Randomized Controlled Trials and the Flow of Information

The transferability problem – whether the results of an experiment will transfer to a treatment population – affects not only Randomized Controlled Trials but any type of study. The problem for any given type of study can also, potentially, be addressed to some degree through many different types of study. The transferability problem for a given RCT can be investigated further through another RCT, but the variables to use in the further experiment must be discovered. This suggests we could do better on the epistemological problem of transferability by promoting, in the repeated process of formulating public health guidelines, feedback loops of information from the implementation setting back to researchers who are defining new studies

Epistemic Self-Doubt

When we get evidence that tells us our belief-forming mechanisms may not be reliable this presents a thorny set of questions about whether and how to revise our original belief. This article analyzes aspects of the problem and a variety of approaches to its solution

Bayesian Recalibration: A Generalization

Roush (2009) derived a probabilistic framework for updating one’s first-order degree of belief in q in light of evidence about one’s own reliability in making q-like claims, thus providing the probabilistic rationality constraint for resolving epistemic self-doubt. In this note the argument is generalized to the case where the evidence about one’s reliability or one’s degree of belief in q is uncertain, by development of a Jeffrey-style version of the Re-Calibration equation. This allows illustrative applications of the framework to examples where higher-order evidence is of varying qualities. The equation is applied here to the familiar examples of hypoxia and peer disagreement

Hypochondria and Self-Recalibration

Health anxiety is, among other things, a response to a universal epistemological problem about whether changes in one’s body indicate serious illness, a problem that grows more challenging to the individual with age and with every advance in medical science, detection, and treatment. There is growing evidence that dysfunctional metacognitive beliefs – beliefs about thinking – are the driving factor, with dysfunctional substantive beliefs about the probability of illness a side‐effect, and that Metacognitive Therapy (MCT) is more effective than Cognitive Behavioral Therapy (CBT). However, hypochondria is distinct from other forms of anxiety, I argue, in ways that make some reality‐checking techniques of CBT and MCT of limited usefulness. I propose a Re‐Calibration Technique (RCT) that complements these therapies by focusing on a metacognitive belief that has not been studied: the patient’s presumption of his own personal reliability in judging symptoms, an assumption exposed every time he disagrees with a doctor. I propose a technique whereby a patient keeps a long‐term register of every episode of alarm about symptoms and its resolution, possibly years later. When healthcare‐seeking impulses arise the patient then uses his own track record to re‐calibrate his confidence that medical attention is needed. The new technique allows one to improve self‐judgment about whether one has an illness or not by improving self‐knowledge of one’s own reliability

Coherence, Truthfulness, and Efficiency in Communication

Why should we make our beliefs consistent or, more generally, probabilistically coherent? That it will prevent sure losses in betting and that it will maximize one’s chances of having accurate beliefs are popular answers. However, these justifications are self-centered, focused on the consequences of our coherence for ourselves. I argue that incoherence has consequences for others because it is liable to mislead others, to false beliefs about one’s beliefs and false expectations about one’s behavior. I argue that the moral obligation of truthfulness thus constrains us to either conform to the logic our audience assumes we use, educate them in a new logic, or give notice that one will do neither. This does not show that probabilistic coherence is uniquely suited to making truthful communication possible, but I argue that classical probabilistic coherence is superior to other logics for maximizing efficiency in communication

Sensitivity and Closure

This paper argues that if knowledge is defined in terms of probabilistic tracking then the benefits of epistemic closure follow without the addition of a closure clause. (This updates my definition of knowledge in Tracking Truth 2005.) An important condition on this result is found in "Closure Failure and Scientific Inquiry" (2017)

The Difference Between Knowledge and Understanding

In the aftermath of Gettier’s examples, knowledge came to be thought of as what you would have if in addition to a true belief and your favorite epistemic goody, such as justifiedness, you also were ungettiered, and the theory of knowledge was frequently equated, especially by its detractors, with the project of pinning down that extra bit. It would follow that knowledge contributes something distinctive that makes it indispensable in our pantheon of epistemic concepts only if avoiding gettierization has a value that can be explained without presupposing the value of knowledge. Tracking-type knowledge has a value that no other logically possible conditions on true belief does. As an Evolutionarily Stable Strategy it preserves appropriate belief states through time and changing circumstances. If we characterize gettierization through the concept of relevance matching, then we see that avoiding gettierization has a value independent of that of knowledge, namely, understanding, and that it is unnecessary to add a clause to the tracking conditions to make them suppress gettierization directly, though fallibly. The bright line of value is between gettierization avoidance and understanding on the one hand and knowledge on the other, and so should be the bright line defining concepts. The concept of relevance matching is key to a definition of what it is to understand why p is true, as opposed merely to knowing that p is true. Perfect tracking implies perfect relevance matching, so knowledge and understanding are intimately connected but understanding also requires that one own states that accomplish the relevance matching rather than achieving it vicariously. The theory of understanding based on relevance matching implies that understanding requires appreciation of not only p but its connections to other matters, and explains how it is possible to know that p is true without understanding why. The view implies that understanding is literally simulation, and is suggestive about understanding other minds

Constructive Empiricism and the Role of Social Values in Science

One of the most common criticisms one hears of the idea of granting a legitimate role for social values in theory choice in science is that it just doesn’t make sense to regard social preferences as relevant to the truth or to the way things are. “What is at issue,” wrote Susan Haack, is “whether it is possible to derive an ‘is’ from an ‘ought.’ ” One can see that this is not possible, she concludes, “as soon as one expresses it plainly: that propositions about what states of affairs are desirable or deplorable could be evidence that things are, or are not, so” (Haack 1993a, 35, emphasis in original). The purpose of this chapter is not to determine whether this widespread view is correct, but rather to show that even if we grant it (which I do), we may still consistently believe that social values have a legitimate role in theory choice in science. I will defend this conclusion by outlining a view about social values and theory choice that is available to a Constructive Empiricist anti-realist, but not to a realist

Positive Relevance Defended

This paper addresses two examples due to Peter Achinstein purporting to show that the positive relevance view of evidence is too strong, that is, that evidence need not raise the probability of what it is evidence for. The first example can work only if it makes a false assumption. The second example fails because what Achinstein claims is evidence is redundant with information we already have. Without these examples Achinstein is left without motivation for his account of evidence, which uses the concept of explanation in addition to that of probability