Stephan Hartmann

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?

The papers in this collection were presented at a workshop on Bayesian Epistemology at the 26th International Wittgenstein Symposium in Kirchberg, Austria (August 4–7, 2003), at a workshop on Philosophy and Probability at the conference GAP5 in Bielefeld, Germany (September 20–22, 2003), at a workshop on Bayesian Epistemology at the Centre for Philosophy of Natural and Social Science, London School of Economics and Political Science in London, UK (June 28, 2004), or at the seminar of the research group Philosophy, Probability and Modeling, Center for Junior Research Fellows, University of Konstanz, Germany. We acknowledge the generous support for these events by the Austrian Ludwig Wittgenstein Society, the Gesellschaft für analytische Philosophie e.V., the Centre for Philosophy of Natural and Social Science, the Alexander von Humboldt Foundation, the Federal Ministry of Education and Research and the Program for the Investment in the Future (ZIP) of the German Government through a Sofja Kovalevskaja Award. We thank the organizers of the conferences in Kirchberg and Bielefeld for their support, the authors and referees of the papers for their work, and John Symons for his interest in dedicating a special issue of this journal to Bayesian Epistemology.

The Common Cause Principle, stating that correlations are either consequences of a direct causal link between the correlated events or are due to a common cause, is assessed from the perspective of its viability and it is argued that at present we do not have strictly empirical evidence that could be interpreted as disconfirming the principle. In particular it is not known whether spacelike correlations predicted by quantum field theory can be explained by properly localized common causes, and EPR correlations are known to be unexplainable only by common causes that are required to satisfy conditions that are empirically not tested.

This book consists in seventeen chapters devoted to physical, metaphysical, and methodological questions concerning open systems. The chapters in the volume address questions such as: Are (theories of) open systems more fundamental than (theories of) closed systems? How have concepts of open and closed systems have been used throughout the history of physics, and how should we understand their use in contemporary physical theories? Must the universe be a closed system? Must there be a such thing as the universe at all? Contributors: Emily Adlam, Luis C. Barbado, Caslav Brukner, Eddy Keming Chen, Michael E. Cuffaro, Gemma De les Coves, George Ellis, Doreen Fraser, Henrique Gomes, Sean Gryb, William L. Harper, Stephan Hartmann, Molly Kao, Adam Koberinski, James Ladyman, Simon Langenscheidt, Olimpia Lombardi, Jorn Klovfjell Mjelva, Wayne C. Myrvold, Daniele Oriti, Josh Quirke, David Sloan, Karim P. Y. Thebault, Lev Vaidman, David Wallace, and Alastair Wilson.

Theoretical models are an important tool for many aspects of scientific activity. They are used, i.a., to structure data, to apply theories or even to construct new theories. But what exactly is a model? It turns out that there is no proper definition of the term "model" that covers all these aspects. Thus, I restrict myself here to evaluate the function of models in the research process while using "model" in the loose way physicists do. To this end, I distinguish four kinds of models. These are (1) models as special theories, (2) models as a substitute for a theory, (3) toy models and (4) developmental models. I argue that models of the types (3) and (4) are considerably useful in the process of theory construction. This will be demonstrated in an extended case-study from High-Energy Physics.

Let me first state that I like Antti Revonsuo’s discussion of the various methodological and interpretational problems in neuroscience. It shows how careful and methodologically reflected scientists have to proceed in this fascinating field of research. I have nothing to add here. Furthermore, I am very sympathetic towards Revonsuo’s general proposal to call for a Philosophy of Neuroscience that stresses foundational issues, but also focuses on methodological and explanatory strategies.2 In a footnote of his paper, Revonsuo complains – as many others do today – about what is sometimes called “physics imperialism”. This is the view that physics dominates the philosophy of science. I am not sure if this is still the case nowadays, but it is certainly historically correct that almost all work in the field of methodology centered around cases from physics. Although this has been changing, there are still plenty of special sciences philosophers did not worry about much. Admittedly, I am myself a trained physicist and not a neuroscientist and will therefore probably be biased negatively. As it is, I will discuss some examples from physics in order to illustrate my points

Models are of central importance in many scientific contexts. The centrality of models such as the billiard ball model of a gas, the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, or general equilibrium models of markets in their respective domains are cases in point. Scientists spend a great deal of time building, testing, comparing and revising models, and much journal space is dedicated to introducing, applying and interpreting these valuable tools. In short, models are one of the principal instruments of modern science. Philosophers are acknowledging the importance of models with increasing attention and are probing the assorted roles that models play in scientific practice. The result has been an incredible proliferation of model-types in the philosophical literature. Probing models, phenomenological models, computational models, developmental models, explanatory models, impoverished models, testing models, idealized models, theoretical models, scale models, heuristic models, caricature models, didactic models, fantasy models, toy models, imaginary models, mathematical models, substitute models, iconic models, formal models, analogue models and instrumental models are but some of the notions that are used to categorize models. While at first glance this abundance is overwhelming, it can quickly be brought under control by recognizing that these notions pertain to different problems that arise in connection with models. For example, models raise questions in semantics (what is the representational function that models perform?), ontology (what kind of things are models?), epistemology (how do we learn with models?), and, of course, in philosophy of science (how do models relate to theory?; what are the implications of a model based approach to science for the debates over scientific realism, reductionism, explanation and laws of nature?).

Shows how the degree of confidence that information from multiple sources is a function of the plausibility and the coherence of the information as well as of the reliability of the sources. There is a long-standing question in epistemology about how to construct a measure that yields a coherence ordering over sets of propositions and there are various proposals in the literature. Presents an impossibility result to the effect that there cannot exist such a measure. This has implications for the coherence theory of justification.