Probabilistic Reasoning in Cosmology

Cosmology raises novel philosophical questions regarding the use of probabilities in inference. This work aims at identifying and assessing lines of arguments and problematic principles in probabilistic reasoning in cosmology. The first, second, and third papers deal with the intersection of two distinct problems: accounting for selection effects, and representing ignorance or indifference in probabilistic inferences. These two problems meet in the cosmology literature when anthropic considerations are used to predict cosmological parameters by conditionalizing the distribution of, e.g., the cosmological constant on the number of observers it allows for. However, uniform probability distributions usually appealed to in such arguments are an inadequate representation of indifference, and lead to unfounded predictions. It has been argued that this inability to represent ignorance is a fundamental flaw of any inductive framework using additive measures. In the first paper, I examine how imprecise probabilities fare as an inductive framework and avoid such unwarranted inferences. In the second paper, I detail how this framework allows us to successfully avoid the conclusions of Doomsday arguments in a way no Bayesian approach that represents credal states by single credence functions could. There are in the cosmology literature several kinds of arguments referring to self- locating uncertainty. In the multiverse framework, different pocket-universes may have different fundamental physical parameters. We don’t know if we are typical observers and if we can safely assume that the physical laws we draw from our observations hold elsewhere. The third paper examines the validity of the appeal to the Sleeping Beauty problem and assesses the nature and role of typicality assumptions often endorsed to handle such questions. A more general issue for the use of probabilities in cosmology concerns the inadequacy of Bayesian and statistical model selection criteria in the absence of well-motivated measures for different cosmological models. The criteria for model selection commonly used tend to focus on optimizing the number of free parameters, but they can select physically implausible models. The fourth paper examines the possibility for Bayesian model selection to circumvent the lack of well-motivated priors

The Bayesian Who Knew Too Much

In several papers, John Norton has argued that Bayesianism cannot handle ignorance adequately due to its inability to distinguish between neutral and disconfirming evidence. He argued that this inability sows confusion in, e.g., anthropic reasoning in cosmology or the Doomsday argument, by allowing one to draw unwarranted conclusions from a lack of knowledge. Norton has suggested criteria for a candidate for representation of neutral support. Imprecise credences (families of credal probability functions) constitute a Bayesian-friendly framework that allows us to avoid inadequate neutral priors and better handle ignorance. The imprecise model generally agrees with Norton's representation of ignorance but requires that his criterion of self-duality be reformulated or abandoned

Report on a Boston University Conference December 7-8, 2012 on 'How Can the History and Philosophy of Science Contribute to Contemporary U.S. Science Teaching?'

This is an editorial report on the outcomes of an international conference sponsored by a grant from the National Science Foundation (NSF) (REESE-1205273) to the School of Education at Boston University and the Center for Philosophy and History of Science at Boston University for a conference titled: How Can the History and Philosophy of Science Contribute to Contemporary U.S. Science Teaching? The presentations of the conference speakers and the reports of the working groups are reviewed. Multiple themes emerged for K-16 education from the perspective of the history and philosophy of science. Key ones were that: students need to understand that central to science is argumentation, criticism, and analysis; students should be educated to appreciate science as part of our culture; students should be educated to be science literate; what is meant by the nature of science as discussed in much of the science education literature must be broadened to accommodate a science literacy that includes preparation for socioscientific issues; teaching for science literacy requires the development of new assessment tools; and, it is difficult to change what science teachers do in their classrooms. The principal conclusions drawn by the editors are that: to prepare students to be citizens in a participatory democracy, science education must be embedded in a liberal arts education; science teachers alone cannot be expected to prepare students to be scientifically literate; and, to educate students for scientific literacy will require a new curriculum that is coordinated across the humanities, history/social studies, and science classrooms.Comment: Conference funded by NSF grant REESE-1205273. 31 page

Blurring Out Cosmic Puzzles

The Doomsday argument and anthropic reasoning are two puzzling examples of probabilistic confirmation. In both cases, a lack of knowledge apparently yields surprising conclusions. Since they are formulated within a Bayesian framework, they constitute a challenge to Bayesianism. Several attempts, some successful, have been made to avoid these conclusions, but some versions of these arguments cannot be dissolved within the framework of orthodox Bayesianism. I show that adopting an imprecise framework of probabilistic reasoning allows for a more adequate representation of ignorance in Bayesian reasoning and explains away these puzzles.Comment: 15 pages, 1 figure. To appear in Philosophy of Science (PSA 2014

An Empiricist Criterion of Meaning

The meaning of scientific propositions is not always expressible in terms of observable phenomena. Such propositions involve generalizations, and also terms that are theoretical constructs. I study here how to assess the meaning of scientific propositions, that is, the specific import of theoretical terms. Empiricists have expressed a concern that scientific propositions, and theoretical terms, should always be, to some degree, related to observable consequences. We can see that the former empiricist criterion of meaning only implies for theoretical terms not to be definable in terms of observable, but that their use put a constraint on the observable consequences of a theory. To that effect, Ramsey's method of formal elimination of theoretical terms can be an interesting tool. It has faced important logical objections, which have mostly been addressed with respect to the problem of the ontological commitment of the second-order quantification they imply. I show here that these criticisms can be overcome, and that there can be a successful Ramsey elimination of theoretical terms with first order sentences, making Ramsey's method a relevant tool to assess the empirical meaning of scientific propositions

Rudolf Carnap, Testabilité et signification

International audienc

Perspectives of History and Philosophy on Teaching Astronomy

The didactics of astronomy is a relatively young field with respect to that of other sciences. Historical issues have most often been part of the teaching of astronomy, although that often does not stem from a specific didactics. The teaching of astronomy is often subsumed under that of physics. One can easily consider that, from an educational standpoint, astronomy requires the same mathematical or physical strategies. This approach may be adequate in many cases but cannot stand as a general principle for the teaching of astronomy. This chapter offers in a first part a brief overview of the status of astronomy education research and of the role of the history and philosophy of science (HPS) in astronomy education. In a second part, it attempts to illustrate possible ways to structure the teaching of astronomy around its historical development so as to pursue a quality education and contextualized learning