自 1934 年成立以来，科学哲学及其赞助社会科学哲学协会一直致力于从科学哲学的不同角度推进研究和自由讨论。该杂志包含散文、讨论文章和书评。

Since its establishment in 1934, the journal "Philosophy of Science" and its sponsoring society for the philosophy of social science have been dedicated to advancing research and free discussion from various perspectives of the philosophy of science. The journal includes essays, discussion articles, and book reviews.

自 1934 年成立以来，《Philosophy of Science》及其赞助的社会科学哲学协会一直致力于从科学哲学的不同角度推动研究和自由讨论。该杂志包含散文、讨论文章和书评。

Philosophy of Science

Stewart Shapiro

Philosophy of Mathematics. Paul Benacerraf , Hilary Putnam

“According to [Bayesian] models” in cognitive neuroscience, says a recent textbook, “the human mind behaves like a capable data scientist.” Do they? That is, do such models show we are rational? I argue that Bayesian models of cognition, perhaps surprisingly, don’t and indeed can’t show that we are Bayes-rational. The key reason is that they appeal to approximations, a fact that carries significant implications. After outlining the argument, I critique two responses, seen in recent cognitive neuroscience. One says that the mind can be seen asapproximatelyBayes-rational, while the other reconceives norms of rationality.

Arnon Levy

Philos. sci.

Can Bayesian Models of Cognition Show That We Are (Epistemically) Rational?

Representation has been one of the main themes in the recent discussion of models. Several authors have argued for a pragmatic approach to representation that takes users and their interpretations into account. It appears to me, however, that this emphasis on representation places excessive limitations on our view of models and their epistemic value. Models should rather be thought of as epistemic artifacts through which we gain knowledge in diverse ways. Approaching models this way stresses their materiality and media‐specificity. Focusing on models as multifunctional artifacts releases them from any preestablished and fixed representational relationships and leads me to argue for a twofold approach to representation.

Tarja Knuuttila

Models, Representation, and Mediation

I argue that Open Science as currently conceptualized and implemented does not take sufficient account of epistemic diversity within research. I use three case studies to exemplify how Open Science threatens to privilege some forms of inquiry over others, thus exasperating divides within and across systems of practice, and overlooking important sources and forms of epistemic diversity. Building on insights from pluralist philosophy, I then identify four aspects of diverse research practices that should serve as reference points for debates around Open Science: (1) specificity to local conditions, (2) entrenchment within repertoires, (3) permeability to newcomers, and (4) demarcation strategies.

Sabina Leonelli

Open Science and Epistemic Diversity: Friends or Foes?

Cognitive psychologists, like biologists, frequently describe mechanisms when explaining phenomena. Unlike biologists, who can often trace material transformations to identify operations, psychologists face a more daunting task in identifying operations that transform information. Behavior provides little guidance as to the nature of the operations involved. While not itself revealing the operations, identification of brain areas involved in psychological mechanisms can help constrain attempts to characterize the operations. In current memory research, evidence that the same brain areas are involved in what are often taken to be different memory phenomena or in other cognitive phenomena is playing such a heuristic function.

William Bechtel

Mechanisms in Cognitive Psychology: What Are the Operations?

This essay has two goals. The first is to define the behavioristic study of natural events and to classify behavior. The second is to stress the importance of the concept of purpose. Given any object, relatively abstracted from its surroundings for study, the behavioristic approach consists in the examination of the output of the object and of the relations of this output to the input. By output is meant any change produced in the surroundings by the object. By input, conversely, is meant any event external to the object that modifies this object in any manner. The above statement of what is meant by the behavioristic method of study omits the specific structure and the instrinsic organization of the object. This omission is fundamental because on it is based the distinction between the behavioristic and the alternative functional method of study. In a functional analysis, as opposed to a behavioristic approach, the main goal is the intrinsic organization of the entity studied, its structure and its properties; the relations between the object and the surroundings are relatively incidental. From this definition of the behavioristic method a broad definition of behavior ensues. By behavior is meant any change of an entity with respect to its surroundings. This change may be largely an output from the object, the input being then minimal, remote or irrelevant; or else the change may be immediately traceable to a certain input. Accordingly, any modification of an object, detectable externally, may be denoted as behavior. The term would be, therefore, too extensive for usefulness were it not that it may be restricted by apposite adjectives — i.e., that behavior may be classified. The consideration of the changes of energy involved in behavior affords a basis for classification. Active behavior is that in which the object is the source of the output energy involved in a given specific reaction. The object may store energy supplied by a remote or relatively immediate input, but the input does not energize the output directly. In passive behavior, on the contrary, the object is not a source of energy; all the energy in the output can be traced to the immediate input (e.g., the throwing of an object), or else the object may control energy which remains external to it throughout the reaction (e.g., the soaring flight of a bird). Active behavior may be subdivided into two classes: purposeless (or random) and purposeful. The term purposeful is meant to denote that the act or behavior may be interpreted as directed to the attainment of a goal — i.e., to a final condition in which the behaving object reaches a definite correlation in time or in space with respect to another object or event. Purposeless behavior then is that which is not interpreted as directed to a goal. The vagueness of the words may be interpreted as used above might be considered so great that the distinction would be useless. Yet the recognition that behavior may sometimes be purposeful is unavoidable and useful, as follows. — The basis of the concept of purpose is the awareness of voluntary activity. Now, the purpose of voluntary acts is not a matter of arbitrary interpretation but a physiological fact. When we perform a voluntary action what we select voluntarily is a specific purpose, not a specific movement. Thus, if we decide to take a glass containing water and carry it to our mouth we do not command certain muscles to contract to a certain degree and in a certain sequence; we merely trip the purpose and the reaction follows automatically. Indeed, experimental physiology has so far been largely incapable of explaining the mechanism of voluntary activity. We submit that this failure is due to the fact that when an experimenter stimu-

Arturo Rosenblueth

Norbert Wiener

Julian Bigelow

Behavior, Purpose and Teleology

While philosophers have focused on epistemological and ethical challenges of using artificial intelligence (AI) in science, scientists have focused largely on opportunities. I argue that this disconnect between philosophical pessimism and scientific optimism is driven by failures to critically examine the practice of AI-infused science. To appreciate the epistemic justification for AI-powered breakthroughs, philosophers must analyze the role of AI as part of a wider process of discovery. I demonstrate the importance of this with two cases from the scientific literature, and show that epistemic opacity need not diminish AI’s capacity to lead scientists to significant and justifiable breakthroughs.

Eamon Duede

Deep Learning Opacity in Scientific Discovery

The Perception of the Visual World. James J. Gibson

The evolutionary problem of the units of selection has elicited a good deal of conceptual work from philosophers. We review this work to determine where the issues now stand.

Elliott Sober

David Sloan Wilson

A Critical Review of Philosophical Work on the Units of Selection Problem

Standard articulations of Inference to the Best Explanation (IBE) imply the uniqueness claim that exactly one explanation should be inferred in response to an explanandum. This claim has been challenged as being both too strong (sometimes agnosticism between candidate explanatory hypotheses seems the rational conclusion) and too weak (in cases where multiple hypotheses might sensibly be conjointly inferred). I propose a novel interpretation of IBE that retains the uniqueness claim while also allowing for agnostic and conjunctive conclusions. I then argue that a particular probabilistic explication of explanatory goodness helpfully guides us in navigating such options when using IBE.

Jonah N. Schupbach

On the Logical Structure of Best Explanations

Thomas Polger and Lawrence Shapiro claim that unlike human-made artifacts cases of multiple realization in naturally occurring systems are uncommon. Drawing on cases from systems biology, I argue that multiple realization in naturally occurring systems is not as uncommon as Polger and Shapiro initially thought. The relevant cases, which I draw from systems biology, involve generalizable design principles called network motifs, which recur in different organisms and species and perform specific functions. I show that network motifs with entirely different underlying causal structures can perform the same function of interest. The article also considers the scope problem of multiple realization.

Wei Fang

Multiple Realization in Systems Biology

Philosophers and psychologists have long worried that the human tendency to anthropomorphize leads us to err in our understanding of nonhuman minds. This tendency, which I call intuitive anthropomorphism, is a heuristic used by our unconscious folk psychology to understand nonhuman animals. The dominant understanding of intuitive anthropomorphism underestimates its complexity. If we want to understand and control intuitive anthropomorphism, we must treat it as a cognitive bias and look to the empirical evidence. This evidence suggests that the most common control for intuitive anthropomorphism, Morgan’s Canon, should be rejected, while others are incomplete. It also suggests new approaches.

Mike Dacey

Anthropomorphism as Cognitive Bias

Constructive field theory aims to rigorously construct concrete, nontrivial solutions to Lagrangians used in particle physics. I examine the relationship of solutions in constructive field theory to both axiomatic and Lagrangian quantum field theory (QFT). I argue that Lagrangian QFT provides conditions for what counts as a successful constructive solution and other information that guides constructive field theorists to solutions. Solutions matter because they describe the behavior of QFT systems and thus what QFT says the world is like. Constructive field theory clarifies existing disputes about which parts of QFT are philosophically relevant and how rigor relates to these disputes.

Leif Hancox-Li

Solutions in Constructive Field Theory

Polarization is a topic of intense interest among social scientists, but there is significant disagreement regarding the character of the phenomenon and little understanding of underlying mechanics. A first problem, we argue, is that polarization appears in the literature as not one concept but many. In the first part of the article, we distinguish nine phenomena that may be considered polarization, with suggestions of appropriate measures for each. In the second part of the article, we apply this analysis to evaluate the types of polarization generated by the three major families of computational models proposing specific mechanisms of opinion polarization.

Aaron Bramson

Patrick Grim

Daniel J. Singer

William J. Berger

Graham Sack

Steven Fisher

Carissa Flocken

Bennett Holman

Understanding Polarization: Meanings, Measures, and Model Evaluation

Recent claims, mainly from computer scientists, concerning a largely automated and model-free data-intensive science have been criticized by several philosophers of science. The debate suffers from lack of detail regarding the actual methods used in data-intensive science and in which ways these presuppose theoretical assumptions. I examine two widely used algorithms, classificatory trees and nonparametric regression, and argue that they are theory laden in an external sense, regarding the framing of research questions, but not in an internal sense, concerning the causal structure of the examined phenomenon. With respect to the novelty of data-intensive science, I draw an analogy to exploratory experimentation.

Wolfgang Pietsch

Aspects of Theory-Ladenness in Data-Intensive Science

Experiments are commonly thought to have epistemic privilege over simulations. Two ideas underpin this belief: first, experiments generate greater inferential power than simulations, and second, simulations cannot surprise us the way experiments can. In this article I argue that neither of these claims is true of experiments versus simulations in general. We should give up the common practice of resting in-principle judgments about the epistemic value of cases of scientific inquiry on whether we classify those cases as experiments or simulations, per se. To the extent that either methodology puts researchers in a privileged epistemic position, this is context sensitive.

短名	Philos. Sci.
Journal Impact	1.37
国际分区	HISTORY & PHILOSOPHY OF SCIENCE(Q1)
期刊索引	SCI Q1中科院 2 区

ISSN	0031-8248, 1539-767X
h-index	84
国内分区	哲学(2区)哲学科学史与科学哲学(2区)

涉及主题	哲学认识论计算机科学数学物理量子力学心理学生物政治学法学社会学经济
出版信息	出版商: Cambridge University Press，出版周期: Bimonthly，期刊类型: journal，开源期刊: 非开源
基本数据	创刊年份: 1996，原创研究文献占比: 97.62%，自引率:14.30%， Gold OA占比: 22.36%
平均审稿周期	网友分享经验：较慢,6-12周
平均录用比例	网友分享经验：容易

Philosophy of Science

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On the Logical Structure of Best Explanations

Can Bayesian Models of Cognition Show That We Are (Epistemically) Rational?

Deep Learning Opacity in Scientific Discovery

Open Science and Epistemic Diversity: Friends or Foes?

Multiple Realization in Systems Biology

Anthropomorphism as Cognitive Bias

Solutions in Constructive Field Theory

Understanding Polarization: Meanings, Measures, and Model Evaluation

Aspects of Theory-Ladenness in Data-Intensive Science

Experiments, Simulations, and Epistemic Privilege

Philosophy of Mathematics. Paul Benacerraf , Hilary Putnam

Can Bayesian Models of Cognition Show That We Are (Epistemically) Rational?

Models, Representation, and Mediation

Open Science and Epistemic Diversity: Friends or Foes?

Mechanisms in Cognitive Psychology: What Are the Operations?

Behavior, Purpose and Teleology

Deep Learning Opacity in Scientific Discovery

The Perception of the Visual World. James J. Gibson

A Critical Review of Philosophical Work on the Units of Selection Problem

On the Logical Structure of Best Explanations