Numerous articles in top IS journals note as a limitation and lack of generalizability that their findings are specific to a certain type of technology, culture, and so on. We argue that this generalizability concern is about limited scope. The IS literature notes this preference for generalizability as a characteristic of good science and it is sometimes confused with statistical generalizability. We argue that such generalizability can be in conflict with explanation or prediction accuracy. An increase in scope can decrease (...) explanation or prediction accuracy. Thus, in sciences such as cancer research, where explanation and prediction accuracy are highly valued, the cancer accounts have become increasingly narrower. IS thinking has not yet benefitted from these considerations. Whether generalizability is valued should be linked with the research aims. If the aim is practical applicability through explanation or prediction accuracy, then “limited” generalizability could be a strength rather than a weakness. (shrink)

We introduce two notions–the shadows and the shallows of explanation–in opening up explanation to broader, interdisciplinary investigation. The shadows of explanation refer to past philosophical efforts to provide either a conceptual analysis of explanation or in some other way to pinpoint the essence of explanation. The shallows of explanation refer to the phenomenon of having surprisingly limited everyday, individual cognitive abilities when it comes to explanation. Explanations are ubiquitous, but they typically are not accompanied by the depth that we might, (...) prima facie, expect. We explain the existence of the shadows and shallows of explanation in terms of there being a theoretical abyss between explanation and richer, theoretical structures that are often attributed to people. We offer an account of the shallows, in particular, both in terms of shorn-down, internal, mental machinery, and in terms of an enriched, public symbolic environment, relative to the currently dominant ways of thinking about cognition and the world. (shrink)

We argue that the health sciences make causal claims on the basis of evidence both of physical mechanisms, and of probabilistic dependencies. Consequently, an analysis of causality solely in terms of physical mechanisms or solely in terms of probabilistic relationships, does not do justice to the causal claims of these sciences. Yet there seems to be a single relation of cause in these sciences - pluralism about causality will not do either. Instead, we maintain, the health sciences require a theory (...) of causality that unifies its mechanistic and probabilistic aspects. We argue that the epistemic theory of causality provides the required unification. (shrink)

James Woodward offers a conception of explanation and mechanism in terms of interventionist counterfactuals. Based on a case from ecology, I show that ecologists’ approach to that case satisfies Woodward’s conditions for explanation and mechanism, but his conception does not fully capture what ecologists view as explanatory. The new mechanistic philosophy likewise aims to describe central aspects of mechanisms, but I show that it is not sufficient to account for ecological mechanisms. I argue that in ecology explanation involves identification of (...) invariant and insensitive causal relationships and descriptions of the mechanistic characteristics that make these relations possible. †To contact the author, please write to: Department of Philosophy, University of Dayton, 300 College Park, Dayton, OH 45469‐1546; e‐mail: [email protected]. (shrink)

The concept of mechanism in biology has three distinct meanings. It may refer to a philosophical thesis about the nature of life and biology (‘mechanicism’), to the internal workings of a machine-like structure (‘machine mechanism’), or to the causal explanation of a particular phenomenon (‘causal mechanism’). In this paper I trace the conceptual evolution of ‘mechanism’ in the history of biology, and I examine how the three meanings of this term have come to be featured in the philosophy of biology, (...) situating the new ‘mechanismic program’ in this context. I argue that the leading advocates of the mechanismic program (i.e., Craver, Darden, Bechtel, etc.) inadvertently conflate the different senses of ‘mechanism’. Specifically, they all inappropriately endow causal mechanisms with the ontic status of machine mechanisms, and this invariably results in problematic accounts of the role played by mechanism-talk in scientific practice. I suggest that for effective analyses of the concept of mechanism, causal mechanisms need to be distinguished from machine mechanisms, and the new mechanismic program in the philosophy of biology needs to be demarcated from the traditional concerns of mechanistic biology. (shrink)

The innovation systems approach has swiftly spread out worldwide in the last three decades and stood as an important framework for policy-making in the fields of science, technology, and innovation. At the same time, there have been serious and untreated concerns in the literature about the theoretical soundness of this approach. Our discussion in this paper is based on the belief that a detailed analysis on epistemological foundations of the approach could shed a judgmental light on the aforementioned concerns. To (...) provide that analysis, we reconstructed and studied the nature and evolution of innovation systems approach as a case of conceptual revolution against the more established traditions in economics of innovation. Our analyses show that the rise of the systemic view of innovation include radical changes in conceptual structures pertaining to orthodox view of innovation. These developments also go so far as to include significant shifts in epistemological foundations of the old paradigm. These later shifts provide us with an epistemological base from which we can shelter the innovation systems approach from the hard criticisms rooted in a quasi-positivist point of view. That epistemological base also shows the path for the future development of the approach towards more robustness and precision. (shrink)

This paper contrasts biomedical and epidemiological approaches to the diagnosis and treatment of disease, and uses Collingwoods principle of the relativity of causes to show how different approaches focus on different causal factors reflecting different interests. By distinguishing between the etiology of a disease and an epidemic, the paper argues that, from an epidemiological perspective, poverty is an important causal factor in the African AIDS epidemic and that emphasizing this should not be considered incompatible with recognizing the causal necessity of (...) HIV for the AIDS disease. (shrink)

In an instant classic paper ; 2002: 179–182) biologist Yuri Lazebnik deplores the poor effectiveness of the approach adopted by biologists to understand and “fix” biological systems. Lazebnik suggests that to remedy this state of things biologist should take inspiration from the approach used by engineers to design, understand, and troubleshoot technological systems. In the present paper I substantiate Lazebnik’s analysis by concretely showing how to apply the engineering approach to biological problems. I use an actual example of electronic circuit (...) troubleshooting to ground the thesis that, in engineering, the crucial phases of any non-trivial troubleshooting process are aimed at generating a mechanistic explanation of the functioning of the system, which makes extensive recourse to problem-driven qualitative reasoning possibly based on cognitive artifacts applied to systems that are known to have been designed for function. To show how to translate these findings into biological practice I consider a concrete example of biological model building and “troubleshooting”, aimed at the identification of a “fix” for the human immune system in presence of progressing cancer, autoimmune disease, and transplant rejection. The result is a novel immune system model—the danger model with regulatory cells— and new, original hypotheses concerning the development, prophylaxis, and therapy of these unwanted biological processes. Based on the manifest efficacy of the proposed approach, I suggest a refocusing of the activity of theoretical biologists along the engineering-inspired lines illustrated in the paper. (shrink)

Human microbiome research makes causal connections between entire microbial communities and a wide array of traits that range from physiological diseases to psychological states. To evaluate these causal claims, we first examine a well-known single-microbe causal explanation: of Helicobacter pylori causing ulcers. This apparently straightforward causal explanation is not so simple, however. It does not achieve a key explanatory standard in microbiology, of Koch’s postulates, which rely on manipulations of single-microorganism cultures to infer causal relationships to disease. When Koch’s postulates (...) are framed by an interventionist causal framework, it is clearer what the H. pylori explanation achieves and where its explanatory strengths lie. After assessing this ‘simple’, single-microbe case, we apply the interventionist framework to two key areas of microbiome research, in which obesity and mental health states are purportedly explained by microbiomes. Despite the experimental data available, interventionist criteria for explanation show that many of the causal claims generated by microbiome research are weak or misleading. We focus on the stability, specificity and proportionality of proposed microbiome causal explanations, and evaluate how effectively these dimensions of causal explanation are achieved in some promising avenues of research. We suggest some conceptual and explanatory strategies to improve how causal claims about microbiomes are made. (shrink)

Three studies examined the co-existence of natural and supernatural explanations for illness and disease transmission, from a developmental perspective. The participants (5-, 7-, 11-, and 15-year-olds and adults; N = 366) were drawn from 2 Sesotho-speaking South African communities, where Western biomedical and traditional healing frameworks were both available. Results indicated that, although biological explanations for illness were endorsed at high levels, witchcraft was also often endorsed. More important, bewitchment explanations were neither the result of ignorance nor replaced by biological (...) explanations. Instead, both natural and supernatural explanations were used to explain the same phenomena, and bewitchment explanations were highest among adults. Taken together, these data provide insight into how diverse, culturally constructed belief systems about illness co-exist across development. (shrink)

The nosological diagnosis is a particular type of nontheoretical diagnosis consisting of identifying the disease that afflicts the patient without explaining the underlying etiopathological mechanisms. Its origins are within the essentialist point of view on the nature of diseases, which dates back at least to 18th-century taxonomy studies. In this article, we propose a model of nosological diagnosis as a two-phase process composed of the categorization of inductive inferences and argumentations by analogy. In the inductive phase, disease entities are identified (...) by means of typicality-based categorization processes, and meaningful clinical samples are learned ; in the subsequent phase, those samples are used as the bases of argumentations by analogy to obtain a diagnosis for a given patient. This model extends the prototype resemblance theory of disease including also the exemplar theory proposed in cognitive science and, moreover, it frames the clinical activity of nosological diagnosis and how it can be explained within the theory of argumentation. According to it, diagnosis based on the recognition of a typical syndrome is explained in terms of the prototype theory of categorization and the antisymmetrical argumentation by analogy, while diagnosis based on a comparison with a previous clinical case is explained by the exemplar theory of categorization and by the symmetrical argumentation by analogy. (shrink)

Classical models of decision making deal fairly well with uncertainty, where settings are well-structured in terms of goals, alternatives, and consequences. Conversely, the typical ill-structured nature of strategy choices remains a challenge for extant models. Such cases can hardly build on the past, and their novelty makes the prediction of consequences a very difficult and poorly robust task. The weakness of the classical expected utility model in representing such problems has not been adequately solved by recent extensions. In this paper (...) we offer an explanatory coherence model for decision making in ill-structured problems. We model alternatives as sets of concurrent causal explanations of reality that act as justifications for action. According to these premises, choice is based on an evaluation of the internal coherence and the consistency of competing explanations of the available evidence. This model is psychologically grounded on causal inference and builds on the connectionist tradition of explanatory coherence. To illustrate the model, we consider the decision of investing in a new technology and we discuss how changes in the structure of alternatives may impact on the solution. We show how the final choice depends on collecting the relevant evidence, making the suitable hypotheses, and drawing the consistent causal explanations linking the two. (shrink)

Human microbiome research makes causal connections between entire microbial communities and a wide array of traits that range from physiological diseases to psychological states. To evaluate these causal claims, we first examine a well-known single-microbe causal explanation: of Helicobacter pylori causing ulcers. This apparently straightforward causal explanation is not so simple, however. It does not achieve a key explanatory standard in microbiology, of Koch’s postulates, which rely on manipulations of single-microorganism cultures to infer causal relationships to disease. When Koch’s postulates (...) are framed by an interventionist causal framework, it is clearer what the H. pylori explanation achieves and where its explanatory strengths lie. After assessing this ‘simple’, single-microbe case, we apply the interventionist framework to two key areas of microbiome research, in which obesity and mental health states are purportedly explained by microbiomes. Despite the experimental data available, interventionist criteria for explanation show that many of the causal claims generated by microbiome research are weak or misleading. We focus on the stability, specificity and proportionality of proposed microbiome causal explanations, and evaluate how effectively these dimensions of causal explanation are achieved in some promising avenues of research. We suggest some conceptual and explanatory strategies to improve how causal claims about microbiomes are made. (shrink)

This article focuses on the assessment of mechanistic relations with specific attention to medicine, where mechanistic models are widely employed. I first survey recent contributions in the philosophical literature on mechanistic causation, and then take issue with Federica Russo and Jon Williamson’s thesis that two types of evidence, probabilistic and mechanistic, are at stake in the health sciences. I argue instead that a distinction should be drawn between previously acquired knowledge of mechanisms and yet-to-be-discovered knowledge of mechanisms and that both (...) probabilistic evidence and manipulation are essential with respect to newly discovered mechanisms. (shrink)

The notion of causal evidence in medicine has been the subject of wide philosophical debate in recent years. The notion of evidence has been discussed mostly in connection with Evidence Based Medicine and, more in general, with the assessment of causal nexus in medical, and especially research contexts. “Manipulative evidence” is one of the notions of causal evidence that has stimulated much debate. It has been defined in slightly different ways, attributed different relevance, and recently placed at the core of (...) Gillies’ “action-related theory of causality”, a view specifically meant to address causation in medicine. While in general sympathetic to Gillies’ account, and totally convinced of the relevance of manipulative evidence and different sorts of interventions in the biomedical sciences, we believe that some further qualifications are needed to allow the notion of manipulative evidence to better express features of medical practice. In particular, we provide some qualification of the role of “interventional evidence” proposed by Gillies, suggesting a distinction between “interventional evidence” and “evidence for interventions”. A case study from research on rare diseases is analyzed in depth and a multifaceted notion of manipulative evidence put forward that allows better understanding of what manipulations in medical contexts amount to and what their targets are. (shrink)

This contribution claims that the two fundamental notions of causation at work in the health sciences are manipulative and mechanistic, and investigates what kinds of evidence matter for the assessment of causal relations. This article is a development of our 2007 article, ‘Plurality of Causality’, where we argue for a pluralistic account of causation with an eye to econometrics and a single medical example. The present contribution has a wider focus, and considers the notion of evidence within a whole range (...) of disciplines belonging to the health sciences. Section 1 addresses the relations between kinds of evidence and causal accounts, and it is shown how different notions of causation can be employed in various medical cases. Section 2 calls attention to issues crucial for any adequate epistemological theory of causation, such as the distinctions between types and tokens, observational and experimental regimes, explanation and prediction. Lastly, the notion of context is articulated, highlighted in its role in the assessment of causal links. All these issues are tackled in the framework of what we label a ‘bottom–up’ epistemology. (shrink)

Recent discussion of mechanism has suggested new approaches to several issues in the philosophy of science, including theory structure, causal explanation, and reductionism. Here, I apply what I take to be the fruits of the Ônew mechanical philosophyÕ to an analysis of a contemporary debate in evolutionary biology about the role of natural selection in speciation. Traditional accounts of that debate focus on the geographic context of genetic divergence— namely, whether divergence in the absence of geographic isolation is possible (or (...) significant). Those accounts are at best incomplete, I argue, because they ignore the mechanisms producing divergence and miss what is at stake in the biological debate. I argue that the biological debate instead concerns the scope of particular speciation mechanisms which assign different roles to natural selection at various stages of divergence. The upshot is a new interpretation of the crux of that debate—namely, whether divergence with gene flow is possible (or significant) and whether the isolating mechanisms producing it are adaptive. Ó 2005 Elsevier Ltd. All rights reserved. (shrink)

Although cases are central to the epistemic practices utilized within clinical medicine, they appear to be limited in their ability to provide evidence about causal relations because they provide detailed accounts of particular patients without explicit filtering of those attributes most likely to be relevant for explaining the phenomena observed. This paper uses a series of recent case reports to explore the role of cases in casual attribution in medical diagnosis. It is argued that cases are brought together by practitioners (...) to generate causal attributions and testable predictions using a manipulability view of causation. (shrink)

This chapter provides a brief overview of the history of behavioral neurology, dividing it roughly into six eras. In the ancient and classical eras, emphasis is placed on two transitions: firstly, from descriptions of head trauma and attempted neurosurgical treatments to the exploratory dissections during the Hellenistic period and the replacement of cardiocentrism; and secondly, to the more systematic investigations of Galenus and the rise of pneumatic ventricular theory. In the medieval through post-Renaissance eras, the scholastic consolidation of knowledge and (...) the role of compendia are emphasized, along with the use of new methods from within a mechanistic framework. With the discovery of electrical conductance and the rise of experimentalism, we frame the modern era as period of intense debate over localization, decomposition, and other mechanistic principles, and marked by rapid discovery about the brain. The chapter ends with a discussion of the contemporary era, focusing on the establishment of behavioral neurology research on aphasia, apraxia, and neuropsychiatric conditions. (shrink)

In recent years both philosophers and scientists have asked whether or not our current kinds of mental disorder—e.g., schizophrenia, depression, bipolar disorder—are natural kinds; and, moreover, whether or not the search for natural kinds of mental disorder is a realistic desideratum for psychiatry. In this dissertation I clarify the sense in which a kind can be said to be “natural” or “real” and argue that, despite a few notable exceptions, kinds of mental disorder cannot be considered natural kinds. Furthermore, I (...) contend that psychopathological phenomena do not cluster together into kinds in the way that paradigmatic natural kinds (e.g., chemical kinds and species) do; and, in light of this fact, I conclude that the normative ideal of natural classification—i.e. classifying real or natural kinds—is not appropriate as a general strategy for psychiatry. In the conclusion to this dissertation I propose an alternative way forward. Rather than assuming a priori that psychopathological phenomena cluster together into kinds, a more tractable and theoretically promising approach would be to first explain how particular kinds of experiences and behaviours—e.g, hallucinations, delusions, disorganized thinking, mania, low mood, etc.—are produced. (shrink)

Part I Introduction The specific problem adressed in this thesis is: what is the rational use of theory and experiment in the process of scientific discovery, in theory and in the practice of drug research for Parkinson’s disease? The thesis aims to answer the following specific questions: what is: 1) the structure of a theory?; 2) the process of scientific reasoning?; 3) the route between theory and experiment? In the first part I further discuss issues about rationality in science as (...) introduction to part II, and I present an overview of my case-study of neuropharmacology, for which I interviewed researchers from the Groningen Pharmacy Department, as an introduction to part III. Part II Discovery In this part I discuss three theoretical models of scientific discovery according to studies in the fields of Logic, Cognition, and Computation. In those fields the structure of a theory is respectively explicated as: a set of sentences; a set of associated memory chunks; and as a computer program that can generate the observed data. Rationality in discovery is characterized by: finding axioms that imply observation sentences; heuristic search for a hypothesis, as part of problem solving, by applying memory chunks and production rules that represent skill; and finding the shortest program that generates the data, respectively. I further argue that reasoning in discovery includes logical fallacies, which are neccesary to introduce new hypotheses. I also argue that, while human subjects often make errors in hypothesis evaluation tasks from a logical perspective, these evaluations are rational given a probabilistic interpretation. Part III Neuropharmacology In this last part I discusses my case-study and a model of discovery in a practice of drug research for Parkinson’s disease. I discuss the dopamine theory of Parkinson’s disease and model its structure as a qualitative differential equation. Then I discuss the use and reasons for particular experiments to both test a drug and explore the function of the brain. I describe different kinds of problems in drug research leading to a discovery. Based on that description I distinguish three kinds of reasoning tasks in discovery, inference to: the best explanation, the best prediction and the best intervention. I further demonstrate how a part of reasoning in neuropharmacology can be computationally modeled as qualitative reasoning, and aided by a computer supported discovery system. (shrink)

According to one large family of views, scientific explanations explain a phenomenon (such as an event or a regularity) by subsuming it under a general representation, model, prototype, or schema (see Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441; Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press; Darden (2006); Hempel, C. G. (1965). Aspects of scientific (...) explanation. In C. G. Hempel (Ed.), Aspects of scientific explanation (pp. 331–496). New York: Free Press; Kitcher (1989); Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25). My concern is with the minimal suggestion that an adequate philosophical theory of scientific explanation can limit its attention to the format or structure with which theories are represented. The representational subsumption view is a plausible hypothesis about the psychology of understanding. It is also a plausible claim about how scientists present their knowledge to the world. However, one cannot address the central questions for a philosophical theory of scientific explanation without turning one’s attention from the structure of representations to the basic commitments about the worldly structures that plausibly count as explanatory. A philosophical theory of scientific explanation should achieve two goals. The first is explanatory demarcation. It should show how explanation relates with other scientific achievements, such as control, description, measurement, prediction, and taxonomy. The second is explanatory normativity. It should say when putative explanations succeed and fail. One cannot achieve these goals without undertaking commitments about the kinds of ontic structures that plausibly count as explanatory. Representations convey explanatory information about a phenomenon when and only when they describe the ontic explanations for those phenomena. (shrink)

Causal claims in biomedical contexts are ubiquitous albeit they are not always made explicit. This paper addresses the question of what causal claims mean in the context of disease. It is argued that in medical contexts causality ought to be interpreted according to the epistemic theory. The epistemic theory offers an alternative to traditional accounts that cash out causation either in terms of “difference-making” relations or in terms of mechanisms. According to the epistemic approach, causal claims tell us about which (...) inferences (e.g., diagnoses and prognoses) are appropriate, rather than about the presence of some physical causal relation analogous to distance or gravitational attraction. It is shown that the epistemic theory has important consequences for medical practice, in particular with regard to evidence-based causal assessment. (shrink)

Inconsistencies between scientific theories have been studied, by and large, from the perspective of paraconsistent logic. This approach considered the formal properties of theories and the structure of inferences one can legitimately draw from theories. However, inconsistencies can be also analysed from the perspective of modelling practices, in particular how modelling practices may lead scientists to form opinions and attitudes that are different, but not necessarily inconsistent. In such cases, it is preferable to talk about disagreement, rather than inconsistency. Disagreement (...) may originate in, or concern, a number of epistemic, socio-political or psychological factors. In this paper, we offer an account of the ‘loci and reasons’ for disagreement at different stages of the scientific process. We then present a controversial episode in the health sciences: the studies on hypercholesterolemia. The causes and effects of high levels of cholesterol in blood have been long and hotly debated, to the point of deserving the name of ‘cholesterol wars’; the debate, to be sure, isn’t settled yet. In this contribution, we focus on some selected loci and reasons for disagreement that occurred between 1920 and 1994 in the studies on hypercholesterolemia. We hope that our analysis of ‘loci and reasons’ for disagreement may shed light on the cholesterol wars, and possibly on other episodes of scientific disagreement. (shrink)