cette discussion semblait souvent étrangère à la pratique scientifique. (shrink)

Philosophers of science increasingly believe that much of science is concerned with understanding the mechanisms responsible for the production of natural phenomena. An adequate understanding of scientific research requires an account of how scientists develop and test models of mechanisms. This paper offers a general account of the nature of mechanical models, discussing the representational relationship that holds between mechanisms and their models as well as the techniques that can be used to test and refine such models. The analysis is (...) supported by study of two competing models of a mechanism of speech perception. (shrink)