Philosophy strives to give us a firmer hold on our concepts. But what about their hold on us? Why place ourselves under the sway of a concept and grant it the authority to shape our thought and conduct? Another conceptualization would carry different implications. What makes one way of thinking better than another? This book develops a framework for concept appraisal. Its guiding idea is that to question the authority of concepts is to ask for reasons of a special kind: (...) reasons for concept use, which tell us which concepts to adopt, adhere to, or abandon, thereby shoring up—or undercutting—the reasons for action and belief that guide our deliberations. Traditionally, reasons for concept use have been sought either in timeless rational foundations or in concepts’ inherent virtues, such as precision and consistency. Against this, the book advances two main claims: that we find reasons for concept use in the conceptual needs we discover when we critically distance ourselves from a concept by viewing it from the autoethnographic stance; and that sometimes, concepts that conflict, or exhibit other vices such as vagueness or superficiality, are just what we need. By considering not what concepts are absolutely best, but what concepts we now need, we can reconcile ourselves to the contingency of our concepts, determine the proper place of efforts to tidy up thought, and adjudicate between competing conceptions of things,—even things as contested as liberty or free will. A needs-based approach separates helpful clarification from hobbling tidy-mindedness, and authoritative definition from conceptual gerrymandering. (shrink)

Many philosophers have drawn parallels between scientific models and fictions. In this paper I will be concerned with a recent version of the analogy, which compares models to the imagined characters of fictional literature. Though versions of the position differ, the shared idea is that modeling essentially involves imagining concrete systems analogously to the way that we imagine characters and events in response to works of fiction. Advocates of this view argue that imagining concrete systems plays an ineliminable role in (...) the practice of modeling that cannot be captured by other accounts. The approach thus leaves open what we should say about the ontological status of model-systems, and here advocates differ among themselves, defending a variety of realist or anti-realist positions. I argue that this debate over the ontological status of model-systems is misguided. If model-systems are the kinds of objects fictional realists posit, they can play no role in explaining the epistemology of modeling for an advocate of this approach. So they are at best superfluous. Defenders of the approach should focus on developing an account of the epistemological role of imagining model-systems. (shrink)

In this paper, I shall defend two main claims. First, Friedman’s famous paper “On the methodology of positive economics” (“F53”) cannot be properly understood without taking into account the influence of three authors who are neither cited nor mentioned in the paper: Max Weber, Frank Knight, and Karl Popper. I shall trace both their substantive influence on F53 and the historical route by which this influence took place. Once one has understood these ingredients, especially Weber’s ideal types, many of F53’s (...) astonishing sentences like “the more significant the theory, the more unrealistic the assumptions”, make good sense. Second, I shall claim that the much-discussed question whether Friedman’s essay espouses an instrumentalist or a realist position, is the wrong question to be asked. I shall illustrate that by a comparison with examples from physics in which also unrealistic assumptions are made. Also there, the question whether these assumptions are indicators of instrumentalism or realism is not appropriate. Cleared from these misunderstandings, F53 presents itself as an interesting and reasonable but much less controversial contribution to the methodology of economics. (shrink)

Mitchell & Gronenborn propose that we account for the presence of multiple models of protein structure, each produced in different contexts, through the framework of integrative pluralism. I argue that two interpretations of this framework are available, neither of which captures the relationship between a model and the protein structure it represents or between multiple models of protein structure. Further, it inclines us toward concluding prematurely that models of protein structure are right in their contexts and makes extrapolation of findings (...) from one context to another seem unwarranted. Instead, protein structure determination ought to be understood as modestly monistic. There is one model for every protein in each physicochemical context, and models of the same protein produced in different contexts are compatible with one another. ‘Integrating’ multiple models amounts to extrapolating from one context to another; this is possible because the effect of context on protein folding is relatively weak and predictable. Modest monism better describes the practice of protein structure determination than integrative pluralism and enables greater attention to how context affects protein folding. (shrink)

This article examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation that are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman, Woodward, and Lange. By integrating Lange’s and Woodward’s accounts, I offer a new way to elucidate the distinction between causal and non-causal explanation, and to address concerns about the explanatory sufficiency of non-mechanistic models in neuroscience. I also use this framework to (...) shed light on the dispute over the interpretation of dynamical models of the brain. _1_ Introduction _1.1_ Efficient coding explanation in computational neuroscience _1.2_ Defining non-causal explanation _2_ Case I: Hybrid Computation _3_ Case II: The Gabor Model Revisited _4_ Case III: A Dynamical Model of Prefrontal Cortex _4.1_ A new explanation of context-dependent computation _4.2_ Causal or non-causal? _5_ Causal and Non-causal: Does the Difference Matter? (shrink)

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...) also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)

There has been much criticism of the idea that Friston's free-energy principle can unite the life and mind sciences. Here, we argue that perhaps the greatest problem for the totalizing ambitions of its proponents is a failure to recognize the importance of evolutionary dynamics and to provide a convincing adaptive story relating free-energy minimization to organismal fitness.

Many scientists and philosophers of science think that beauty should play a role in theory selection. Physicists like Paul Dirac and Steven Weinberg explicitly claim that the ultimate explanations of the physical world must be beautiful. And philosophers of science like Peter Lipton say that we should expect the loveliest theory to also be the most likely. In this paper, I contend that these arguments from loveliness bear a striking similarity to Thomas Aquinas’ arguments from fittingness; both seem to presume (...) that the most beautiful theory is also the most probable. To do this, I first explain the explanatory virtues that are commonly thought to be constitutive of a lovely theory. Second, I elucidate Aquinas’ arguments from fittingness and show how they work in light of his account of beauty. Lastly, I connect the two kinds of aesthetic arguments and show the extent to which they overlap. (shrink)

This book presents a collection of studies by Romanian philosophers, addressing foundational issues currently debated in contemporary philosophy of science. It offers a historical survey of the tradition of scientific philosophy in Romania. It examines some problems in the foundations of logic, mathematics, linguistics, the natural and social sciences. Among the more specific topics, it discusses scientific explanation, models, and mechanisms, as well as memory, artifacts, and rules of research. The book is useful to those interested in the philosophy of (...) real science, but also to those interested in Romanian philosophy. (shrink)

At the beginning of the 20th century, Gestalt psychologists put forward the concept of psychoneural isomorphism, which was meant to replace Fechner’s obscure notion of psychophysical parallelism and provide a heuristics that may facilitate the search for the neural correlates of the mind. However, the concept has generated much confusion in the debate, and today its role is still unclear. In this contribution, I will attempt a little conceptual spadework in clarifying the concept of psychoneural isomorphism, focusing exclusively on conscious (...) visual perceptual experience and its neural correlates. Firstly, I will outline the history of our concept, and its alleged metaphysical and epistemic roles. Then, I will clarify the nature of isomorphism and rule out its metaphysical role. Finally, I will review some epistemic roles of our concept, zooming in on the work of Jean Petitot, and suggest that it does not play a relevant heuristic role. I conclude suggesting that psychoneural isomorphism might be an indicator of robustness for certain mathematical descriptions of perceptual content. (shrink)

This paper argues against the view that the standard explanation of phase transitions in statistical mechanics may be considered a causal explanation, a distortion that can nevertheless successfully represent causal relations.

This article offers a characterization of what I call multiple-models juxtaposition, a strategy for managing trade-offs among modeling desiderata. MMJ displays models of distinct phenomena to...

I argue for a theory of the optimal function of the speech act of referring, called the edenic theory. First, the act of singular reference is defined directly in terms of Gricean communicative intentions. Second, I propose a doxastic constraint on the optimal performance of such acts, stating, roughly, that the speaker must not have any relevant false beliefs about the identity or distinctness of the intended object. In uttering a singular term on an occasion, on this theory, one represents (...) oneself as not having any confused beliefs about the object to which one intends to refer. This paves the way for an intentionalist theory of reference that circumvents well-known problems, which have not been adequately addressed before in the literature. (shrink)

This paper adds to the philosophical literature on mechanistic explanation by elaborating two related explanatory functions of idealisation in mechanistic models. The first function involves explaining the presence of structural/organizational features of mechanisms by reference to their role as difference-makers for performance requirements. The second involves tracking counterfactual dependency relations between features of mechanisms and features of mechanistic explanandum phenomena. To make these functions salient, we relate our discussion to an exemplar from systems biological research on the mechanism for countering (...) heat shock—the heat shock response system—in Escherichia coli bacteria. This research also reinforces a more general lesson: ontic constraint accounts in the literature on mechanistic explanation provide insufficiently informative normative appraisals of mechanistic models. We close by outlining an alternative view on the explanatory norms governing mechanistic representation. (shrink)

This paper has two aims. The first is critical: it argues that our mainstream epistemology of disagreement does not have the resources to explain what goes wrong in cases of group-level epistemic injustice. The second is positive: we argue that a functionalist account of group belief and group justification delivers an account of the epistemic peerhood relation between groups that accommodates minority and oppressed groups, and diagnoses the epistemic injustice cases correctly as cases of unwarranted belief on the part of (...) the oppressor group. (shrink)

There are two traditions of thinking about idealization offering almost opposite views on their functioning and epistemic status. While one tradition views idealizations as epistemic deficiencies, the other one highlights the epistemic benefits of idealization. Both of these, however, identify idealization with misrepresentation. In this article, we instead approach idealization from the artifactual perspective, comparing it to the distortion-to-reality accounts of idealization, and exemplifying it through the case of the Hodgkin and Huxley model of nerve impulse. From the artifactual perspective, (...) the epistemic benefits and deficiencies introduced by idealization frequently come in a package due to the way idealization draws together different resources in model construction. Accordingly, idealization tends to be holistic in that it is not often easily attributable to just some specific parts of the model. Instead, the idealizing process tightly embeds theoretical concepts and formal tools into the construction of a model. (shrink)

In his famous article “The Unreasonable Effectiveness of Mathematics in the Natural Sciences” Eugen Wigner argues for a unique tie between mathematics and physics, invoking even religious language: “The miracle of the appropriateness of the language of mathematics for the formulation of the laws of physics is a wonderful gift which we neither understand nor deserve”. The possible existence of such a unique match between mathematics and physics has been extensively discussed by philosophers and historians of mathematics. Whatever the merits (...) of this claim are, a further question can be posed with regard to mathematization in science more generally: What happens when we leave the area of theories and laws of physics and move over to the realm of mathematical modeling in interdisciplinary contexts? Namely, in modeling the phenomena specific to biology or economics, for instance, scientists often use methods that have their origin in physics. How is this kind of mathematical modeling justified? (shrink)

What is scientific progress? This paper advances an interpretation of this question, and an account that serves to answer it. Roughly, the question is here understood to concern what type of cognitive change with respect to a topic X constitutes a scientific improvement with respect to X. The answer explored in the paper is that the requisite type of cognitive change occurs when scientific results are made publicly available so as to make it possible for anyone to increase their understanding (...) of X. This account is briefly compared to two rival accounts of scientific progress, based respectively on increasing truthlikeness and accumulating knowledge, and is argued to be preferable to both. (shrink)

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. (shrink)

Dellsén (2017) has recently argued for an understanding-based account of scientific progress, the noetic account, according to which science (or a particular scientific discipline) makes cognitive progress precisely when it increases our understanding of some aspect of the world. Dellsén contrasts this account with Bird’s (2007, 2015) epistemic account, according to which such progress is made precisely when our knowledge of the world is increased or accumulated. In a recent paper, Park (2017) criticizes various aspects of Dellsén’s account and his (...) arguments in favor of the noetic account as against Bird’s epistemic account. This paper responds to Park’s objections. Since a number of Park’s arguments rely on the idea that scientific progress may merely consists in “achieving the means to increase knowledge” (Park 2017: 570), I will start by discussing this “means-end thesis”. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different "degrees of contingency". We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For illustrative (...) purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different “degrees of contingency”. We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For illustrative (...) purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

This contribution sheds light on the role of infinite idealization in structural analysis, by exploring how infinite elements and finite element methods are combined in civil engineering models. This combination, I claim, should be read in terms of a ‘complementarity function’ through which the representational ideal of completeness is reached in engineering model-building. Taking a cue from Weisberg’s definition of multiple-model idealization, I highlight how infinite idealizations are primarily meant to contribute to the prediction of structural behavior in Multiphysics approaches.

The advancement of computing technology makes it possible to build extremely accurate digital reconstructions of brain circuits. Are such unprecedented levels of biological accuracy essential for brain simulations to play the roles they are expected to play in neuroscientific research? The main goal of this paper is to clarify this question by distinguishing between various roles played by large-scale simulations in contemporary neuroscience, and by reflecting about what makes a simulation biologically accurate. It is argued that large-scale simulations may play (...) model-oriented and prediction-oriented roles in brain research, and that the concept of biological accuracy can be interpreted as related to the plausibility of the theoretical model implemented in the simulation system, to the accuracy of the computer implementation, and to the level of details of the implemented model. Building on these observations and distinctions, it is argued that biological accuracy is not essential for a computer simulation to play the epistemic roles it is expected to play in brain research. (shrink)

I develop an account of productive surprise as an epistemic virtue of scientific investigations which does not turn on psychology alone. On my account, a scientific investigation is potentially productively surprising when results can conflict with epistemic expectations, those expectations pertain to a wide set of subjects. I argue that there are two sources of such surprise in science. One source, often identified with experiments, involves bringing our theoretical ideas in contact with new empirical observations. Another, often identified with simulations, (...) involves articulating and bringing together different parts of our knowledge. Both experiments and simulations, then, can surprise. (shrink)

Paleobiologists often provide simple narratives to explain complex, contingent episodes. These narratives are sometimes ‘one-shot hypotheses’ which are treated as being mutually exclusive with other possible explanations of the target episode, and are thus extended to accommodate as much about the episode as possible. I argue that a provisional preference for such hypotheses provides two kinds of productive scaffolding. First, they generate ‘hypothetical difference-makers’: one-shot hypotheses highlight and isolate empirically tractable dependencies between variables. Second, investigations of hypothetical difference-makers provision explanatory (...) resources, the ‘raw materials’ for constructing more complex—and likely more adequate—explanations. Provisional preferences for simple, one-shot hypotheses in historical science, then, is defeasibly justified on indirect—strategic—grounds. My argument is made in reference to recent developments regarding the K–Pg extinction. (shrink)

Geologists, Paleontologists and other historical scientists are frequently concerned with narrative explanations targeting single cases. I show that two distinct explanatory strategies are employed in narratives, simple and complex. A simple narrative has minimal causal detail and is embedded in a regularity, whereas a complex narrative is more detailed and not embedded. The distinction is illustrated through two case studies: the ‘snowball earth’ explanation of Neoproterozoic glaciation and recent attempts to explain gigantism in Sauropods. This distinction is revelatory of historical (...) science. I argue that at least sometimes which strategy is appropriate is not a pragmatic issue, but turns on the nature of the target. Moreover, the distinction reveals a counterintuitive pattern of progress in some historical explanation: shifting from simple to complex. Sometimes, historical scientists rightly abandon simple, unified explanations in favour of disunified, complex narratives. Finally I compare narrative and mechanistic explanation, arguing that mechanistic approaches are inappropriate for complex narrative explanations. (shrink)

. Philosophers and scientists often cite ontic factors when explaining the methods and success of scientific inquiry. That is, the adoption of a method or approach is explained in reference to the kind of system in which the scientist is interested: these are explanations of why scientists do what they do, that appeal to properties of their target systems. We present a framework for understanding such “Opticks to his Principia. Newton’s optical work is largely experiment-driven, while the Principia is primarily (...) mathematical, so usually, each work is taken to exemplify a different kind of science. However, Newton himself often presented them in terms of a largely consistent method. We use our framework to articulate an original and plausible position: that the differences between the Opticks and the Principia are due to the kinds of systems targeted. That is, we provide an ontic-driven explanation of methodological differences. We suspect that ontic factors should have a more prominent role in historical explanations of scientific method and development. (shrink)

Our epistemic access to the past is infamously patchy: historical information degrades and disappears and bygone eras are often beyond the reach of repeatable experiments. However, historical scientists have been remarkably successful at uncovering and explaining the past. I argue that part of this success is explained by the exploitation of dependencies between historical events, entities, and processes. For instance, if sauropod dinosaurs were hot blooded, they must have been gluttons; the high-energy demands of endothermy restrict sauropod grazing strategies. Understanding (...) such dependencies extends our reach into the past in spite of incomplete data. In addition, this serves as a counterexample to two accounts of method in the historical sciences. By one, historical science proceeds by identifying ‘smoking guns’: traces that discriminate between live hypotheses. By the other, historical hypotheses are supported by consilience: the convergence of independent lines of evidence. However, testing for ‘coherency’ between past hypotheses also plays a critical role in historical confirmation. Just as historical scientists exploit dependencies between past entities and present entities to infer what the past was like, they also exploit dependencies between past entities themselves. I do not suggest that archetypical historical science proceeds in this manner. Rather, the lesson I draw is that historical methodology cannot be characterized as archetypically relying on one method or another. Historical science is at base opportunistic, and is resistant to unitary analyses. 1 Introduction2 Snowballs and Explosions: The Basic Idea3 Were Sauropods Endothermic?4 Dependent Entities and Interdependent Explanations5 Smoking Guns and Consilience. (shrink)

I develop an account of the relationship between aesthetics and knowledge, focusing on scientific practice. Cognitivists infer from ‘partial sensitivity’—aesthetic appreciation partly depends on doxastic states—to ‘factivity’, the idea that the truth or otherwise of those beliefs makes a difference to aesthetic appreciation. Rejecting factivity, I develop a notion of ‘epistemic engagement’: partaking genuinely in a knowledge-directed process of coming to epistemic judgements, and suggest that this better accommodates the relationship between the aesthetic and the epistemic. Scientific training (and other (...) knowledge-directed activities), I argue, involves ‘attunement’: the co-option of aesthetic judgements towards epistemic ends. Thus, the connection between aesthetic appreciation and knowledge is psychological and contingent. This view has consequences for the warrant of aesthetic judgement in science, namely, the locus of justification is those processes of attunement, not the aesthetic judgements themselves. (shrink)

In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in (...) computational neuroscience that creates difficulties for the mechanist project. Carandini and Heeger (Nat Rev Neurosci 13:51–62, 2012) propose that many neural response properties can be understood in terms of canonical neural computations. These are “standard computational modules that apply the same fundamental operations in a variety of contexts.” Importantly, these computations can have numerous biophysical realisations, and so straightforward examination of the mechanisms underlying these computations carries little explanatory weight. Through a comparison between this modelling approach and minimal models in other branches of science, I argue that computational neuroscience frequently employs a distinct explanatory style, namely, efficient coding explanation. Such explanations cannot be assimilated into the mechanistic framework but do bear interesting similarities with evolutionary and optimality explanations elsewhere in biology. (shrink)

This paper defends the viability of de-idealization strategies in economic modeling against recent criticism. De-idealization occurs when an idealized assumption of a theoretical model is replaced with a more realistic one. Recently, some scholars have raised objections against the possibility or fruitfulness of de-idealizing economic models, suggesting that economists do not employ this kind of strategy. We present a detailed case study from the theory of industrial organization, discussing three different models, two of which can be construed as de-idealized versions (...) of the first (the so-called Bertrand model of oligopoly). We conclude that recent pessimism about de-idealization in economics is largely unfounded, and that de-idealization strategies are not only possible but also widely employed in economics. (shrink)

The Scientific Revolution spawned not just one methodology, but two. We have emphasized Bacon's inductivism at the expense of Galileo's more abstract, sophisticated method of successive approximation, and so have failed to appreciate Galileo's contribution to the ceteris paribus problem in philosophy of science. My purpose here is to help redress this imbalance. I first briefly review the old unsolved problems, and then point out the Baconian basis of ceteris paribus, as this clause is conventionally understood, and its history from (...) Aristotle to twentieth century Positivism. Then I explore Galileo's method of dealing with unwanted impediments, and the more general problem of 'accidents.' I trace his methodology back to Archimedes and forward through the economic theories of Adam Smith (18th century), J. S. Mill (19th) and Milton Friedman (20th). Finally, I point out ways in which I think Galileo's scientific method sheds light on, and provides a partial solution to, the ceteris paribus problem. (shrink)

Berkeley in his Introduction to the Principles of Human knowledge uses geometrical examples to illustrate a way of generating “universal ideas,” which allegedly account for the existence of general terms. In doing proofs we might, for example, selectively attend to the triangular shape of a diagram. Presumably what we prove using just that property applies to all triangles.I contend, rather, that given Berkeley’s view of extension, no Euclidean triangles exist to attend to. Rather proof, as Berkeley would normally assume, requires (...) idealizing diagrams; treating them as if they obeyed Euclidean constraints. This convention solves the problem of representative generalization. View HTML Send article to KindleTo send this article to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle. Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply. Find out more about the Kindle Personal Document Service.Berkeley and Proof in GeometryVolume 51, Issue 3RICHARD J. BROOK DOI: https://doi.org/10.1017/S0012217312000686Your Kindle email address Please provide your Kindle [email protected]@kindle.com Available formats PDF Please select a format to send. By using this service, you agree that you will only keep articles for personal use, and will not openly distribute them via Dropbox, Google Drive or other file sharing services. Please confirm that you accept the terms of use. Cancel Send ×Send article to Dropbox To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to Dropbox. Berkeley and Proof in GeometryVolume 51, Issue 3RICHARD J. BROOK DOI: https://doi.org/10.1017/S0012217312000686Available formats PDF Please select a format to send. By using this service, you agree that you will only keep articles for personal use, and will not openly distribute them via Dropbox, Google Drive or other file sharing services. Please confirm that you accept the terms of use. Cancel Send ×Send article to Google Drive To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to Google Drive. Berkeley and Proof in GeometryVolume 51, Issue 3RICHARD J. BROOK DOI: https://doi.org/10.1017/S0012217312000686Available formats PDF Please select a format to send. By using this service, you agree that you will only keep articles for personal use, and will not openly distribute them via Dropbox, Google Drive or other file sharing services. Please confirm that you accept the terms of use. Cancel Send ×Export citation Request permission. (shrink)

Some recent arguments for paternalist government interventions have been based in empirical results in psychology and behavioral economics that would seem to show that adult human beings are far removed from the ideals of rationality presupposed by much of philosophical and economic theory. In this paper it is argued that we need to move to a different conception of human decision-making competence than the one that lies behind that common line of philosophical and economic thinking, and which actually still lies (...) in the background of some of these recent approaches to paternalist interventions. An alternative picture of human decision-making competence is outlined and four criteria for identifying areas where paternalist interventions have a basic moral and political legitimacy are then identified on the basis of this picture. (shrink)

This essay analyzes and develops recent views about explanation in biology. Philosophers of biology have parted with the received deductive-nomological model of scientific explanation primarily by attempting to capture actual biological theorizing and practice. This includes an endorsement of different kinds of explanation (e.g., mathematical and causal-mechanistic), a joint study of discovery and explanation, and an abandonment of models of theory reduction in favor of accounts of explanatory reduction. Of particular current interest are philosophical accounts of complex explanations that appeal (...) to different levels of organismal organization and use contributions from different biological disciplines. The essay lays out one model that views explanatory integration across different disciplines as being structured by scientific problems. I emphasize the philosophical need to take the explanatory aims pursued by different groups of scientists into account, as explanatory aims determine whether different explanations are competing or complementary and govern the dynamics of scientific practice, including interdisciplinary research. I distinguish different kinds of pluralism that philosophers have endorsed in the context of explanation in biology, and draw several implications for science education, especially the need to teach science as an interdisciplinary and dynamic practice guided by scientific problems and explanatory aims. (shrink)

Epistemic accounts of scientific collaboration usually assume that, one way or another, two heads really are more than twice better than one. We show that this hypothesis is unduly strong. We present a deliberately crude model with unfavorable hypotheses. We show that, even then, when the priority rule is applied, large differences in successfulness can emerge from small differences in efficiency, with sometimes increasing marginal returns. We emphasize that success is sensitive to the structure of competing communities. Our results suggest (...) that purely epistemic explanations of the efficiency of collaborations are less plausible but have much more powerful socioepistemic versions. (shrink)

The Darwinian theory of evolution is itself evolving and this book presents the details of the core of modern Darwinism and its latest developmental directions. The authors present current scientific work addressing theoretical problems and challenges in four sections, beginning with the concepts of evolution theory, its processes of variation, heredity, selection, adaptation and function, and its patterns of character, species, descent and life. The second part of this book scrutinizes Darwinism in the philosophy of science and its usefulness in (...) understanding ecosystems, whilst the third section deals with its application in disciplines beyond the biological sciences, including evolutionary psychology and evolutionary economics, Darwinian morality and phylolinguistics. The final section addresses anti-Darwinism, the creationist view and issues around teaching evolution in secondary schools. The reader learns how current experimental biology is opening important perspectives on the sources of variation, and thus of the very power of natural selection. This work examines numerous examples of the extension of the principle of natural selection and provides the opportunity to critically reflect on a rich theory, on the methodological rigour that presides in its extensions and exportations, and on the necessity to measure its advantages and also its limits. Scholars interested in modern Darwinism and scientific research, its concepts, research programs and controversies will find this book an excellent read, and those considering how Darwinism might evolve, how it can apply to the human sciences and other disciplines beyond its origins will find it particularly valuable. Originally produced in French (Les Mondes Darwiniens), the scope and usefulness of the book have led to the production of this English text, to reach a wider audience. This book is a milestone in the impressive penetration by Francophone scholars into the world of Darwinian science, its historiography and philosophy over the last two decades. Alex Rosenberg, R. Taylor Cole Professor of Philosophy, Duke University Until now this useful and comprehensive handbook has only been available to francophones. Thanks to this invaluable new translation, this collection of insightful and original essays can reach the global audience it deserves. Tim Lewens, University of Cambridge. (shrink)

In this essay, I examine the role of dissimilarity in scientific representation. After briefly reviewing some of the philosophical literature which places a strong emphasis on the role of similarity, I turn to examine some work from Carroll and Borges which demonstrates that perfect similarity is not valuable in the representational use of maps. Expanding on this insight, I go on to argue that this shows that dissimilarity is an important part of the representational use of maps—a point I then (...) extend to the case of scientific representation. Relying on some work from Latour, I argue that dissimilarity plays an essential role in representational practice, by providing novel forms of manipulation and use which affords the achievement of various epistemic and nonepistemic aims. After showing how this point connects to some other literature on scientific representation, I discuss some examples of the value of dissimilarity in the use of representational vehicles. Overall, I argue that to understand scientific representation, we will need to consider more than just similarity. We will need to explore dissimilarities as well. (shrink)

Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would allow one to (...) use models for possibilistic prediction and salvage widespread scientific practice. (shrink)

Richard Levins’s (Am Sci 54(4):421–431, 1966) paper sets a landmark for the significance of scientific model-making in biology. Colombo and Palacios (Biol Philos 36(5):1–26. 10.1007/s10539-021-09818-x, 2021) have recently built their critique of the explanatory power of the Free Energy Principle on Levins’s insight into the relationship between generality, realism, and precision. This paper addresses the issue of the plausibility of biological explanations that are grounded in the Free Energy Principle (FEP) and deals with the question of the realist fortitude of (...) FEP’s theoretical framework. It indicates that what is required for establishing the plausibility of the explanation of a target system given a model of that system is the dosage or the harmony between the generality and accuracy of explanatory models. This would also provide a basis for seeing how scientific realism could be a viable option with respect to FEP. (shrink)

The robust behavior of the patent citation network is a complex target of recent bottom-up models in science. This paper investigates the purpose and testing of three especially simple bottom-up models of the citation count distribution observed in the patent citation network. The complex causal webs in the models generate weakly emergent patterns of behavior, and this explains both the need for empirical observation of computer simulations of the models and the epistemic harmlessness of the resulting epistemic opacity.

In this paper, we explore an under-investigated question concerning the class of formal models that aim at providing normative guidance. We call such models normative models. In particular, we examine the question of how normative models can successfully exert normative guidance. First, we highlight the absence of a discussion of this question – which is surprising given the extensive debate about the success conditions of descriptive models – and motivate its importance. Second, we introduce and discuss two potential accounts of (...) the success conditions of normative models. Our tentative conclusion is that the second account is more promising. (shrink)

This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story (...) explains patterns across extremely diverse systems and shows how minimal models can be used to understand real systems. (shrink)

Infinite idealizations appear in our best scientific explanations of phase transitions. This is thought by some to be paradoxical. In this paper I connect the existing literature on the phase transition paradox to work on the concept of indispensability, which arises in discussions of realism and anti-realism within the philosophy of science and the philosophy of mathematics. I formulate a version of the phase transition paradox based on the idea that infinite idealizations are explanatorily indispensable to our best science, and (...) so ought to attract a realist attitude. I go on to offer a solution to the paradox by drawing a distinction between two types of indispensability: constructive and substantive indispensability. I argue that infinite idealizations are constructively indispensable to explanations of phase transitions, but not substantively indispensable. This helps to resolve the paradox, I maintain, since realist commitment tracks substantive, and not constructive, indispensability. (shrink)

Daniel M. Hausman holds that preferences in economics are total subjective comparative evaluations—subjective judgments to the effect that something is better than something else all things told—and that economists are right to employ this conception of preference. Here, I argue against both parts of Hausman’s thesis. The failure of Hausman’s account, I continue, reflects a deeper problem, that is, that preferences in economics do not need an explicit definition of the kind that he seeks. Nonetheless, Hausman’s labors were not in (...) vain: his accomplishment is that he has articulated a useful model of the theory. (shrink)

The Epistemology Of Computer Simulation has developed as an epistemological and methodological analysis of simulative sciences using quantitative computational models to represent and predict empirical phenomena of interest. In this paper, Executable Cell Biology and Agent-Based Modelling are examined to show how one may take advantage of qualitative computational models to evaluate reachability properties of reactive systems. In contrast to the thesis, advanced by EOCS, that computational models are not adequate representations of the simulated empirical systems, it is shown how (...) the representational adequacy of qualitative models is essential to evaluate reachability properties. Justification theory, if not playing an essential role in EOCS, is exhibited to be involved in the process of advancing and corroborating model-based hypotheses about empirical systems in ECB and ABM. Finally, the practice of evaluating model-based hypothesis by testing the simulated systems is shown to constitute an argument in favour of the thesis that computer simulations in ECB and ABM can be put on a par with scientific experiments. (shrink)

This paper addresses the methodological problem of analysing what it is to explain observed behaviours of engineered computing systems, focusing on the crucial role that abstraction and idealization play in explanations of both correct and incorrect BECS. First, it is argued that an understanding of explanatory requests about observed miscomputations crucially involves reference to the rich background afforded by hierarchies of functional specifications. Second, many explanations concerning incorrect BECS are found to abstract away from descriptions of physical components and processes (...) of computing systems that one finds below the logic circuit and gate layer of functional specification hierarchies. Third, model-based explanations of both correct and incorrect BECS that are provided in the framework of formal verification methods often involve idealizations. Moreover, a distinction between restrictive and permissive idealizations is introduced and their roles in BECS explanations are analysed. (shrink)