Online research in philosophy

A comparison of Plato's "Phaedo" and the "Mahāparanibbāna Sutta" of the Pāli Canon juxtaposes the character and teachings of Socrates and the Buddha as revealed by both texts, set just before their deaths. Discussed at length are similarities in technique (dialogue), personality (open-mindedness and compassion), and doctrine (especially regarding the purification of the soul over numerous lifetimes), as well as the subsequent development of Platonism and Buddhism after the deaths of the masters.

Chow's book makes a provocative contribution to the debate on the role of statistical significance, but it involves some important misconceptions in the presentation of the Fisher and Neyman/Pearson's theories. Moreover, the author's caricature-like considerations about “Bayesianism” are completely irrelevant for discarding the Bayesian statistical theory. These facts call into question the objectivity of his contribution.

Standard models of statistical explanation face two intractable difficulties. In his 1984 Salmon argues that because statistical explanations are essentially probabilistic we can make sense of statistical explanation only by rejecting the intuition that scientific explanations are contrastive. Further, frequently the point of a statistical explanation is to identify the etiology of its explanandum, but on standard models probabilistic explanations often fail to do so. This paper offers an alternative conception of statistical explanations on which explanations of the frequency of a property consist in the derivation of that frequency from a statistical specification of the mechanism by which instances of the relevant property are produced. Such explanations are contrastive precisely because they identify the determinate causal etiologies of their explananda.

Through his S–R model of statistical relevance, Wesley Salmon offers a solution to the scientific explanation of objectively improbable events.

This book deals not so much with statistical methods as with the central concept of chance, or statistical probability, which statistical theories apply to nature.

Since the mid-1990s, there has been a rise in the number of deaths of undocumented Mexican migrants crossing the U.S./Mexican border. Who is responsible for these deaths? This article examines the culpability of (1) migrants, (2) humanitarian volunteers, (3) the Mexican government, (4) the U.S. government, and (5) U.S. businesses. A significant portion of the blame is assigned to U.S. free trade policies and U.S. businesses employing undocumented immigrants.

By emphasising the intentions underlying suicidal behaviour, suicidal death is distinguished from accidental death in standard philosophical accounts on the nature of suicide. A crucial third class of self-produced deaths, deaths in which agents act neither intentionally nor accidentally to produce their own deaths, is left out by such accounts. Based on findings from psychiatry, many life-threatening behaviours, if and when they lead to the agent’s death, are suggested to be neither intentional nor accidental, with many apparently suicidal behaviours being of this sort, especially the so-called “cries for help”. This category may be usefully analogised to the existing legal category of manslaughter.

This note is viewing survival data of a natural cohort as being generated by a possibly nonlinear, nonhomogeneous death process. It proves that the usual conditional distributions of the number of survivors at a certain age are binomial if and only if the death process is linear. Thus the customary statistical methods for the analysis of life table data are, strictly speaking, invalid whenever the underlying death process is nonlinear. For example, if a contagious disease is the cause of some or all of the deaths, the deaths will not be independent and the death process, not linear. One should then base the statistical analysis on a model for the spread of the disease rather than the routine binomial model.

This paper takes a critical look at the idea that evolutionary theory is a statistical theory. It argues that despite the strong instrumental motivation for statistical theories, they are not necessary to explain deterministic systems. Biological evolution is fundamentally a result of deterministic processes. Hence, a statistical theory is not necessary for describing the evolutionary forces of genetic drift and natural selection, nor is it needed for describing the fitness of organisms. There is a computational advantage to the statistical theory of population genetics, but population genetics succeeds only by eliminating causes from its account of evolutionary change.