The Laplace-Jaynes approach to induction

An approach to induction is presented, based on the idea of analysing the context of a given problem into `circumstances'. This approach, fully Bayesian in form and meaning, provides a complement or in some cases an alternative to that based on de Finetti's representation theorem and on the notion of infinite exchangeability. In particular, it gives an alternative interpretation of those formulae that apparently involve `unknown probabilities' or `propensities'. Various advantages and applications of the presented approach are discussed, especially in comparison to that based on exchangeability. Generalisations are also discussed.Comment: 38 pages, 1 figure. V2: altered discussion on some points, corrected typos, added reference

Of Miracles and Evidential Probability: Hume’s “Abject Failure” Vindicated

This paper defends David Hume's "Of Miracles" from John Earman's (2000) Bayesian attack by showing that Earman misrepresents Hume's argument against believing in miracles and misunderstands Hume's epistemology of probable belief. It argues, moreover, that Hume's account of evidence is fundamentally non-mathematical and thus cannot be properly represented in a Bayesian framework. Hume's account of probability is show to be consistent with a long and laudable tradition of evidential reasoning going back to ancient Roman law

Hume’s problem, epistemic deductivism and the validation of induction

Contrary to Owen (2000), Hume's problem is, as has traditionally been supposed, a problem for the justification of inductive inference. But, contrary to tradition, induction on Hume's account is not deductively invalid. Furthermore, on a more modem conception of inductive or ampliative inference, it is a mistake to suppose that the proper construal of an argument explicating the supposed justification for such inferences should in general be non-deductive. On a general requirement for argument cogency that arguments should be suitably constructed so as to make it clear to the audience that the subject is justified, on whatever basis is cited, in regarding the hypothesis with whatever epistemic attitude the arguer purports to be so justified, arguments in general, fully explicated and properly construed, should be deductively valid. Hume’s problem does not prevent such justification because his crucial argument establishes only that our basic assumptions cannot be justified, in the sense of being 'proven', or shown by non-question-begging argument to be just. It does not establish that our basic assumptions, properly explicated, are not just, or that they are not (at least to the satisfaction of most of us) clearly so. Nor does Goodman's 'new riddle' of induction pose a serious problem for the justification of our inductive inferences, as is still commonly suggested, since Jackson figured out the solution to the riddle thirty years ago. There is an analogous problem to Hume’s for the provability of principles or claims of deductive inferability, and if my analysis of the proper construal structure of argument (in the natural sense) is correct, this will block Howson's (2000) proposed escape route. Nevertheless, as with the case of induction, the unprovability of basic claims and principles of deductive inferability does not bar their deployment in cogent justifications

Problems in Argument Analysis and Evaluation

We are pleased to publish this WSIA edition of Trudy’s Govier’s seminal volume, Problems in Argument Analysis and Evaluation. Originally published in 1987 by Foris Publications, this was a pioneering work that played a major role in establishing argumentation theory as a discipline. Today, it is as relevant to the field as when it first appeared, with discussions of questions and issues that remain central to the study of argument. It has defined the main approaches to many of those issues and guided the ways in which we might respond to them. From this foundation, it sets the stage for further investigations and emerging research. This is a second edition of the book that is corrected and updated by the author, with new prefaces to each chapter

Inductive reasoning in humans and large language models

The impressive recent performance of large language models has led many to wonder to what extent they can serve as models of general intelligence or are similar to human cognition. We address this issue by applying GPT-3.5 and GPT-4 to a classic problem in human inductive reasoning known as property induction. Over two experiments, we elicit human judgments on a range of property induction tasks spanning multiple domains. Although GPT-3.5 struggles to capture many aspects of human behaviour, GPT-4 is much more successful: for the most part, its performance qualitatively matches that of humans, and the only notable exception is its failure to capture the phenomenon of premise non-monotonicity. Our work demonstrates that property induction allows for interesting comparisons between human and machine intelligence and provides two large datasets that can serve as benchmarks for future work in this vein.Comment: 61 pages, 5 figure

Meta-Empirical Support for Eliminative Reasoning

Eliminative reasoning is a method that has been employed in many significant episodes in the history of science. It has also been advocated by some philosophers as an important means for justifying well-established scientific theories. Arguments for how eliminative reasoning is able to do so, however, have generally relied on a too narrow conception of evidence, and have therefore tended to lapse into merely heuristic or pragmatic justifications for their conclusions. This paper shows how a broader conception of evidence not only can supply the needed justification but also illuminates the methodological significance of eliminative reasoning in a variety of contexts

Single or dual processing in reasoning development? An application of state-trace analysis to a systematic database of studies

This item is only available electronically.Influential dual-process theories of higher cognition posit that two qualitatively different processes underlie human reasoning. In contrast, single-process theories postulate that reasoners draw upon common cognitive mechanisms when making inferences. To test the competing theories, a rigorous method – state-trace analysis – has been proposed and proven to be a useful tool for beginning to diagnose the number of underlying psychological processes. This approach has been previously applied in exclusively adult based populations, suggesting that single-process theories of reasoning cannot be ruled out. However, to date it remains unclear whether such results hold across the period of child development. Therefore, the current study aimed to build a database of published developmental reasoning studies and to re-evaluate the data using state-trace analysis, to determine whether they best support the single-process or dual-process accounts. An electronic search of the PsycINFO and Scopus databases was undertaken to obtain empirical studies that have applied dual-process theories to examine reasoning in children or young adolescents (6-15 years). Two screening processes identified 10 papers that provided suitable summary data, forming a database of 78 datasets. State-trace analysis was applied to each dataset. Much of the developmental reasoning data were found to be consistent with a single-process account with one underlying latent variable, thus providing limited evidence for dual-process accounts of reasoning. More targeted experimental design and more stringent statistical tools are recommended for future research, to better understand the cognitive mechanisms underlying reasoning and its development.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 202