William L. Harper

Objective Bayesian methodology is widely used in statistics, physics, engineering and artificial intelligence. However, every justification for this method has contained glaring holes. This paper offered an entirely new, decision-theoretic justification of objective Bayesianism. Kyburg goes half-way towards objective Bayesianism. He accepts that frequencies constrain rational belief to an interval but stops short of isolating an optimal degree of belief within this interval. I examine the case for going the whole hog.

This book examines Newton’s argument for universal gravity and his application of it to resolve the problem of deciding between geocentric and heliocentric world systems by measuring masses of the sun and planets. Newton’s inferences from phenomena realize an ideal of empirical success that is richer than prediction. To achieve this rich sort of empirical success a theory needs, not only to accurately predict the phenomena it purports to explain, but also, to have those phenomena accurately measure the parameters which explain them. Newton’s method aims to turn theoretical questions into ones which can be empirically answered by measurement from phenomena. Newton employs theory-mediated measurements to turn data into far more informative evidence than can be achieved by confirmation from prediction alone. Propositions inferred from phenomena are provisionally accepted as guides to further research. This methodology, guided by its rich ideal of empirical success, supports a conception of scientific progress that does not require construing it as progress toward Laplace’s ideal limit of a final theory of everything and is not threatened by the classic argument against convergent realism. Newton’s method endorses the radical theoretical transformation from his theory to Einstein’s. It is strikingly realized in the development and application of testing frameworks for relativistic theories of gravity. In addition, it is very much at work in cosmology today.

Isaac Newton's Scientific Method examines Newton's argument for universal gravity and his application of it to resolve the problem of deciding between geocentric and heliocentric world systems by measuring masses of the sun and planets. William L. Harper suggests that Newton's inferences from phenomena realize an ideal of empirical success that is richer than prediction. Any theory that can achieve this rich sort of empirical success must not only be able to predict the phenomena it purports to explain, but also have those phenomena accurately measure the parameters which explain them. Harper explores the ways in which Newton's method aims to turn theoretical questions into ones which can be answered empirically by measurement from phenomena, and to establish that propositions inferred from phenomena are provisionally accepted as guides to further research. This methodology, guided by its rich ideal of empirical success, supports a conception of scientific progress that does not require construing it as progress toward Laplace's ideal limit of a final theory of everything, and is not threatened by the classic argument against convergent realism. Newton's method endorses the radical theoretical transformation from his theory to Einstein's. Harper argues that it is strikingly realized in the development and application of testing frameworks for relativistic theories of gravity, and very much at work in cosmology today.

One of the salient features of Carnap's systems of inductive logic is a conditionalization learning model, which also plays a fundamental role in the orthodox Bayesian account of rationality. This learning model does not allow for revision of previously accepted evidence. It is, therefore, not adequate to represent all rational learning by an agent who accepts corrigible propositions as evidence. Recently I used a generalization of the concept of conditional belief to extend the model so that rational revision of previously accepted evidence can be accommodated. My generalization of conditional belief carries with it a natural way of representing an agent's conceptual framework. In this paper I exploit this representation to produce learning models that can accommodate rational conceptual change.The idea of conceptual change has received considerable attention in recent work of philosophers of science. The following quotation from Hilary Putnam is instructive.

This paper depicts a case study of an organizational strategy for the promotion of ethical practice when introducing a new, high-risk, ethically-charged medical practice like Medical Assistance in Dying (MAiD). We describe the development of an interprofessional program that enables the delivery of high-quality, whole-person MAiD care that is values-based and sustainable. A “care ecology” strategy recognizes the interconnected web of relationships and structures necessary to support a quality experience of MAiD for patients, families, and clinicians. This program exemplifies a care ecology approach that addresses common barriers to entry to MAiD practice, and also meets the needs of a variety of stakeholders through the creation of patient and family resources, team supports, standards of practice, professional development opportunities, organizational infrastructure, and community partnerships. We also describe how a thriving care ecology evolves to remain resilient, and to enable integration as the needs of the organization, team and program change over time. The design and development of this program may be adapted to other jurisdictions and organizations where MAiD is introduced, or where new patient populations become eligible for MAiD. This care ecology model may also be applicable to the creation of sustainable programs that provide other morally controversial or novel clinical services.

We argue that causal decision theory is no worse off than evidential decision theory in handling entanglement, regardless of one’s preferred interpretation of quantum mechanics. In recent works, Ahmed and Ahmed and Caulton : 4315–4352, 2014) have claimed the opposite; we argue that they are mistaken. Bell-type experiments are not instances of Newcomb problems, so CDT and EDT do not diverge in their recommendations. We highlight the fact that a Causal Decision Theorist should take all lawlike correlations into account, including potentially acausal entanglement correlations. This paper also provides a brief introduction to CDT with a motivating “small” Newcomb problem. The main point of our argument is that quantum theory does not provide grounds for favouring EDT over CDT.

In “On Calling God ‘Mother’” (this journal), I argued that the practice of referring to God exclusively in male terms is morally acceptable. Isham claims that I have argued that “God should be referred to exclusively in male terms.” He claims that the Bible refers to God in female terms. He hints that I may have engaged in “gender devaluation.” He claims that there is a “need for a deity with which women can both relate and identify.” The first of Isham’s claims is simply false. I address the remaining criticisms at greater length.

Recent advances in philosophy, artificial intelligence, mathematical psychology, and the decision sciences have brought a renewed focus to the role and interpretation of probability in theories of uncertain reasoning. Henry E. Kyburg, Jr. has long resisted the now dominate Bayesian approach to the role of probability in scientific inference and practical decision. The sharp contrasts between the Bayesian approach and Kyburg's program offer a uniquely powerful framework within which to study several issues at the heart of scientific inference, decision, and reasoning under uncertainty. The commissioned essays for this volume take measure of the scope and impact of Kyburg's views on probability and scientific inference, and include several new and important contributions to the field. Contributors: Gert de Cooman, Clark Glymour, William Harper, Isaac Levi, Ron Loui, Enrique Miranda, John Pollock, Teddy Seidenfeld, Choh Man Teng, Mariam Thalos, Gregory Wheeler, Jon Williamson, and Henry E. Kyburg, Jr.

I attempt to persuade the reader that externalism admits of no plausible interpretation. I argue that reliability is a concept with very different contours from epistemic justification, and that attempts to explicate justification in terms of reliability must fail. I address several other forms of externalism, and also mixed forms of justification. I then argue that externalist theories of justification cannot close the gap between mere true belief and knowledge. I suggest that a fourth condition on knowledge is required, regardless of whether justification is internalist or externalist. I argue that with such a fourth condition a strong internalist theory of justification is adequate to the task that remains in making true belief knowledge. Additionally, strong internalism is more satisfying to our intuitions than externalism and mixed forms of justification.

As van Fraassen pointed out in his opening remarks, Henry Kyburg's lottery paradox has long been known to raise difficulties in attempts to represent full belief as a probability greater than or equal to p, where p is some number less than 1. Recently, Patrick Maher has pointed out that to identify full belief with probability equal to 1 presents similar difficulties. In his paper, van Fraassen investigates ways of representing full belief by personal probability which avoid the difficulties raised by Maher's measure-theoretic version of the lottery paradox. Van Fraassen's more subtle representation dissolves the simple identification of full belief with maximal personal probability. His investigation exploits the richer resources for representing opinion provided by taking conditional, rather than unconditional, personal probability as fundamental. It has interesting implications for equivalent alternative approaches based on non-Archimedean probability, as well as for equivalent approaches in which assumption contexts representing full belief relative to suppositions are taken as fundamental.

Newton's methodology emphasized propositions "inferred from phenomena." These rest on systematic dependencies that make phenomena measure theoretical parameters. We consider the inferences supporting Newton's inductive argument that gravitation is proportional to inertial mass. We argue that the support provided by these systematic dependencies is much stronger than that provided by bootstrap confirmation; this kind of support thus avoids some of the major objections against bootstrapping. Finally we examine how contemporary testing of equivalence principles exemplifies this Newtonian methodological theme