European Journal for Philosophy of Science最新文献

Scientists, either working alone or in groups, require rich cognitive, social, cultural, and material environments to accomplish their epistemic aims. There is research in the cognitive sciences that examines intelligent behavior as a function of the environment (“environmental perspectives”), which can be used to examine how scientists integrate “cognitive-cultural” resources as they create environments for problem-solving. In this paper, I advance the position that an expanded framework of distributed cognition can provide conceptual, analytical, and methodological tools to investigate how scientists enhance natural cognitive capacities by creating specific kinds of environments to address their epistemic goals. In a case study of a pioneering neuroengineering lab seeking to understand learning in living networks of neurons, I examine how the researchers integrated conceptual, methodological, and material resources from engineering, neuroscience, and computational science to create different kinds of distributed problem-solving environments that enhanced their natural cognitive capacities, for instance, for reasoning, visualization, abstraction, imagination, and memory, to attain their epistemic aims.

According to High-Dimensional Wavefunction Fundamentalism (HDWF) the wavefunction field evolving in configuration space is all that exists fundamentally. The main argument in favor of HDWF is an argument from separability and locality: separability is a desirable feature of a fundamental metaphysics and HDWF is indeed such a separable metaphysics. Separability in turn is desirable because it is simple and intuitive. Tim Maudlin has recently argued that intuitiveness and simplicity cannot motivate separability. In particular, our intuitions stem from our interactions with the three-dimensional world which is non-separable. Therefore, he concludes, there is nothing else HDWF theorists can appeal to motivate separability. I call this Maudlin’s challenge. The present paper addresses Maudlin’s challenge by showing how the facts that some plurality of entities are separable entail that its constituents are fundamental, for well-motivated notions of fundamentality.

Hempel never met Ramsey, but he knew his work. In his 1958 The Theoretician’s Dilemma: a study in the logic of theory construction, Hempel introduces the term Ramsey sentence, referring to Ramsey’s attempt in Theories to get rid of theoretical terms in formal accounts of scientific theories. In this paper, I draw the attention to another connection between Ramsey’s and Hempel’s works. Hempel’s Deductive-Nomological (DN) account of scientific explanation resembles very closely Ramsey’s account of a certain type of conditional sentences. In the first part of the paper, by introducing a fictional story, I highlight the similarities and differences between the two. In the last part of the paper, I claim that the most relevant difference between Ramsey and Hempel can be used to offer original solutions to Hempel’s Raven Paradox. Two possibilities are presented, arguing that the second, which requires a reconsideration of the formalisation of laws, is the most promising.

Although the philosophical literature on science and values has flourished in recent years, the central concept of “values” has remained ambiguous. This paper endeavors to clarify the nature of values as they are discussed in this literature and then highlights some of the major implications of this clarification. First, it elucidates four major concepts of values and discusses some of their strengths and weaknesses. Second, it clarifies the relationships between these concepts of values and a wide variety of related concepts that are sometimes used interchangeably in the philosophical literature. Third, it argues that this conceptual clarification reveals that much of the literature on science and values has discussed different concepts of values without making these differences clear. The paper illustrates this point by analyzing the different concepts of values at play in different arguments against the value-free ideal and in proposals for managing values. Understanding the literature on values in science as a patchwork of related discourses rather than a single discourse can help researchers more thoughtfully choose a concept of values that best fits their philosophical targets and goals, rather than conflating different discourses because of the common terminology of “values.”

Recent work in explainable artificial intelligence (XAI) attempts to render opaque AI systems understandable through a divide-and-conquer strategy. However, this fails to illuminate how trained AI systems work as a whole. Precisely this kind of functional understanding is needed, though, to satisfy important societal desiderata such as safety. To remedy this situation, we argue, AI researchers should seek mechanistic interpretability, viz. apply coordinated discovery strategies familiar from the life sciences to uncover the functional organisation of complex AI systems. Additionally, theorists should accommodate for the unique costs and benefits of such strategies in their portrayals of XAI research.

Some authors have defended the claim that one needs to be able to define ‘physical coordinate systems’ and ‘observables’ in order to make sense of general relativity. Moreover, in Rovelli (Physical Review D, 65(4), 044017 2002), Rovelli proposes a way of implementing these ideas by making use of a system of satellites that allows defining a set of ‘physical coordinates’, the GPS coordinates. In this article I oppose these views in four ways. First, I defend an alternative way of understanding general relativity which implies that we have a perfectly fine interpretation of the models of the theory even in the absence of ‘physical coordinate systems’. Second, I analyze and challenge the motivations behind the ‘observable’ view. Third, I analyze Rovelli’s proposal and I conclude that it does not allow extracting any physical information from our models that wasn’t available before. Fourth, I draw an analogy between general relativistic spacetimes and Newtonian spacetimes, which allows me to argue that as ‘physical observables’ are not needed in Newtonian spacetime, then neither are they in general relativity. In this sense, I conclude that the ‘observable’ view of general relativity is unmotivated.

I will argue that the development of Feynman diagrams came from the physicist’s capacity of visualizing phenomena and that such visualization-skill contributed to the forming of a narrative explanation in the sense of Wise (2011) and Morgan (2001). The second part of the paper explores the extent to which Feynman diagrams can be considered as weak representations of quantum phenomena. I will review some of the most common arguments in support of the instrumentalist view and I will suggest that a form of weak representation that does not imply ontological commitment can be applied to the diagrams. Such a form of weak representation will be characterized as non-denotative, intentional, and as conveying a physical interpretation through narrative explanations.

The program of reconstructing quantum theory based on information-theoretic principles enjoys much popularity in the foundations of physics. Surprisingly, this endeavor has only received very little attention in philosophy. Here I argue that this should change. This is because, on the one hand, reconstructions can help us to better understand quantum mechanics, and, on the other hand, reconstructions are themselves in need of interpretation. My overall objective, thus, is to motivate the reconstruction program and to show why philosophers should care. My specific aims are threefold. (i) Clarify the relationship between reconstructing and interpreting quantum mechanics, (ii) show how the informational reconstruction of quantum theory puts pressure on standard realist interpretations, (iii) defend the quantum reconstruction program against possible objections.

Simpson’s paradox (SP) is a statistical phenomenon where the association between two variables reverses, disappears, or emerges, after conditioning on a third variable. It has been proposed (by, e.g., Judea Pearl) that SP should be analyzed using the framework of graphical causal models (i.e., causal DAGs) in which SP is diagnosed as a symptom of confounding bias. This paper contends that this confounding-based analysis cannot fully capture SP: there are cases of SP that cannot be explained away in terms of confounding. Previous works have argued that some cases of SP do not require causal analysis at all. Despite being a logically valid counterexample, we argue that this type of cases poses only a limited challenge to Pearl’s analysis of SP. In our view, a more powerful challenge to Pearl comes from cases of SP that do require causal analysis but can arise without confounding. We demonstrate with examples that accidental associations due to genetic drift, the use of inappropriate aggregate variables as causes, and interactions between units (i.e., inter-unit causation) can all give rise to SP of this type. The discussion is also extended to the amalgamation paradox (of which SP is a special form) which can occur due to the use of non-collapsible association measures, in the absence of confounding.

The problem this paper addresses is that scientists have to take normatively charged decisions which can have a significant impact on individual members of the public or the public as a whole. And yet mechanisms to exercise democratic control over them are often absent. Given the normative nature of these choices, this is often perceived to be at odds with basic democratic principles. I show that this problem applies in similar ways to civil service institutions and draw on political philosophy literature on the civil service (e.g. Rosanvallon, 2011; Heath, 2022) to discuss when such normative judgements can nevertheless be said to be democratically legitimate. Concretely, I seek to show that normative judgements in research need not be democratically legitimated in order for science to be democratically legitimate. Indeed, it can be democratically legitimate for scientists to go against the expressed views of the public or political representatives if this is justified in light of, firstly, the role science has been asked to fulfil and, secondly, when it is in line with public institutions’ key principles. This is a counter-position to views currently held in the values in science debate (e.g. by Kitcher, 2011; Intemann, 2015; Schroeder, 2021; Lusk, 2021) which argue that value-laden judgements in science are legitimate if they are aligned with the public’s views or directly decided by public.