Neuroscience is a laboratory-based science that spans multiple levels of analysis from molecular genetics to behavior. At every level of analysis experiments are designed in order to answer empirical questions about phenomena of interest. Understanding the nature and structure of experimentation in neuroscience is fundamental for assessing the quality of the evidence produced by such experiments and the kinds of claims that are warranted by the data. This article provides a general conceptual framework for thinking about evidence and experimentation in (...) neuroscience with a particular focus on two research areas: cognitive neuroscience and cognitive neurobiology. (shrink)

Descriptive accounts of the nature of explanation in neuroscience and the global goals of such explanation have recently proliferated in the philosophy of neuroscience and with them new understandings of the experimental practices of neuroscientists have emerged. In this paper, I consider two models of such practices; one that takes them to be reductive; another that takes them to be integrative. I investigate those areas of the neuroscience of learning and memory from which the examples used to substantiate these models (...) are culled, and argue that the multiplicity of experimental protocols used in these research areas presents specific challenges for both models. In my view, these challenges have been overlooked largely because philosophers have hitherto failed to pay sufficient attention to fundamental features of experimental practice. I demonstrate that when we do pay attention to such features, evidence for reduction and integrative unity in neuroscience is simply not borne out. I end by suggesting some new directions for the philosophy of neuroscience that pertain to taking a closer look at the nature of neuroscientific experiments. (shrink)

H. B. D. Kettlewell's (1955, 1956) classic field experiments on industrial melanism in polluted and unpolluted settings using the peppered moth, Biston betularia, are routinely cited as establishing that the melanic (dark) form of the moth rose in frequency downwind of industrial centers because of the cryptic advantage dark coloration provides against visual predators in soot-darkened environments. This paper critiques three common myths surrounding these investigations: (1) that Kettlewell used a model that identified crypsis as the only selective force responsible (...) for the spread of the melanic gene, (2) that Kettlewell's field experiments alone established that selection for crypsis was the most important factor in the spread of melanic forms, and (3) that Kettlewell's investigations in an unpolluted wood near Dorset constituted a control for his earlier Birmingham studies (contra Hagen 1993, 1996). This analysis further identifies two features that distinguish manipulative experiments in evolutionary biology from experiments in other contexts. First, experiments in evolutionary biology rest on a wealth of information provided by strictly observational ecological studies; in the absence of such information experiments in evolutionary biology make no sense. Second, there is a trade-off between how much control investigators have over the conditions being studied and how informative the results of the experiment will be with regard to natural populations. (shrink)

This undergraduate textbook introduces some fundamental issues in philosophy of science for students of philosophy and science students. The book is divided into two parts. Part 1 deals with knowledge and values. Chap. 1 presents the classical conception of knowledge as initiated by the ancient Greeks and elaborated during the development of science, introducing the central concepts of truth, belief and justification. Aspects of the quest for objectivity are taken up in the following two chapters. Moral issues are broached in (...) Chap. 4, which discusses some aspects of the use and abuse of science, taking up the responsibilities of scientists in properly conducting their business and decision-makers in their concerns with the import of science for society. Part 2 contrasts the view of scientific progress as the rejecting of old hypotheses and theories and replacing them with new ones, represented by Karl Popper, with the conception of progress as accumulating knowledge, saving as much as possible from older theories, represented by Pierre Duhem. A concluding chapter defends the natural attitude of taking the theories of modern science to be literally true, i.e. realism, in the face of arguments drawn partly from the history of scientific progress in criticism of this stance. (shrink)

This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry interested in questions (...) related to the general practice of computer simulations. (shrink)

The ArgumentIn its reconstruction of scientific practice, philosophy of science has traditionally placed scientific theories in a central role, and has reduced the problem of mediating between theories and the world to formal considerations. Many applications of scientific theories, however, involve complex mathematical models whose constitutive equations are analytically unsolvable. The study of these applications often consists in developing representations of the underlying physics on a computer, and using the techniques of computer simulation in order to learn about the behavior (...) of these systems. In many instances, these computer simulations are not simple number-crunching techniques. They involve a complex chain of inferences that serve to transform theoretical structures into specific concrete knowledge of physical systems. In this paper I argue that this process of transformation has its own epistemology. I also argue that this kind of epistemology is unfamiliar to most philosophy of science, which has traditionally concerned itself with the justification of theories, not with their application. Finally, I urge that the nature of this epistemology suggests that the end results of some simulations do not bear a simple, straightforward relation to the theories from which they stem. (shrink)

There are a variety of topics in the philosophy of science that need to be rethought, in varying degrees, after one pays careful attention to the ways in which computer simulations are used in the sciences. There are a number of conceptual issues internal to the practice of computer simulation that can benefit from the attention of philosophers. This essay surveys some of the recent literature on simulation from the perspective of the philosophy of science and argues that philosophers have (...) a lot to learn by paying closer attention to the practice of simulation. (shrink)

John D. Norton defends an empiricist epistemology of thought experiments, the central thesis of which is that thought experiments are nothing more than arguments. Philosophers have attempted to provide counterexamples to this claim, but they haven’t convinced Norton. I will point out a more fundamental reason for reformulation that criticizes Norton’s claim that a thought experiment is a good one when its underlying logical form possesses certain desirable properties. I argue that by Norton’s empiricist standards, no thought experiment is ever (...) justified in any deep sense due to the properties of its logical form. Instead, empiricists should consider again the merits of evaluating thought experiments more like laboratory experiments, and less like arguments. (shrink)

I describe two traditions of philosophical accounts of evidence: one characterizes the notion in terms of signs of success, the other characterizes the notion in terms of conditions of success. The best examples of the former rely on the probability calculus, and have the virtues of generality and theoretical simplicity. The best examples of the latter describe the features of evidence which scientists appeal to in practice, which include general features of methods, such as quality and relevance, and general features (...) of evidence, such as patterns in data, concordance with other evidence, and believability of the evidence. Two infamous episodes from biomedical research help to illustrate these features. Philosophical characterization of these latter features—conditions of success—has the virtue of potential relevance to, and descriptive accuracy of, practices of experimental scientists. (shrink)

Some accounts of evidence regard it as an objective relationship holding between data and hypotheses, perhaps mediated by a testing procedure. Mayo’s error-statistical theory of evidence is an example of such an approach. Such a view leaves open the question of when an epistemic agent is justified in drawing an inference from such data to a hypothesis. Using Mayo’s account as an illustration, I propose a framework for addressing the justification question via a relativized notion, which I designate security , (...) meant to conceptualize practices aimed at the justification of inferences from evidence. I then show how the notion of security can be put to use by showing how two quite different theoretical approaches to model criticism in statistics can both be viewed as strategies for securing claims about statistical evidence. (shrink)

The aim of this paper is to discuss a key question in the history and philosophy of medicine, namely how scholars should treat the practices and experimental hypotheses of modern life science laboratories. The paper seeks to introduce some prominent historiographical methods and theoretical approaches associated with biomedical research. Although medical scientists need no convincing that experimentation has a significant function in their laboratory work, historians, philosophers, and sociologists long neglected its importance when examining changes in medical theories or progress (...) in scientific knowledge. The reason appears to have been the academic influence of the then dominant tradition in the history of ideas, but was also due to a misconception of what could usefully be termed the view on “historical ontology.” During the last two decades, there have been many books and research articles that have turned towards the subject, so that the study of experimental practice has become a major trend in the contemporary history and philosophy of medicine. A closer look at the issue of laboratory research shows that concepts in medicine and the life sciences cannot be understood as historically constant, free-standing ideas, but have to be regarded as dependent on local research settings. They often carry particular “social memories” with them and thus acquire important ethical implications. (shrink)

This paper deals with calibration in scientific practices which investigate relatively well-understood natural phenomena by means of already standardized instrumental devices. Calibration is a crucial topic, since it conditions the reliability of instrumental procedures in science. Yet although important, calibration is a relatively neglected topic. We think more attention should be devoted to calibration. The paper attempts to take a step in this direction. The aims are two-fold: (1) to characterize calibration in a relatively simple kind of scientific practices; (2) (...) to provide conceptual and taxonomic tools of broader scope that help to get a better understanding of calibration in more complex cases and other kinds of scientific practices. For this purpose, we first provide indications for why a conceptual framework is needed. Second, a bibliographic survey of works dealing with calibration is attempted. Third, we introduce different tools to enable a better understanding of calibration. Fourth, we turn to the elaboration of what we call a “simple exemplar” of calibration, illustrated through the case of the calibration of an equal-arm balance. Fifth, the tools previously introduced, and the framework of the simple exemplar, are applied to a more complex case of calibration: calibration procedures in X-ray experiments. This serves to show the work accomplished by the simple exemplar and to emphasize features of more complex cases of calibration. Eventually, we revisit and specify the nature, status, scope and value of the proposed framework. (shrink)

: This paper explores the calibration of laboratory models in population genetics as an experimental strategy for justifying experimental results and claims based upon them following Franklin (1986, 1990) and Rudge (1996, 1998). The analysis provided undermines Coyne et al.'s (1997) critique of Wade and Goodnight's (1991) experimental study of Wright's (1931, 1932) Shifting Balance Theory. The essay concludes by further demonstrating how this analysis bears on Diamond's (1986) claims regarding the weakness of laboratory experiments as evidence, and further how (...) the calibration strategy fits within Lloyd's (1987, 1988) account of the confirmation of ecological and evolutionary models. (shrink)

Computer simulation and thought experiments seem to produce knowledge about the world without intervening in the world. This has called for a comparison between the two methods. However, Chandrasekharan et al. argue that the nature of contemporary science is too complex for using TEs. They suggest CS as the tool for contemporary sciences and conclude that it will replace TEs. In this paper, by discussing a few TEs from the history of science, I show that the replacement thesis about TE (...) is a failure. The paper is divided into three sections. The first section discusses the arguments of Chandrasekharan et al. and demonstrates the three distinct aspects of the replacement thesis. The second section examines the argument against TE and shows that they are inadequate to prove the withering of TE from science. The third section discusses Albert Einstein’s Magnet and Conductor TE and demonstrates that replacing such TE with CS yield no advantage. (shrink)

Experimentation represents today a ‘hot’ topic in computing. If experiments made with the support of computers, such as computer simulations, have received increasing attention from philosophers of science and technology, questions such as “what does it mean to do experiments in computer science and engineering and what are their benefits?” emerged only recently as central in the debate over the disciplinary status of the discipline. In this work we aim at showing, also by means of paradigmatic examples, how the traditional (...) notion of controlled experiment should be revised to take into account a part of the experimental practice in computing along the lines of experimentation as exploration. Taking inspiration from the discussion on exploratory experimentation in the philosophy of science—experimentation that is not theory-driven—we advance the idea of explorative experiments that, although not new, can contribute to enlarge the debate about the nature and role of experimental methods in computing. In order to further refine this concept we recast explorative experiments as socio-technical experiments, that test new technologies in their socio-technical contexts. We suggest that, when experiments are explorative, control should be intended in a posteriori form, in opposition to the a priori form that usually takes place in traditional experimental contexts. (shrink)

In recent years there has been a revival of the debate about the relation between history and philosophy of science. This article seeks to contribute to the discussion by approaching the issue from a new angle. To rethink the relation between the two domains of study, I apply an important insight about scientific practice to the practice of integrating the history and philosophy of science: the insight that the scientific paper does not give a faithful account of the actual research (...) pursued in the laboratory or in the field. Arguably, the scholarly article about science is also not a transparent window to the activity of producing such an article. But if it is not, we need to redirect our attention. If we want to understand the nature and merits of integrating the history and philosophy of science, we need to examine both the actual activity of integrating and the scholarly paper produced by it. To consider what one can learn from such an inquiry, I reflect on my own activity of studying scientific justification through the combination of historical and philosophical analysis. Probing a concrete historical episode, micro-anatomical research on the retina in the 19th century, I pursue two related questions, the first-order question, 'What exactly is scientific justification in the given case?' and the meta-question, 'How do I go about analysing justificatory practices?' I then characterise the nature of my analysis and consider what can be learned from the study of the practice of integrating the history and philosophy of science. (shrink)

H. B. D. Kettlewell's (1955, 1956) classic field experiments on industrial melanism in polluted and unpolluted settings using the peppered moth, Biston betularia, are routinely cited as establishing that the melanic (dark) form of the moth rose in frequency downwind of industrial centers because of the cryptic advantage dark coloration provides against visual predators in soot-darkened environments. This paper critiques three common myths surrounding these investigations: (1) that Kettlewell used a model that identified crypsis as the only selective force responsible (...) for the spread of the melanic gene, (2) that Kettlewell's field experiments alone established that selection for crypsis was the most important factor in the spread of melanic forms, and (3) that Kettlewell's investigations in an unpolluted wood near Dorset constituted a control for his earlier Birmingham studies (contra Hagen 1993, 1996). This analysis further identifies two features that distinguish manipulative experiments in evolutionary biology from experiments in other contexts. First, experiments in evolutionary biology rest on a wealth of information provided by strictly observational ecological studies; in the absence of such information experiments in evolutionary biology make no sense. Second, there is a trade-off between how much control investigators have over the conditions being studied and how informative the results of the experiment will be with regard to natural populations. (shrink)

: Bayesians and error statisticians have relied heavily upon examples from physics in developing their accounts of scientific inference. The present essay demonstrates it is possible to analyze H.B.D. Kettlewell's classic study of natural selection from Deborah Mayo's error statistical point of view (Mayo 1996). A comparison with a previous analysis of this episode from a Bayesian perspective (Rudge 1998) reveals that the error statistical account makes better sense of investigations such as Kettlewell's because it clarifies how core elements in (...) the design of experiments are used to minimize erroneous inferences rather than dwelling on whether the strategies used are reasonable. (shrink)

: Most work done in philosophy of experiment has focused on experiments taken from the domain of physics. The present essay tests whether Allan Franklin's (1984, 1986, 1989, 1990) philosophy of experiment developed in the context of high energy physics can be extended to include examples from evolutionary biology, such as H. B. D. Kettlewell's (1955, 1956, 1958) famous studies of industrial melanism in the peppered moth, Biston betularia. The analysis demonstrates that many of the techniques used by evolutionary biologists (...) exemplify the strategies Franklin lists, and identifies an additional strategy that can likewise be justified by appeal to Bayes's Theorem. (shrink)

This survey of major developments in North American philosophy of science begins with the mid-1960s consolidation of the disciplinary synthesis of internalist history and philosophy of science (HPS) as a response to criticisms of logical empiricism. These developments are grouped for discussion under the following headings: historical metamethodologies, scientific realisms, philosophies of the special sciences, revivals of empiricism, cognitivist naturalisms, social epistemologies, feminist theories of science, studies of experiment and the disunity of science, and studies of science as practice and (...) culture. A unifying theme of the survey is the relation between historical metamethodologists and scientific realists, which dominated philosophical work in the late 1970s. I argue that many of the alternative cognitive naturalisms, social epistemologies, and feminist theories that have been proposed can be understood as analogues to the differences between metamethodological theories of scientific rationality and realist accounts of successful reference to real causal processes. Recent work on experiment, scientific practice, and the culture of science may, however, challenge the underlying conception of the field according to which realism and historical rationalism (or their descendants) are the important alternatives available, and thus may take philosophy of science in new directions. (shrink)

In a 1993 paper, I argued that empirical treatments of the epistemologyused by scientists in experimental work are too abstract in practice tocounter relativist efforts to explain the outcome of scientificcontroversies by reference to sociological forces. This was because, atthe rarefied level at which the methodology of scientists is treated byphilosophers, multiple mutually inconsistent instantiations of theprinciples described by philosophers are employed by contestingscientists. These multiple construals change within a scientificcommunity over short time frames, and these different versions ofscientific methodology (...) can determine the outcome of a controversy. Iillustrated with a comparatively detailed analysis of the methodologyused by biologists debating the existence of an entity called thebacterial mesosome between the mid-1950s and the mid-1970s. This 1993piece has drawn several critiques in the philosophical literature. Inthis present piece I respond to these critiques and argue that they failto address the core argument of the original paper, and I reflectfurther on the methodologies of philosophers of science pursuingempirical or `naturalistic' epistemology. (shrink)

Prediction is more than testing established theory by examining whether the prediction matches the data. To show this, I examine the practices of a community of scientists, known as threaders, who are attempting to predict the final, folded structure of a protein from its primary structure, i.e., its amino acid sequence. These scientists employ a careful and deliberate methodology of prediction. A key feature of the methodology is calibration. They calibrate in order to construct better models. The construction leads to (...) knowledge of how to construct or build an object. Thus, prediction serves a cognitive goal of model construction and not just model or theory testing. The kind of knowledge that results is relevantly different than theoretical knowledge. (shrink)

The roles of classical realism, logical positivism, and pragmatic instrumentalism in the shaping of fundamental ideas in quantum physics are examined in the light of some recent historical and sociological studies of the factors that influenced their development. It is shown that those studies indicate that the conventionalistic form of instrumentalism that has dominated all the major post-World War II developments in quantum physics is not an outgrowth of the Copenhagen school, and that despite the “schism” in twentieth century physics (...) created by the Bohr-Einstein “disagreements” on foundational issues in quantum theory, both their philosophical stands were very much opposed to those of conventionalistic instrumentalism. Quotations from the writings of Dirac, Heisenberg, Popper, Russell, and other influential thinkers, are provided, illustrating the fact that, despite the various divergencies in their opinions, they all either opposed the instrumentalist concept of “truth” in general, or its conventionalistic version in post-World War II quantum physics in particular. The basic epistemic ideas of a quantum geometry approach to quantum physics are reviewed and discussed from the point of view of a quantum realism that seeks to reconcile Bohr's “positivism” with Einstein's “realism” by emphasizing the existence of an underlying quantum reality, in which they both believed. This quantum geometry framework seeks to introduce geometro-stochastic concepts that are specifically designed for the systematic description of that underlying quantum reality, by developing the conceptual and mathematical tools that are most appropriate for such a use. (shrink)

The 1980s, it is often claimed, was the decade when experimentation finally became a philosophical topic. This was the responsibility, the claim continues, of one particular movement within philosophy of science, called “new experimentalism.” The aim of this article is to complicate this historical narrative. We argue that in the 1980s, the study of experimentation was carried out not by one movement with one particular aim but rather in a diverse and open-ended way by people with different aims and backgrounds. (...) We then argue that from the late 1990s onward, this diversity disappeared and made room for disciplinary divisions—questions concerning experimentation became philosophical, others sociological, and so on. The reason for this, we claim, was that science and technology studies, philosophy of technology, and philosophy of science took over aspects of the 1980s study of experimentation. In this way, we argue, these elements became institutionalized, whereas others were forgotten. The importance of this process of institutionalization is illustrated by means of a discussion of other, similar approaches to the philosophy of experimentation that have not been able to ensure continuity because they did not find an institutional home. (shrink)

Experiments may not reveal their full import at the time that they are performed. The scientists who perform them usually are testing a specific hypothesis and quite often have specific expectations limiting the possible inferences that can be drawn from the experiment. Nonetheless, as Hacking has said, experiments have lives of their own. Those lives do not end with the initial report of the results and consequences of the experiment. Going back and rethinking the consequences of the experiment in a (...) new context, theoretical or empirical, has great merit as a strategy for investigation and for scientific problem analysis. I apply this analysis to the interplay between Fizeau's classic optical experiments and the building of special relativity. Einstein's understanding of the problems facing classical electrodynamics and optics, in part, was informed by Fizeau's 1851 experiments. However, between 1851 and 1905, Fizeau's experiments were duplicated and reinterpreted by a succession of scientists, including Hertz, Lorentz, and Michelson. Einstein's analysis of the consequences of the experiments is tied closely to this theoretical and experimental tradition. However, Einstein's own inferences from the experiments differ greatly from the inferences drawn by others in that tradition. (shrink)

I examine the role of inference from experiment in theory building. What are the options open to the scientific community when faced with an experimental result that appears to be in conflict with accepted theory? I distinguish, in Laudan's (1977), Nickels's (1981), and Franklin's (1993) sense, between the context of pursuit and the context of justification of a scientific theory. Making this distinction allows for a productive middle position between epistemic realism and constructivism. The decision to pursue a new or (...) a revised theory in response to the new evidence may not be fully rationally determined. Nonetheless, it is possible to distinguish the question of whether there is reason to pursue a theory from the question of whether that theory, once it has been pursued over time, solves a problem of interest to science. I argue that, in this context, there is a solid way to distinguish between the contexts of pursuit and of justification, on the basis of a theory's evidential support and problem-solving ability. (shrink)

Allan Franklin has identified a number of strategies that scientists use to build confidence in experimental results. This paper shows that Franklin's strategies have direct analogues in the context of computer simulation and then suggests that one of his strategies—the so-called 'Sherlock Holmes' strategy—deserves a privileged place within the epistemologies of experiment and simulation. In particular, it is argued that while the successful application of even several of Franklin's other strategies (or their analogues in simulation) may not be sufficient for (...) justified belief in results, the successful application of a slightly elaborated version of the Sherlock Holmes strategy is sufficient. (shrink)

Computer simulation and philosophy of science Content Type Journal Article Pages 1-4 DOI 10.1007/s11016-011-9567-8 Authors Wendy S. Parker, Department of Philosophy, Ellis Hall 202, Ohio University, Athens, OH 45701, USA Journal Metascience Online ISSN 1467-9981 Print ISSN 0815-0796.

Amedeo Giorgi has published a review article devoted to Phenomenology as Qualitative Research: A Critical Analysis of Meaning Attribution. However, anyone reading this article, but unfamiliar with the book, will get a distorted view of what it is about, whom it is addressed to, what it seeks to achieve and how it goes about presenting its arguments. Not mildly distorted, in need of the odd correction here and there, but systematically misrepresented. The article is a study in misreading. Giorgi misreads (...) the book's mise en scène; he misreads its narrative arc; he misreads individual arguments; he misreads short, simple passages; he misreads the philosophy of the science literature; he misreads his own data; he misreads the title; he misreads the blurb; he misreads the acknowledgements. In addition, there are serious failures of scholarship (ironically, he demonstrates how unacquainted he is with the relevant literature at the very moment he is accusing me of being ill‐informed). In this reply, I provide several examples of these errors, but my primary aim is to understand why Giorgi's misreading is as ubiquitous as it is. To this end, I explain his mistakes by reference to the hermetic epistemology within which he is confined. (shrink)

Many philosophers have claimed that Bayesianism can provide a simple justification for hypothetico-deductive inference, long regarded as a cornerstone of the scientific method. Following up a remark of van Fraassen, we analyze a problem for the putative Bayesian justification of H-D inference in the case where what we learn from observation is logically stronger than what our theory implies. Firstly, we demonstrate that in such cases the simple Bayesian justification does not necessarily apply. Secondly, we identify a set of sufficient (...) conditions for the mismatch in logical strength to be justifiably ignored as a "harmless idealization''. Thirdly, we argue, based upon scientific examples, that the pattern of H-D inference of which there is a ready Bayesian justification is only rarely the pattern that one actually finds at work in science. Whatever the other virtues of Bayesianism, the idea that it yields a simple justification of a pervasive pattern of scientific inference appears to have been oversold. (shrink)

It is customary to distinguish experimental from purely observational sciences. The former include physics and molecular biology, the latter astronomy and palaeontology. Experiments involve actively intervening in the course of nature, as opposed to observing events that would have happened anyway. When a molecular biologist inserts viral DNA into a bacterium in his laboratory, this is an experiment; but when an astronomer points his telescope at the heavens, this is an observation. Without the biologist’s handiwork the bacterium would never have (...) contained foreign DNA; but the planets would have continued orbiting the sun whether or not the astronomer had directed his telescope skyward. The observational/experimental distinction would probably be difficult to make precise 1, as the notion of an ‘intervention’ is not easily defined, but it is intuitively fairly clear, and is frequently invoked by scientists and historians of science. Experimentation, or ‘putting questions to nature’, is often cited as a hallmark of the modern scientific method, something that permitted the enormous advances of the last 350 years. And it is sometimes said that the social sciences lag behind the natural because controlled experiments cannot be done so readily in the former. Moreover in certain sciences, e.g. epidemiology, students are explicitly taught that experimental data is preferable to observational data, particularly for doing causal inference. So the distinction between observational and experimental science has quite wide currency, and is often regarded as methodologically significant. Surprisingly, mainstream philosophy of science has had rather little to say about the observational/experimental distinction. 2 For example, discussions of confirmation usually invoke a notion of ‘evidence’, to be contrasted with ‘theory’ or ‘hypothesis’; the aim is to understand how the evidence bears on the hypothesis. But whether this ‘evidence’ comes from observation or experiment generally plays no role in the discussion; this is true …. (shrink)

This paper defends an account of explanatory reasoning generally, and inference to the best explanation in particular, according to which it first and foremost justifies pursuing hypotheses rather than accepting them as true. This side-steps the problem of why better explanations should be more likely to be true. I argue that this account faces no analogous problems. I propose an account of justification for pursuit and show how this provides a simple and straightforward connection between explanatoriness and justification for pursuit.

This article highlights a use of analogies in science that so far has received relatively little systematic discussion: providing reasons for pursuing a model or theory. Using the development of the liquid drop model as a test case, I critically assess two extant pursuit worthiness accounts: that analogies justify pursuit by supporting plausibility arguments and that analogies can serve as a guide to potential theoretical unification. Neither of these fit the liquid drop model case. Instead, I develop an alternative account, (...) based on the idea that analogies facilitate the transfer of a well-understood modelling strategy to a new domain. 1Introduction2Case Study: The Development of the Liquid Drop Model3Plausibility Accounts 3.1Bartha on plausibility and analogical inference3.2Plausibility and the drop analogy4Analogies as a Guide to Unification5Generative Accounts 5.1Analogy-based modelling strategies5.2Did analogies play a merely generative role?6A New Pursuit Worthiness Account of Analogies 6.1Transferring understanding-with through analogies6.2Understanding-with and the liquid drop model7Conclusion. (shrink)

The rejection of the idea that the so‐called Duhem‐Quine thesis in fact expresses a thesis upheld by either Duhem or Quine invites a more detailed comparison of their views. It is suggested that the arguments of each have a certain impact on the positions maintained by the other. In particular, Quine's development of his notion of ontological commitment is enlisted in the interpretation of Duhem's position. It is argued that this counts against the instrumentalist construal usually put on what Duhem (...) says about approximation and historical continuity. (shrink)

What is noise? A tumultuous crowd is noisy or, more cheerfully, a group of students on holiday, or a flock of migrating birds. A loud conversation or loud laughter can be noisy if we are reading a philosophy article, or we are performing a physics experiment, or we are concentrating on a yoga exercise. In all such cases, noise is something that others do and that we unwillingly suffer, something that we perceive as an invasion of our perceptual space, or (...) an interference with it.2 We instinctively associate noise with an idea of impediment, or obstacle—with a source of distraction that prevents us from accomplishing the task or occupation we are focusing on. These associations are confirmed by dictionary... (shrink)

The ArgumentWhile there has been muchattention given to experiment in modern science studies, there has been astoundingly little concern spared over the practice ofquanitataivemeasurment.Thus myths about the unreasonable effectiveness of mathematice in science still abound. This paper presents: An explicit mathematical model of the stabilization of quantitative constants in a mathematical science to rival older Bayesian and classical accounts;a framework for writing a history of pracitces with regard to treatment of quantitative measurement erroe; resourece for the comparative sociology of differing (...) discipliness in this regard;and a prolegonmena to a critique of orthodox economics and accounting theories. The key to all these diverse themes is the realization that no one individual alone is capable of fixing the magnitude of a quantitative error estimate, and therefore the social construction of error must be given a more precise meaning, an therefore the social construction of error must be given a more precise menaing, and that this occurs through the istrumentality of meta-analysis. (shrink)

In 1966 the population biologist Richard Levins gave a forceful and in?uential defence of a method called “robustness analysis” (RA). RA is a way of assessing the result of a model by showing that different but related models give the same result. As Levins put it, “our truth is the intersection of independent lies” (1966, 423). Steven Orzack and Elliott Sober (1993) responded with an equally forceful critique of this method, concluding that the idea of robustness “lacks proper de?nition and (...) that its bearing on the question of whether a proposition is true is highly problematic” (533). Replies to Orzack and Sober, from Levins (1993) and Weisberg (2006b), have rejected the idea that RA, on its own, can con?rm the results of models. I argue that these replies have not properly addressed Orzack and Sober’s real criticisms, which focus not on the role of empirical data in RA but on the problem of ensuring that the models used in RA are independent. By drawing on accounts of RA in experimental science, I argue that there is in fact a fallible but viable form of RA that can both con?rm the results of models and incorporate empirical data. However, for reasons other than those of Orzack and Sober, I conclude that RA may be of limited use in model-based science, as the example of ?shery biology can show. (shrink)

Rolston (1988) argues that in order to act ethically in the environment, moral agents must assume that their actions are potentially harmful, and then strive to prove otherwise before implementing that action. In order to determine whether or not an action in the environment is harmful requires the tools of applied epistemology in order to act in accord with Rolston’s ethical prescription. This link between ethics and epistemology demands a closer look at the relationship between confirmation theory, particularly notions of (...) plausibility, in the philosophy of science and environmental ethics. Upon taking this look, I conclude that, at least logically, we are no better off assuming that actions are maximally risky (Rolston) than when we assume minimal risk. “Our vulnerability to error is greatest not from the things that we include in the model, but from the prophecies we leave out entirely.” Francis Bretherton “What is the main epistemic problem concerning science? I take it that it is the explication of how we compare and evaluate theories, as a basis either for theory acceptance or for practical action.” Bas van Fraassen “We face two kinds of ethical difficulties. One is where we know what ought to be done but not how to get the company to do it. The other is where we do not know what is right. We do not know the facts, or how to weigh the facts, especially statistical ones. We do not know the probabilities for development of future technologies. We do not know how to attach values to facts, or how to trade this good off against that one.” Holmes Rolston. (shrink)

With the careful use of the polywater episode in the history of chemistry as a case study, I will show that the distinction recently made in the philosophy of science between experimenting on an entity and manipulating that entity is best seen as a distinction between experimenting on, and experimenting with, that entity. The polywater case also reveals that Ian Hacking's 1983 manipulability criterion is not a necessary condition for realism, and that scientists can, and do, justifiably change their minds (...) as to the reality of theoretical entities. (shrink)

In this paper I argue that aim-oriented empiricism (AOE), a conception of natural science that I have defended at some length elsewhere[1], is a kind of synthesis of the views of Popper, Kuhn and Lakatos, but is also an improvement over the views of all three. Whereas Popper's falsificationism protects metaphysical assumptions implicitly made by science from criticism, AOE exposes all such assumptions to sustained criticism, and furthermore focuses criticism on those assumptions most likely to need revision if science is (...) to make progress. Even though AOE is, in this way, more Popperian than Popper, it is also, in some respects, more like the views of Kuhn and Lakatos than falsificationism is. AOE is able, however, to solve problems which Kuhn's and Lakatos's views cannot solve. [Back to Top]. (shrink)

This paper investigates some metaphysical and epistemological assumptions behind Bogen and Woodward’s data-to-phenomena inferences. I raise a series of points and suggest an alternative possible Kantian stance about data-to-phenomena inferences. I clarify the nature of the suggested Kantian stance by contrasting it with McAllister’s view about phenomena as patterns in data sets.