Stephan Hartmann

Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models is that it is an unsettled question what the epistemic goal of toy modeling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this paper is to precisely articulate and to defend this claim. In particular, we will distinguish between autonomous and embedded toy models, and, then, argue that important examples of autonomous toy models are sometimes best interpreted to provide how-possibly understanding, while embedded toy models yield how-actually understanding, if certain conditions are satisfied.

Die Frage, was eine wissenschaftliche Erklärung ist, stellt seit mehr als einem halben Jahrhundert ein zentrales Thema der Wissenschaftsphilosophie dar. Die heutige Diskussion begann mit einer richtungsweisenden Arbeit von Carl Hempel im Jahre 1942 über den Erklärungsbegriff in der Geschichtswissenschaft. In dieser Arbeit gab Hempel, frühere Überlegungen von John Stuart Mill, Karl Popper und anderen präzisierend, eine formale Definition der Erklärung eines singulären Faktums.1 Mit seiner dem zugrunde liegenden Auffassung, dass die Wissenschaften sehr wohl in der Lage sind, Erklärungen zu liefern, setzt sich Hempel ab von den zur damaligen Zeit vorherrschenden antimetaphysisch gestimmten Erklärungsskeptikern wie Pierre Duhem. Es ist jedoch wichtig zu betonen, dass Hempels Definition des Konzeptes der wissenschaftlichen Erklärung nicht wesentlich über das Deskriptive hinaus geht und damit auch und gerade von Empiristen akzeptiert werden kann. Bekanntlich versteht man unter einer Erklärung in Hempels Deduktiv-Nomologischen (D-N) Modell ein deduktiv gültiges Argument, zu dessen Prämisse (dem sog. Explanans) mindestens ein universelles Gesetz und eine Menge von singulären Sätzen gehört und dessen Konklusion das zu erklärende Faktum (das sog. Explanandum) ist.

We provide welfarist evaluations of decision rules for federations of states and consider models, under which the interests of people from different states are stochastically dependent. We concentrate on two welfarist standards; they require that the expected utility for the federation be maximized or that the expected utilities for people from different states be equal. We discuss an analytic result that characterizes the decision rule with maximum expected utility, set up a class of models that display interstate dependencies and run simulations for different dependency scenarios in the European Union. We find that, under positive correlations, the welfare distribution tends to be less sensitive to the choice of the decision rule, whereas it can be important under negative correlations. The results that Beisbart and Bovens (SCW 29, p. 581, 2007) have found for two types of models without interstate dependencies are relatively stable. There are exceptions, though, under which the way the welfare distribution is shaped by a decision rule is significantly affected by dependencies.

Corroborating Testimony, Probability and Surprise’, Erik J. Olsson ascribes to L. Jonathan Cohen the claims that if two witnesses provide us with the same information, then the less probable the information is, the more confident we may be that the information is true (C), and the stronger the information is corroborated (C*). We question whether Cohen intends anything like claims (C) and (C*). Furthermore, he discusses the concurrence of witness reports within a context of independent witnesses, whereas the witnesses in Olsson's model are not independent in the standard sense. We argue that there is much more than, in Olsson's words, ‘a grain of truth’ to claim (C), both on his own characterization as well as on Cohen's characterization of the witnesses. We present an analysis for independent witnesses in the contexts of decision-making under risk and decision-making under uncertainty and generalize the model for n witnesses. As to claim (C*), Olsson's argument is contingent on the choice of a particular measure of corroboration and is not robust in the face of alternative measures. Finally, we delimit the set of cases to which Olsson's model is applicable. 1 Claim (C) examined for Olsson's characterization of the relationship between the witnesses 2 Claim (C) examined for two or more independent witnesses 3 Robustness and multiple measures of corroboration 4 Discussion.

Quantum mechanical entangled configurations of particles that do not satisfy Bell’s inequalities, or equivalently, do not have a joint probability distribution, are familiar in the foundational literature of quantum mechanics. Nonexistence of a joint probability measure for the correlations predicted by quantum mechanics is itself equivalent to the nonexistence of local hidden variables that account for the correlations (for a proof of this equivalence, see Suppes and Zanotti, 1981). From a philosophical standpoint it is natural to ask what sort of concept can be used to provide a “joint” analysis of such quantum correlations. In other areas of application of probability, similar but different problems arise. A typical example is the introduction of upper and lower probabilities in the theory of belief. A person may feel uncomfortable assigning a precise probability to the occurrence of rain tomorrow, but feel comfortable saying the probability should be greater than ½ and less than ⅞. Rather extensive statistical developments have occurred for this framework. A thorough treatment can be found in Walley (1991) and an earlier measurement-oriented development in Suppes (1974). It is important to note that this focus on beliefs, or related Bayesian ideas, is not concerned, as we are here, with the nonexistence of joint probability distributions. Yet earlier work with no relation to quantum mechanics, but focused on conditions for existence has been published by many people. For some of our own work on this topic, see Suppes and Zanotti (1989). Still, this earlier work naturally suggested the question of whether or not upper and lower measures could be used in quantum mechanics, as a generalization of..

We appeal to the theory of Bayesian Networks to model different strategies for obtaining confirmation for a hypothesis from experimental test results provided by less than fully reliable instruments. In particular, we consider (i) repeated measurements of a single test consequence of the hypothesis, (ii) measurements of multiple test consequences of the hypothesis, (iii) theoretical support for the reliability of the instrument, and (iv) calibration procedures. We evaluate these strategies on their relative merits under idealized conditions and show some surprising repercussions on the variety-of-evidence thesis and the Duhem-Quine thesis.

Fundamental aspects of modern life owe their existence to the achievements of scientific reason. In other words, science is an integral element of the modern world and simultaneously the epitome of the rational nature of a technical culture that makes up the essence of the modern world. Without science, the modern world would lose its very nature and modern society its future. Right from the start, physics forms the core of European scientific development. It is the original paradigm of science, the foundation of technology and a constitutive part of a rational culture. It will remain a model methodological discipline in the future and its strengths will be used fruitfully in interdisciplinary and transdisciplinary collaboration.

Wann ist Kohärenz ein Indiz für Wahrheit? Können Informationsmengen immer entsprechend ihrer Kohärenz geordnet werden? Welche Rolle spielt Kohärenz bei der Theoriewahl in der Wissenschaft? Unter welchen Umständen kann eine wissenschaftliche Theorie mit nur teilweise zuverlässigen Messinstrumenten bestätigt werden? Ist die Belegvielfaltsthese wahr? Warum sind übereinstimmende Aussagen unabhängiger Zeugen so gewichtig? Dies sind einige der Fragen, die in diesem Buch in einem wahrscheinlichkeitstheoretischen Kontext und auf der Grundlage konkreter Modelle behandelt werden. Darüber hinaus bietet das Buch eine elementare Einführung in die Theorie der Bayesianischen Netzwerke und zeigt Verbindungen auf zu Debatten in der Kognitionswissenschaft (Tversky und Kahnemans Linda Problem), Sozialwahltheorie (Condorcet Jury Theorem) und Informatik (Repräsentation von Glaubenssystemen). Das Buch richtet sich an alle, die sich für die Anwendung wahrscheinlichkeitstheoretischer Methoden in der Philosophie interessieren.

Computer simulations have changed the face of many scientific disciplines. This has attracted the attention of a number of philosophers of science, who have started discussing the philosophical implications of the use of computational methods in science. It was the aim of the conference Models and Simulations that took place at the IHPST in Paris, France, on 12 and 13 June 2006 to bring researchers working in this field together and provide a platform to discuss these issues in a focused way. The papers in this special issue were presented at this conference. We would like to thank the IHPST and the CNRS (département SHS, ATIP “Physique et Calcul”) for generous financial support of the conference. We also would like to thank the authors and referees of the papers for their work, and Vincent Hendricks and John Symons for their support of the project of a special issue of this journal to the topic of the conference.

How does a predecessor theory relate to its successor? According to Heinz Post’s General Correspondence Principle, the successor theory has to account for the em- pirical success of its predecessor. After a critical discussion of this principle, I outline and discuss various kinds of correspondence relations that hold between successive scientific theories. I then look in some detail at a case study from contemporary physics: the various proposals for a theory of high-temperature superconductivity. The aim of this case study is to understand better the prospects and the place of a methodological principle such as the Generalized Correspondence Principle. Generalizing from the case study, I will then argue that some such principle has to be considered, at best, as one tool that might guide scientists in their theoriz- ing. Finally I present a tentative account of why principles such as the Generalized Correspondence Principle work so often and why there is so much continuity in scientific theorizing.

Life-science phenomena are often explained by specifying the mechanisms that bring them about. The new mechanistic philosophers have done much to substantiate this claim and to provide us with a better understanding of what mechanisms are and how they explain. Although there is disagreement among current mechanists on various issues, they share a common core position and a seeming commitment to some form of scientific realism. But is such a commitment necessary? Is it the best way to go about mechanistic explanation? In this article, we propose an alternative antirealist account that also fits explanatory practice in the life sciences. We pay special attention to mechanistic models, i.e. scientific models that involve a mechanism, and to the role of coherence considerations in building such models. To illustrate our points, we consider the mechanism for the action potential. 1 Introduction2 Some Core Features of Mechanistic Explanation3 Scientific Realism and Mechanistic Explanation4 Antirealist Mechanistic Explanation: The Case of the Action Potential5 Some Outstanding Issues for the Antirealist Mechanist6 Two Problems for the Realist Mechanist7 Conclusions.

Bayesian epistemology addresses epistemological problems with the help of the mathematical theory of probability. It turns out that the probability calculus is especially suited to represent degrees of belief (credences) and to deal with questions of belief change, confirmation, evidence, justification, and coherence. Compared to the informal discussions in traditional epistemology, Bayesian epis- temology allows for a more precise and fine-grained analysis which takes the gradual aspects of these central epistemological notions into account. Bayesian epistemology therefore complements traditional epistemology; it does not re- place it or aim at replacing it.

Inductive Logic is number ten in the 11-volume Handbook of the History of Logic. While there are many examples were a science split from philosophy and became autonomous (such as physics with Newton and biology with Darwin), and while there are, perhaps, topics that are of exclusively philosophical interest, inductive logic — as this handbook attests — is a research field where philosophers and scientists fruitfully and constructively interact. This handbook covers the rich history of scientific turning points in Inductive Logic, including probability theory and decision theory. Written by leading researchers in the field, both this volume and the Handbook as a whole are definitive reference tools for senior undergraduates, graduate students and researchers in the history of logic, the history of philosophy, and any discipline, such as mathematics, computer science, cognitive psychology, and artificial intelligence, for whom the historical background of his or her work is a salient consideration.

Intertheoretic relations are an important topic in the philosophy of science. However, since their classical discussion by Ernest Nagel, such relations have mostly been restricted to relations between pairs of theories in the natural sciences. In this paper, we present a model of a new type of intertheoretic relation, called 'Montague Reduction', which is assumed in Montague's framework for the analysis and interpretation of natural language syntax. To motivate the adoption of our new model, we show that this model extends the scope of application of the Nagelian (or related) models, and that it shares the epistemological advantages of the Nagelian model. The latter is achieved in a Bayesian framework.

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.