Since the late 1980s, the human genetics and genomics research community has been promising to usher in a “new paradigm for health care”—one that uses molecular profiling to identify human genetic variants implicated in multifactorial health risks. After the completion of the Human Genome Project in 2003, a wide range of stakeholders became committed to this “paradigm shift,” creating a confluence of investment, advocacy, and enthusiasm that bears all the marks of a “scientific/intellectual social movement” within biomedicine. Proponents of this (...) movement usually offer four ways in which their approach to medical diagnosis and health care improves upon current practices, arguing that it is more “personalized,” “predictive,” “preventive,” and “participatory” than the medical status quo. Initially, it was personalization that seemed to best sum up the movement's appeal. By 2012, however, powerful opinion leaders were abandoning “personalized medicine” in favor of a new label: “precision medicine.” The new label received a decisive seal of approval when, in January 2015, President Obama unveiled plans for a national “precision medicine initiative” to promote the development and use of genomic tools in health care. (shrink)

The price of sequencing all the DNA in a person's genome is falling so fast that, according to one biotech leader, soon it won't cost much more than flushing a toilet. Getting all that genomic data at an ever‐lower cost excites the imaginations not only of biotech investors and researchers but also of the President and many members of Congress. They envision the data ushering in an age of “personalized medicine,” where medical care is tailored to persons’ genomes. The new (...) name “precision medicine” should not distract us from the fact that the genomic information it depends on is more complex, uncertain, and ambiguous than anyone had hoped, nor should it distract us from an important ethical point. Unfortunately, several developments of the last fifteen years suggest that, in our excitement about the technological capacity to gather genomic data at an ever lower cost, we are drifting away from what has long been a basic ethical commitment: to offer persons a process that enables them to provide informed consent before they or anyone else accesses their genetic information. (shrink)

The idea of personalised medicine (PM) has gathered momentum recently, attracting funding and generating hopes as well as scepticism. As PM gives rise to differing interpretations, there have been several attempts to clarify the concept. In an influential paper published in this journal, Schleidgen and colleagues have proposed a precise and narrow definition of PM on the basis of a systematic literature review. Given that their conclusion is at odds with those of other recent attempts to understand PM, we consider (...) whether their systematic review gives them an edge over competing interpretations. We have found some methodological weaknesses and questionable assumptions in Schleidgen and colleagues’ attempt to provide a more specific definition of PM. Our perplexities concern the lack of criteria for assessing the epistemic strength of the definitions that they consider, as well as the logical principles used to extract a more precise definition, the narrowness of the pool from which they have drawn their empirical data, and finally their overlooking the fact that definitions depend on the context of use. We are also worried that their ethical assumption that only patients’ interests are legitimate is too simplistic and drives all other stakeholders’ interests—including those that are justifiable—underground, thus compromising any hope of a transparent and fair negotiation among a plurality of actors and interests. As an alternative to the shortcomings of attempting a semantic disciplining of the concept we propose a pragmatic approach. Rather than considering PM to be a scientific concept in need of precise demarcation, we look at it as an open and negotiable concept used in a variety of contexts including at the level of orienting research goals and policy objectives. We believe that since PM is still more an ideal than an achieved reality, a plurality of visions is to be expected and we need to pay attention to the people, reasons and interests behind these alternative conceptions. In other words, the logic and politics of PM cannot be disentangled and disagreements need to be tackled addressing the normative and strategic conflicts behind them. (shrink)

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science , but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of (...) models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before. (shrink)

John Worrall and Nancy Cartwright have both argued that randomized controlled trials are “testing the wrong theory.” They claim that RCTs are designed to test inferences about the causal relationships in the study population, but this does not guarantee a justified inference about the causal relationships in the more diverse population in clinical practice. In this article I argue that the epistemology of theory testing in trials is more complicated than either Worrall’s or Cartwright’s accounts suggest. I illustrate this more (...) complex theoretical structure with case studies in medical theory testing from Alzheimer’s research and anticancer drugs in personalized medicine. (shrink)

one takes to be the most salient, any pair could be judged more similar to each other than to the third. Goodman uses this second problem to showthat there can be no context-free similarity metric, either in the trivial case or in a scientifically ...

In this paper, I offer one example of conceptual change. Specifically, I contend that the discovery that viruses could cause cancer represents an excellent example of branch jumping, one of Thagard’s nine forms of conceptual change. Prior to about 1960, cancer was generally regarded as a degenerative, chronic, non-infectious disease. Cancer causation was therefore usually held to be a gradual process of accumulating cellular damage, caused by relatively non-specific component causes, acting over long periods of time. Viral infections, on the (...) other hand, were generally understood to be acute processes, whereby single, specific and necessary causal agents acted alone to produce disease. However, during the 1960s and 1970s, a number of cancers were discovered to have an infectious aetiology. Of particular note were two—Burkitt’s lymphoma and cervical cancer—which I will discuss in detail later in this piece. Together, these discoveries led, in the short term, to a tentative aetiological reclassification of some types of cancer as infectious diseases and, in the longer term, to a full-blown reclassification of cancer as an aetiological disease branch in its own right. This process of reclassification forms the empirical basis for my concluding remarks on the influence of classification upon causation in medicine. Through this, I aim to demonstrate that conceptual change, far from being a purely abstract concern of the philosopher of science, is of substantial import to scientific practitioners. (shrink)

Proponents of mechanistic explanation all acknowledge the importance of organization. But they have also tended to emphasize specificity with respect to parts and operations in mechanisms. We argue that in understanding one important mode of organization—patterns of causal connectivity—a successful explanatory strategy abstracts from the specifics of the mechanism and invokes tools such as those of graph theory to explain how mechanisms with a particular mode of connectivity will behave. We discuss the connection between organization, abstraction, and mechanistic explanation and (...) illustrate our claims by looking at an example from recent research on so-called network motifs. (shrink)

Even though the evidence‐based medicine movement (EBM) labels mechanisms a low quality form of evidence, consideration of the mechanisms on which medicine relies, and the distinct roles that mechanisms might play in clinical practice, offers a number of insights into EBM itself. In this paper, I examine the connections between EBM and mechanisms from several angles. I diagnose what went wrong in two examples where mechanistic reasoning failed to generate accurate predictions for how a dysfunctional mechanism would respond to intervention. (...) I then use these examples to explain why we should expect this kind of mechanistic reasoning to fail in systematic ways, by situating these failures in terms of evolved complexity of the causal system(s) in question. I argue that there is still a different role in which mechanisms continue to figure as evidence in EBM: namely, in guiding the application of population‐level recommendations to individual patients. Thus, even though the evidence‐based movement rejects one role in which mechanistic reasoning serves as evidence, there are other evidentiary roles for mechanistic reasoning. This renders plausible the claims of some critics of evidencebased medicine who point to the ineliminable role of clinical experience. Clearly specifying the ways in which mechanisms and mechanistic reasoning can be involved in clinical practice frames the discussion about EBM and clinical experience in more fruitful terms. (shrink)

Philosophers of science have insisted that evidence of underlying mechanisms is required to support claims about the effects of medical interventions. Yet evidence about mechanisms does not feature on dominant evidence-based medicine “hierarchies.” After arguing that only inferences from mechanisms (“mechanistic reasoning”)—not mechanisms themselves—count as evidence, I argue for a middle ground. Mechanistic reasoning is not required to establish causation when we have high-quality controlled studies; moreover, mechanistic reasoning is more problematic than has been assumed. Yet where the problems can (...) be overcome, mechanistic reasoning can and should be used as evidence. (shrink)

In this sequence of philosophical essays about natural science, the author argues that fundamental explanatory laws, the deepest and most admired successes of modern physics, do not in fact describe regularities that exist in nature. Cartwright draws from many real-life examples to propound a novel distinction: that theoretical entities, and the complex and localized laws that describe them, can be interpreted realistically, but the simple unifying laws of basic theory cannot.

Using an example of a computer simulation of the convective structure of a red giant star, this paper argues that simulation is a rich inferential process, and not simply a "number crunching" technique. The scientific practice of simulation, moreover, poses some interesting and challenging epistemological and methodological issues for the philosophy of science. I will also argue that these challenges would be best addressed by a philosophy of science that places less emphasis on the representational capacity of theories (and ascribes (...) that capacity instead to models) and more emphasis on the role of theory in guiding (rather than determining) the construction of models. (shrink)

Empirical agreement is often used as an important criterion when assessing the validity of scientific models. However, it is by no means a sufficient criterion as a model can be so adjusted as to fit available data even though it is based on hypotheses whose plausibility is known to be questionable. Our aim in this paper is to investigate into the uses of empirical agreement within the process of model validation.

Using an example of a computer simulation of the convective structure of a red giant star, this paper argues that simulation is a rich inferential process, and not simply a “number crunching” technique. The scientific practice of simulation, moreover, poses some interesting and challenging epistemological and methodological issues for the philosophy of science. I will also argue that these challenges would be best addressed by a philosophy of science that places less emphasis on the representational capacity of theories and more (...) emphasis on the role of theory in guiding the construction of models. (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)

Recently, individualized or personalized medicine (PM) has become a buzz word in the academic as well as public debate surrounding health care. However, PM lacks a clear definition and is open to interpretation. This conceptual vagueness complicates public discourse on chances, risks and limits of PM. Furthermore, stakeholders might use it to further their respective interests and preferences. For these reasons it is important to have a shared understanding of PM. In this paper, we present a sufficiently precise as well (...) as adequate definition of PM with the potential of wide acceptance. (shrink)

Evidence based medicine has been a topic of considerable controversy in medical and health care circles over its short lifetime, because of the claims made by its exponents about the criteria used to assess the evidence for or against the effectiveness of medical interventions. The central epistemological debates underpinning the debates about evidence based medicine are reviewed by this paper, and some areas are suggested where further work remains to be done. In particular, further work is needed on the theory (...) of evidence and inference; causation and correlation; clinical judgment and collective knowledge; the structure of medical theory; and the nature of clinical effectiveness. (shrink)

Ever since David Hume, empiricists have barred powers and capacities from nature. In this book Cartwright argues that capacities are essential in our scientific world, and, contrary to empiricist orthodoxy, that they can meet sufficiently strict demands for testability. Econometrics is one discipline where probabilities are used to measure causal capacities, and the technology of modern physics provides several examples of testing capacities (such as lasers). Cartwright concludes by applying the lessons of the book about capacities and probabilities to the (...) explanation of the role of causality in quantum mechanics. (shrink)

Designing and manufacturing medical devices for specific patients is becoming increasingly feasible with developments in 3D printing and 3D imaging software. This raises the question of how patient-specific devices can be evaluated, since our ‘gold standard’ method for evaluation, the randomised controlled trial, requires that an intervention is standardised across a number of individuals in an experimental group. I distinguish several senses of patient-specific device, and focus the discussion on understanding the problem of variations between instances of an intervention for (...) RCT evaluation. I argue that, despite initial appearances, it is theoretically possible to use RCTs to evaluate some patient-specific medical devices. However, the argument reveals significant difficulties for ensuring the validity of such trials, with implications for how we should think about methods of evidence gathering and regulatory approaches for these technologies. (shrink)

Evidence-based medicine (EBM) makes use of explicit procedures for grading evidence for causal claims. Normally, these procedures categorise evidence of correlation produced by statistical trials as better evidence for a causal claim than evidence of mechanisms produced by other methods. We argue, in contrast, that evidence of mechanisms needs to be viewed as complementary to, rather than inferior to, evidence of correlation. In this paper we first set out the case for treating evidence of mechanisms alongside evidence of correlation in (...) explicit protocols for evaluating evidence. Next we provide case studies which exemplify the ways in which evidence of mechanisms complements evidence of correlation in practice. Finally, we put forward some general considerations as to how the two sorts of evidence can be more closely integrated by EBM. (shrink)

This paper raises questions about the epistemological foundations of evidence-based medicine . We argue that EBM is based upon reliabilist epistemological assumptions, and that this is appropriate - we should focus on identifying the most reliable processes for generating and collecting medical knowledge. However, we note that this should not be reduced to narrow questions about which research methodologies are the best for gathering evidence. Reliable processes for generating medical evidence might lie outside of formal research methods. We also question (...) the notion of the knower that is assumed by EBM. We argue that EBM assumes an enlightenment conception of knowers as autonomous, substitutable individuals. This conception is troubled by the way that clinicians learn the role of anecdote in health care and the role of patient choice, all of which bring into play features of clinicians and patients as situated individuals with particular backgrounds and experiences. EBM's enlightenment conception of the knower is also troubled by aspects of the way evidence is produced. Given these limitations, we argue that EBM should retain its reliabilist bent, but should look beyond formal research methodologies in identifying processes that yield reliable evidence for clinical practice. We suggest looking to feminist epistemology, with its focus on the standpoints of individual situated knowers, and the role of social context in determining what counts as knowledge. (shrink)

Most clinicians take for granted a simple, reductionist understanding of medical knowledge that is at odds with how they actually practice medicine; routine medical decisions incorporate more complicated kinds of information than most standard accounts of medical reasoning suggest. A better understanding of the structure and function of knowledge in medicine can lead to practical improvements in clinical medicine. This understanding requires some familiarity with epistemology, the study of knowledge and its structure, in medicine. Michael Polanyi's theory of tacit knowing (...) is advanced as the basis for developing a more accurate understanding of medical knowledge. Tacit knowing, which explores the taken-for-granted background knowledge that underlies all human knowing, is explained in detail with a focus on its relevance for clinical medicine. The implications of recognizing tacit knowing in medicine and medical decisions are discussed. These include the ability to explain the importance of the clinical encounter in medical practice, mechanisms for analysing patient and doctor as persons, and the need for humility given the uncertainty that the tacit dimension injects into all medical decisions. This more robust medical epistemology allows clinicians to better articulate the nature and importance of patient-centred care, to avoid pitfalls inherent in reductionist approaches to medical knowledge, and to think more clearly about the relationships between medicine and health care at the individual and population levels. (shrink)

Philosophy of medicine encompasses a broad range of methodological approaches and theoretical perspectives—from the uses of statistical reasoning and probability theory in epidemiology and evidence-based medicine to questions about how to recognize the uniqueness of individual patients in medical humanities, person-centered care, and values-based practice; and from debates about causal ontology to questions of how to cultivate epistemic and moral virtue in practice. Apart from being different ways of thinking about medical practices, do these different philosophical approaches have anything in (...) common? Are they committed to incompatible assumptions about the nature of science and its relationship to experience, value, and the art of medicine, or are different approaches nonetheless complementary? The chapter examines the questions these different approaches and perspectives raise and considers why so many theorists of medicine seek to find a “base” or “center” for medicine—as though there is, or should be, some general conceptual thread linking the things we call “usual clinical practice” in the real world. It further considers whether there are alternative approaches to the philosophy of medicine, which do not embody this philosophical assumption; and it suggests that a key tool for evaluating approaches must be their ability to contribute something of genuine value to clinical medicine. (shrink)

In recent years, personalized medicine (PM) has become a highly regarded line of development in medicine. Yet, it is still a relatively new field. As a consequence, the discussion of its future developments, in particular of its ethical implications, in most cases can only be anticipative. Such anticipative discussions, however, pose several challenges. Nevertheless, they play a crucial role for shaping PM’s further developments. Therefore, it is vital to understand how the ethical discourse on PM is conducted, i.e. on what (...) – empirical and normative – assumptions ethical arguments are based regarding PM’s current and future developments. (shrink)

Causal relations among components and activities are intentionally misrepresented in mechanistic explanations found routinely across the life sciences. Since several mechanists explicitly advocate accurately representing factors that make a difference to the outcome, these idealizations conflict with the stated rationale for mechanistic explanation. We argue that these idealizations signal an overlooked feature of reasoning in molecular and cell biology—mechanistic explanations do not occur in isolation—and suggest that explanatory practices within the mechanistic tradition share commonalities with model-based approaches prevalent in population (...) biology. (shrink)

Western philosophers have traditionally concentrated on theory as the means for expressing knowledge about a variety of phenomena. This absorbing book challenges this fundamental notion by showing how objects themselves, specifically scientific instruments, can express knowledge. As he considers numerous intriguing examples, Davis Baird gives us the tools to "read" the material products of science and technology and to understand their place in culture. Making a provocative and original challenge to our conception of knowledge itself, _Thing Knowledge _demands that we (...) take a new look at theories of science and technology, knowledge, progress, and change. Baird considers a wide range of instruments, including Faraday's first electric motor, eighteenth-century mechanical models of the solar system, the cyclotron, various instruments developed by analytical chemists between 1930 and 1960, spectrometers, and more. (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 sequencing of the human genome and decreasing costs of sequencing technology have led to the notion of ‘personalized medicine’. This has been taken by some authors to indicate that personalized medicine will provide individualized treatments solely based on one’s DNA sequence. We argue this is overly optimistic and misconstrues the notion of personalization. Such interpretations fail to account for economic, policy and structural constraints on the delivery of healthcare. Furthermore, notions of individualization based on genomic data potentially take us (...) down the road of genetic reductionism obscuring the role of environmental factors in disease and ill health. We propose that one should see personalized medicine as a way of using personal genomic information to stratify individuals into subpopulations and suggest that personalized medicine be seen within a broader idea of personalized healthcare, reflecting healthcare that integrates personal genomic data into cultural, environmental and personal contexts. (shrink)

In the context of scientists' reflections on genomics, we examine some fundamental issues in the emerging postgenomic discipline of systems biology. Systems biology is best understood as consisting of two streams. One, which we shall call ‘pragmatic systems biology’, emphasises large‐scale molecular interactions; the other, which we shall refer to as ‘systems‐theoretic biology’, emphasises system principles. Both are committed to mathematical modelling, and both lack a clear account of what biological systems are. We discuss the underlying issues in identifying systems (...) and how causality operates at different levels of organisation. We suggest that resolving such basic problems is a key task for successful systems biology, and that philosophers could contribute to its realisation. We conclude with an argument for more sociologically informed collaboration between scientists and philosophers. BioEssays 27:1270–1276, 2005. © 2005 Wiley Periodicals, Inc. (shrink)

People increasingly live online, sharing publicly what might have once seemed private, but at the same time are enraged by extremes of government surveillance and the corresponding invasion into our private lives. In this enlightening work, Adam Henschke re-examines privacy and property in the age of surveillance in order to understand not only the importance of these social conventions, but also their moral relevance. By analyzing identity and information, and presenting a case for a relation between the two, he explains (...) the moral importance of virtual identities and offers an ethically robust solution to design surveillance technologies. This book should be read by anyone interested in surveillance technology, new information technology more generally, and social concepts like privacy and property. (shrink)

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science, but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of models (...) and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before. (shrink)

an observation to formulate a theory, it is no surprise that the resulting theory accurately captures that observation. However, when the theory makes a novel prediction—when it predicts an observation that was not used in its formulation—this seems to provide more substantial confirmation of the theory. This paper presents a new approach to the vexed problem of understanding the epistemic difference between prediction and accommodation. In fact, there are several problems that need to be disentangled; in all of them, the (...) key is the concept of overfitting. We float the hypothesis that accommodation is a defective methodology only when the methods used to accommodate the data fail to guard against the risk of overfitting. We connect our analysis with the proposals that other philosophers have made. We also discuss its bearing on the conflict between instrumentalism and scientific realism. Introduction Predictivisms—a taxonomy Observations Formulating the problem What might Annie be doing wrong? Solutions Observations explained Mayo on severe tests The miracle argument and scientific realism Concluding comments. (shrink)

Systems medicine, which is based on computational modelling of biological systems, is emerging as an increasingly prominent part of the personalized medicine movement. It is often promoted as ‘P4 medicine’. In this article, we test promises made by some of its proponents that systems medicine will be able to develop a scientific, quantitative metric for wellness that will eliminate the purported vagueness, ambiguity, and incompleteness—that is, normativity—of previous health definitions. We do so by examining the most concrete and relevant evidence (...) for such a metric available: a patent that describes a systems medicine method for assessing health and disease. We find that although systems medicine is promoted as heralding an era of transformative scientific objectivity, its definition of health seems at present still normatively based. As such, we argue that it will be open to influence from various stakeholders and that its purported objectivity may conceal important scientific, philosophical, and political issues. We also argue that this is an example of a general trend within biomedicine to create overly hopeful visions and expectations for the future. (shrink)

Proponents of evidence-based medicine and some philosophers of science seem to agree that knowledge of mechanisms can help solve the problem of applying results of controlled studies to target populations (‘the problem of extrapolation’). We describe the problem of extrapolation, characterize mechanisms, and outline how mechanistic knowledge might be used to solve the problem. Our main thesis is that there are four often overlooked problems with using mechanistic knowledge to solve the problem of extrapolation. First, our understanding of mechanisms is (...) often (and arguably, likely to remain) incomplete. Secondly, knowledge of mechanisms is not always applicable outside the tightly controlled laboratory conditions in which it is gained. Thirdly, mechanisms can behave paradoxically. Fourthly, as Daniel Steel points out, using mechanistic knowledge faces the problem of the ‘extrapolator’s circle’. At the same time, when the problems with mechanistic knowledge have been addressed, such knowledge can and should be used to mitigate (nothing can entirely solve) the problem of extrapolation. (shrink)

Over the last decade, personalized medicine has become a buzz word, which covers a broad spectrum of meanings and generates many different opinions. The purpose of this article is to achieve a better understanding of the reasons why personalized medicine gives rise to such conflicting opinions. We show that a major issue of personalized medicine is the gap existing between its claims and its reality. We then present and analyze different possible reasons for this gap. We propose an hypothesis inspired (...) by the Windelband’s distinction between nomothetic and idiographic methodology. We argue that the fuzzy situation of personalized medicine results from a mix between idiographic claims and nomothetic methodological procedures. Hence we suggest that the current quandary about personalized medicine cannot be solved without getting involved in a discussion about the complex epistemological and methodological status of medicine. To conclude, we show that the Gadamer’s view of medicine as a dialogical process can be fruitfully used and reveals that personalization is not a theoretical task, but a practical one, which takes place within the clinical encounter. (shrink)

What are the relative epistemic merits of building prosthetic models versus building nonprosthetic models and simulations? I argue that prosthetic models provide a sufficient test of affordance validity, that is, of whether the target system affords mechanisms that can be commandeered by a prosthesis. In other respects, prosthetic models are epistemically on par with nonprosthetic models. I focus on prosthetics in neuroscience, but the results are general. The goal of understanding how brain mechanisms work under ecologically and physiologically relevant conditions (...) is narrow compared to the search for maker's knowledge about how the brain can be made to work for us. (shrink)

The phrase "personalized medicine" has a built-in positive spin. Simple genetic tests can sometimes predict whether a particular individual will have a positive response to a particular drug or, alternatively, suffer costly and debilitating side effects. But little attention has been given to some challenging issues of justice raised by personalized medicine. How should we determine who would have a just claim to access particular treatments, especially very expensive ones? How effective do those treatments need to be?If there were a (...) thick, bright line separating minimal responders from maximal responders, then we could allocate these treatments in a fair and cost-effective way. But there is no bright line. The .. (shrink)

This article considers the prospects of inference to the best explanation as a method of confirming causal claims vis-à-vis the medical evidence of mechanisms. I show that IBE is actually descriptive of how scientists reason when choosing among hypotheses, that it is amenable to the balance/weight distinction, a pivotal pair of concepts in the philosophy of evidence, and that it can do justice to interesting features of the interplay between mechanistic and population level assessments.