Studies causation both as a concept and as it is 'in the objects.' Offers new accounts of the logic of singular causal statements, the form of causal regularities, the detection of causal relationships, the asymmetry of cause and effect, and necessary connection, and it relates causation to functional and statistical laws and to teleology.

"How do we go about weighing evidence, testing hypotheses and making inferences? According to the model of 'inference to the Best explanation', we work out what to inter from the evidence by thinking about what would actually explain that evidence, and we take the ability of a hypothesis to explain the evidence as a sign that the hypothesis is correct. In inference to the Best Explanation, Peter Lipton gives this important and influential idea the development and assessment it deserves." "The (...) second edition has been substantially enlarged and reworked, with a new chapter on the relationship between explanation and Bayesianism, and an extension and defence of the account of contractive explanation. It also includes an expanded defence of the claims that our inferences really are guided by diverse explanatory considerations, and that this pattern of inference can take us towards the truth. This edition of Inference to the Best Explanation has also been updated throughout and incudes a new bibliography."--BOOK JACKET. (shrink)

This paper discusses the epistemic import of highly abstract and simplified theoretical models using Thomas Schelling’s checkerboard model as an example. We argue that the epistemic contribution of theoretical models can be better understood in the context of a cluster of models relevant to the explanatory task at hand. The central claim of the paper is that theoretical models make better sense in the context of a menu of possible explanations. In order to justify this claim, we introduce a distinction (...) between causal scenarios and causal mechanism schemes. These conceptual tools help us to articulate the basis for modelers’ intuitive confidence that their models make an important epistemic contribution. By focusing on the role of the menu of possible explanations in the evaluation of explanatory hypotheses, it is possible to understand how a causal mechanism scheme can improve our explanatory understanding even in cases where it does not describe the actual cause of a particular phenomenon. (shrink)

Comparisons of rival explanations or theories often involve vague appeals to explanatory power. In this paper, we dissect this metaphor by distinguishing between different dimensions of the goodness of an explanation: non-sensitivity, cognitive salience, precision, factual accuracy and degree of integration. These dimensions are partially independent and often come into conflict. Our main contribution is to go beyond simple stipulation or description by explicating why these factors are taken to be explanatory virtues. We accomplish this by using the contrastive-counterfactual approach (...) to explanation and the view of understanding as an inferential ability. By combining these perspectives, we show how the explanatory power of an explanation in a given dimension can be assessed by showing the range of answers it provides to what-if-things-had-been-different questions and the theoretical and pragmatic importance of these questions. Our account also explains intuitions linking explanation to unification or to exhibition of a mechanism. (shrink)

This article discusses agent-based simulation (ABS) as a tool of sociological understanding. I argue that agent-based simulations can play an important role in the expansion of explanatory understanding in the social sciences. The argument is based on an inferential account of understanding (Ylikoski 2009, Ylikoski & Kuorikoski 2010), according to which computer simulations increase our explanatory understanding by expanding our ability to make what-if inferences about social processes and by making these inferences more reliable. The inferential account also suggests a (...) number of ways in which the use of simulation methodology might give rise to illusory understanding. The structure of the article is as .. (shrink)

This article compares causal and constitutive explanation. While scientific inquiry usually addresses both causal and constitutive questions, making the distinction is crucial for a detailed understanding of scientific questions and their interrelations. These explanations have different kinds of explananda and they track different sorts of dependencies. Constitutive explanations do not address events or behaviors, but causal capacities. While there are some interesting relations between building and causal manipulation, causation and constitution are not to be confused. Constitution is a synchronous and (...) asymmetric relation between relata that cannot be conceived as independent existences. However, despite their metaphysical differences, the same key ideas about explanation largely apply to both. Causal and constitutive explanations face similar challenges (such as the problems of relevance and explanatory regress) and both are in the business of mapping networks of counterfactual dependence—i.e. mechanisms—although the relevant counterfactuals are of a different sort. In the final section the issue of developmental explanation is discussed. It is argued that developmental explanations deserve their own place in taxonomy of explanations, although ultimately developmental dependencies can be analyzed as combinations of causal and constitutive dependencies. Hence, causal and constitutive explanation are distinct, but not always completely separate forms of explanation. (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.

Despite their best efforts, scientists may be unable to construct models that simultaneously exemplify every theoretical virtue. One explanation for this is the existence of tradeoffs: relationships of attenuation that constrain the extent to which models can have such desirable qualities. In this paper, we characterize three types of tradeoffs theorists may confront. These characterizations are then used to examine the relationships between parameter precision and two types of generality. We show that several of these relationships exhibit tradeoffs and discuss (...) what consequences those tradeoffs have for theoretical practice. (shrink)

Biologists studying complex causal systems typically identify some factors as causes and treat other factors as background conditions. For example, when geneticists explain biological phenomena, they often foreground genes and relegate the cellular milieu to the background. But factors in the milieu are as causally necessary as genes for the production of phenotypic traits, even traits at the molecular level such as amino acid sequences. Gene-centered biology has been criticized on the grounds that because there is parity among causes, the (...) “privileging” of genes reflects a reductionist bias, not an ontological difference. The idea that there is an ontological parity among causes is related to a philosophical puzzle identified by John Stuart Mill: what, other than our interests or biases, could possibly justify identifying some causes as the actual or operative ones, and other causes as mere background? The aim of this paper is to solve this conceptual puzzle and to explain why there is not an ontological parity among genes and the other factors. It turns out that solving this puzzle helps answer a seemingly unrelated philosophical question: what kind of causal generality matters in biology? (shrink)

Models such as the simple pendulum, isolated populations, and perfectly rational agents, play a central role in theorising. It is now widely acknowledged that a study of scientific representation should focus on the role of such imaginary entities in scientists’ reasoning. However, the question is most of the time cast as follows: How can fictional or abstract entities represent the phenomena? In this paper, I show that this question is not well posed. First, I clarify the notion of representation, and (...) I emphasise the importance of what I call the “format” of a representation for the inferences agents can draw from it. Then, I show that the very same model can be presented under different formats, which do not enable scientists to perform the same inferences. Assuming that the main function of a representation is to allow one to draw predictions and explanations of the phenomena by reasoning with it, I conclude that imaginary models in abstracto are not used as representations: scientists always reason with formatted representations. Therefore, the problem of scientific representation does not lie in the relationship of imaginary entities with real systems. One should rather focus on the variety of the formats that are used in scientific practice. (shrink)

This paper defends an inferential conception of scientific representation. It approaches the notion of representation in a deflationary spirit, and minimally characterizes the concept as it appears in science by means of two necessary conditions: its essential directionality and its capacity to allow surrogate reasoning and inference. The conception is defended by showing that it successfully meets the objections that make its competitors, such as isomorphism and similarity, untenable. In addition the inferential conception captures the objectivity of the cognitive representations (...) used by science, it sheds light on their truth and completeness, and it explains the source of the analogy between scientific and artistic modes of representation. (shrink)

People feel they understand complex phenomena with far greater precision, coherence, and depth than they really do; they are subject to an illusion—an illusion of explanatory depth. The illusion is far stronger for explanatory knowledge than many other kinds of knowledge, such as that for facts, procedures or narratives. The illusion for explanatory knowledge is most robust where the environment supports real‐time explanations with visible mechanisms. We demonstrate the illusion of depth with explanatory knowledge in Studies 1–6. Then we show (...) differences in overconfidence about knowledge across different knowledge domains in Studies 7–10. Finally, we explore the mechanisms behind the initial confidence and behind overconfidence in Studies 11 and 12, and discuss the implications of our findings for the roles of intuitive theories in concepts and cognition. (shrink)

This paper examines a series of Schelling-like models of residential segregation, in which agents prefer to be in the minority. We demon- strate that as long as agents care about the characteristics of their wider community, they tend to end up in a segregated state. We then investigate the process that causes this, and conclude that the result hinges on the similarity of informational states amongst agents of the same type. This is quite di erent from Schelling-like behavior, and sug- (...) gests (in his terms) that segregation is an instance of macro behavior which can arise from a wide variety of micro motives. (shrink)

This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), and these relations are coordinated by (...) a model commentary. Surrogate models are often about single mechanisms. They are distinguishable from substitute models, which are examined without any concern about their connections with the target. Models as credible worlds are surrogate models that are believed to provide access to their targets on account of their credibility (of which a few senses are distinguished). (shrink)

This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), and these relations are coordinated by (...) a model commentary. Surrogate models are often about single mechanisms. They are distinguishable from substitute models, which are examined without any concern about their connections with the target. Models as credible worlds are surrogate models that are believed to provide access to their targets on account of their credibility (of which a few senses are distinguished). (shrink)

Modelers often rely on robustness analysis, the search for predictions common to several independent models. Robustness analysis has been characterized and championed by Richard Levins and William Wimsatt, who see it as central to modern theoretical practice. The practice has also been severely criticized by Steven Orzack and Elliott Sober, who claim that it is a nonempirical form of confirmation, effective only under unusual circumstances. This paper addresses Orzack and Sober's criticisms by giving a new account of robustness analysis and (...) showing how the practice can identify robust theorems. Once the structure of robust theorems is clearly articulated, it can be shown that such theorems have a degree of confirmation, despite the lack of direct empirical evidence for their truth. (shrink)

How do we go about weighing evidence, testing hypotheses, and making inferences? The model of " inference to the best explanation " -- that we infer the hypothesis that would, if correct, provide the best explanation of the available evidence--offers a compelling account of inferences both in science and in ordinary life. Widely cited by epistemologists and philosophers of science, IBE has nonetheless remained little more than a slogan. Now this influential work has been thoroughly revised and updated, and features (...) a new introduction and two new chapters. Inference to the Best Explanation is an unrivaled exposition of a theory of particular interest in the fields both of epistemology and the philosophy of science. (shrink)

Robert Sugden argues that robustness analysis cannot play an epistemic role in grounding model-world relationships because the procedure is only a matter of comparing models with each other. We posit that this argument is based on a view of models as being surrogate systems in too literal a sense. In contrast, the epistemic importance of robustness analysis is easy to explicate if modelling is viewed as extended cognition, as inference from assumptions to conclusions. Robustness analysis is about assessing the reliability (...) of our extended inferences, and when our confidence in these inferences changes, so does our confidence in the results. Furthermore, we argue that Sugden’s inductive account relies tacitly on robustness considerations. (shrink)

Odenbaugh and Alexandrova provide a challenging critique of the epistemic benefits of robustness analysis, singling out for particular criticism the account we articulated in Kuorikoski et al.. Odenbaugh and Alexandrova offer two arguments against the confirmatory value of robustness analysis: robust theorems cannot specify causal mechanisms and models are rarely independent in the way required by robustness analysis. We address Odenbaugh and Alexandrova’s criticisms in order to clarify some of our original arguments and to shed further light on the properties (...) of robustness analysis and its epistemic rationale. (shrink)

We claim that the process of theoretical model refinement in economics is best characterised as robustness analysis: the systematic examination of the robustness of modelling results with respect to particular modelling assumptions. We argue that this practise has epistemic value by extending William Wimsatt's account of robustness analysis as triangulation via independent means of determination. For economists robustness analysis is a crucial methodological strategy because their models are often based on idealisations and abstractions, and it is usually difficult to tell (...) which idealisations are truly harmful. (shrink)

The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on the (...) relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation. (shrink)

The rise of appeals to intuitive theories in many areas of cognitive science must cope with a powerful fact. People understand the workings of the world around them in far less detail than they think. This illusion of knowledge depth has been uncovered in a series of recent studies and is caused by several distinctive properties of explanatory understanding not found in other forms of knowledge. Other experimental work has shown that people do have skeletal frameworks of expectations that constrain (...) richer ad hoc theory construction on the fly. These frameworks are supplemented by an ability to evaluate and rely on the division of cognitive labour in one's culture, an ability shown to be present even in young children. (shrink)

Craver claims that mechanistic explanation is ontic, while Bechtel claims that it is epistemic. While this distinction between ontic and epistemic explanation originates with Salmon, the ideas have changed in the modern debate on mechanistic explanation, where the frame of the debate is changing. I will explore what Bechtel and Craver’s claims mean, and argue that good mechanistic explanations must satisfy both ontic and epistemic normative constraints on what is a good explanation. I will argue for ontic constraints by drawing (...) on Craver’s work in Sect. 2.1, and argue for epistemic constraints by drawing on Bechtel’s work in Sect. 2.2. Along the way, I will argue that Bechtel and Craver actually agree with this claim. I argue that we should not take either kind of constraints to be fundamental, in Sect. 3, and close in Sect. 4 by considering what remains at stake in making a distinction between ontic and epistemic constraints on mechanistic explanation. I suggest that we should not concentrate on either kind of constraint, to the neglect of the other, arguing for the importance of seeing the relationship as one of integration. (shrink)

A general account of modeling in physics is proposed. Modeling is shown to involve three components: denotation, demonstration, and interpretation. Elements of the physical world are denoted by elements of the model; the model possesses an internal dynamic that allows us to demonstrate theoretical conclusions; these in turn need to be interpreted if we are to make predictions. The DDI account can be readily extended in ways that correspond to different aspects of scientific practice.

Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language-like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms "law" and "theory," I distinguish principles, specific conditions, models, (...) hypotheses, and generalizations. I argue that scientists use designated similarities between models and aspects of the world to form both hypotheses and generalizations. (shrink)

Discussions of representation in science tend to draw on examples from art. However, such examples need to be handled with care given a) the differences between works of art and scientific theories and b) the accommodation of these examples within certain philosophies of art. I shall examine the claim that isomorphism is neither necessary nor sufficient for representation and I shall argue that there exist accounts of representation in both art and science involving isomorphism which accommodate the apparent counterexamples and, (...) moreover, allow us to understand how “impossible” artistic objects and inconsistent scientific theories can be said to represent. (shrink)

Recent discussions of the nature of representation in science have tended to import pre-established decompositions from analyses of representation in the arts, language, cognition and so forth. Which of these analyses one favours will depend on how one conceives of theories in the first place. If one thinks of them in terms of an axiomatised set of logico-linguistic statements, then one might be naturally drawn to accounts of linguistic representation in which notions of denotation, for example, feature prominently. If, on (...) the other hand, one conceives of theories in non-linguistic terms, as in the model-theoretic approach, then one might look to analyses of representation in the arts where notions of resemblance tend to be brought to the fore. Thus van Fraassen, for example, has imported such an analysis into his discussion of representation in science and argued that an appropriate account of resemblance can be given in terms of the set-theoretic relation of isomorphism. This has been strongly criticised by Suarez, who argues that just as isomorphism cannot capture representation in art, so it is inappropriate in the scientific context as well. Similarly Hughes draws on Goodman`s rejection of resemblance in art in favour of denotation and, rather confusingly perhaps, favours the latter whilst also maintaining the model-theoretic view of theories. In this paper, I shall examine the debate in terms of four claims: 1. Isomorphism is not sufficient for representation; 2. Isomorphism is not necessary for representation; 3. Models represent but theories do not; 4. Models denote and do not resemble. Each of these claims will be questioned and I will conclude by suggesting that, through appropriate modifications, a form of isomorphism can serve to underpin representation in both the arts and science. (shrink)

What would something unlike us--a chimpanzee, say, or a computer--have to be able to do to qualify as a possible knower, like us? To answer this question at the very heart of our sense of ourselves, philosophers have long focused on intentionality and have looked to language as a key to this condition. Making It Explicit is an investigation into the nature of language--the social practices that distinguish us as rational, logical creatures--that revises the very terms of this inquiry. Where (...) accounts of the relation between language and mind have traditionally rested on the concept of representation, this book sets out an alternate approach based on inference, and on a conception of certain kinds of implicit assessment that become explicit in language. Making It Explicit is the first attempt to work out in detail a theory that renders linguistic meaning in terms of use--in short, to explain how semantic content can be conferred on expressions and attitudes that are suitably caught up in social practices. -/- At the center of this enterprise is a notion of discursive commitment. Being able to talk--and so in the fullest sense being able to think--is a matter of mastering the practices that govern such commitments, being able to keep track of one’s own commitments and those of others. Assessing the pragmatic significance of speech acts is a matter of explaining the explicit in terms of the implicit. As he traces the inferential structure of the social practices within which things can be made conceptually explicit, the author defines the distinctively expressive role of logical vocabulary. This expressive account of language, mind, and logic is, finally, an account of who we are. (shrink)

The aim of this paper is to defend the structural concept of representation, as defined by homomorphisms, against its main objections, namely: logical objections, the objection from misrepresentation, theobjection from failing necessity, and the copy theory objection. The logical objections can be met by reserving the relation ‘to be homomorphic to’ for the explication of potential representation (or, of the representational content). Actual reference objects (‘targets’) of representations are determined by (intentional or causal) representational mechanisms. Appealing to the independence of (...) the dimensions of ‘content’ and ‘target’ also helps to see how the structural concept can cope with misrepresentation. Finally, I argue that homomorphic representations are not necessarily ‘copies’ of their representanda, and thus can convey scientific insight. (shrink)

The paper defends the structural concept of representation, defined by homomorphisms, against the main objections that have been raised against it: Logical objections, the objection from misrepresentation, the objection from failing necessity, and the copy theory objection. Homomorphic representations are not necessarily ‘copies’ of their representanda, and thus can convey scientific insight.

Julian Reiss correctly identified a trilemma about economic models: we cannot maintain that they are false, but nevertheless explain and that only true accounts explain. In this reply we give reasons to reject the second premise ? that economic models explain. Intuitions to the contrary should be distrusted.

During the last two centuries, the way economic science is done has changed radically: it has become a social science based on mathematical models in place of words. This book describes and analyses that change - both historically and philosophically - using a series of case studies to illuminate the nature and the implications of these changes. It is not a technical book; it is written for the intelligent person who wants to understand how economics works from the inside out. (...) This book will be of interest to economists and science studies scholars. But it also aims at a wider readership in the public intellectual sphere, building on the current interest in all things economic and on the recent failure of the so-called economic model, which has shaped our beliefs and the world we live in. (shrink)

Jonathan Walkan challenges cognitive science's dominant model of mental representation and proposes a novel, well-devised alternative. The traditional view in the cognitive sciences uses a linguistic model of mental representation. That logic-based model of cognition informs and constrains both the classical tradition of artificial intelligence and modeling in the connectionist tradition. It falls short, however, when confronted by the frame problem---the lack of a principled way to determine which features of a representation must be updated when new information becomes available. (...) So far, proposed alternatives, including the imagistic model, have not resolved the problem. Waskan proposes the Intrinsic Cognitive Models hypothesis, according to which representational states can be concpetualized as the cognitive equivalent of scale models.Waskan argues further that the proposal that humans harbor and manipulate cognitive counterparts to scale models offers the only viable explanation for what most clearly differentiates humans from other creatures: the capacity to engage in truth-preserving manipulation of representations. The ICM hypothesis, he claims, can be distinguished from sentence-based accounts of truth preservation in a way that is fully compatible with what is known about the brain. (shrink)

This book offers a comprehensive overview of the structure, strategy and methods of assessment of orthodox theoretical economics. In Part I Professor Hausman explains how economists theorise, emphasising the essential underlying commitment of economists to a vision of economics as a separate science. In Part II he defends the view that the basic axioms of economics are 'inexact' since they deal only with the 'major' causes; unlike most writers on economic methodology, the author argues that it is the rules that (...) economists espouse rather than their practice that is at fault. Part III links the conception of economics as a separate science to the fact that economic theories offer reasons and justifications for human actions, not just their causes. With its lengthy appendix introducing relevant issues in philosophy of science, this book is a major addition to philosophy of economics and of social science. (shrink)

Approaches to explanation -- Causal and explanatory relevance -- The kairetic account of /D making -- The kairetic account of explanation -- Extending the kairetic account -- Event explanation and causal claims -- Regularity explanation -- Abstraction in regularity explanation -- Approaches to probabilistic explanation -- Kairetic explanation of frequencies -- Kairetic explanation of single outcomes -- Looking outward -- Looking inward.

Hutchins examines a set of phenomena that have fallen between the established disciplines of psychology and anthropology, bringing to light a new set of relationships between culture and cognition.

In Supersizing the Mind, Andy Clark argues that the human mind is not bound inside the head but extends into body and environment.

During the past decade, social mechanisms and mechanism-based ex- planations have received considerable attention in the social sciences as well as in the philosophy of science. This article critically reviews the most important philosophical and social science contributions to the mechanism approach. The first part discusses the idea of mechanism- based explanation from the point of view of philosophy of science and relates it to causation and to the covering-law account of explanation. The second part focuses on how the idea (...) of mechanisms has been used in the social sciences. The final part discusses recent developments in analytical sociology, covering the nature of sociological explananda, the role of theory of action in mechanism-based explanations, Merton’s idea of middle-range theory, and the role of agent-based simulations in the development of mechanism-based explanations. (shrink)

The aim of this paper is to defend the structural concept of representation, as defined by homomorphisms, against its main objections, namely: logical objections, the objection from misrepresentation, theobjection from failing necessity, and the copy theory objection. The logical objections can be met by reserving the relation.

My two daughters would love to go tobogganing down the hill by themselves, but they are just toddlers and I am an apprehensive parent, so, before letting them do so, I want to ensure that the toboggan won’t go too fast. But how fast will it go? One way to try to answer this question would be to tackle the problem head on. Since my daughters and their toboggan are initially at rest, according to classical mechanics, their final velocity will (...) be determined by the forces they will be subjected to between the moment the toboggan will be released at the top of the hill and the moment it will reach its highest speed. The problem is that, throughout their downhill journey, my daughters and the toboggan will be subjected to an incredibly large number of forces—from the gravitational pull of any massive object in the universe to the weight of the snowflake that is sitting on the tip of one of my youngest daughter’s hairs—so that any attempt to apply the theory directly to the real-world system in all its complexity seems to be doomed to failure. (shrink)

Whether simulation models provide the right kind of understanding comparable to that of analytic models has been and remains a contentious issue. The assessment of understanding provided by simulations is often hampered by a conflation between the sense of understanding and understanding proper. This paper presents a deflationist conception of understanding and argues for the need to replace appeals to the sense of understanding with explicit criteria of explanatory relevance and for rethinking the proper way of conceptualizing the role of (...) a single human mind in the collective scientific understanding. (shrink)