The rehabilitation of deductive reasoning

The paper aims at the rehabilitation of deductive reasoning. As a paradigm of reliable reasoning, it should be applicable in every confirmation context. In particular, it should transmit inductive justification, so that if D justifies a hypothesis H, then D also justifies all deductive conclusions from H. Nevertheless, most current philosophers of science reject such a transmission principle as false. They argue against it by providing apparent counter-examples and also by showing that it is incompatible with common confirmation theories such as HD-confirmation and Bayesianism. I argue in the opposite direction that we should stick to the transmission principle and revise instead our justification theories towards more cautious justification procedures that respect the transmission principle. This will avoid further paradoxes of these theories and, in particular, will enable us to apply our confirmed hypotheses to new situations in a well-founded way

Bayesian confirmation, connexivism and an unkindness of ravens

Bayesian confirmation theories (BCTs) might be the best standing theories of confirmation to date, but they are certainly not paradox-free. Here I recognize that BCTs’ appeal mainly comes from the fact that they capture some of our intuitions about confirmation better than those the- ories that came before them and that the superiority of BCTs is suffi- ciently justified by those advantages. Instead, I will focus on Sylvan and Nola’s claim that it is desirable that our best theory of confirmation be as paradox-free as possible. For this reason, I will show that, as they respond to different interests, the project of the BCTs is not incompatible with Sylvan and Nola’s project of a paradox-free confirmation logic. In fact, it will turn out that, provided we are ready to embrace some degree of non-classicality, both projects complement each other nicely

A Framework for Inductive Reasoning in Model-Based Science

This paper argues that the linguistic approach to analyzing induction, according to which induction is a type of inference or argument composed of statements or propositions, is unsuitable to account for scientific reasoning. Consequently, a novel approach to induction in model-based science is suggested. First, in order to show their adherence to the linguistic treatment of induction, two strategies are reviewed: (i) Carnap and Reichenbach’s attempts to justify induction and (ii) Norton’s recent material theory of induction. Second, three reasons are provided to support the claim that the linguistic treatment of induction is insufficient in accounting for model-based reasoning in science. Finally, a framework focused on models—rather than statements or propositions—is suggested to address induction in science. William Whewell’s theory of induction is briefly outlined as an example of a non-propositional treatment of induction that is consistent with model-based scientific practice

Bayesian confirmation, connexivism and an unkindness of ravens

Bayesian confirmation theories (BCTs) might be the best standing theories of confirmation to date, but they are certainly not paradox-free. Here I recognize that BCTs’ appeal mainly comes from the fact that they capture some of our intuitions about confirmation better than those the- ories that came before them and that the superiority of BCTs is suffi- ciently justified by those advantages. Instead, I will focus on Sylvan and Nola’s claim that it is desirable that our best theory of confirmation be as paradox-free as possible. For this reason, I will show that, as they respond to different interests, the project of the BCTs is not incompatible with Sylvan and Nola’s project of a paradox-free confirmation logic. In fact, it will turn out that, provided we are ready to embrace some degree of non-classicality, both projects complement each other nicely

Die erkenntnistheoretischen Grundlagen induktiven Schließens: Induktion, Falsifikation, Signifikanztests, kausales Schließen, Abduktion, HD-Bestätigung, Bayesianismus

Das vorliegende Buch stellt eine überarbeitete und deutlich erweiterte zweite Ausgabe meines gleichnamigen Buches von 2012 dar. Es wendet sich in Form eines Lehrbuchs sowohl an Anfänger wie Fortgeschrittene der Wissenschaftstheorie sowie an Wissenschaftler, die sich dafür interessieren, wann Daten eine bestimmte Theorie begründen und wie stark die Bestätigung der Theorie durch die Daten ist. Im Vordergrund steht dabei immer die erkenntnistheoretische Frage, ob bestimmte Begründungsverfahren die Ziele der Wissenschaften in überzeugender Weise verfolgen oder ob es dagegen substantielle Einwände gibt. Leider wird sich herausstellen, dass kein Verfahren ohne Fehl und Tadel ist, und wir sollten die Schwächen unserer Begründungsverfahren genau kennen, um sie korrekt einsetzen zu können