Uncomfortable bedfellows: Objective quantum Bayesianism and the von Neumann–Lüders projection postulate

In this paper I critically evaluate the justification of the von Neumann–Lüders projection postulate for state changes in projective measurement contexts from the objective quantum Bayesian perspective. I point out that the justification provided so far for the von Neumann–Lüders projection postulate is insufficient. I argue that the best way to correct this problem is to make an assumption, Benign Realism, which is contradictory to the objective quantum Bayesian interpretation of quantum states.     

Deflationary representation,inference, and practice

This paper defends the deflationary character of two recent views regarding scientific representation, namely RIG Hughes' DDI model and the inferential conception. It is first argued that these views' deflationism is akin to the homonymous position in discussions regarding the nature of truth. There, we are invited to consider the platitudes that the predicate “true” obeys at the level of practice, disregarding any deeper, or more substantive, account of its nature. More generally, for any concept X, a deflationary approach is then defined in opposition to a substantive approach, where a substantive approach to X is an analysis of X in terms of some property P, or relation R, accounting for and explaining the standard use of X. It then becomes possible to characterize a deflationary view of scientific representation in three distinct senses, namely: a “no-theory” view, a “minimalist” view, and a “use-based” view—in line with three standard deflationary responses in the philosophical literature on truth. It is then argued that both the DDI model and the inferential conception may be suitably understood in any of these three different senses. The application of these deflationary ‘hermeneutics’ moreover yields significant improvements on the DDI model, which bring it closer to the inferential conception. It is finally argued that what these approaches have in common—the key to any deflationary account of scientific representation—is the denial that scientific representation may be ultimately reduced to any substantive explanatory property of sources, or targets, or their relations.     

Public scientific testimony in the scientific image

In this paper, I draw on philosophy of science to address a challenge for science communication. Empirical research indicates that some people who trust a meteorologist's report that they are in the path of a storm do not trust a climate scientist's report that we are on a path to global warming. Such selective skepticism about climate science exemplifies a more general challenge:

The Challenge of Selective UptakeLaypersons who generally accept public scientific testimony nevertheless fail to accept public scientific testimony concerning select, equally well warranted, scientific hypotheses.

A prominent response arising from the novel interdisciplinary science of science communication is a principle called Consensus Reporting. According to this principle, science reporters should, whenever feasible, report the scientific consensus or lack thereof for a reported scientific view.However, philosophy of science may offer a different perspective on the issue. This perspective is critical insofar as it indicates some inadequacies of Consensus Reporting. But it is also constructive insofar as it guides the development of an alternative principle, Justification Reporting, according to which science reporters should, whenever feasible, report aspects of the nature and strength of scientific justification or lack thereof for a reported scientific view. A central difference between these proposals is that Consensus Reporting appeals to the authority of the scientists whereas Justification Reporting appeals to the authority of scientific justification. As such, Justification Reporting reflects the image of science.The paper considers the philosophical and empirical motivation for Justification Reporting and its limitations. This includes prospects and problems for implementing it in a way that addresses The Challenge of Selective Uptake. From a methodological point of view, the paper illustrates how empirically informed philosophy of science may help address challenges for science communication.     

Extensional scientific realism vs. intensional scientific realism

Extensional scientific realism is the view that each believable scientific theory is supported by the unique first-order evidence for it and that if we want to believe that it is true, we should rely on its unique first-order evidence. In contrast, intensional scientific realism is the view that all believable scientific theories have a common feature and that we should rely on it to determine whether a theory is believable or not. Fitzpatrick argues that extensional realism is immune, while intensional realism is not, to the pessimistic induction. I reply that if extensional realism overcomes the pessimistic induction at all, that is because it implicitly relies on the theoretical resource of intensional realism. I also argue that extensional realism, by nature, cannot embed a criterion for distinguishing between believable and unbelievable theories.     

Asymptotic normal and bootstrap inference in structural VAR analysis

The aim of the paper is to examine the performance of bootstrap and asymptotic parametric inference methods in structural VAR analysis. The results obtained through a Monte Carlo experiment suggest that the two approaches are largely equivalent in most, but not all, cases. While the asymptotic method turns out to be surprisingly robust with respect to the distribution of the errors, the bootstrap does deliver results superior in terms of both length of the confidence interval and coverage when highly non-linear statistics (such as the components of the variance of the forecast error) are considered.     

Bias and values in scientific research

When interests and preferences of researchers or their sponsors cause bias in experimental design, data interpretation or dissemination of research results, we normally think of it as an epistemic shortcoming. But as a result of the debate on science and values, the idea that all ‘extra-scientific’ influences on research could be singled out and separated from pure science is now widely believed to be an illusion. I argue that nonetheless, there are cases in which research is rightfully regarded as epistemologically deficient due to the influence of preferences on its outcomes. I present examples from biomedical research and offer an analysis in terms of social epistemology.     

Subjectivity and emotion in scientific research

A persistent puzzle for philosophers of science is the well-documented appeal made by scientists to their aesthetic emotions in the course of scientific research. Emotions are usually viewed as irremediably subjective, and thus of no epistemological interest. Yet, by denying an epistemic role for scientists’ emotional dispositions, philosophers find themselves in the awkward position of ignoring phenomena which scientists themselves often insist are of importance. This paper suggests a possible solution to this puzzle by challenging the wholesale identification of emotion with subjectivity. The proposed method is a naturalistic and externalist one, calling for empirical investigation into the intersubjective processes by which scientists’ emotional dispositions become refined and attuned to specific objects of attention. The proposal is developed through a critical discussion of Michael Polanyi’s theory of scientific passions, as well as plant geneticist Barbara McClintock’s celebrated “feeling for the organism.”     

Priority and privilege in scientific discovery

The priority rule in science has been interpreted as a behavior regulator for the scientific community, which benefits society by adequately structuring the distribution of intellectual labor across pre-existing research programs. Further, it has been lauded as an intuitively fair way to reward scientists for their contributions, as a special case of society’s “grand reward scheme”. However, we will argue that the current formal framework utilized to model the priority rule idealizes away important aspects of credit attribution, and does so in a way that impacts the conclusions drawn regarding its function in scientific communities. In particular, we consider the social dynamics of credit attribution in order to show that the priority rule can foster structural disadvantages in socially diverse science, as well as drive the distribution of intellectual labor away from optimal.     

Transfer and templates in scientific modelling

The notion of template has been advocated by Paul Humphreys and others as an illuminating unit of analysis in the philosophy of scientific modelling. Templates are supposed to have the dual functions of representing target systems and of facilitating quantitative manipulation. A resulting worry is that wide-ranging cross-disciplinary use of templates might compromise their representational function and reduce them to mere formalisms. In this paper, we argue that templates are valuable units of analysis in reconstructing cross-disciplinary modelling. Central to our discussion are the ways in which Lotka-Volterra models are used to analyse processes of technology diffusion. We illuminate both the similarities and differences between contributions to this case of cross-disciplinary modelling by reconstructing them as transfer of a template, without reducing the template to a mere formalism or a computational model. This requires differentiating the interpretation of templates from that of the models based on them. This differentiation allows us to claim that the LV models of technology diffusion that we review are the result of template transfer - conformist in some contributions, creative in others.     

Hempel on scientific understanding

Hempel seems to hold the following three views: (H1) Understanding is pragmatic/relativistic: Whether one understands why X happened in terms of Explanation E depends on one's beliefs and cognitive abilities; (H2) Whether a scientific explanation is good, just like whether a mathematical proof is good, is a nonpragmatic and objective issue independent of the beliefs or cognitive abilities of individuals; (H3) The goal of scientific explanation is understanding: A good scientific explanation is the one that provides understanding. Apparently, H1, H2, and H3 cannot be all true. Some philosophers think that Hempel is inconsistent, while some others claim that Hempel does not actually hold H3. I argue that Hempel does hold H3 and that he can consistently hold all of H1, H2, and H3 if he endorses what I call the “understanding argument.” I also show how attributing the understanding argument to Hempel can make more sense of his D-N model and his philosophical analysis of the pragmatic aspects of scientific explanation.     

Value-entanglement and the integrity of scientific research

Throughout much of the 20th century, philosophers of science maintained a position known as the value-free ideal, which holds that non-epistemic (e.g., moral, social, political, or economic) values should not influence the evaluation and acceptance of scientific results. In the last few decades, many philosophers of science have rejected this position by arguing that non-epistemic values can and should play an important role in scientific judgment and decision-making in a variety of contexts, including the evaluation and acceptance of scientific results. Rejecting the value-free ideal creates some new and vexing problems, however. One of these is that relinquishing this philosophical doctrine may undermine the integrity of scientific research if practicing scientists decide to allow non-epistemic values to impact their judgment and decision-making. A number of prominent philosophers of science have sought to show how one can reject the value-free ideal without compromising the integrity of scientific research. In this paper, we examine and critique their views and offer our own proposal for protecting and promoting scientific integrity. We argue that the literature on research ethics and its focus on adherence to norms, rules, policies, and procedures that together promote the aims of science can provide a promising foundation for building an account of scientific integrity. These norms, rules, policies, and procedures provide a level of specificity that is lacking in most philosophical discussions of science and values, and they suggest an important set of tasks for those working in science and values—namely, assessing, justifying, and prioritizing them. Thus, we argue that bringing together the literature on research ethics with the literature on science and values will enrich both areas and generate a more sophisticated and detailed account of scientific integrity.