The work of scientists and doctors in advancing genetic research and its applications has been accompanied by plenty of discussion in the popular press—from Good Housekeeping and Forbes to Ms. and the Congressional Record—about such ...

Anorexia nervosa remains an enigma among Western cultures. Various causal explanations have been offered, encompassing biological, psychological, and sociocultural models. These explanations, however, focus on the immediate or proximal mechanisms of causation. A more thorough understanding of anorexia nervosa can be achieved by understanding the relationship between these factors and ultimate causation, the level of explanation which deals with individual reproductive fitness. This paper reviews the biological, psychological, sociocultural, and evolutionary models and indicates a necessary synthesis between proximate and ultimate (...) levels of causation in examining the anorexia nervosa puzzle. (shrink)

Richard Jeffrey espoused an antifoundationalist variant of Bayesian thinking that he termed ‘Radical Probabilism’. Radical Probabilism denies both the existence of an ideal, unbiased starting point for our attempts to learn about the world and the dogma of classical Bayesianism that the only justified change of belief is one based on the learning of certainties. Probabilistic judgment is basic and irreducible. Bayesian conditioning is appropriate when interaction with the environment yields new certainty of belief in some proposition but leaves (...) one’s conditional beliefs untouched (the ‘Rigidity’ condition). Although Richard Jeffrey denied the general applicability of this condition, one of his main contributions to probabilistic thinking is a form of belief updating—now typically called ‘Jeffrey conditioning’ or ‘probability kinematics’—that is appropriate in circumstances in which Rigidity is satisfied, but where the interaction causes one to reevaluate one’s probability judgments over some partition of the possibility space without conferring certainty on any particular element. The most familiar occasion for Jeffrey conditioning is receipt of uncertain evidence: things partially perceived or remembered. But it also serves to illuminate belief updating occasioned by a change in one’s degrees of conditional belief, a kind of belief change largely ignored by classical Bayesianism. I argue that such changes in conditional belief can also be basic (in the sense of not being analyzable as a consequence of conditioning on factual information) and offer a kinematical model for a particular kind change in conditional belief. Both are applied to changes in preference. Finally, I argue that Rigidity can fail when changes of belief give inferential grounds for changes in conditional belief (and vice versa). These failures show that conditioning methods are properly regarded, not as valid rules of inference, but as tools in the ‘art of judgment’. (shrink)

In the last third of the twentieth century, humanists and social scientists argued that attention to genetics would heighten already‐existing genetic determinism, which in turn would intensify negative social outcomes, especially sexism, racism, ableism, and harshness to criminals. They assumed that laypeople are at risk of becoming genetic essentialists. I will call this the “laypeople are genetic essentialists model.” This model has not accurately predicted psychosocial impacts of findings from genetics research. I will be arguing that the failure of the (...) model can be traced to its inability to recognize the complexity of laypeople’s attitudes; its incorrect theory of how beliefs, attitudes, and discourse function; and its blindness to how academics’ own interests can override the available evidence. More specifically, I suggest that the substantial data about laypeople’s deployment of genetics supports what I will call the “laypeople are strategic essentialists model” better than the “laypeople are genetic essentialists model.” The strategic essentialists model holds that people tend to store multiple categories, including multiple causal forces, that they deploy “strategically” to serve context‐dependent goals. It will be difficult for academics to reorient ourselves to model laypeople as sophisticated strategic essentialists rather than as naïve genetic essentialists. Perhaps a little shift, however, will be of value. (shrink)

The human propensity for casting our social worlds as "us against them" is perhaps the primary impediment to deep and broadly inclusive understandings of the workings of rhetoric. Many decades ago, Kenneth Burke assailed that barrier with regard to Adolf Hitler. Surrounded by the satisfactions of vituperation against the leader of one of the world's most heinous social movements, Burke begged his readers to make space for understanding how Hitler's rhetoric brought about what it did. Philippe-Joseph Salazar's Words Are Weapons (...) makes a similar contribution for our era. He asks us to resist the gratifications generated by dismissing as "Islamaphobic" any critical analysis of what he calls "caliphal"... (shrink)

Richard Jeffrey's generalization of Bayes' rule of conditioning follows, within the theory of belief functions, from Dempster's rule of combination and the rule of minimal extension. Both Jeffrey's rule and the theory of belief functions can and should be construed constructively, rather than normatively or descriptively. The theory of belief functions gives a more thorough analysis of how beliefs might be constructed than Jeffrey's rule does. The inadequacy of Bayesian conditioning is much more general than Jeffrey's examples of (...) uncertain perception might suggest. The ``parameter α '' that Hartry Field has introduced into Jeffrey's rule corresponds to the "weight of evidence" of the theory of belief functions. (shrink)

Randomized Controlled Trials are currently the gold standard within evidence-based medicine. Usually, they are conducted as sequential trials allowing for monitoring for early signs of effectiveness or harm. However, evidence from early stopped trials is often charged with being biased towards implausibly large effects. To our mind, this skeptical attitude is unfounded and caused by the failure to perform appropriate conditioning in the statistical analysis of the evidence. We contend that a shift from unconditional hypothesis tests in the style (...) of Neyman and Pearson to conditional hypothesis tests gives a superior appreciation of the obtained evidence and significantly improves the practice of sequential medical trials, while staying firmly rooted in frequentist methodology. (shrink)

In his recent book, Time and Chance, David Albert claims that by positing that there is a uniform probability distribution defined, on the standard measure, over the space of microscopic states that are compatible with both the current macrocondition of the world, and with what he calls the “past hypothesis”, we can explain the time asymmetry of all of the thermodynamic behavior in the world. The principal purpose of this paper is to dispute this claim. I argue that Albert's proposal (...) fails in his stated goal—to show how to use the time‐reversible dynamics of Newtonian physics to “underwrite the actual content of our thermodynamic experience” (Albert 2000, 159). Albert's proposal can satisfactorily explain why the overall entropy of the universe as a whole is increasing, but it does not and cannot explain the increasing entropy of relatively small, relatively short‐lived systems in energetic isolation without making use of a principle that leads to reversibility objections. (shrink)

Clark Glymour, together with his students Peter Spirtes and Richard Scheines, did pioneering work on graphical causal models . One of the central advances provided by these models is the ability to simply represent the effects of interventions. In an elegant paper , Glymour and his student Christopher Meek applied these methods to problems in decision theory. One of the morals they drew was that causal decision theory should be understood in terms of interventions. I revisit their proposal, and extend (...) the analysis by showing how graphical causal models might be used to address decision problems that arise in “exotic” situations, such as those involving crystal balls or time travelers. (shrink)

This paper investigates the viability of the Bayesian model of belief change. Van Benthem (2003) has shown that a particular kind of information change typical for dynamic epistemic logic cannot be modelled by Bayesian conditioning. I argue that the problems described by van Benthem come about because the information change alters the semantics in which the change is supposed to be modelled by conditioning: it induces a shift in meanings. I then show that meaning shifts can be modelled (...) in terms of conditioning by employing a semantics that makes these changes in meaning explicit, and that the appropriate probability kinematics can be described by Dempster’s rule. The new model thereby facilitates a better understanding between probabilistic epistemology and dynamic epistemic logic. (shrink)