This article argues that the basic account of mechanism and mechanistic explanation, involving sequential execution of qualitatively characterized operations, is itself insufficient to explain biological phenomena such as the capacity of living organisms to maintain themselves as systems distinct from their environment. This capacity depends on cyclic organization, including positive and negative feedback loops, which can generate complex dynamics. Understanding cyclically organized mechanisms with complex dynamics requires coordinating research directed at decomposing mechanisms into parts and operations with research using computational (...) models to recompose mechanisms and determine their dynamic behavior. This coordinated endeavor yields dynamic mechanistic explanations. (shrink)

Employing the techniques of modern economic analysis, Professor Buchanan reveals the conceptual basis of an individual's social rights by examining the ...

The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes a robust (...) argument for scientific realism akin to the argument that convinced twentieth-century physical scientists of the existence of atoms and molecules. To do justice to such notions as irreducibly statistical laws and statistical explanation, he offers a novel account of physical randomness. The transition from the "reviewed view" of scientific explanation to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts. (shrink)

Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.

Canadian Journal of Philosophy, 26, pp. 333-66. 1996.

Thought experiments in the history of science display a striking asymmetry between chemistry and physics, namely that chemistry seems to lack well-known examples, whereas physics presents many famous examples. This asymmetry, I argue, is not independent data concerning the chemistry/physics distinction. The laws of chemistry such as the periodic table are incurably special, in that they make testable predictions only for a very restricted range of physical conditions in the universe which are necessarily conditioned by the contingences of chemical investigation. (...) The argument depends on how ‚thought experiment’ is construed. Here, several recent accounts of thought experiments are surveyed to help formulate what I call ‚crucial’ thought experiments. These have a historical role in helping to judge between hypotheses in physics, but are not helpful in chemistry past or present. (shrink)

To explain the phenomena in the world of our experience, to answer the question “why?” rather than only the question “what?”, is one of the foremost objectives of all rational inquiry; and especially, scientific research in its various branches strives to go beyond a mere description of its subject matter by providing an explanation of the phenomena it investigates. While there is rather general agreement about this chief objective of science, there exists considerable difference of opinion as to the function (...) and the essential characteristics of scientific explanation. In the present essay, an attempt will be made to shed some light on these issues by means of an elementary survey of the basic pattern of scientific explanation and a subsequent more rigorous analysis of the concept of law and of the logical structure of explanatory arguments. (shrink)

La autora presenta algunas criticas generales al proyecto de reducir las leyes causales a probabilidades. Además, muestra que las leyes causales son imprescindibles para poder diferenciar las strategias efectivas de las que no lo son y da un criterio para considerar cuando podemos deducir causalidad a través de datos estadísticos.

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

This paper defends an invariance-based account of laws of nature: Laws are generalizations that remain invariant under various sorts of changes. Alternatively, laws are generalizations that exhibit a certain kind of independence from background conditions.

This article explores some issues associated with Philip Kitcher's unificationist theory of explanation, including the contrast between epistemic and ontic approaches to explanation, and the implications of Kitcher’s theory for the autonomy of the special sciences.

A widely shared view in the cognitive sciences is that discovering and assessing explanations of cognitive phenomena whose production involves uncertainty should be done in a Bayesian framework. One assumption supporting this modelling choice is that Bayes provides the best approach for representing uncertainty. However, it is unclear that Bayes possesses special epistemic virtues over alternative modelling frameworks, since a systematic comparison has yet to be attempted. Currently, it is then premature to assert that cognitive phenomena involving uncertainty are best (...) explained within the Bayesian framework. As a forewarning, progress in cognitive science may be hindered if too many scientists continue to focus their efforts on Bayesian modelling, which risks to monopolize scientific resources that may be better allocated to alternative approaches. (shrink)

We might think that thought experiments are at their most powerful or most interesting when they produce new knowledge. This would be a mistake; thought experiments that seek understanding are just as powerful and interesting, and perhaps even more so. A growing number of epistemologists are emphasizing the importance of understanding for epistemology, arguing that it should supplant knowledge as the central notion. In this chapter, I bring the literature on understanding in epistemology to bear on explicating the different ways (...) that thought experiments increase three important kinds of understanding: explanatory, objectual and practical. (shrink)

How do novel scientific concepts arise? In Creating Scientific Concepts, Nancy Nersessian seeks to answer this central but virtually unasked question in the problem of conceptual change. She argues that the popular image of novel concepts and profound insight bursting forth in a blinding flash of inspiration is mistaken. Instead, novel concepts are shown to arise out of the interplay of three factors: an attempt to solve specific problems; the use of conceptual, analytical, and material resources provided by the cognitive-social-cultural (...) context of the problem; and dynamic processes of reasoning that extend ordinary cognition. Focusing on the third factor, Nersessian draws on cognitive science research and historical accounts of scientific practices to show how scientific and ordinary cognition lie on a continuum, and how problem-solving practices in one illuminate practices in the other. (shrink)

In this paper we explore the constraints that our preferred account of scientific representation places on the ontology of scientific models. Pace the Direct Representation view associated with Arnon Levy and Adam Toon we argue that scientific models should be thought of as imagined systems, and clarify the relationship between imagination and representation.

A philosophical theory of explanation should provide solutions to a series of problems, both descriptive and normative. The aim of this essay is to establish the claim that this can be best done if one theorizes in terms of explanatory games rather than focusing on the explication of the concept of explanation. The position that is adopted is that of an explanatory pluralism and it is elaborated in terms of the rules that incorporate the normative standards that guide the processes (...) of discovery and justification of explanations as well as the modes of their communication, dissemination, and adoption. They constitute the rules of the explanatory game that the participants are playing. The philosophical project consists in describing and normatively appraising the rules that constitute these games. (shrink)

Explaining phenomena is one of the main activities in which scientists engage. This book proposes a new philosophical theory of scientific explanation by developing and defending the position of explanatory pluralism with the help of the notion of 'explanatory games'. Mantzavinos provides a descriptive account of the explanatory activity of scientists in different domains and shows how they differ from commonsensical explanations offered in everyday life by ordinary people and also from explanations offered in religious contexts. He also shows how (...) an evaluation and a critical appraisal of explanations put forward in different social arenas can take place on the basis of different values. Explanatory Pluralism provides solutions to all important descriptive and normative problems of the philosophical theory of explanation as illustrated in sophisticated case studies from economics and medicine, but also from mythology and religion. (shrink)

Objectivity has a history, and it is full of surprises. In Objectivity, Lorraine Daston and Peter Galison chart the emergence of objectivity in the mid-nineteenth-century sciences--and show how the concept differs from its alternatives, truth-to-nature and trained judgment. This is a story of lofty epistemic ideals fused with workaday practices in the making of scientific images. From the eighteenth through the early twenty-first centuries, the images that reveal the deepest commitments of the empirical sciences--from anatomy to crystallography--are those featured in (...) scientific atlases, the compendia that teach practitioners what is worth looking at and how to look at it. Galison and Daston use atlas images to uncover a hidden history of scientific objectivity and its rivals. Whether an atlas maker idealizes an image to capture the essentials in the name of truth-to-nature or refuses to erase even the most incidental detail in the name of objectivity or highlights patterns in the name of trained judgment is a decision enforced by an ethos as well as by an epistemology. As Daston and Galison argue, atlases shape the subjects as well as the objects of science. To pursue objectivity--or truth-to-nature or trained judgment--is simultaneously to cultivate a distinctive scientific self wherein knowing and knower converge. Moreover, the very point at which they visibly converge is in the very act of seeing not as a separate individual but as a member of a particular scientific community. Embedded in the atlas image, therefore, are the traces of consequential choices about knowledge, persona, and collective sight. Objectivity is a book addressed to anyone interested in the elusive and crucial notion of objectivity-- and in what it means to peer into the world scientifically. Lorraine Daston is Director at the Max Planck Institute for the History of Science in Berlin, Germany. She is the coauthor of Wonders and the Order of Nature, 1150-1750 and the editor of Things That Talk: Object Lessons from Art and Science. Peter Galison is Pellegrino University Professor of the History of Science and of Physics at Harvard University. He is the author of Einstein's Clocks, Poincaré's Maps: Empires of Time, How Experiments End, and Image and Logic: A Material Culture of Microphysics, and other books, and coeditor of The Architecture of Science. (shrink)

The paper presents an argument for treating certain types of computer simulation as having the same epistemic status as experimental measurement. While this may seem a rather counterintuitive view it becomes less so when one looks carefully at the role that models play in experimental activity, particularly measurement. I begin by discussing how models function as “measuring instruments” and go on to examine the ways in which simulation can be said to constitute an experimental activity. By focussing on the connections (...) between models and their various functions, simulation and experiment one can begin to see similarities in the practices associated with each type of activity. Establishing the connections between simulation and particular types of modelling strategies and highlighting the ways in which those strategies are essential features of experimentation allows us to clarify the contexts in which we can legitimately call computer simulation a form of experimental measurement. (shrink)

Time magazine's "Man of the Century", Albert Einstein is the founder of modern physics and his theory of relativity is the most important scientific idea of the modern era. In this short book, Einstein explains, using the minimum of mathematical terms, the basic ideas and principles of the theory that has shaped the world we live in today. Unsurpassed by any subsequent books on relativity, this remains the most popular and useful exposition of Einstein's immense contribution to human knowledge. With (...) a new foreword by Derek Raine. (shrink)

Philosophers have traditionally addressed the issue of scientific unification in terms of theoretical reduction. Reductive models, however, cannot explain the occurrence of unification in areas of science where successful reductions are hard to find. The goal of this essay is to analyse a concrete example of integration in biology—the developmental synthesis—and to generalize it into a model of scientific unification, according to which two fields are in the process of being unified when they become explanatorily relevant to each other. I (...) conclude by suggesting that this methodological conception of unity, which is independent of the debate on the metaphysical foundations of science, is closely connected to the notion of interdisciplinarity. 1 Introduction2 Some Troubles with Theory Reduction3 Interfield and Mechanistic Unification4 Foundations of the Developmental Synthesis5 Explanatory Relevance6 Concluding Remarks. (shrink)

It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be (...) explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal–mechanical explanation. 1 Introduction2 What a Great Many Phenomena Bayesian Decision Theory Can Model3 The Case of Information Integration4 How Do Bayesian Models Unify?5 Bayesian Unification: What Constraints Are There on Mechanistic Explanation?5.1 Unification constrains mechanism discovery5.2 Unification constrains the identification of relevant mechanistic factors5.3 Unification constrains confirmation of competitive mechanistic models6 ConclusionAppendix. (shrink)

Science depends on judgments of the bearing of evidence on theory. Scientists must judge whether an observation or the result of an experiment supports, disconfirms, or is simply irrelevant to a given hypothesis. Similarly, scientists may judge that, given all the available evidence, a hypothesis ought to be accepted as correct or nearly so, rejected as false, or neither. Occasionally, these evidential judgments can be made on deductive grounds. If an experimental result strictly contradicts a hypothesis, then the truth of (...) the data deductively entails the falsity of the hypothesis. In the great majority of cases, however, the connection between evidence and hypothesis is non-demonstrative, or inductive. In particular, this is so whenever a general hypothesis is inferred to be correct on the basis of the available data, since the truth of the data will not deductively entail the truth of the hypothesis. It always remains possible that the hypothesis is false even though the data are correct. (shrink)

Thought experiments have played a prominent role in numerous cases of conceptual change in science. I propose that research in cognitive psychology into the role of mental modeling in narrative comprehension can illuminate how and why thought experiments work. In thought experimenting a scientist constructs and manipulates a mental simulation of the experimental situation. During this process, she makes use of inferencing mechanisms, existing representations, and general world knowledge to make realistic transformations from one possible physical state to the next. (...) The simulation reveals the impossibility of integrating multiple constraints drawn from existing representations and the world and pinpoints the locus of the required conceptual reform. (shrink)

I argue that thought experiments are a form of experimental reasoning similar to real experiments. They require the same ability to participate by following a narrative as real experiments do. Participation depends in turn on using what we already know to visualize, manipulate and understand what is unfamiliar or problematic. I defend the claim that visualization requires embodiment by an example which shows how tacit understanding of the properties of represented objects and relations enables us to work out how such (...) objects might behave in a postulated world. This knowledge is that of embodied agents. That thought experiments require embodied participation is what makes them experiments rather than arguments. Unlike real experiments, from which ordinary perception has been displaced by instrumentation, thought experiments still appeal to relatively unmediated common sense, even when their purpose is to criticize or subvert common sense notions. (shrink)

Offering a new approach to scientific explanation, this book focuses initially on the explaining act itself.

Thought experiments are ordinary argumentation disguised in a vivid pictorial or narrative form. This account of their nature will allow me to show that empiricism has nothing to fear from thought experiments. They perform no epistemic magic. In so far as they tell us about the world, thought experiments draw upon what we already know of it, either explicitly or tacitly; they then transform that knowledge by disguised argumentation. They can do nothing more epistemically than can argumentation. I defend my (...) account of thought experiments in Section 3 by urging that the epistemic reach of thought experiments turns out to coincide with that of argumentation and that this coincidence is best explained by the simple view that thought experiments just are arguments. Thought experiments can err—-a fact to be displayed by the thought experiment - anti thought experiment pairs of Section 2. Nonetheless thought experiments can be used reliably and, I urge in Section 4., this is only possible if they are governed by some very generalized logic. I will suggest on evolutionary considerations that their logics are most likely the familiar logics of induction and deduction, recovering the view that thought experiment is argumentation. Finally in Section 5 I defend this argument based epistemology of thought experiments against competing accounts. I suggest that these other accounts can offer a viable epistemology only insofar as they already incorporate the notion that thought experimentation is governed by a logic, possibly of very generalized form. (shrink)

This monograph offers a critical introduction to current theories of how scientific models represent their target systems. Representation is important because it allows scientists to study a model to discover features of reality. The authors provide a map of the conceptual landscape surrounding the issue of scientific representation, arguing that it consists of multiple intertwined problems. They provide an encyclopaedic overview of existing attempts to answer these questions, and they assess their strengths and weaknesses. The book also presents a comprehensive (...) statement of their alternative proposal, the DEKI account of representation, which they have developed over the last few years. They show how the account works in the case of material as well as non-material models; how it accommodates the use of mathematics in scientific modelling; and how it sheds light on the relation between representation in science and art. The issue of representation has generated a sizeable literature, which has been growing fast in particular over the last decade. This makes it hard for novices to get a handle on the topic because so far there is no book-length introduction that would guide them through the discussion. Likewise, researchers may require a comprehensive review that they can refer to for critical evaluations. This book meets the needs of both groups. (shrink)

Explanations contribute to our understanding of the world byembedding phenomena into general nomic patterns that we recognize in the world. Manyof these patterns are, of course, causal ones, but the declaration as ``causal'' often fails to determinethe explanatory power of the pattern. More important is the systematization capacity and the empiricalcontent of the pattern or theory with respect to explanations. We can specify these parameters moreprecisely within the framework of the structuralist view of theories.

This book is an expanded and revised edition of the author's critically acclaimed volume Nuts and Bolts for the Social Sciences. In twenty-six succinct chapters, Jon Elster provides an account of the nature of explanation in the social sciences. He offers an overview of key explanatory mechanisms in the social sciences, relying on hundreds of examples and drawing on a large variety of sources - psychology, behavioral economics, biology, political science, historical writings, philosophy and fiction. Written in accessible and jargon-free (...) language, Elster aims at accuracy and clarity while eschewing formal models. In a provocative conclusion, Elster defends the centrality of qualitative social sciences in a two-front war against soft and hard forms of obscurantism. (shrink)

Bas C. van Fraassen presents an original exploration of how we represent the world.