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International audience ; This paper proposes a critical assessment of the concept of "conspiracy theory" as a coherent object of investigation, and evaluates the prospects for an integration of various avenues of research-sociological, epistemological, psychological-that deal with it. Because of the threat posed by conspiracy theories to public health and political stability, academic efforts to understand the sociological and cognitive basis for the adoption of such views, as well as their epistemological flaws, are undoubtedly needed. But the preliminary question of the unity, and of the specificity of the class of things called "conspiracy theories", is often overlooked. It is addressed in this paper. Starting from a tentative classification of the various ideations labelled "conspiracy theories", we then focus on a particularly important subclass thereof, namely those promoting anti-scientific views. From this, we draw a first, sceptical conclusion as to the existence of a clear-cut boundary between conspiracy thinking and healthy rational critique of science (both sociological and philosophical). This leads us to evaluate the attempt of analysing conspiracy theories' epistemic flaws in the light of philosophical standards for scientific theories. Having shown that this route is a dead-end, we highlight what appears as a major divide among philosophical and psychological accounts of CTs, namely whether one should treat them as irrational, or as merely wrong (in the latter, rationalist approach, CTs would just be wrong statements resulting from rational operations). Focusing again on anti-science CTs, we finally argue in favour of a politically and socially contextualised approach to the growth and spread of conspiracy ideations, over a scheme considering CTs as abstract entities, independently from the socially situated agents who hold them.

International audience ; This paper proposes a critical assessment of the concept of "conspiracy theory" as a coherent object of investigation, and evaluates the prospects for an integration of various avenues of research-sociological, epistemological, psychological-that deal with it. Because of the threat posed by conspiracy theories to public health and political stability, academic efforts to understand the sociological and cognitive basis for the adoption of such views, as well as their epistemological flaws, are undoubtedly needed. But the preliminary question of the unity, and of the specificity of the class of things called "conspiracy theories", is often overlooked. It is addressed in this paper. Starting from a tentative classification of the various ideations labelled "conspiracy theories", we then focus on a particularly important subclass thereof, namely those promoting anti-scientific views. From this, we draw a first, sceptical conclusion as to the existence of a clear-cut boundary between conspiracy thinking and healthy rational critique of science (both sociological and philosophical). This leads us to evaluate the attempt of analysing conspiracy theories' epistemic flaws in the light of philosophical standards for scientific theories. Having shown that this route is a dead-end, we highlight what appears as a major divide among philosophical and psychological accounts of CTs, namely whether one should treat them as irrational, or as merely wrong (in the latter, rationalist approach, CTs would just be wrong statements resulting from rational operations). Focusing again on anti-science CTs, we finally argue in favour of a politically and socially contextualised approach to the growth and spread of conspiracy ideations, over a scheme considering CTs as abstract entities, independently from the socially situated agents who hold them.

This anthology of essays presents a sample of studies from recent philosophy of medicine addressing issues which attempt to answer very general (interdependent) questions: (a) what is a disease and what is health? (b) How do we (causally) explain diseases? (c) And how do we distinguish diseases, i.e. define classes of diseases and recognize that an instance X of disease belongs to a given class B? (d) How do we assess and choose cure/ therapy?0The book is divided into three sections: classification, disease, and evidence. In general, attention is focused on statistics in medicine and epidemiology, issues in psychiatry, and connecting medicine with evolutionary biology and genetics. Many authors position the theories that they address within their historical contexts.00

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.

International audience ; In the current context of global change and a biodiversity crisis, there are increasing demands for greater predictive power in ecology, in both the scientific literature and at the science–policy interface. The implicit assumption is that this will increase knowledge and, in turn, lead to better decision‐making. However, the justification for this assumption remains uncertain, not least because the definition of 'prediction' is unclear. We propose that two types of prediction should be distinguished: corroboratory‐prediction is linked to the validation of theories; and anticipatory‐prediction is linked to the description of possible futures. We then discuss four families of obstacles to prediction, linked to the specific features of ecosystems: 1) they are historical entities, 2) they are complex, 3) their dynamics are stochastic, and 4) they are influenced by socio‐economic drivers. A naïve understanding of ecological science suggests that the two types of predictions are simply two phases in a sequence in which scientists first improve their knowledge of ecological systems via corroboratory‐predictions, and then apply this knowledge in order to forecast future states of ecosystems via anticipatory‐predictions in order to help policy makers taking decisions. This sequence is however not straightforward, partly because corroboration and anticipation are not affected by the obstacles to prediction in the same way. We thus invite to reconsider the role of ecological prediction as a tool in a deliberative model of decision‐making rather than as external scientific information aimed at enlightening the political process. Doing so would be beneficial for both the policy‐relevance of anticipatory‐prediction and the theoretical‐relevance of corroboratory‐prediction.