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The epidemiology of science
Pergamon
Stud. Hist. Phil. Biol. & Biomed. Sci., Vol. 32, No. 3, pp. 575–581, 2001 Printed in Great Britain
www.elsevier.com/locate/shpsc
Essay Review The Epidemiology of Science Peter Kosso*
Paul Thagard, How Scientists Explain Disease (Princeton, NJ: Princeton University Press, 1999), 268 pp. ISBN 0-691-00261-4. Hardback, $29.95, £18.95. A stomach ulcer is almost a cliche´. Everybody knows that ulcers are caused by stress or maybe too much spicy food. And if pressed, most would say that the common denominator in these factors is the acids they somehow produce in the stomach. The superabundant acids then eat away at the lining of the stomach and there’s your ulcer. There is some sophistication in this popular account of the cause of an ulcer; it allows for a complex of possible causes and a multi-step causal chain. But it’s false. Acid is not the cause of stomach ulcers, and, when the word gets out, ulcers should no longer be the punchline of jokes about worry and anxiety. The medical explanation of ulcers has changed. Ulcers are caused by a bacterium, specifically the H. pylori bacterium. Consequently, ulcers must now be categorized as an infectious disease, and they can be treated with antibiotics. This new account of the disease, first proposed in 1983 and now generally accepted in the community of medical scientists, makes for a fascinating case study in Paul Thagard’s How Scientists Explain Disease. The focus is on the introduction, testing and acceptance of new beliefs in science, specifically medical science. Explaining a disease is a causal analysis. But it is more than the identification of the singular cause that is the immediate mechanism affecting the body. In all cases, there is a network of causes, conditions and dispositions to be identified and fitted together into a coherent causal structure. Thagard clarifies this style of explanation, and gives us a clear language for talking about explanation in medical science, by categorizing distinct patterns of causal networks and their associated * Department of Philosophy, Northern Arizona University, Flagstaff, AZ 86011, U.S.A.
PII: S1369-8486(01)00019-X 575
576
Studies in History and Philosophy of Biological and Biomedical Sciences
‘explanation schemas’ (p. 5). And there is a larger issue than that of understanding diseases or even understanding the ways of medical science. The account of medical explanation is to be a case study of science in general, and the goal is to understand how science itself works. Philosophy of science is always enriched and clarified by evidence from science itself, and here is a bookful of real, detailed examples to make sure that our theorizing about science matches what really happens in the laboratory. Thagard works with an intriguing analogy. Science is like a disease. Beliefs are the symptoms, and like the symptoms of a disease, we can explain (and we should explain) what causes them. In both cases it would miss the point and be inaccurate to dwell on just a single causal factor, because all cases require ‘complex schemas that assemble multiple interacting causes’ (p. xv). To explain science we must consider the complementary roles of both cognitive and social influences, and the constraints of the natural world. It is a useful analogy for describing the process of science, and if we ever need to know the cure for science this will surely be the way to find it. But an important weakness in the analogy is revealed when we ask about the goal of explanation. Why do we want to explain a disease? Why do we want to explain science? And what amounts to genuine understanding in each case? In medical science, a full description of the confluence of causes amounts to a full explanation of a disease. Identifying the causes and conditions and the structure of their interactions is all it takes to understand the disease, since the goal of understanding is prevention and treatment. But understanding science itself is different, as the goal is different. A full description of the causes of beliefs, of the mechanisms of discovery and acceptance of ideas, is not all it takes to really understand science. Science is more than a physical or even a psychological phenomenon; it is an epistemic phenomenon in which the symptoms are not mere beliefs but knowledge. In this way it is not like a disease, and we do not really understand science if the epistemic component is missing. Describing the causes of beliefs and the patterns of acceptance provides essential evidence for explaining science, but genuine understanding requires some epistemological theorizing as well. To make the best use of the evidence we should clarify the details of explanation in medical science and what amounts to genuine understanding of a disease. Thagard uses the explanation of stomach ulcers as an exemplar for this analysis, and he briefly surveys other diseases such as scurvy, AIDS and Mad Cow Disease to show variations on the theme. The key to understanding the explanation of diseases is the taxonomy of causal mechanisms and their associated explanatory schemata. There are four basic types of diseases, classified in terms of the nature of their causes (p. 35). Some diseases (such as ulcers) are infectious, some (such as scurvy) are caused by nutritional imbalances, some (such as cancer) are caused by molecular–genetic malfunctions, and the rest (such as lupus) are autoimmune diseases in which the body’s own
The Epidemiology of Science
577
immune system attacks the body. The first step in explaining a disease is putting it into its proper causal category, and the progress in understanding ulcers came in moving them out of the category of nutritional diseases and into the category of infectious disease. There is a kind of unification in this accomplishment of grouping the causes of diseases into four basic types, and it bears a superficial resemblance to the sort of unification that is the goal in physics. The description of the physical world is unified to the extent that the great variety of interactions we observe can be explained as different manifestations of only four fundamental forces. There is unity in the description of diseases when the vast variety of physical maladies can be organized into a system of just four fundamental types of causes. But Thagard points out important differences between unification in medical science and in physics (p. 20). Each of the four types of causes of disease is just that, a type of cause but not a singular mechanism. Each category covers its own variety of different causal agents. Infectious diseases, for example, include viral and bacterial phenomena, and each of those covers a diverse population of different viruses or bacteria. The unity in physics is more ontologically parsimonious in that each of the four fundamental interactions describes exactly one causal mechanism that is at work each time that force is applied. Furthermore, the unity in physics is a work in progress, and there is reason to believe that the four fundamental forces can be described as four different manifestations of a single basic interaction, unified under a single theory of everything. There is no such expectation in medical science. Infections are fundamentally different phenomena from nutritional deficiencies, and it is very unlikely that eating too little vitamin C and getting scurvy is somehow a manifestation of the same basic causal mechanism as getting tetanus from the bacteria that enter an open wound. Once placed in the right explanatory schema, a particular disease is explained by identifying the particular cause. In practice this is a matter of observing a correlation between some condition and the symptoms of the disease. The crucial inference is then from correlation to cause, and then, both as test of the inference and as application of the medical knowledge, to the ability to control the symptoms. The inference from correlation to cause is a fundamental step in all sciences, and it is a focus of the debates about scientific realism. The key to the inference, according to Thagard, is explanatory coherence. Conclusions about causes, including the complex network of contributing conditions and the specific causal mechanism, must fit into a coherent explanatory system. Claims about causes are scientific theories, and ‘the justification of scientific theories, including their postulation of theoretical entities, is a matter of explanatory coherence, in which a theory is accepted because it provides a better explanation of the evidence’ (pp. 110–1). There is an important and revealing equivocation in this statement about explanatory coherence. It starts out being about justification but ends up being about acceptance of the scientific theory. Explanatory coherence may well be sufficient
578
Studies in History and Philosophy of Biological and Biomedical Sciences
reason to accept a theory and its claims about causes, but it could at the same time fall short of providing good reason to believe that the theory is true in what it says about causes. This distinction between belief and acceptance is clarified by van Fraassen (1980, p. 69). He points out that empirical adequacy of a theory—and explanatory coherence is a very robust kind of empirical adequacy—is good reason to use a theory and to behave as if it is true in what it says about causes, but that is all. Empirical adequacy is not in itself good reason to actually believe that the theory is true. So there is some epistemological work to be done on behalf of explanatory coherence to make it a sign not just of acceptability but also of justification. In fact, just this sort of work is undertaken in BonJour’s coherence model of justification. He calls it the ‘metajustification’ (BonJour, 1985, p. 171), the argument that links coherence to truth. In the setting of medical science, empirical adequacy is every good reason to accept a causal theory, since control of observable agents and symptoms is the goal. Truth about unobservables is not really the issue when prevention and cure of disease is the challenge. In an applied science such as medicine, in other words, acceptance is enough. If antibiotics clear up the symptoms of the ulcer, that is, if nature can be manipulated in the sense that Hacking refers to as intervening (Hacking, 1983), then the scientists have succeeded. There can be no argument against empirical success, including explanatory coherence and the resulting ability to control the phenomena, as a good reason to accept such a theory for practical application. But it is not yet good reason to believe that the theory is true in what it says about unobservable causes. In the pragmatics of medical science we can put the issues of realism aside. Claims about alternative medicines with alternative treatments can be argued at the level of observable efficacy. Perhaps in all the sciences, if we focus on the issues within the science itself and of concern to the scientists, we can put realism aside. And if we ask what amounts to an explanation of disease, that is, an explanation in medical science, then Thagard’s taxonomy of explanatory schemata and descriptions of the discovery and acceptance of theories of causal mechanisms is just the thing. It is a rich and detailed source of evidence for understanding what it is to become, in van Fraassen’s words, ‘immersed in theory’ (van Fraassen, 1980, p. 82) and to live in the world as if the theory is true. But Thagard wants more than an account of explanation in science; he aims for an explanation of science as well. The idea is to use medical science as the exemplar and to generalize on the causes of beliefs in science, that is, to account for the discovery, acceptance and justification of scientific theories. Just as diseases are explained in terms of interrelated networks of causes and a variety of interacting mechanisms, science is explained by interacting and complementary influences. The social, cognitive and natural constraints on theorizing do not demand exclusive or competing accounts of science. They must all be considered to account for the full causal network and to give an accurate explanation of science. Thagard is
The Epidemiology of Science
579
most concerned with putting the cognitive and social factors together in a unified explanation of science. And though he begins the analysis with an eye on three explanatory schemata for science—social, cognitive and philosophical (p. 4)—the philosophical factor disappears as early as the end of the first chapter where the argument is summarized: ‘Cognitive and social explanations can, however, be complementary rather than competitive’ (p. 19). This loss of the philosophical perspective is, using the medical analogy, a symptom, an early warning sign, that the results will lack the epistemological value to really explain science. The social and cognitive explanations are nonetheless interesting and important parts of the description of science. In the category of cognitive processes, Thagard describes the details of arguments by analogy, directed questioning and explanatory coherence. These are the individual parts of the scientific reasoning process that make up the social whole. And the social processes exhibit properties of their own, properties not found at the individual cognitive level but equally important for understanding how science works. These social properties include different types of collaboration, conferencing, and exchange of ideas over the internet. Taken together, the cognitive and social components provide a rich interwoven description of the ways of science. There is a wealth of data to draw on in this, but it is not clear what it is evidence for. The full description of the processes does not suffice to explain science as it does to explain disease. Science is a different sort of phenomenon, an epistemic phenomenon, and as (Goldman, 1992, p. 193) points out, an analysis of the causes of beliefs does not amount to an analysis of the justification of the beliefs. The epistemic component is missing, and we cannot claim to understand science without it. Describing the mechanisms of acceptance of theories, even explaining why theories are accepted, simply does not add up to explaining the justification of theories. Thagard claims that there is a normative epistemological component in his analysis, referring, for example, to ‘social rationality’ (p. 18). This is assessed by such standards as reliability and efficiency of various social processes in science (p. 172). But it is the reliability or efficiency of a practice in producing results that are ‘acceptable by a scientist’s peers’ (p. 172) that is evaluated. These are only valuable qualities, and the analysis is only normative, with the presumption that science, characterized by intersubjectivity, is a good thing and should be perpetuated. Any normative aspects of the social+cognitive analysis depend upon the antecedent normative status of science itself. We learn of the effective ways to perpetuate science without really understanding what kind of enterprise, truth-provider or merely instrumental problem-solver is being perpetuated. The missing epistemological component is most apparent in Thagard’s revision of Goldman’s standards for epistemic appraisal (p. 171). Goldman proposes what he calls ‘veritism’ (Goldman, 1992, p. 195), an assessment of the effectiveness of a social process at delivering theories that are true. Thagard claims that the goal
580
Studies in History and Philosophy of Biological and Biomedical Sciences
of truth is not only asking too much of a theory but is not really what scientists consider themselves to be doing. And so it is both more in touch with real science and a more assessable evaluation if we lower the goal to the attainment of ‘results’ rather than truth. Scientific results are simply claims that enjoy general acceptance among the appropriate peers. There is no doubt that the assessment of a claim being a result relies on more immediately accessible information than does assessment of its being true. But the consequence of making the task easier is to diminish the accomplishment. The assessment has been made too easy. If general agreement on the issues is the goal in science, then collaboration and all of the other sociological aspects of science are obviously an asset. The goal of the process has been changed to accommodate the practice. This is exactly what tempts many philosophers of science into a dismissive attitude toward sociology. There is no challenge left if we are allowed to lower the standards to match the abilities of the players. There is plenty of analytic room to maneuver between Goldman’s veritism and Thagard’s retrenchment into results. Between truth and practice lies justification. To genuinely understand science we need to know the accessible and assessable indicators of the truth. This is the missing epistemic component in the cognitive+social explanation of science. The appraisal left in the hands of just the sociological and cognitive analysis verges on being a long ad hominem. It’s all about who believes what, and why they believe it. The focus is on their reasons rather than on the reasons themselves and on the logic of the reasoning. Thagard rightly points out that there is a reciprocal relation in science between the contributions of theory and evidence (p. 82). This is as true for explanations of science as for explanations in science, and this points to the most useful relation between the social+cognitive account of science and philosophy of science. It’s not that the social (or cognitive) account is incompatible with the philosophical, as Thagard suggests (p. 94). The one is the essential evidence for the other’s theorizing. Thus ‘[philosophical] [p]rinciples of rationality are not to be derived a priori but should develop in interaction with increasing understanding of human cognitive processes’ (p. 236). The interaction is exactly the reciprocity described earlier between evidence and theory. Thagard’s case study of the cognitive and social processes in the discovery and acceptance of the belief that ulcers are caused by bacteria is an extensive and detailed source of data to both motivate and test epistemological theory. It is an answer to any complaint that coherence theories of justification are vague and short on details of the structure of the explanatory coherence itself. Here are the details and clear taxonomy to characterize the structure. On its own, the cognitive+social explanation of science is data without theory, and so it isn’t really evidence for or against anything. Sure enough, ‘attention to the psychological and social complexities of scientific practice does not undermine the validity of scientific knowledge’ (p. 220). But neither does it offer any support. To return to the analogy between science and disease, the social and cognitive
The Epidemiology of Science
581
processes are clearly seen to be responsible for communicating the disease from one host to another. We have identified the vector, but what exactly is it carrying? We have yet to identify the agent of the disease, the germ itself. We see correlation between the social/cognitive processes and the acceptance of belief, but we cannot yet infer that truth is part of the picture. We have done the epidemiology of science, but not the microbiology. We know how to control the disease but not really what’s going on. References BonJour, L. (1985) The Structure of Empirical Knowledge (Cambridge, MA: Harvard University Press). Goldman, A. (1992) Liaisons: Philosophy Meets the Cognitive and Social Sciences (Cambridge, MA: MIT Press). Hacking, I. (1983) Representing and Intervening (Cambridge: Cambridge University Press). Van Fraassen, B. (1980) The Scientific Image (Oxford: Clarendon Press).
Stud. Hist. Phil. Biol. & Biomed. Sci., Vol. 32, No. 3, pp. 575–581, 2001 Printed in Great Britain
www.elsevier.com/locate/shpsc
Essay Review The Epidemiology of Science Peter Kosso*
Paul Thagard, How Scientists Explain Disease (Princeton, NJ: Princeton University Press, 1999), 268 pp. ISBN 0-691-00261-4. Hardback, $29.95, £18.95. A stomach ulcer is almost a cliche´. Everybody knows that ulcers are caused by stress or maybe too much spicy food. And if pressed, most would say that the common denominator in these factors is the acids they somehow produce in the stomach. The superabundant acids then eat away at the lining of the stomach and there’s your ulcer. There is some sophistication in this popular account of the cause of an ulcer; it allows for a complex of possible causes and a multi-step causal chain. But it’s false. Acid is not the cause of stomach ulcers, and, when the word gets out, ulcers should no longer be the punchline of jokes about worry and anxiety. The medical explanation of ulcers has changed. Ulcers are caused by a bacterium, specifically the H. pylori bacterium. Consequently, ulcers must now be categorized as an infectious disease, and they can be treated with antibiotics. This new account of the disease, first proposed in 1983 and now generally accepted in the community of medical scientists, makes for a fascinating case study in Paul Thagard’s How Scientists Explain Disease. The focus is on the introduction, testing and acceptance of new beliefs in science, specifically medical science. Explaining a disease is a causal analysis. But it is more than the identification of the singular cause that is the immediate mechanism affecting the body. In all cases, there is a network of causes, conditions and dispositions to be identified and fitted together into a coherent causal structure. Thagard clarifies this style of explanation, and gives us a clear language for talking about explanation in medical science, by categorizing distinct patterns of causal networks and their associated * Department of Philosophy, Northern Arizona University, Flagstaff, AZ 86011, U.S.A.
PII: S1369-8486(01)00019-X 575
576
Studies in History and Philosophy of Biological and Biomedical Sciences
‘explanation schemas’ (p. 5). And there is a larger issue than that of understanding diseases or even understanding the ways of medical science. The account of medical explanation is to be a case study of science in general, and the goal is to understand how science itself works. Philosophy of science is always enriched and clarified by evidence from science itself, and here is a bookful of real, detailed examples to make sure that our theorizing about science matches what really happens in the laboratory. Thagard works with an intriguing analogy. Science is like a disease. Beliefs are the symptoms, and like the symptoms of a disease, we can explain (and we should explain) what causes them. In both cases it would miss the point and be inaccurate to dwell on just a single causal factor, because all cases require ‘complex schemas that assemble multiple interacting causes’ (p. xv). To explain science we must consider the complementary roles of both cognitive and social influences, and the constraints of the natural world. It is a useful analogy for describing the process of science, and if we ever need to know the cure for science this will surely be the way to find it. But an important weakness in the analogy is revealed when we ask about the goal of explanation. Why do we want to explain a disease? Why do we want to explain science? And what amounts to genuine understanding in each case? In medical science, a full description of the confluence of causes amounts to a full explanation of a disease. Identifying the causes and conditions and the structure of their interactions is all it takes to understand the disease, since the goal of understanding is prevention and treatment. But understanding science itself is different, as the goal is different. A full description of the causes of beliefs, of the mechanisms of discovery and acceptance of ideas, is not all it takes to really understand science. Science is more than a physical or even a psychological phenomenon; it is an epistemic phenomenon in which the symptoms are not mere beliefs but knowledge. In this way it is not like a disease, and we do not really understand science if the epistemic component is missing. Describing the causes of beliefs and the patterns of acceptance provides essential evidence for explaining science, but genuine understanding requires some epistemological theorizing as well. To make the best use of the evidence we should clarify the details of explanation in medical science and what amounts to genuine understanding of a disease. Thagard uses the explanation of stomach ulcers as an exemplar for this analysis, and he briefly surveys other diseases such as scurvy, AIDS and Mad Cow Disease to show variations on the theme. The key to understanding the explanation of diseases is the taxonomy of causal mechanisms and their associated explanatory schemata. There are four basic types of diseases, classified in terms of the nature of their causes (p. 35). Some diseases (such as ulcers) are infectious, some (such as scurvy) are caused by nutritional imbalances, some (such as cancer) are caused by molecular–genetic malfunctions, and the rest (such as lupus) are autoimmune diseases in which the body’s own
The Epidemiology of Science
577
immune system attacks the body. The first step in explaining a disease is putting it into its proper causal category, and the progress in understanding ulcers came in moving them out of the category of nutritional diseases and into the category of infectious disease. There is a kind of unification in this accomplishment of grouping the causes of diseases into four basic types, and it bears a superficial resemblance to the sort of unification that is the goal in physics. The description of the physical world is unified to the extent that the great variety of interactions we observe can be explained as different manifestations of only four fundamental forces. There is unity in the description of diseases when the vast variety of physical maladies can be organized into a system of just four fundamental types of causes. But Thagard points out important differences between unification in medical science and in physics (p. 20). Each of the four types of causes of disease is just that, a type of cause but not a singular mechanism. Each category covers its own variety of different causal agents. Infectious diseases, for example, include viral and bacterial phenomena, and each of those covers a diverse population of different viruses or bacteria. The unity in physics is more ontologically parsimonious in that each of the four fundamental interactions describes exactly one causal mechanism that is at work each time that force is applied. Furthermore, the unity in physics is a work in progress, and there is reason to believe that the four fundamental forces can be described as four different manifestations of a single basic interaction, unified under a single theory of everything. There is no such expectation in medical science. Infections are fundamentally different phenomena from nutritional deficiencies, and it is very unlikely that eating too little vitamin C and getting scurvy is somehow a manifestation of the same basic causal mechanism as getting tetanus from the bacteria that enter an open wound. Once placed in the right explanatory schema, a particular disease is explained by identifying the particular cause. In practice this is a matter of observing a correlation between some condition and the symptoms of the disease. The crucial inference is then from correlation to cause, and then, both as test of the inference and as application of the medical knowledge, to the ability to control the symptoms. The inference from correlation to cause is a fundamental step in all sciences, and it is a focus of the debates about scientific realism. The key to the inference, according to Thagard, is explanatory coherence. Conclusions about causes, including the complex network of contributing conditions and the specific causal mechanism, must fit into a coherent explanatory system. Claims about causes are scientific theories, and ‘the justification of scientific theories, including their postulation of theoretical entities, is a matter of explanatory coherence, in which a theory is accepted because it provides a better explanation of the evidence’ (pp. 110–1). There is an important and revealing equivocation in this statement about explanatory coherence. It starts out being about justification but ends up being about acceptance of the scientific theory. Explanatory coherence may well be sufficient
578
Studies in History and Philosophy of Biological and Biomedical Sciences
reason to accept a theory and its claims about causes, but it could at the same time fall short of providing good reason to believe that the theory is true in what it says about causes. This distinction between belief and acceptance is clarified by van Fraassen (1980, p. 69). He points out that empirical adequacy of a theory—and explanatory coherence is a very robust kind of empirical adequacy—is good reason to use a theory and to behave as if it is true in what it says about causes, but that is all. Empirical adequacy is not in itself good reason to actually believe that the theory is true. So there is some epistemological work to be done on behalf of explanatory coherence to make it a sign not just of acceptability but also of justification. In fact, just this sort of work is undertaken in BonJour’s coherence model of justification. He calls it the ‘metajustification’ (BonJour, 1985, p. 171), the argument that links coherence to truth. In the setting of medical science, empirical adequacy is every good reason to accept a causal theory, since control of observable agents and symptoms is the goal. Truth about unobservables is not really the issue when prevention and cure of disease is the challenge. In an applied science such as medicine, in other words, acceptance is enough. If antibiotics clear up the symptoms of the ulcer, that is, if nature can be manipulated in the sense that Hacking refers to as intervening (Hacking, 1983), then the scientists have succeeded. There can be no argument against empirical success, including explanatory coherence and the resulting ability to control the phenomena, as a good reason to accept such a theory for practical application. But it is not yet good reason to believe that the theory is true in what it says about unobservable causes. In the pragmatics of medical science we can put the issues of realism aside. Claims about alternative medicines with alternative treatments can be argued at the level of observable efficacy. Perhaps in all the sciences, if we focus on the issues within the science itself and of concern to the scientists, we can put realism aside. And if we ask what amounts to an explanation of disease, that is, an explanation in medical science, then Thagard’s taxonomy of explanatory schemata and descriptions of the discovery and acceptance of theories of causal mechanisms is just the thing. It is a rich and detailed source of evidence for understanding what it is to become, in van Fraassen’s words, ‘immersed in theory’ (van Fraassen, 1980, p. 82) and to live in the world as if the theory is true. But Thagard wants more than an account of explanation in science; he aims for an explanation of science as well. The idea is to use medical science as the exemplar and to generalize on the causes of beliefs in science, that is, to account for the discovery, acceptance and justification of scientific theories. Just as diseases are explained in terms of interrelated networks of causes and a variety of interacting mechanisms, science is explained by interacting and complementary influences. The social, cognitive and natural constraints on theorizing do not demand exclusive or competing accounts of science. They must all be considered to account for the full causal network and to give an accurate explanation of science. Thagard is
The Epidemiology of Science
579
most concerned with putting the cognitive and social factors together in a unified explanation of science. And though he begins the analysis with an eye on three explanatory schemata for science—social, cognitive and philosophical (p. 4)—the philosophical factor disappears as early as the end of the first chapter where the argument is summarized: ‘Cognitive and social explanations can, however, be complementary rather than competitive’ (p. 19). This loss of the philosophical perspective is, using the medical analogy, a symptom, an early warning sign, that the results will lack the epistemological value to really explain science. The social and cognitive explanations are nonetheless interesting and important parts of the description of science. In the category of cognitive processes, Thagard describes the details of arguments by analogy, directed questioning and explanatory coherence. These are the individual parts of the scientific reasoning process that make up the social whole. And the social processes exhibit properties of their own, properties not found at the individual cognitive level but equally important for understanding how science works. These social properties include different types of collaboration, conferencing, and exchange of ideas over the internet. Taken together, the cognitive and social components provide a rich interwoven description of the ways of science. There is a wealth of data to draw on in this, but it is not clear what it is evidence for. The full description of the processes does not suffice to explain science as it does to explain disease. Science is a different sort of phenomenon, an epistemic phenomenon, and as (Goldman, 1992, p. 193) points out, an analysis of the causes of beliefs does not amount to an analysis of the justification of the beliefs. The epistemic component is missing, and we cannot claim to understand science without it. Describing the mechanisms of acceptance of theories, even explaining why theories are accepted, simply does not add up to explaining the justification of theories. Thagard claims that there is a normative epistemological component in his analysis, referring, for example, to ‘social rationality’ (p. 18). This is assessed by such standards as reliability and efficiency of various social processes in science (p. 172). But it is the reliability or efficiency of a practice in producing results that are ‘acceptable by a scientist’s peers’ (p. 172) that is evaluated. These are only valuable qualities, and the analysis is only normative, with the presumption that science, characterized by intersubjectivity, is a good thing and should be perpetuated. Any normative aspects of the social+cognitive analysis depend upon the antecedent normative status of science itself. We learn of the effective ways to perpetuate science without really understanding what kind of enterprise, truth-provider or merely instrumental problem-solver is being perpetuated. The missing epistemological component is most apparent in Thagard’s revision of Goldman’s standards for epistemic appraisal (p. 171). Goldman proposes what he calls ‘veritism’ (Goldman, 1992, p. 195), an assessment of the effectiveness of a social process at delivering theories that are true. Thagard claims that the goal
580
Studies in History and Philosophy of Biological and Biomedical Sciences
of truth is not only asking too much of a theory but is not really what scientists consider themselves to be doing. And so it is both more in touch with real science and a more assessable evaluation if we lower the goal to the attainment of ‘results’ rather than truth. Scientific results are simply claims that enjoy general acceptance among the appropriate peers. There is no doubt that the assessment of a claim being a result relies on more immediately accessible information than does assessment of its being true. But the consequence of making the task easier is to diminish the accomplishment. The assessment has been made too easy. If general agreement on the issues is the goal in science, then collaboration and all of the other sociological aspects of science are obviously an asset. The goal of the process has been changed to accommodate the practice. This is exactly what tempts many philosophers of science into a dismissive attitude toward sociology. There is no challenge left if we are allowed to lower the standards to match the abilities of the players. There is plenty of analytic room to maneuver between Goldman’s veritism and Thagard’s retrenchment into results. Between truth and practice lies justification. To genuinely understand science we need to know the accessible and assessable indicators of the truth. This is the missing epistemic component in the cognitive+social explanation of science. The appraisal left in the hands of just the sociological and cognitive analysis verges on being a long ad hominem. It’s all about who believes what, and why they believe it. The focus is on their reasons rather than on the reasons themselves and on the logic of the reasoning. Thagard rightly points out that there is a reciprocal relation in science between the contributions of theory and evidence (p. 82). This is as true for explanations of science as for explanations in science, and this points to the most useful relation between the social+cognitive account of science and philosophy of science. It’s not that the social (or cognitive) account is incompatible with the philosophical, as Thagard suggests (p. 94). The one is the essential evidence for the other’s theorizing. Thus ‘[philosophical] [p]rinciples of rationality are not to be derived a priori but should develop in interaction with increasing understanding of human cognitive processes’ (p. 236). The interaction is exactly the reciprocity described earlier between evidence and theory. Thagard’s case study of the cognitive and social processes in the discovery and acceptance of the belief that ulcers are caused by bacteria is an extensive and detailed source of data to both motivate and test epistemological theory. It is an answer to any complaint that coherence theories of justification are vague and short on details of the structure of the explanatory coherence itself. Here are the details and clear taxonomy to characterize the structure. On its own, the cognitive+social explanation of science is data without theory, and so it isn’t really evidence for or against anything. Sure enough, ‘attention to the psychological and social complexities of scientific practice does not undermine the validity of scientific knowledge’ (p. 220). But neither does it offer any support. To return to the analogy between science and disease, the social and cognitive
The Epidemiology of Science
581
processes are clearly seen to be responsible for communicating the disease from one host to another. We have identified the vector, but what exactly is it carrying? We have yet to identify the agent of the disease, the germ itself. We see correlation between the social/cognitive processes and the acceptance of belief, but we cannot yet infer that truth is part of the picture. We have done the epidemiology of science, but not the microbiology. We know how to control the disease but not really what’s going on. References BonJour, L. (1985) The Structure of Empirical Knowledge (Cambridge, MA: Harvard University Press). Goldman, A. (1992) Liaisons: Philosophy Meets the Cognitive and Social Sciences (Cambridge, MA: MIT Press). Hacking, I. (1983) Representing and Intervening (Cambridge: Cambridge University Press). Van Fraassen, B. (1980) The Scientific Image (Oxford: Clarendon Press).