And this paper is an attempt to say precisely how, thus addressing a philosophical problem which is commonly taken to be a serious one. It does so, however, in quite an idiosyncratic way. It is based on the account of inductive schemes I have given in (1988) and (1990a) and on the conception of causation I have presented in (1980), (1983), and (1990b), and it intends to fill one of many gaps which have been left by these papers. Still, I (...) have tried to make this paper self-contained. Section 1 explains the philosophical question this paper is about; in more general terms it asks what might be meant by objectifying epistemic states or features of them and to which extent epistemic states can be objectified. The next sections introduce the basis I rely on with formal precision and some explanation; section 2 deals with induction and section 3 with causation. Within these confines, section 4 attempts to give an explication of the relevant sense of objectification and section 5 investigates the extent to which various features of epistemic states are objectifiable. The two most salient results are roughly that the relation "A is a reason for B" cannot be objectified at all and that the relation "A is a cause of B" can be objectified only under substantial, though reasonable restrictions. What has all of this to do with probability? A lot. The paper trades on a pervasive duality between probabilistic and deterministic epistemology, between a probabilistic representation of epistemic states together with a theory of probabilistic causation and another representation of epistemic states which I call deterministic because it lends itself, in a perfectly parallel fashion, to a theory of deterministic causation. Here I explicitly deal only with the deterministic side, but the duality should pave the way for further conclusions concerning objective probabilities and statistical laws. This outlook is briefly expanded in the final section 6. (shrink)

Name der Zeitschrift: Deutsche Zeitschrift für Philosophie Jahrgang: 65 Heft: 3 Seiten: 436-455.

The article takes the term "technoscience" literally and investigates a conception of science that takes it not only as practice, but as production in the sense of a material labor process. It will explore in particular the material connection between science and ordinary production. It will furthermore examine how the historical development of science as a social enterprise was shaped by its technoscientific character. In this context, in an excursus, the prevailing notion will be questioned that social relations must be (...) conceived of as pure interactions. Finally, the article will go into the relationship between the epistemic dimension of science and its technoscientific character. (shrink)

This book addresses controversies concerning the epistemological foundations of data science: Is it a genuine science? Or is data science merely some inferior practice that can at best contribute to the scientific enterprise, but cannot stand on its own? The author proposes a coherent conceptual framework with which these questions can be rigorously addressed. Readers will discover a defense of inductivism and consideration of the arguments against it: an epistemology of data science more or less by definition has to be (...) inductivist, given that data science starts with the data. As an alternative to enumerative approaches, the author endorses Federica Russo’s recent call for a variational rationale in inductive methodology. Chapters then address some of the key concepts of an inductivist methodology including causation, probability and analogy, before outlining an inductivist framework. The inductivist framework is shown to be adequate and useful for an analysis of the epistemological foundations of data science. The author points out that many aspects of the variational rationale are present in algorithms commonly used in data science. Introductions to algorithms and brief case studies of successful data science such as machine translation are included. Data science is located with reference to several crucial distinctions regarding different kinds of scientific practices, including between exploratory and theory-driven experimentation, and between phenomenological and theoretical science. Computer scientists, philosophers and data scientists of various disciplines will find this philosophical perspective and conceptual framework of great interest, especially as a starting point for further in-depth analysis of algorithms used in data science. (shrink)

In the historiography of mathematics the concept of deviation is relative to a normal way of developing mathematics. Similarly, the concept of abbreviation is necessarily connected to some aim to which this development is directed. We should not speak of deviation or abbreviation without regard for such contects. In the history of mathematics there are some cases of treating impossible objects as possible objects of a new theory. Therefore it may be expected that there are more ways out than in (...) normal life. Some examples are given. *** DIRECT SUPPORT *** A02GD002 00011. (shrink)

My aim is to show that theories which try to construct truthmakers out of objects and properties/relations alone are not tenable: The Frege–Wittgenstein idea of incompleteness does not yield truthmakers. Armstrong’s theory of partial identity and the theory of moments, i.e., of non-transferable properties, yield truthmakers, but these theories have counter-intuitive consequences. I conclude that the notion of a truthmaker makes ontological demands beyond objects and properties/relations and propose that truthmakers are exemplification relations which are necessarily tied to objects and (...) properties/relations. (shrink)

Morality in context is a timely topic. A debate between philosophers and social scientists is a good way to approach it. Why is there such a booming interest in morality and why does it focus on context? One starting point is the change in the sociostructural and sociocultural conditions of modern societies. This involves change in the empirical conditions of moral action and in the social demand on morality. As these changes are accounted for and analyzed in the social sciences, (...) new perspectives emerge that give rise to new ways of framing issues and problems. These problems are best addressed by way of cooperation between philosophers and social scientists. As Habermas (1990) has pointed out in a much cited paper, philosophers depend on social science to fill in the data they require to answer the questions raised by philosophy in its "placeholder" function. The reverse also holds true: Social science needs the conceptual clarifications that philosophy can provide. With respect to morality, such mutual interchanges are of particular importance the contributions to this book show convincingly. (shrink)

This article contributes to the analysis of refusal to license cases as abuse of a dominant position pursuant Article 82 EC from an economic perspective. In the Microsoft case, the European Commission introduced an "Incentives Balance Test" to assess whether the refusal to give access to interface information can be justified by arguing that this information is protected by Intellectual Property Rights (IPRs): The Commission argued that if the overall innovative effects evoked by a compulsory license are significantly higher than (...) without this access, the IPR owner is obliged to license. This should be assessed through balancing the different incentives to innovate between the dominant firm and its competitors. In the paper we pursue two objectives: Firstly, we analyze to what extent the decision of the Court of First Instance, which confirmed the decision of the Commission, helps to clarify the criteria in refusal to license cases; in fact, it is disappointing in this regard. Secondly, we demonstrate that the basic idea of the Incentives Balance Test can be interpreted as a test whether the specific IPRs of the dominant firm can be defended from the perspective of the economics of IPRs. This implies that Article 82 allows competition law to correct economically not optimal IPRs through a specific economic analysis. This is followed by a broad overview on theoretical and empirical insights from economics of IPRs, innovation economics and competition and network economics that can help to develop a more general and sophisticated Innovation Effects Test that can be applied in Article 82 refusal to license cases. (shrink)