Understanding beyond grasping propositions: A discussion of chess and fish

In this paper, we argue that, contra Strevens (2013), understanding in the sciences is sometimes partially constituted by the possession of abilities; hence, it is not (in such cases) exhausted by the understander's bearing a particular psychological or epistemic relationship to some set of structured propositions. Specifically, the case will be made that one does not really understand why a modeled phenomenon occurred unless one has the ability to actually work through (meaning run and grasp at each step) a model simulation of the underlying dynamic.     

Explanation,understanding, and unrealistic models

How can false models be explanatory? And how can they help us to understand the way the world works? Sometimes scientists have little hope of building models that approximate the world they observe. Even in such cases, I argue, the models they build can have explanatory import. The basic idea is that scientists provide causal explanations of why the regularity entailed by an abstract and idealized model fails to obtain. They do so by relaxing some of its unrealistic assumptions. This method of ‘explanation by relaxation’ captures the explanatory import of some important models in economics. I contrast this method with the accounts that Daniel Hausman and Nancy Cartwright have provided of explanation in economics. Their accounts are unsatisfactory because they require that the economic model regularities obtain, which is rarely the case. I go on to argue that counterfactual regularities play a central role in achieving ‘understanding by relaxation.’ This has a surprising implication for the relation between explanation and understanding: Achieving scientific understanding does not require the ability to explain observed regularities.     

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.     

Understanding and explanation: Living apart together?

This introductory essay to the special issue on ‘understanding without explanation’ provides a review of the debate in philosophy of science concerning the relation between scientific explanation and understanding, and an overview of the themes addressed in the papers included in this issue. In recent years, the traditional consensus that understanding is a philosophically irrelevant by-product of scientific explanations has given way to a lively debate about the relation between understanding and explanation. The papers in this issue defend or challenge the idea that understanding is a cognitive achievement in its own right, rather than simply a derivative or side-effect of scientific explanations.     

Colligation in modelling practices: From Whewell’s tides to the San Francisco Bay Model

“Colligation”, a term first introduced in philosophy of science by William Whewell (1840), today sparks a renewed interest beyond Whewell scholarship. In this paper, we argue that adopting the notion of colligation in current debates in philosophy of science can contribute to our understanding of scientific models. Specifically, studying colligation allows us to have a better grasp of how integrating diverse model components (empirical data, theory, useful idealization, visual and other representational resources) in a creative way may produce novel generalizations about the phenomenon investigated. Our argument is built both on the theoretical appraisal of Whewell’s philosophy of science and the historical rehabilitation of his scientific work on tides. Adopting a philosophy of science in practice perspective, we show how colligation emerged from Whewell’s empirical work on tides. The production of idealized maps (“cotidal maps”) illustrates the unifying and creative power of the activity of colligating in scientific practice. We show the importance of colligation in modelling practices more generally by looking at its epistemic role in the construction of the San Francisco Bay Model.     

Scientific progress: Knowledge versus understanding

What is scientific progress? On Alexander Bird's epistemic account of scientific progress, an episode in science is progressive precisely when there is more scientific knowledge at the end of the episode than at the beginning. Using Bird's epistemic account as a foil, this paper develops an alternative understanding-based account on which an episode in science is progressive precisely when scientists grasp how to correctly explain or predict more aspects of the world at the end of the episode than at the beginning. This account is shown to be superior to the epistemic account by examining cases in which knowledge and understanding come apart. In these cases, it is argued that scientific progress matches increases in scientific understanding rather than accumulations of knowledge. In addition, considerations having to do with minimalist idealizations, pragmatic virtues, and epistemic value all favor this understanding-based account over its epistemic counterpart.     

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.     

Permissible idealizations for the purpose of prediction

Every model leaves out or distorts some factors that are causally connected to its target phenomenon—the phenomenon that it seeks to predict or explain. If we want to make predictions, and we want to base decisions on those predictions, what is it safe to omit or to simplify, and what ought a causal model to describe fully and correctly? A schematic answer: the factors that matter are those that make a difference to the target phenomenon. There are several ways to understand differencemaking. This paper advances a view as to which is the most relevant to the forecaster and the decision-maker. It turns out that the right notion of differencemaking for thinking about idealization in prediction is also the right notion for thinking about idealization in explanation; this suggests a carefully circumscribed version of Hempel’s famous thesis that there is a symmetry between explanation and prediction.     

A pragmatic account of mechanistic artifact explanation

A mechanistic artifact explanation is an explanation that accounts for an artifact behavior by describing the underlying mechanism. The article shows that there are different kinds of mechanistic artifact explanation: top-down and bottom-up explanation, and I also distinguish between less and more inclusive top-down explanations. To illustrate these different kinds of explanation, the behavior of a simple, fictional artifact is explained in different ways. I defend that which explanation is ideal, depends on pragmatic factors (e.g., the background knowledge of the explainee and the specific goal for which the explanation will be used). For each kind of explanation, the situations, goals and interests for which it is most appropriate are specified, resulting in a pragmatic theory of mechanistic artifact explanation. This theory is compared to Jeroen de Ridder’s account of the pragmatics of mechanistic artifact explanation.     

Scrutinizing thing knowledge

In his book Thing Knowledge Davis Baird argues that our accustomed understanding of knowledge as justified true beliefs is not enough to understand progress in science and technology. To be more accurate he argues that scientific instruments are to be seen as a form of “objective knowledge” in the sense of Karl Popper.I want to examine if this idea is plausible. In a first step I want to show that this proposal implies that nearly all man-made artifacts are materialized objective knowledge. I argue that this radical change in our concept of knowledge demands strong reasons and that Baird does not give them. I take a look at the strongest strand of arguments of Baird's book—the arguments from cognitive autonomy—and conclude that they do not suffice to make Baird's view of scientific instruments tenable.     

The estimative functions of physical theory

Attention is drawn to two closely related functions served by scientific theory (called here ‘mensurative’ and ‘reconstructive estimation’) which are of fundamental importance in physical science but as yet little discussed in philosophy. As indicated by their names, they constitute the theoretical basis of physical measurements.After analysing some historically important examples and sketching the historical development of these ideas, this paper examines the similarities and differences between the estimate functions of theory and such well-known functions as prediction and explanation. The pervasiveness of the estimative functions even when theory is but poorly developed is noted; and some of the problems raised by the physical equivalence of the measuring instrument to the object measured are discussed. The relations of estimation to ‘reductive logic’ are also considered.We then apply this understanding of estimative functioning to distinguishing experimental errors from those genuine anomalies which result in discovery. It is also shown that there can be no facts established nor any verification of predictions except on the basis of valid estimates derived, in turn, from antecedently accepted theories.     

Circularity,indispensability, and mathematical explanation in science

In this paper I consider the objection that the Enhanced Indispensability Argument (EIA) is circular and hence fails to support mathematical platonism. The objection is that the explanandum in any mathematical explanation of a physical phenomenon is itself identified using mathematical concepts. Hence the explanandum is only genuine if the truth of some mathematical theory is already presupposed. I argue that this objection deserves to be taken seriously, that it does sometimes undermine support for EIA, but that there is no reason to think that circularity is an unavoidable feature of mathematical explanation in science.     

Associationism without associative links: Thomas Brown and the associationist project

There are two roles that association played in 18th–19th century associationism. The first dominates modern understanding of the history of the concept: association is a causal link posited to explain why ideas come in the sequence they do. The second has been ignored: association is merely regularity in the trains of thought, and the target of explanation. The view of association as regularity arose in several forms throughout the tradition, but Thomas Brown (1778–1820) makes the distinction explicit. He argues that there is no associative link, and association is mere sequence. I trace this view of association through the tradition, and consider its implications: Brown's views, in particular, motivate a rethinking of the associationist tradition in psychology. Associationism was a project united by a shared explanandum phenomenon, rather than a theory united by a shared theoretical posit.     

General Relativity and the Standard Model: Why evidence for one does not disconfirm the other

General Relativity and the Standard Model often are touted as the most rigorously and extensively confirmed scientific hypotheses of all time. Nonetheless, these theories appear to have consequences that are inconsistent with evidence about phenomena for which, respectively, quantum effects and gravity matter. This paper suggests an explanation for why the theories are not disconfirmed by such evidence. The key to this explanation is an approach to scientific hypotheses that allows their actual content to differ from their apparent content. This approach does not appeal to ceteris-paribus qualifiers or counterfactuals or similarity relations. And it helps to explain why some highly idealized hypotheses are not treated in the way that a thoroughly refuted theory is treated but instead as hypotheses with limited domains of applicability.     

Understanding as explanatory knowledge: The case of Bjorken scaling

In this paper, we develop and refine the idea that understanding is a species of explanatory knowledge. Specifically, we defend the idea that S understands why p if and only if S knows that p, and, for some q, S’s true belief that q correctly explains p is produced/maintained by reliable explanatory evaluation. We then show how this model explains the reception of James Bjorken’s explanation of scaling by the broader physics community in the late 1960s and early 1970s. The historical episode is interesting because Bjorken’s explanation initially did not provide understanding to other physicists, but was subsequently deemed intelligible when Feynman provided a physical interpretation that led to experimental tests that vindicated Bjorken’s model. Finally, we argue that other philosophical models of scientific understanding are best construed as limiting cases of our more general model.     

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.     

Magnification: how to turn a spyglass into an astronomical telescope

According to the received view, the first spyglass was assembled without any theory of how the instrument magnifies. Galileo, who was the first to use the device as a scientific instrument, improved the power of magnification up to 30 times. How did he accomplish this feat? Galileo does not tell us what he did. We hold that such improvement of magnification is too intricate a problem to be solved by trial and error, accidentally stumbling upon a complex procedure. We construct a plausibility argument and submit that Galileo had a theory of the telescope. He could develop it by analogical reasoning based on the phenomenon of reflection in mirrors—as it was put to use in surveying instruments—and applied to refraction in sets of lenses. Galileo could appeal to this analogy and assume Della Porta’s theory of refraction. He could thus turn the spyglass into a revolutionary scientific instrument—the telescope.     

Perspectivism, inconsistent models, and contrastive explanation

It is widely recognized that scientific theories are often associated with strictly inconsistent models, but there is little agreement concerning the epistemic consequences. Some argue that model inconsistency supports a strong perspectivism, according to which claims serving as interpretations of models are inevitably and irreducibly perspectival. Others argue that in at least some cases, inconsistent models can be unified as approximations to a theory with which they are associated, thus undermining this kind of perspectivism. I examine the arguments for perspectivism, and contend that its strong form is defeasible in principle, not merely in special cases. The argument rests on the plausibility of scientific knowledge concerning non-perspectival, dispositional facts about modelled systems. This forms the basis of a novel suggestion regarding how to understand the knowledge these models afford, in terms of a contrastive theory of what-questions.     

Taming theory with thought experiments: Understanding and scientific progress

I claim that one way thought experiments contribute to scientific progress is by increasing scientific understanding. Understanding does not have a currently accepted characterization in the philosophical literature, but I argue that we already have ways to test for it. For instance, current pedagogical practice often requires that students demonstrate being in either or both of the following two states: 1) Having grasped the meaning of some relevant theory, concept, law or model, 2) Being able to apply that theory, concept, law or model fruitfully to new instances. Three thought experiments are presented which have been important historically in helping us pass these tests, and two others that cause us to fail. Then I use this operationalization of understanding to clarify the relationships between scientific thought experiments, the understanding they produce, and the progress they enable. I conclude that while no specific instance of understanding (thus conceived) is necessary for scientific progress, understanding in general is.     

Chemistry in the French tradition of philosophy of science: Duhem, Meyerson, Metzger and Bachelard

At first glance twentieth-century philosophy of science seems virtually to ignore chemistry. However this paper argues that a focus on chemistry helped shape the French philosophical reflections about the aims and foundations of scientific methods. Despite patent philosophical disagreements between Duhem, Meyerson, Metzger and Bachelard it is possible to identify the continuity of a tradition that is rooted in their common interest for chemistry. Two distinctive features of the French tradition originated in the attention to what was going on in chemistry.French philosophers of science, in stark contrast with analytic philosophers, considered history of science as the necessary basis for understanding how the human intellect or the scientific spirit tries to grasp the world. This constant reference to historical data was prompted by a fierce controversy about the chemical revolution, which brought the issue of the nature of scientific changes centre stage.A second striking—albeit largely unnoticed—feature of the French tradition is that matter theories are a favourite subject with which to characterize the ways of science. Duhem, Meyerson, Metzger and Bachelard developed most of their views about the methods and aims of science through a discussion of matter theories. Just as the concern with history was prompted by a controversy between chemists, the focus on matter was triggered by a scientific controversy about atomism in the late nineteenth-century.