Collaborative explanation and biological mechanisms

This paper motivates and outlines a new account of scientific explanation, which I term ‘collaborative explanation.’ My approach is pluralist: I do not claim that all scientific explanations are collaborative, but only that some important scientific explanations are—notably those of complex organic processes like development. Collaborative explanation is closely related to what philosophers of biology term ‘mechanistic explanation’ (e.g., Machamer et al., Craver, 2007). I begin with minimal conditions for mechanisms: complexity, causality, and multilevel structure. Different accounts of mechanistic explanation interpret and prioritize these conditions in different ways. This framework reveals two distinct varieties of mechanistic explanation: causal and constitutive. The two have heretofore been conflated, with philosophical discussion focusing on the former. This paper addresses the imbalance, using a case study of modeling practices in Systems Biology to reveals key features of constitutive mechanistic explanation. I then propose an analysis of this variety of mechanistic explanation, in terms of collaborative concepts, and sketch the outlines of a general theory of collaborative explanation. I conclude with some reflections on the connection between this variety of explanation and social aspects of scientific practice.     

Collaborative explanation,explanatory roles,and scientific explaining in practice

Scientific explanation is a perennial topic in philosophy of science, but the literature has fragmented into specialized discussions in different scientific disciplines. An increasing attention to scientific practice by philosophers is (in part) responsible for this fragmentation and has put pressure on criteria of adequacy for philosophical accounts of explanation, usually demanding some form of pluralism. This commentary examines the arguments offered by Fagan and Woody with respect to explanation and understanding in scientific practice. I begin by scrutinizing Fagan's concept of collaborative explanation, highlighting its distinctive advantages and expressing concern about several of its assumptions. Then I analyze Woody's attempt to reorient discussions of scientific explanation around functional considerations, elaborating on the wider implications of this methodological recommendation. I conclude with reflections on synergies and tensions that emerge when the two papers are juxtaposed and how these draw attention to critical issues that confront ongoing philosophical analyses of scientific explanation.     

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.     

Can the behavioral sciences self-correct? A social epistemic study

Advocates of the self-corrective thesis argue that scientific method will refute false theories and find closer approximations to the truth in the long run. I discuss a contemporary interpretation of this thesis in terms of frequentist statistics in the context of the behavioral sciences. First, I identify experimental replications and systematic aggregation of evidence (meta-analysis) as the self-corrective mechanism. Then, I present a computer simulation study of scientific communities that implement this mechanism to argue that frequentist statistics may converge upon a correct estimate or not depending on the social structure of the community that uses it. Based on this study, I argue that methodological explanations of the “replicability crisis” in psychology are limited and propose an alternative explanation in terms of biases. Finally, I conclude suggesting that scientific self-correction should be understood as an interaction effect between inference methods and social structures.     

Representing with imaginary models: Formats matter

Models such as the simple pendulum, isolated populations, and perfectly rational agents, play a central role in theorising. It is now widely acknowledged that a study of scientific representation should focus on the role of such imaginary entities in scientists’ reasoning. However, the question is most of the time cast as follows: How can fictional or abstract entities represent the phenomena? In this paper, I show that this question is not well posed. First, I clarify the notion of representation, and I emphasise the importance of what I call the “format” of a representation for the inferences agents can draw from it. Then, I show that the very same model can be presented under different formats, which do not enable scientists to perform the same inferences. Assuming that the main function of a representation is to allow one to draw predictions and explanations of the phenomena by reasoning with it, I conclude that imaginary models in abstracto are not used as representations: scientists always reason with formatted representations. Therefore, the problem of scientific representation does not lie in the relationship of imaginary entities with real systems. One should rather focus on the variety of the formats that are used in scientific practice.     

Mathematical formalisms in scientific practice: From denotation to model-based representation

The present paper argues that ‘mature mathematical formalisms’ play a central role in achieving representation via scientific models. A close discussion of two contemporary accounts of how mathematical models apply—the DDI account (according to which representation depends on the successful interplay of denotation, demonstration and interpretation) and the ‘matching model’ account—reveals shortcomings of each, which, it is argued, suggests that scientific representation may be ineliminably heterogeneous in character. In order to achieve a degree of unification that is compatible with successful representation, scientists often rely on the existence of a ‘mature mathematical formalism’, where the latter refers to a—mathematically formulated and physically interpreted—notational system of locally applicable rules that derive from (but need not be reducible to) fundamental theory. As mathematical formalisms undergo a process of elaboration, enrichment, and entrenchment, they come to embody theoretical, ontological, and methodological commitments and assumptions. Since these are enshrined in the formalism itself, they are no longer readily obvious to either the novice or the proficient user. At the same time as formalisms constrain what may be represented, they also function as inferential and interpretative resources.     

A different kind of revolutionary change: transformation from object to process concepts

I propose a new perspective with which to understand scientific revolutions. This is a conversion from an object-only perspective to one that properly treats object and process concepts as distinct kinds. I begin with a re-examination of the Copernican revolution. Recent findings from the history of astronomy suggest that the Copernican revolution was a move from a conceptual framework built around an object concept to one built around a process concept. Drawing from studies in the cognitive sciences, I then show that process concepts are independent of object concepts, grounded in specific regions of the brain and involving unique representational mechanisms. There are cognitive obstacles to the transformation from object to process concepts, and an object bias—a tendency to treat processes as objects—makes this kind of conceptual change difficult. Consequently, transformation from object to process concepts is disruptive and revolutionary. Finally, I explore the implications of this new perspective on scientific revolutions for both the history and philosophy of science.     

关于当代生命科学研究对象的划界、分析与表征的讨论

本文讨论了当代生命科学研究对象的划界、分析与表征的问题,首先围绕三个分主题对国际生物学的历史、哲学和社会学研究协会(ISHPSSB)2011年双年会的相关报告进行介绍并评论:关于基因组学与后基因组学的本体论和认识论问题;关于自然选择理论的扩展性的研究;对于生物学个体性概念的讨论,然后介绍了作者在共生语境下对生物学个体概念的研究进展。文章指出,对同一对象的不同表征模式以及对同一概念的不同分析方式,体现了两种科学传统在当代生命科学中的冲突与交汇;系统生物学力图综合不同研究范式,整合不同生命组织的层次,对其独特的研究方法、概念体系进行分析和讨论,将是未来生物学哲学的热门研究课题。     

“整体子思维”——中国古代“尊道”“贵德”的生存方式与当代启示

基于长期对宇宙生命存在的体悟,中国古代已孕育出以尊道贵德为内涵的整体子思维方式,衍生了整体性的生存哲学。这种整体子思维既包含人们在宇宙中的尊道之理和贵德之质,又蕴藏着社会实践活动中的尊道与贵德合一的存在秩序。而且,人类的生命价值完善就在于以尊道贵德为内容的实践过程。它不但为古代社会秩序建构提供一定的理论依据,同时还成为当代社会有序建构的可能理论来源。     

Abstract

Scientific Culture and Cultural Science (p.1)Today science advances at an astonishingly high speed, and driven by the ideological and industrial revolutions, it even becomes a dominant culture in the ...