Stephan Hartmann

Explicating the concept of coherence and establishing a measure for assessing the coherence of an information set are two of the most important tasks of coherentist epistemology. To this end, several principles have been proposed to guide the specification of a measure of coherence. We depart from this prevailing path by challenging two well-established and prima facie plausible principles: Agreement and Dependence. Instead, we propose a new probabilistic measure of coherence that combines basic intuitions of both principles, but without strictly satisfying either of them. It is then shown that the new measure outperforms alternative measures in terms of its truth-tracking properties. We consider this feature to be central and argue that coherence matters because it is likely to be our best available guide to truth, at least when more direct evidence is unavailable.

Various scientific theories stand in a reductive relation to each other. In a recent article, we have argued that a generalized version of the Nagel-Schaffner model (GNS) is the right account of this relation. In this article, we present a Bayesian analysis of how GNS impacts on confirmation. We formalize the relation between the reducing and the reduced theory before and after the reduction using Bayesian networks, and thereby show that, post-reduction, the two theories are confirmatory of each other. We then ask when a purported reduction should be accepted on epistemic grounds. To do so, we compare the prior and posterior probabilities of the conjunction of both theories before and after the reduction and ask how well each is confirmed by the available evidence.

Recent critiques have cast doubt on the viability of probabilistic measures of explanatory power. We respond by developing a coherence-based family of measures that sidesteps these challenges. Rather than assessing explanatory power solely by how well a hypothesis accounts for the evidence, these measures evaluate how well the entire explanatory package, including multiple explanantia, coheres with the explanandum compared to how well it coheres with its negation. We show that this approach accommodates key scientific judgments while preserving the virtues of a probabilistic framework.

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.

This volume sheds light on still unexplored issues and raises new questions in the main areas addressed by the philosophy of science. Bringing together selected papers from three main events, the book presents the most advanced scientific results in the field and suggests innovative lines for further investigation. It explores how discussions on several notions of the philosophy of science can help different scientific disciplines in learning from each other. Finally, it focuses on the relationship between Cambridge and Vienna in twentieth century philosophy of science. The areas examined in the book are: formal methods, the philosophy of the natural and life sciences, the cultural and social sciences, the physical sciences and the history of the philosophy of science.

Lakatos argued that Carnap’s research programme in inductive logic was degenerative because it underwent a degenerative problem-shift by dealing with ever more specific internal problems and thereby moving further and further away from its original goals. Here I show that this criticism (which may apply to Carnap) cannot be levelled at the contemporary successor to Carnap’s programme, Bayesianism. To this end, I discuss various challenges and show how they can be addressed within the Bayesian research programme. In each of these cases, I argue, one can speak of a ‘progressive problem shift’. I therefore conclude that the Bayesian research programme in the methodology of science is progressive. Nevertheless, it is essential to continue to explore alternatives to it and to develop criteria for comparing competing research programmes.

Probabilistic models have much to offer to epistemology and philosophy of science. Arguably, the coherence theory of justification claims that the more coherent a set of propositions is, the more confident one ought to be in its content, ceteris paribus. An impossibility result shows that there cannot exist a coherence ordering. A coherence quasi-ordering can be constructed that respects this claim and is relevant to scientific-theory choice. Bayesian-Network models of the reliability of information sources are made applicable to Condorcet-style jury voting, Tversky and Kahneman’s Linda puzzle, the variety-of-evidence thesis, the Duhem–Quine thesis, and the informational value of testimony.

There are various ways to reach a group decision on a factual yes–no question. One way is to vote and decide what the majority votes for. This procedure receives some epistemological support from the Condorcet Jury Theorem. Alternatively, the group members may prefer to deliberate and will eventually reach a decision that everybody endorses—a consensus. While the latter procedure has the advantage that it makes everybody happy (as everybody endorses the consensus), it has the disadvantage that it is difficult to implement, especially for larger groups. Besides, the resulting consensus may be far away from the truth. And so we ask: Is deliberation truth-conducive in the sense that majority voting is? To address this question, we construct a highly idealized model of a particular deliberation process, inspired by the movie Twelve Angry Men, and show that the answer is ‘yes’. Deliberation procedures can be truth-conducive just as the voting procedure is. We then explore, again on the basis of our model and using agent-based simulations, under which conditions it is better epistemically to deliberate than to vote. Our analysis shows that there are contexts in which deliberation is epistemically preferable and we will provide reasons for why this is so.

Various scientific theories stand in a reductive relation to each other. In a recent article, we have argued that a generalized version of the Nagel-Schaffner model (GNS) is the right account of this relation. In this article, we present a Bayesian analysis of how GNS impacts on confirmation. We formalize the relation between the reducing and the reduced theory before and after the reduction using Bayesian networks, and thereby show that, post-reduction, the two theories are confirmatory of each other. We then ask when a purported reduction should be accepted on epistemic grounds. To do so, we compare the prior and posterior probabilities of the conjunction of both theories before and after the reduction and ask how well each is confirmed by the available evidence.

Suppose that we acquire various items of information from various sources and that our degree of confidence in the content of the information set is sufficiently high to believe the information. Now a new item of information is being presented by a new information source. Are we justified to add this new item of information to what we already believe? Consider the following parable: “I go to a lecture about wildlife in Greenland which was supposed to be delivered by an expert in the field. When I arrive I notice that the expert has excused himself and that the biology department has sent a newcomer to fill in for him. I have no beliefs about wildlife in Greenland, but I do have some beliefs about Greenland’s climate and about the kinds of climate conditions that various types of wildlife favor. Suppose that our newcomer proclaims that large colonies of elk roam in a particular valley on the southern tip of Greenland. Then I would certainly be willing to accept this item of information. But suppose that he proclaims that large colonies of armadillos roam in the same valley. Then I would not be willing to accept this item of information. Why do I accept the former but not the latter item of information? Let us suppose that neither item of information is logically inconsistent with what I already believe. Clearly, the former item of information is not inconsistent. But also the latter item is not: after all, there may be small pockets of Greenland with special climatological conditions and some Texan sailors may have set loose a pair of armadillos as a practical joke. But whereas the former item of information is quite plausible given my previous beliefs, the latter item of information is not. And this is what makes for the difference. Now suppose that our newcomer proclaims that large colonies of wild boars roam this valley in Greenland. I am not that sure anymore. Given everything I believe, I find wild boars in Greenland more plausible than armadillos in Greenland, but certainly less plausible than elk in Greenland. Had our expert presented the lecture and provided precisely the 2 same information, then I would have been willing to adopt the belief that there are wild boars in Greenland, but, with our newcomer delivering the lecture, I am not willing to do so. It is not that I would believe anything out of the expert’s mouth: also he could not have convinced me that there are armadillos in Greenland. But when it comes to wild boars, the difference between the expert’s and the newcomer’s credentials simply makes for the difference.” What we learn from this story is that the question of belief expansion has something to do with the reliability of the information source as well as with the plausibility of the new information, given what I already believe. The more reliable the information source is, the less plausible the new information needs to be given what I already believe, for me to be justified to add the belief to our belief set. The more plausible the new information is given what I already believe, the less reliable the information source needs to be given what I already believe, for me to be justified to add the belief to my belief set. The challenge is: can a precise account of this relationship be provided?