In this paper we show that the coherence measures of Olsson (J Philos 94:246–272, 2002), Shogenji (Log Anal 59:338–345, 1999), and Fitelson (Log Anal 63:194–199, 2003) satisfy the two most important adequacy requirements for the purpose of assessing theories. Following Hempel (Synthese 12:439–469, 1960), Levi (Gambling with truth, New York, A. A. Knopf, 1967), and recently Huber (Synthese 161:89–118, 2008) we require, as minimal or necessary conditions, that adequate assessment functions favor true theories over false theories and true and informative (...) theories over true but uninformative theories. We then demonstrate that the coherence measures of Olsson, Shogenji, and Fitelson satisfy these minimal conditions if we confront the hypotheses with a separating sequence of observational statements. In the concluding remarks we set out the philosophical relevance, and limitations, of the formal results. Inter alia, we discuss the problematic implications of our precondition that competing hypotheses must be confronted with a separating sequence of observational statements, which also leads us to discuss theory assessment in the context of scientific antirealism. (shrink)

In this paper I argue that it is finally time to move beyond the Nagelian framework and to break new ground in thinking about epistemic reduction in biology. I will do so, not by simply repeating all the old objections that have been raised against Ernest Nagel’s classical model of theory reduction. Rather, I grant that a proponent of Nagel’s approach can handle several of these problems but that, nevertheless, Nagel’s general way of thinking about epistemic reduction in terms of (...) theories and their logical relations is entirely inadequate with respect to what is going on in actual biological research practice. (shrink)

Amalgamating evidence of different kinds for the same hypothesis into an overall confirmation is analogous, I argue, to amalgamating individuals’ preferences into a group preference. The latter faces well-known impossibility theorems, most famously “Arrow’s Theorem”. Once the analogy between amalgamating evidence and amalgamating preferences is tight, it is obvious that amalgamating evidence might face a theorem similar to Arrow’s. I prove that this is so, and end by discussing the plausibility of the axioms required for the theorem.

In 2021, the Bigelow Institute for Consciousness Studies (hereafter, BICS) sponsored an essay competition (hereafter, the Contest) designed to solicit the best evidence for the hypothesis that human consciousness survives bodily death, and more specifically, evidence that would prove this hypothesis beyond a reasonable doubt. The summer 2022 issue of the Journal of Scientific Exploration featured a special subsection on the BICS contest and its winning essays. Robert Bigelow and Colm Kelleher outlined the motivation, design, and judging criteria for the (...) competition. Keith Augustine provided an extensive critical commentary on the contest design and eight of its prominent winning essays. Stephen Braude and several coauthors responded to Augustine’s criticisms, and Augustine provided a reply to Braude and his collaborators. Finally, the subsection concluded with a collaborative paper in which Etienne LeBel, Adam Rock, and Augustine proposed a more rigorous experimental design for testing the survival hypothesis. Augustine presented important criticisms of the Contest and several of its prominent winning essays. Moreover, many of his criticisms apply to survival literature in general and so are instructive for the wider survival debate. And Braude, one of the most important contributors to the survival debate since C.D. Broad and C.J. Ducasse, was at the helm of the reply to Augustine. Although the response to Augustine raised important concerns about skeptical assessments of the evidence for survival, it was hamstrung with several defects. 1. Preliminaries 2. Problematic Aspects of the Reply to Augustine 3. What is the Question? Honing in on the Actual Disagreement 4. Inference to Best Explanation 5. Confirmation Approaches to Evidential Support -- 5.1 Evidential Justification -- 5.2 When Does an Observation Favor One Hypothesis Over Another Hypothesis? The Law of Likelihood -- 5.3 When Should We Up Our Confidence in a Hypothesis? Incremental Confirmation -- 5.4 How Can We Determine the Net Plausibility of a Hypothesis? Bayes’ Theorem and Posterior Probabilities -- 5.5 Which of Two Hypotheses is More Probable than the Other? Principle of Contrastive Posterior Probabilities -- 5.6 When Does Evidence Justify Believing a Hypothesis? Principle of Absolute Confirmation -- 5.7 Survivalist Confusions about Bayesian Analyses -- 5.8 The Washing Out of Priors -- 5.9 The Subjectivism Objection -- 5.10 Summing Up Insights from Confirmation Theory -- 6. Back to Braude 7. Varieties of Skepticism 8. Neurophysiological Evidence and the Survival Hypothesis 9. The Data from Mediumship 10. The Alleged Improbability of Fraud and Other Counterexplanations 11. Mediumship and the Logic of Confirmation 12. The Significance of Failed Tests 13. Survivalist Rescues and Contrastive Confirmation 14. Evidential Support without Predictions -- 15. The Testability Problem -- Concluding Remarks. (shrink)

While Bayesian methods have become very popular in phylogenetic systematics, the foundations of this approach remain controversial. The star-tree paradox in Bayesian phylogenetics refers to the phenomenon that a particular binary phylogenetic tree sometimes has a very high posterior probability even though a star tree generates the data. I argue that this phenomenon reveals an unattractive feature of the Bayesian approach to scientific inference and discuss two proposals for how to address the star-tree paradox. In particular, I defend the polytomy (...) prior as a solution of the paradox and argue that it is preferable to a data-size dependent branch lengths prior from a methodological perspective. However, while this reply dissolves the star-tree paradox, the general challenge to Bayesian confirmation theory remains unmet. (shrink)

Philosophers of science have often favoured reductive approaches to how-possibly explanation. This article identifies three alternative conceptions making how-possibly explanation an interesting phenomenon in its own right. The first variety approaches “how possibly X?” by showing that X is not epistemically impossible. This can sometimes be achieved by removing misunderstandings concerning the implications of one’s current belief system but involves characteristically a modification of this belief system so that acceptance of X does not result in contradiction. The second variety offers (...) a potential how-explanation of X. It is usually followed by a range of further potential how-explanations of the same phenomenon. In recent literature the factual claims implied by the second variety have been downplayed whereas the heuristic role of mapping the space of conceptual possibilities has been emphasized. I will focus especially on this truth-bracketing sense of potentiality when looking closer at the second variety in the paper. The third variety has attracted less interest. It presents a partial how-explanation of X. Typically it aims to establish the existence of a mechanism by which X could be and was generated. The third conception stands out as the natural alternative for the advocate of ontic how-possibly explanations. This article transfers Salmon’s view that explanation-concepts can be broadly divided into epistemic, modal, and ontic to the context of how-possibly explanations. Moreover, it is argued that each of the three above-mentioned varieties of how-possibly explanation occurs in science. To recognize this may be especially relevant for philosophers. We are often misled by the promises of various why-explanation accounts, and seem to have forgotten nearly everything about the diversity of how-possibly explanations. (shrink)

The paper discusses Bayesian convergence when the truth is excluded from the analysis by means of a simple coin-tossing example. In the fair-balance paradox a fair coin is tossed repeatedly. A Bayesian agent, however, holds the a priori view that the coin is either biased towards heads or towards tails. As a result the truth is ignored by the agent. In this scenario the Bayesian approach tends to confirm a false model as the data size goes to infinity. I argue (...) that the fair-balance paradox reveals an unattractive feature of the Bayesian approach to scientific inference and explore a modification of the paradox. (shrink)

Evidence is an objective matter. This is the prevailing view within science, and confirmation theory should aim to capture the objective nature of scientific evidence. Modeling an objective evidence relation in a probabilistic framework faces two challenges: the probabilities must have the right epistemic foundation, and they must be specifiable given the hypotheses and data under consideration. Here I will explore how Sober's approach to confirmation handles these challenges of foundation and specification. In particular, I will argue that the specification (...) problem proves especially difficult, and undermines the law of likelihood as an adequate representation of the objective nature of scientific evidence. (shrink)