Jacob Stegenga

A stereotype is a belief or claim that a group of people has a particular feature. Stereotypes are expressed by sentences that have the form of generic statements, like “Canadians are nice.” Recent work on generics lends new life to understanding generics as statements involving probabilities. I argue that generics (and thus sentences expressing stereotypes) can take one of several forms involving conditional probabilities, and these probabilities have what I call a naturalness requirement. This is the natural probability theory of stereotypes. Each of the two components of the theory entails a family of fallacies that contributes to the spurious reinforcement of stereotypes: inferential slippage within and between the different generic forms, and inferential slippage from facts about frequencies of group traits to beliefs about natural propensities or dispositions of groups. Empirical research suggests that we often commit these fallacies. Moreover, this theory can referee a vitriolic debate between some psychologists, who hold that stereotypes are always false and stereotyping is always wrong, and other psychologists, who hold that stereotypes are often accurate and stereotyping is often reasonable.

Debates about the genuine disease status of controversial diseases rely on intuitions about a range of factors. Adopting tools from experimental philosophy, this paper explores some of the factors that influence judgments about whether low sexual desire should be considered a disease and whether it should be medically treated. Drawing in part on some assumptions underpinning a divide in the literature between viewing low sexual desire as a genuine disease and seeing it as improperly medicalized, we investigate whether health and disease judgments are affected by factors such as an individual’s gender, the cause of the low desire, whether the desire is high or low, and both personal and societal valuations of the condition. Our main findings indicate that (a) the cause of a condition influences whether it is judged a disorder, (b) how the individual values the condition influences whether the condition is seen as a proper target of medical intervention, and (c) perceived dysfunction influences judgments regarding health, disorder classification, medicalization, and medical intervention. Our findings help further illuminate the intricate interplay of factors that influence judgments about health and disease in controversial conditions.

If scientists violate principles and practices of routine science to quickly develop interventions against catastrophic threats, they are engaged in what I call fast science. The magnitude, imminence, and plausibility of a threat justify engaging in and acting on fast science. Yet, that justification is incomplete. I defend two principles to assess fast science, which say: fast science should satisfy as much as possible the reliability-enhancing features of routine science, and the fast science developing an intervention against a threat should not depend on the same problematic assumptions as the fast science which estimates the magnitude, imminence, and plausibility of the threat.

Havstad (2022) argues that the argument from inductive risk for the claim that non-epistemic values have a legitimate role to play in the internal stages of science is deductively valid. She also defends its premises and thus soundness. This is, as far as we are aware, the best reconstruction of the argument from inductive risk in the existing literature. However, there is a small flaw in this reconstruction of the argument from inductive risk which appears to render the argument invalid. This flaw is superficial, and a small amendment to it rescues the claim of validity.

Drug regulation is fraught with inductive risk. Regulators must make a prediction about whether or not an experimental pharmaceutical will be effective and relatively safe when used by typical patients, and such predictions are based on a complex, indeterminate, and incomplete evidential basis. Such inductive risk has important practical consequences. If regulators reject an experimental drug when it in fact has a favourable benefit/harm profile, then a valuable intervention is denied to the public and a company’s material interests are needlessly thwarted. Conversely, if regulators approve an experimental drug when it in fact has an unfavourable benefit/harm profile, then resources are wasted, people are needlessly harmed, and other potentially more effective treatments are underutilized. Given that such regulatory decisions have these practical consequences, non-epistemic values about the relative importance of these consequences impact the way such regulatory decisions are made (similar to the analysis of laboratory studies on the toxic effects of dioxins presented in Douglas [2000]). To balance the competing demands of the pertinent non-epistemic values, regulators must perform what I call an “inductive risk calculus.” At least in the American context this inductive risk calculus is not well-managed.

The value-free ideal in science has been criticised as both unattainable and undesirable. We argue that it can be defended as a practical principle guiding scientific research even if the unattainability and undesirability of a value-free end-state are granted. If a goal is unattainable, then one can separate the desirability of accomplishing the goal from the desirability of pursuing it. We articulate a novel value-free ideal, which holds that scientists should act as if science should be value-free, and we argue that even if a purely value-free science is undesirable, this value-free ideal is desirable to pursue.

The value-free ideal for science holds that values should not influence the core features of scientific reasoning. We defend the difference-to-inference model of value-permeation, which holds that value-permeation in science is problematic when values make a difference to the inferences made about a hypothesis. This view of value-permeation is superior to existing views, and it suggests a corresponding maxim—namely, that scientists should strive to eliminate differences to inference. This maxim is the basis of a novel value-free ideal for science.

Philosophers have committed sins while studying science, it is said – philosophy of science focused on physics to the detriment of biology, reconstructed idealizations of scientific episodes rather than attending to historical details, and focused on theories and concepts to the detriment of experiments. Recent generations of philosophers of science have tried to atone for these sins, and by the 1980s the exculpation was in full swing. Marcel Weber’s Philosophy of Experimental Biology is a zenith mea culpa for philosophy of science: it carefully describes several historical examples from twentieth century biology to address both ‘old’ philosophical topics, like reductionism, inference, and realism, and ‘new’ topics, like discovery, models, and norms. Biology, experiments, history – at last, philosophy of science, free of sin.

The standard view about sex differences in sexual desire is that males are lusty and loose, while females are cool and coy. This is widely believed and is a core premise of some scientific programs like evolutionary psychology. But is it true? A mountain of evidence seems to support the standard view. Yet, this evidence is shot through with methodological and philosophical problems. Developments in the study of sexual desire suggest that some of these problems can be resolved, and when they are, the standard view looks, at best, to be an exaggeration.

It is a plausible speculation that conventional choices in outcome measures might influence the results of meta-analyses. We test that speculation by simulating data from trials on antidepressants. We vary real drug effectiveness while modulating conventional values for outcome measures. We had previously shown that one conventional choice used in meta-analyses of antidepressants falls in a narrow range of values that maximize estimates of effectiveness. Our present analysis investigates why this phenomenon occurs. Moreover, our results suggest the superiority of absolute outcome measures over relative measures. This research program can be extended to test numerous other aspects of clinical research.

There are two competing views regarding the role of mechanistic knowledge in inferences about the effectiveness of interventions. One view holds that inferences about the effectiveness of interventions should be based only on data from population-level studies (often statistical evidence from randomised trials). The other view holds that such inferences must be based in part on mechanistic evidence. The competing views are local principles of inference, the plausibility of which can be assessed by a more general normative principle of inference. Bayesianism tells us to base inferences on both the ‘likelihood’ and the ‘prior’. The likelihood represents statistical evidence. One influence on the prior probability of a hypothesis like ‘d causes x’ is mechanistic knowledge of how d causes x. Thus, reasoning about such inferences by appealing to both statistical and mechanistic evidence is vindicated by our best general theory of inference. The primary contribution of this paper is to assess the merits and weaknesses of the arguments on both sides of the debate, using the Bayesian framework. This analysis lends support to those who argue that we should base our causal inferences about interventions in part on mechanistic evidence.

Medicalisation is a social phenomenon in which conditions that were once under legal, religious, personal or other jurisdictions are brought into the domain of medical authority. Low sexual desire in females has been medicalised, pathologised as a disease, and intervened upon with a range of pharmaceuticals. There are two polarised positions on the medicalisation of low female sexual desire: I call these the mainstream view and the critical view. I assess the central arguments for both positions. Dividing the two positions are opposing models of the aetiology of low female sexual desire. I conclude by suggesting that the balance of arguments supports a modest defence of the critical view regarding the medicalisation of low female sexual desire.

We provide a novel articulation of the epistemic peril of p-hacking using three resources from philosophy: predictivism, Bayesian confirmation theory, and model selection theory. We defend a nuanced position on p-hacking: p-hacking is sometimes, but not always, epistemically pernicious. Our argument requires a novel understanding of Bayesianism, since a standard criticism of Bayesian confirmation theory is that it cannot represent the influence of biased methods. We then turn to pre-analysis plans, a methodological device used to mitigate p-hacking. Some say that pre-analysis plans are epistemically meritorious while others deny this, and in practice pre-analysis plans are often violated. We resolve this debate with a modest defence of pre-analysis plans. Further, we argue that pre-analysis plans can be epistemically relevant even if the plan is not strictly followed—and suggest that allowing for flexible pre-analysis plans may be the best available policy option.

Data from medical research are typically summarized with various types of outcome measures. We present three arguments in favor of absolute over relative outcome measures. The first argument is from cognitive bias: relative measures promote the reference class fallacy and the overestimation of treatment effectiveness. The second argument is decision-theoretic: absolute measures are superior to relative measures for making a decision between interventions. The third argument is causal: interpreted as measures of causal strength, absolute measures satisfy a set of desirable properties, but relative measures do not. Absolute outcome measures outperform relative measures on all counts.

Robustness arguments hold that hypotheses are more likely to be true when they are confirmed by diverse kinds of evidence. Robustness arguments require the confirming evidence to be independent. We identify two kinds of independence appealed to in robustness arguments: ontic independence —when the multiple lines of evidence depend on different materials, assumptions, or theories—and probabilistic independence. Many assume that OI is sufficient for a robustness argument to be warranted. However, we argue that, as typically construed, OI is not a sufficient independence condition for warranting robustness arguments. We show that OI evidence can collectively confirm a hypothesis to a lower degree than individual lines of evidence, contrary to the standard assumption undergirding usual robustness arguments. We employ Bayesian networks to represent the ideal empirical scenario for a robustness argument and a variety of ways in which empirical scenarios can fall short of this ideal.

Measuring the effectiveness of medical interventions faces three epistemological challenges: the choice of good measuring instruments, the use of appropriate analytic measures, and the use of a reliable method of extrapolating measures from an experimental context to a more general context. In practice each of these challenges contributes to overestimating the effectiveness of medical interventions. These challenges suggest the need for corrective normative principles. The instruments employed in clinical research should measure patient-relevant and disease-specific parameters, and should not be sensitive to parameters that are only indirectly relevant. Effectiveness always should be measured and reported in absolute terms (using measures such as 'absolute risk reduction'), and only sometimes should effectiveness also be measured and reported in relative terms (using measures such as 'relative risk reduction')-employment of relative measures promotes an informal fallacy akin to the base-rate fallacy, which can be exploited to exaggerate claims of effectiveness. Finally, extrapolating from research settings to clinical settings should more rigorously take into account possible ways in which the intervention in question can fail to be effective in a target population.

I describe two traditions of philosophical accounts of evidence: one characterizes the notion in terms of signs of success, the other characterizes the notion in terms of conditions of success. The best examples of the former rely on the probability calculus, and have the virtues of generality and theoretical simplicity. The best examples of the latter describe the features of evidence which scientists appeal to in practice, which include general features of methods, such as quality and relevance, and general features of evidence, such as patterns in data, concordance with other evidence, and believability of the evidence. Two infamous episodes from biomedical research help to illustrate these features. Philosophical characterization of these latter features—conditions of success—has the virtue of potential relevance to, and descriptive accuracy of, practices of experimental scientists

A platitude that took hold with Kuhn is that there can be several equally good ways of balancing theoretical virtues for theory choice. Okasha recently modelled theory choice using technical apparatus from the domain of social choice: famously, Arrow showed that no method of social choice can jointly satisfy four desiderata, and each of the desiderata in social choice has an analogue in theory choice. Okasha suggested that one can avoid the Arrow analogue for theory choice by employing a strategy used by Sen in social choice, namely, to enhance the information made available to the choice algorithms. I argue here that, despite Okasha’s claims to the contrary, the information-enhancing strategy is not compelling in the domain of theory choice

Consensus conferences are social techniques which involve bringing together a group of scientific experts, and sometimes also non-experts, in order to increase the public role in science and related policy, to amalgamate diverse and often contradictory evidence for a hypothesis of interest, and to achieve scientific consensus or at least the appearance of consensus among scientists. For consensus conferences that set out to amalgamate evidence, I propose three desiderata: Inclusivity, Constraint, and Evidential Complexity. Two examples suggest that consensus conferences can readily satisfy Inclusivity and Evidential Complexity, but consensus conferences do not as easily satisfy Constraint. I end by discussing the relation between social inclusivity and the three desiderata

Amalgamating evidence of different kinds for the same hypothesis into an overall confirmation is analogous, I argue, to amalgamating individuals’ preferences into a group preference. The latter faces well-known impossibility theorems, most famously “Arrow’s Theorem”. Once the analogy between amalgamating evidence and amalgamating preferences is tight, it is obvious that amalgamating evidence might face a theorem similar to Arrow’s. I prove that this is so, and end by discussing the plausibility of the axioms required for the theorem

The purpose of this chapter is to describe what we see as several important new directions for philosophy of medicine. This recent work (i) takes existing discussions in important and promising new directions, (ii) identifies areas that have not received sufficient and deserved attention to date, and/or (iii) brings together philosophy of medicine with other areas of philosophy (including bioethics, philosophy of psychiatry, and social epistemology). To this end, the next part focuses on what we call the “epistemological turn” in recent work in the philosophy of medicine; the third part addresses new developments in medical research that raise interesting questions for philosophy of medicine; the fourth part is a discussion of philosophical issues within the practice of diagnosis; the fifth part focuses on the recent developments in psychiatric classification and scientific and ethical issues therein, and the final part focuses on the objectivity of medical research.