Is there a universal set of rules for discovering and testing scientific hypotheses? Since the birth of modern science, philosophers, scientists, and other thinkers have wrestled with this fundamental question of scientific practice. Efforts to devise rigorous methods for obtaining scientific knowledge include the twenty-one rules Descartes proposed in his Rules for the Direction of the Mind and the four rules of reasoning that begin the third book of Newton's Principia , and continue today in debates over the very possibility (...) of such rules. Bringing together key primary sources spanning almost four centuries, Science Rules introduces readers to scientific methods that have played a prominent role in the history of scientific practice. Editor Peter Achinstein includes works by scientists and philosophers of science to offer a new perspective on the nature of scientific reasoning. For each of the methods discussed, he presents the original formulation of the method selections written by a proponent of the method together with an application to a particular scientific example and a critical analysis of the method that draws on historical and contemporary sources. The methods included in this volume are Cartesian rationalism with an application to Descartes' laws of motion Newton's inductivism and the law of gravity two versions of hypothetico-deductivism -- those of William Whewell and Karl Popper -- and the nineteenth-century wave theory of light Paul Feyerabend's principle of proliferation and Thomas Kuhn's views on scientific values, both of which deny that there are universal rules of method, with an application to Galileo's tower argument. Included also is a famous nineteenth-century debate about scientific reasoning between the hypothetico-deductivist William Whewell and the inductivist John Stuart Mill and an account of the realism-antirealism dispute about unobservables in science, with a consideration of Perrin's argument for the existence of molecules in the early twentieth century. (shrink)

The received view of an ad hochypothesis is that it accounts for only the observation(s) it was designed to account for, and so non-ad hocness is generally held to be necessary or important for an introduced hypothesis or modification to a theory. Attempts by Popper and several others to convincingly explicate this view, however, prove to be unsuccessful or of doubtful value, and familiar and firmer criteria for evaluating the hypotheses or modified theories so classified are characteristically available. These points (...) are obscured largely because the received view fails to adequately separate psychology from methodology or to recognise ambiguities in the use of ''ad hoc''. (shrink)

The first part of this paper shows that Qualitative Comparative Analysis (QCA)--also in its most recent forms as presented in Ragin (2000, 2008)--, does not correctly analyze data generated by causal chains, which, after all, are very common among causal processes in the social sciences. The incorrect modeling of data originating from chains essentially stems from QCA’s reliance on Quine-McCluskey optimization to eliminate redundancies from sufficient and necessary conditions. Baumgartner (2009a,b) has introduced a Boolean methodology, termed Coincidence Analysis (CNA), that (...) is related to QCA, yet, contrary to the latter, does not eliminate redundancies by means of Quine-McCluskey optimization. The second part of the paper applies CNA to chain-generated data. It will turn out that CNA successfully detects causal chains in small-N data. (shrink)

While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are custom-built for (non-deterministic) probabilistic structures, this paper introduces a Boolean procedure that uncovers deterministic causal structures. Contrary to existing Boolean methodologies, the procedure advanced here successfully analyzes structures of arbitrary complexity. It roughly involves three parts: first, deterministic dependencies are identified in the data; second, these dependencies are suitably minimalized in order to eliminate redundancies; and third, one or—in case of ambiguities—more than one causal structure is assigned (...) to the minimalized deterministic dependencies. (shrink)

This discussion paper proposes that a meaningful distinction between science and technoscience can be found at the level of the objects of research. Both notions intermingle in the attitudes, intentions, programs and projects of researchers and research institutions—that is, on the side of the subjects of research. But the difference between science and technoscience becomes more explicit when research results are presented in particular settings and when the objects of research are exhibited for the specific interest they hold. When an (...) experiment is presented as scientific evidence which confirms or disconfirms a hypothesis, this agrees with traditional conceptions of science. When organic molecules are presented for their capacity to serve individually as electric wires that carry surprisingly large currents, this would be a hallmark of technoscience. Accordingly, we propose research on the ontology of research objects. The focus on the character and significance of research objects makes this a specifically philosophical project. (shrink)

This chapter explores the idea that causal inference is warranted if and only if the mechanism underlying the inferred causal association is identified. This mechanistic stance is discernible in the epidemiological literature, and in the strategies adopted by epidemiologists seeking to establish causal hypotheses. But the exact opposite methodology is also discernible, the black box stance, which asserts that epidemiologists can and should make causal inferences on the basis of their evidence, without worrying about the mechanisms that might underlie their (...) hypotheses. I argue that the mechanistic stance is indeed a bad methodology for causal inference. However, I detach and defend a mechanistic interpretation of causal generalisations in epidemiology as existence claims about underlying mechanisms. (shrink)

In their paper, 'When are thought experiments poor ones?' (Peijnenburg and Atkinson, 2003, Journal of General Philosophy of Science 34, 305-322), Jeanne Peijnenburg and David Atkinson argue that most, if not all, philosophical thought experiments are "poor" ones with "disastrous consequences" and that they share the property of being poor with some (but not all) scientific thought experiments. Noting that unlike philosophy, the sciences have the resources to avoid the disastrous consequences, Peijnenburg and Atkinson come to the conclusion that the (...) use of thought experiments in science is in general more successful than in philosophy and that instead of concocting more "recherché" thought experiments, philosophy should try to be more empirical. In this comment I will argue that Peijnenburg's and Atkinson's view on thought experiments is based on a misleading characterization of both, the dialectical situation in philosophy as well as the history of physics. By giving an adequate account of what the Discussion in contemporary philosophy is about, we will arrive at a considerably different evaluation of philosophical thought experiments. (shrink)

In this article I attempt to reconstruct David Hume's use of the label ?experimental? to characterise his method in the Treatise. Although its meaning may strike the present-day reader as unusual, such a reconstruction is possible from the background of eighteenth-century practices and concepts of natural inquiry. As I argue, Hume's inquiries into human nature are experimental not primarily because of the way the empirical data he uses are produced, but because of the way those data are theoretically processed. He (...) seems to follow a method of analysis and synthesis quite similar to the one advertised in Newton's Opticks, which profoundly influenced eighteenth-century natural and moral philosophy. This method brings him much closer to the methods of qualitative, chemical investigations than to mechanical approaches to both nature and human nature. (shrink)

Current philosophical reflections on science have departed from mainstream history of science with respect to both methodology and conclusions. The article investigates how different approaches to reconstructing commitments can explain these differences and facilitate a mutual understanding and communication of these two perspectives on science. Translating the differences into problems pertaining to principles of charity, the paper offers a platform for clarification and resolution of the differences between the two perspectives. The outlined contextual approach occupies a middle ground between mainstream (...) history and sociology of science, bracketing questions of rationality, and individual coherence-maximizing, rationality-centered approaches. It can satisfy those, who believe that science is an epistemically privileged endeavor, and its epistemic content should not be neglected when reconstructing the scientists’ positions. It can also satisfy those who hold that it is naive to believe that the immediate context, e.g. the challenges to a theory, the expectations of the author about his audience, etc., does not affect the position a scientist takes. Its theoretical considerations are exemplified with a close study of the debate following the 1672 publication of Newton's theory of light and colours, also offering a novel reading of the development of his methodological views concerning the demonstrativity of the famous crucial experiment. Although we only show the capacity of the framework to analyze a direct controversy, given that it is hard to think about any scientific text as detached from an argumentative context, this approach has the potential to be a general guide for interpretation. (shrink)

This is a review essay of what is probably the best contribution to the history of the philosophical investigation into thought experiments.

Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...) These are some of the questions addressed in this article. I maintain that the most significant feature of a simulation is that it allows scientists to imitate one process by another process. "Process" here refers solely to a temporal sequence of states of a system. Given the observation that processes are dealt with by all sorts of scientists, it is apparent that simulations prove to be a powerful interdisciplinarily acknowledged tool. Accordingly, simulations are best suited to investigate the various research strategies in different sciences more carefully. To this end, I focus on the function of simulations in the research process. Finally, a somewhat detailed case-study from nuclear physics is presented which, in my view, illustrates elements of a typical simulation in physics. (shrink)

The first part of this paper reveals a conflict between the core principles of deterministic causation and the standard method of difference, which is widely seen (and used) as a correct method of causally analyzing deterministic structures. We show that applying the method of difference to deterministic structures can giverise to causal inferences that contradict the principles of deterministic causation. The second part then locates the source of this conflict in an inference rule implemented in the method of difference according (...) to which factors that can make a difference to investigated effects relative to one particular test setup are to be identified as causes, provided the causal background of the corresponding setup is homogeneous. The paper ends by modifying the method of difference in a way that renders it compatible with the principles of deterministic causation. (shrink)

Increasingly encompassing models have been suggested for our world. Theories range from generally accepted to increasingly speculative to apparently bogus. The progression of theories from ego- to geo- to helio-centric models to universe and multiverse theories and beyond was accompanied by a dramatic increase in the sizes of the postulated worlds, with humans being expelled from their center to ever more remote and random locations. Rather than leading to a true theory of everything, this trend faces a turning point after (...) which the predictive power of such theories decreases (actually to zero). Incorporating the location and other capacities of the observer into such theories avoids this problem and allows to distinguish meaningful from predictively meaningless theories. This also leads to a truly complete theory of everything consisting of a (conventional objective) theory of everything plus a (novel subjective) observer process. The observer localization is neither based on the controversial anthropic principle, nor has it anything to do with the quantum-mechanical observation process. The suggested principle is extended to more practical (partial, approximate, probabilistic, parametric) world models (rather than theories of everything). Finally, I provide a justification of Ockham's razor, and criticize the anthropic principle, the doomsday argument, the no free lunch theorem, and the falsifiability dogma.", . (shrink)

Using as case studies two early diagrams that represent mechanisms of the cell division cycle, we aim to extend prior philosophical analyses of the roles of diagrams in scientific reasoning, and specifically their role in biological reasoning. The diagrams we discuss are, in practice, integral and indispensible elements of reasoning from experimental data about the cell division cycle to mathematical models of the cycle’s molecular mechanisms. In accordance with prior analyses, the diagrams provide functional explanations of the cell cycle and (...) facilitate the construction of mathematical models of the cell cycle. But, extending beyond those analyses, we show how diagrams facilitate the construction of mathematical models, and we argue that the diagrams permit nomological explanations of the cell cycle. We further argue that what makes diagrams integral and indispensible for explanation and model construction is their nature as locality aids: they group together information that is to be used together in a way that sentential representations do not. (shrink)

We are in a state of impending crisis. And the fault lies in part with academia. For two centuries or so, academia has been devoted to the pursuit of knowledge and technological know-how. This has enormously increased our power to act which has, in turn, brought us both all the great benefits of the modern world and the crises we now face. Modern science and technology have made possible modern industry and agriculture, the explosive growth of the world’s population, global (...) warming, modern armaments and the lethal character of modern warfare, destruction of natural habitats and rapid extinction of species, immense inequalities of wealth and power across the globe, pollution of earth, sea and air, even the aids epidemic (aids being spread by modern travel). All these global problems have arisen because some of us have acquired unprecedented powers to act, via science and technology, without also acquiring the capacity to act wisely. We urgently need to bring about a revolution in universities so that the basic intellectual aim becomes, not knowledge merely, but rather wisdom – wisdom being the capacity to realize what is of value in life, for oneself and others, thus including knowledge and technological know-how, but much else besides. (shrink)

Universities need to change dramatically in order to help humanity make progress towards as good a world as possible.

At present the basic intellectual aim of academic inquiry is to improve knowledge. Much of the structure, the whole character, of academic inquiry, in universities all over the world, is shaped by the adoption of this as the basic intellectual aim. But, judged from the standpoint of making a contribution to human welfare, academic inquiry of this type is damagingly irrational. Three of four of the most elementary rules of rational problem-solving are violated. A revolution in the aims and methods (...) of academic inquiry is needed so that the basic aim becomes to promote wisdom, conceived of as the capacity to realize what is of value, for oneself and others, thus including knowledge and technological know-how, but much else besides. This urgently needed revolution would affect every branch and aspect of the academic enterprise. (shrink)

Two great problems of learning confront humanity: first, learning about the nature of the universe and about ourselves as a part of the universe, and second, learning how to live wisely – learning how to make progress towards as good a world as possible. The first problem was solved, in essence, in the 17th century, with the creation of modern science. A method was discovered for progressively improving knowledge and understanding of the natural world, the famous empirical method of science. (...) But the second great problem of learning has not yet been solved. And this puts us in a situation of unprecedented danger. Indeed, all our current global problems can be traced back, in one way or another, to this source. Solving the first great problem of learning enormously increases our power to act, via the increase of scientific knowledge and technological know-how. But without wisdom – without a solution to the second problem of learning – our immensely increased power to act may have good consequences, but will as often as not have all sorts of harmful consequences as well, whether intended or not. In order to cope with the situation of unprecedented danger we find ourselves in, we need to learn from our solution to the first problem how to solve the second. That is, we need to learn from scientific progress how to make better social progress towards a wiser world. (shrink)

In this article I reply to comments made by Agustin Vicente and Giridhari Lal Pandit on Science and the Pursuit of Wisdom (McHenry 2009 ). I criticize analytic philosophy, go on to expound the argument for the need for a revolution in academic inquiry so that the basic aim becomes wisdom and not just knowledge, defend aim-oriented empiricism, outline my solution to the human world/physical universe problem, and defend the thesis that free will is compatible with physicalism.

What ought to be the aims of science? How can science best serve humanity? What would an ideal science be like, a science that is sensitively and humanely responsive to the needs, problems and aspirations of people? How ought the institutional enterprise of science to be related to the rest of society? What ought to be the relationship between science and art, thought and feeling, reason and desire, mind and heart? Should the social sciences model themselves on the natural sciences: (...) or ought they to take a different form if they are to serve the interests of humanity objectively, sensitively and rigorously? Might it be possible to get into human life, into art, education, politics, industry, international affairs, and other domains of human activity, the same kind of progressive success that is found so strikingly, on the intellectual level, within science? These are some of the questions tackled by What’s Wrong With Science? But the book is no abstruse treatise on the philosophy of science. Most of it takes the form of a passionate debate between a Scientist and a Philosopher, a debate that is by turns humorous, ironical, bitter, dramatically explosive. Even as the argument explores the relationship between thought and feeling, reason and desire, the two main protagonists find it necessary to examine their own feelings and motivations. The book is a delight to read and can be understood by anyone. The book should have a wide appeal. It will be of interest to any scientist concerned about the intellectual and moral integrity of modern science – whether working in a physical, biological or social science. It will be of interest to educationalists, science teachers, students, 6th form pupils, historians, sociologists and philosophers of science, and indeed to anyone concerned about the place and role of science and technology in the modern world. First published in 1976, the book is even more relevant today than it was 33 years ago. This second edition has a new introduction in which the author explains how the book both exploits and develops Karl Popper’s philosophy. (shrink)

Here is an idea that just might save the world. It is that science, properly understood, provides us with the methodological key to the salvation of humanity.

There are these two absolutely basic problems: to learn about the universe and ourselves as a part of the universe, and to learn how to create a civilized world. Essentially, we have solved the first problem. We solved it when we created modern science. That is not to say that we know everything that is to be known, but we created a method for improving our knowledge about the world. But we haven't solved the second problem. And to solve the (...) first problem without solving the second problem is very, very dangerous. The crisis is this: the crisis of having science without civlization. (shrink)

The suggestion that philosophers are responsible for global warming seems, on the face of it, absurd. However, that we might cause global warming has been known for over a century. If we had had in existence a more rigorous kind of academic inquiry devoted to promoting human welfare, giving priority to problems of living, humanity might have become aware of the dangers of global warming long ago, and might have taken steps to meet these dangers decades ago. That we do (...) not have academic inquiry of this type, giving priority to problems of living and able to warn humanity of the impending disaster of global warming, is the result of a philosophical mistake – a mistake about what constitutes rigorous intellectual inquiry. This is a mistake of philosophers. Thus philosophers, in failing to grasp the profound intellectual and humanitarian failings of academia as it is at present organized (the outcome of implementing a seriously defective philosophy of inquiry) can perhaps be said to be responsible for global warming. (shrink)

Even though evidence underdetermines theory, often in science one theory only is regarded as acceptable in the light of the evidence. This suggests there are additional unacknowledged assumptions which constrain what theories are to be accepted. In the case of physics, these additional assumptions are metaphysical theses concerning the comprehensibility and knowability of the universe. Rigour demands that these implicit assumptions be made explicit within science, so that they can be critically assessed and, we may hope improved. This leads to (...) a new conception of science, one which we need to adopt in order to solve the problem of induction. (shrink)

From Knowledge to Wisdom argues that there is an urgent need, for both intellectual and humanitarian reasons, to bring about a revolution in science and the humanities. The outcome would be a kind of academic inquiry rationally devoted to helping humanity learn how to create a better world. Instead of giving priority to solving problems of knowledge, as at present, academia would devote itself to helping us solve our immense, current global problems – climate change, war, poverty, population growth, pollution (...) of sea, earth and air, destruction of natural habitats and rapid extinction of species, injustice, tyranny, proliferation of armaments, conventional, chemical, biological and nuclear, depletion of natural resources. The basic intellectual aim of inquiry would be to seek and promote wisdom – wisdom being the capacity to realize what is of value in life for oneself and others, thus including knowledge and technological know-how, but much else besides. This second edition has been revised throughout, has additional material, a new introduction and three new chapters. (shrink)

From Knowledge to Wisdom argues that there is an urgent need, for both intellectual and humanitarian reasons, to bring about a revolution in science and the humanities. The outcome would be a kind of academic inquiry rationally devoted to helping humanity learn how to create a better world. Instead of giving priority to solving problems of knowledge, as at present, academia would devote itself to helping us solve our immense, current global problems – climate change, war, poverty, population growth, pollution... (...) of sea, earth and air, destruction of natural habitats and rapid extinction of species, injustice, tyranny, proliferation of armaments, conventional, chemical, biological and nuclear, depletion of natural resources. The basic intellectual aim of inquiry would be to seek and promote wisdom – wisdom being the capacity to realize what is of value in life for oneself and others, thus including knowledge and technological know-how, but much else besides. This second edition has been revised throughout, has additional material, a new introduction and three new chapters. (shrink)

For three decades I have expounded and defended aim-oriented empiricism, a view of science which, l claim, solves a number of problems in the philosophy of science and has important implications for science itself and, when generalized, for the whole of academic inquiry, and for our capacity to solve our current global problems. Despite these claims, the view has received scant attention from philosophers of science. Recently, however, David Miller has criticized the view. Miller’s criticisms are, however, not valid.

Popper first developed his theory of scientific method – falsificationism – in his The Logic of Scientific Discovery, then generalized it to form critical rationalism, which he subsequently applied to social and political problems in The Open Society and Its Enemies. All this can be regarded as constituting a major development of the 18th century Enlightenment programme of learning from scientific progress how to achieve social progress towards a better world. Falsificationism is, however, defective. It misrepresents the real, problematic aims (...) of science. We need a new conception of scientific method, a meta-methodology which provides a framework for the improvement of the aims and methods of science as scientific knowledge improves. This aim-oriented empiricist idea can be generalized to form a conception of rationality – aim-oriented rationality – which helps us improve problematic aims and methods whatever we may be doing. In this way, Popper’s version of the Enlightenment programme can be much improved, indeed transformed. (shrink)

Science has been under attack ever since William Blake and Romantic movement. In our time, criticisms of modern science have led to Alan Sokal's spoof, and the so-called science wars. Both sides missed the point. Science deserves to be criticized for seriously misrepresenting its highly problematic aims, which have metaphysical, value and political assumptions associated with them. Instead of repressing these problematic aims, science ought rather to make them explicit, so that they can be critically assessed and, we may hope, (...) improved. (shrink)

In this paper I argue that aim-oriented empiricism (AOE), a conception of natural science that I have defended at some length elsewhere, 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. (shrink)

Steven Yates has criticized my claim that we need to bring about a revolution in the aims and methods of academic inquiry, so that the aim becomes to promote wisdom rather than just acquire knowledge. Yates's main criticism is that the proposed revolution does not have a clear strategy for its implementation, and is, in any case, Utopian, unrealizable and undesirable. It is argued, here, that Yates has misconstrued what the proposed revolution amounts to; in fact it is realizable, urgently (...) needed, and involves exploiting the kind of strategies utilized so effectively by the philosophes of the 18th century French Enlightenment. (shrink)

Is Science Neurotic? sets out to show that science suffers from a damaging but rarely noticed methodological disease — “rationalistic neurosis.” Assumptions concerning metaphysics, human value and politics, implicit in the aims of science, are repressed, and the malaise has spread to affect the whole academic enterprise, with the potential for extraordinarily damaging long-term consequences. The book begins with a discussion of the aims and methods of natural science, and moves on to discuss social science, philosophy, education, psychoanalytic theory and (...) academic inquiry as a whole. It makes an original and compelling contribution to the current debate between those for and those against science, arguing that science would be of greater human value if it were more rigorous — we suffer not from too much scientific rationality, but too little. The author discusses the need for a revolution in the aims of science and academic inquiry in general and, in a lively and accessible style, spells out a thesis with profound importance for the long-term future of humanity. (shrink)

Two great problems of learning confront humanity: learning about the universe, and learning how to live wisely. The first problem was solved with the creation of modern science, but the second problem has not been solved. This combination puts humanity into a situation of unprecedented danger. In order to solve the second problem we need to learn from our solution to the first problem. This requires that we bring about a revolution in the overall aims and methods of academic inquiry, (...) so that it takes up its proper task of promoting wisdom. (shrink)

Neurosis can be interpreted as a methodological condition which any aim-pursuing entity can suffer from. If such an entity pursues a problematic aim B, represents to itself that it is pursuing a different aim C, and as a result fails to solve the problems associated with B which, if solved, would lead to the pursuit of aim A, then the entity may be said to be "rationalistically neurotic". Natural science is neurotic in this sense in so far as a basic (...) aim of science is represented to be to improve knowledge of factual truth as such (aim C), when actually the aim of science is to improve knowledge of explanatory truth (aim B). Science does not suffer too much from this neurosis, but philosophy of science does. Much more serious is the rationalistic neurosis of the social sciences, and of academic inquiry more generally. Freeing social science and academic inquiry from neurosis would have far reaching, beneficial, intellectual, institutional and cultural consequences. (shrink)

When scientists choose one theory over another, they reject out of hand all those that are not simple, unified or explanatory. Yet the orthodox view of science is that evidence alone should determine what can be accepted. Nicholas Maxwell thinks he has a way out of the dilemma.

Many scientists, if pushed, may be inclined to hazard the guess that the universe is comprehensible, even physically comprehensible. Almost all, however, would vehemently deny that science has already established that the universe is comprehensible. It is, nevertheless, just this that I claim to be the case. Once one gets the nature of science properly into perspective, it becomes clear that the comprehensibility of the universe is as secure an item of current scientific knowledge as anything theoretical in science can (...) be, more secure, indeed, than the most firmly established fundamental theories of physics, such as quantum theory or Einstein's general theory of relativity. (shrink)

Nicholas Maxwell believes that while we have developed an excellent way of learning about the nature of the universe, we have so far failed in our attempts to apply this method to create a civilized world.

Humanity is confronted by immense global problems. In order to learn how to tackle them we need a new kind of academic inquiry, rationally organized and devoted to helping us resolve our problems of living in increasingly cooperatively rational ways.

In order to create a good world, we need to learn how to do it - how to resolve our appalling problems and conflicts in more cooperative ways than at present. And in order to do this, we need traditions and institutions of learning rationally devoted to this end. When viewed from this standpoint, what we have at present - academic inquiry devoted to the pursuit of knowledge and technological know-how - is an intellectual and human disaster. We urgently need (...) a new, more rigorous kind of inquiry that gives intellectual priority to the tasks of articulating our problems of living and proposing and critically assessing possible cooperative solutions. This new kind of inquiry would have as its basic aim to improve, not just knowledge, but also personal and global wisdom - wisdom being understood to be the capacity to realize what is of value in life. To develop this new kind of inquiry we will need to change almost every branch and aspect of the academic enterprise. (shrink)

In order to make progress with solving our grave global problems we need to bring about a revolution in academia so that problems of living are given intellectual priority, and the basic intellectual aim becomes to seek and promote wisdom, and not just acquire knowledge.

According to general wisdom, Carnap's quasianalysis is an ingenious but definitively flawed approach to epistemology and philosophy of science. I argue that this assessment is mistaken. Rather, Carnapian quasianalysis can be reconstructed as a special case of a general theory of structural representation. This enables us to exploit some interesting analogies of quasianalysis with the representational theory of measurement. It is shown how Goodman's well-known objections against the quasianalytical approach may be defused in the new framework. As an application, I (...) sketch how the thesis of empirical underdetermination of theories may be elucidated in the framework of quasianalysis. (shrink)

It is sometimes said that simulation can serve as epistemic substitute for experimentation. Such a claim might be suggested by the fast-spreading use of computer simulation to investigate phenomena not accessible to experimentation (in astrophysics, ecology, economics, climatology, etc.). But what does that mean? The paper starts with a clarification of the terms of the issue and then focuses on two powerful arguments for the view that simulation and experimentation are ‘epistemically on a par’. One is based on the claim (...) that, in experimentation, no less than in simulation, it is not the system under study that is manipulated but a system that ‘stands-in’ for it. The other one highlights the pervasive use of models in experimentation. It will be argued that these arguments, as compelling as they might seem, are each based on a mistaken interpretation of experimentation and that, far from simulation and experimentation being epistemically on a par, they do not have the same epistemic function, do not produce the same kind of epistemic results. (shrink)

Epistemic divergence occurs when different investigators give different answers to the same question using evidence-collecting methods that are not public. Without following the principle that scientific methods must be public, scientific communities risk epistemic divergence. I explicate the notion of public method and argue that, to avoid the risk of epistemic divergence, scientific communities should (and do) apply only methods that are public.

Scientists are, by any understanding of the term, experts. But what exactly is an expert, and on what grounds is the nonexpert going to decide whom to trust? Leave it to philosophers to ask such uncomfortable questions, and the volume edited by Selinger and Crease is an excellent starting point for this discussion.

Peter Lipton has attempted to flesh out a model of Inference to the Best Explanation (IBE) by clarifying explanation in terms of a causal model. But Lipton's account of explanation makes an adequate explanation depend on a principle which is virtually identical to Mill's Method of Difference. This has the result of collapsing IBE on Lipton's account of it into causal inference as conceived by the Causal-Inference model of induction. According to this model, many of our inductions are inferences from (...) effects to their probable causes, and Mill's Methods are canons to guide such inferences. Thus, Lipton's account of IBE fails to represent an advance over the already familiar Causal-Inference Model of induction. (shrink)

This paper rejects a view of science called "methodological naturalism." -/- According to many defenders of mainstream science and Darwinian evolution, anti-evolution critics--creationists and intelligent design proponents--are conceptually and epistemologically confusing science and religion, a supernatural view of world. These defenders of evolution contend that doing science requires adhering to a methodology that is strictly and essentially naturalistic: science is essentially committed to "methodological naturalism" and assumes that all the phenomena it investigates are entirely natural and consistent with the laws (...) of physics. Thus encountering any unexplained phenomenon, science assumes a priori that there is some natural cause and will only test a natural hypothesis. Since by definition supernatural causes are assumed to be not subject to the constraints of physical or natural law as understood by science, supernatural hypotheses and explanations must be banned from proper science. Science simply can't say that God did, or did not do it. -/- I argue that the success of science is directly relevant to rational belief in supernatural causes, and that in fact science can and does say in particular cases that "God didn't do it." I suggest that pro-evolution proponents can better defend science and the theory of evolution by rejecting methodological naturalism. -/- . (shrink)

In this piece, I advocate and motivate a new understanding of thought experiments, which avoids problems with the rival accounts of Brown and Norton.

This paper explores the relationship between scepticism and epistemic relativism in the context of recent history and philosophy of science. More specifically, it seeks to show that significant treatments of epistemic relativism by influential figures in the history and philosophy of science draw upon the Pyrrhonian problem of the criterion. The paper begins with a presentation of the problem of the criterion as it occurs in the work of Sextus Empiricus. It is then shown that significant treatments of epistemic relativism (...) in recent history and philosophy of science (critical rationalism, historical philosophy of science and the strong programme) draw upon the problem of the criterion. It is briefly suggested that a particularist response to the problem of the criterion may be put to good use against epistemic relativism. (shrink)

This paper constitutes one extended argument, which touches on various topics of Critical Rationalism as it was initiated by Karl Popper and further developed (although into different directions) in his aftermath. The result of the argument will be that critical rationalism either offers no solution to the problem of induction at all , or that it amounts, in the last resort, to a kind of Critical Rationalist Inductivism as it were, a version of what I call Good Old Induction. One (...) may think of David Miller as a contemporary representative of what I consider as the ‘no solution’ version of critical rationalism, while Alan Musgrave stands for the version of ‘critical rationalist induction’. Popper’s own writings admit of either interpretation. (shrink)

In reviewing Finocchiaro's book, I argue that Galileo deserved to be found guilty for the charges against him. A measure of Finocchiaro's scrupulously fair-minded presentation of the issues surrounding the Galileo Affair is the fact that a contrary case against his own exculpatory evaluation may be inferred from his meticulous scholarship. Specifically, to acknowledge that the standards of evaluation and judgment have changed since 1633 is not in any way to diminish Galileo's greatness but, on the contrary, to recognize his (...) visionary insights. Even to acknowledge that he was justly condemned by standards that we no longer accept in science or theology is not to detract from his deserved place in the firmament of scientific genius. (shrink)

When is clinical research ethical? The difficulty in answering this question lies in the dual nature of research on human subjects, which yields two somewhat conflicting sets of obligations. On the one hand, there is the traditional view of science that includes the idea of an obligation to learn about the world. On the other hand, there is the obligation of care on the part of researchers towards individual participants in the research ...

Philosophers subscribing to particular principles of statistical inference and evidence need to be aware of the limitations and practical consequences of the statistical approach they endorse. The framework proposed (for statistical inference in the field of medicine) allows disparate statistical approaches to emerge in their appropriate context. My dissertation proposes a decision theoretic model, together with methodological guidelines, that provide important considerations for deciding on clinical trial conduct. These considerations do not amount to more stopping rules. Instead, they are principles (...) that address the complexity of interpreting and responding to interim data, based on a broad range of epistemic and ethical factors. While they are not stopping rules, they would assist a Data Monitoring Committee in judging its position with regard to necessary precautionary interpretation of interim data. By vindicating a framework that accommodates a wide range of approaches to statistical inference in one important setting (clinical trials), my results pose a serious challenge for any approach that advocates a single, universal principle of statistical inference. (shrink)

A central issue confronting both philosophers and practitioners in formulating an analysis of causation is the question of what constitutes evidence for a causal association. From the 1950s onward, the biostatistician Jerome Cornfield put himself at the center of a controversial debate over whether cigarette smoking was a causative factor in the incidence of lung cancer. Despite criticisms from distinguished statisticians such as Fisher, Berkson and Neyman, Cornfield argued that a review of the scientific evidence supported the conclusion of a (...) causal association. Cornfield's odds ratio in case‐control studies — as a good estimate of relative risk — together with his argument of ''explanatory common cause'' became important tools to use in confronting the skeptics. In this paper, I revisit this important historical episode as recorded in the Journal of National Cancer Institute and the Journal of the American Statistical Association. More specifically, I examine Cornfield's necessary condition on the minimum magnitudes of relative risk in light of confounders. This episode yields important insight into the nature of causal inference by showing the sorts of evidence appealed to by practitioners in supporting claims of causal association. I discuss this event in light of the manipulationist account of causation. (shrink)

Science is epistemically special, or so I will assume: it is better able to produce knowledge about the workings of the world than other knowledge-directed pursuits. Further, its superior epistemic powers are due to its being in some sense especially empirical: in particular, science puts great weight on a form of inductive reasoning that I call empirical con rmation. My aim in this paper is to investigate the nature of science’s “empiricism”, and to provide a preliminary explanation of the connection (...) between empirical confirmation and epistemic efficacy. I will try to convince you that the place to find an account of empirical confirmation is the dusty, long-neglected instantialist account of scientific inference offered by mid-century logical empiricists. Some revision of instantialism will be required. As for what is advantageous in empirical confirmation, I propose that it is an unusual degree of independence from background belief. (shrink)

A theme of much work taking an ““economic approach”” to the study of science is the interaction between the norms of individual scientists and those of society at large. Though drawing from the same suite of formal methods, proponents of the economic approach offer what are in substantive terms profoundly different explanations of various aspects of the structure of science. The differences are illustrated by comparing Strevens's explanation of the scientific reward system (the ““priority rule””) with Max Albert's explanation of (...) the prevalence of ““high methodological standards”” in science. Some objections to the economic approach as a whole are then briefly considered. (shrink)

Robert Merton observed that better-known scientists tend to get more credit than less well-known scientists for the same achievements; he called this the Matthew effect. Scientists themselves, even those eminent researchers who enjoy its benefits, regard the effect as a pathology: it results, they believe, in a misallocation of credit. If so, why do scientists continue to bestow credit in the manner described by the effect? This paper advocates an explanation of the effect on which it turns out to allocate (...) credit fairly after all, while at the same time making sense of scientists' opinions to the contrary. (shrink)

Science's priority rule rewards those who are first to make a discovery, at the expense of all other scientists working towards the same goal, no matter how close they may be to making the same discovery. I propose an explanation of the priority rule that, better than previous explanations, accounts for the distinctive features of the rule. My explanation treats the priority system, and more generally, any scheme of rewards for scientific endeavor, as a device for achieving an allocation of (...) resources among different research programs that provides as much benefit as possible to society. I show that the priority system is especially well suited to finding an efficient allocation of resources in those situations, characteristic of scientific inquiry, in which any success in an endeavor subsequent to the first success brings little additional benefit to society. (shrink)

This paper justifies the inference of probabilities from symmetries. I supply some examples of important and correct inferences of this variety. Two explanations of such inferences -- an explanation based on the Principle of Indifference and a proposal due to Poincaré and Reichenbach -- are considered and rejected. I conclude with my own account, in which the inferences in question are shown to be warranted a posteriori, provided that they are based on symmetries in the mechanisms of chance setups.

In this paper I challenge Paolo Palmieri’s reading of the Mach-Vailati debate on Archimedes’s proof of the law of the lever. I argue that the actual import of the debate concerns the possible epistemic (as opposed to merely pragmatic) role of mathematical arguments in empirical physics, and that construed in this light Vailati carries the upper hand. This claim is defended by showing that Archimedes’s proof of the law of the lever is not a way of appealing to a non-empirical (...) source of information, but a way of explicating the mathematical structure that can represent the empirical information at our disposal in the most general way. (shrink)

The paper deals with an intellectual and historical approach to the changing meanings of the term “model” in life sciences. The author 1st tries to understand how modeling has gradually spread over life sciences then he particularly focus on the birth of mathematical modeling in this field. This quite new practice offers new insights on the old debate concerning the mathematization of life sciences. Nowadays, through computers, mathematics not only analyze or quantify but model things: what does it mean? The (...) question turns out to be dramatic as far as digital simulation is concerned. That is the reason why he choosed to study a particular case: the history of the individual plant mathematical modeling. On this case, one may discern various epistemological standpoints that caused various reactions to the emergence of computer simulation, from the 50s to the 90s. The author shows that philosophical views often play a role in the history of sciences, especially in the choice of supposed proper mathematical formalisms. This will indicate that contemporary discourses tend to echo each other. That is the reason why he feels authorized to address the Foucault’s concept—épistémè—to denote these convergences between the scientific and the philosophical discourses. Finally, it is suggested that this épistémè gradually is changing because one can currently observe the emergence of a “graphical” thought through these simulation experiments, which tends to replace a more functionalist thought. (shrink)