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)

Big data increasingly enables prediction of the behaviour and characteristics of individuals. This is ethically concerning on privacy grounds. However, this article discusses other reasons for concern. These predictions usually rely on generalisations about what certain sorts of people tend to do. Generalisations of this sort are often under scrutiny in legal cases, where, for example, lawyers argue that people with prior convictions are more likely to be guilty of the crime they are currently on trial for. This article applies (...) criteria for distinguishing acceptable from unacceptable generalisations in legal cases to a number of big data examples. It argues that these criteria are helpful, and highlights three issues that should be taken into account when deciding whether predictions about individuals are ethical. (shrink)

In this paper we compare causal models with reason models in the construction of Bayesian networks for legal evidence. In causal models, arrows in the network are drawn from causes to effects. In a reason model, the arrows are instead drawn towards the evidence, from factum probandum to factum probans. We explore the differences between causal models and reason models and observe several distinct advantages with reason models. Reason models are better aligned with the philosophy of Bayesian inference, as they (...) model reasons for up-dating beliefs. Reason models are better suited for measuring the combined support of the evidence, and a prior probability of guilt that reflects the number of possible perpetrators is accommodated more easily with reason models. (shrink)