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  1. Values and Decisions: Cognitive and Noncognitive Values in Knowledge Generation and Decision Making.José Luis Luján & Oliver Todt - 2014 - Science, Technology, and Human Values 39 (5):720-743.
    The relevance of scientific knowledge for science and technology policy and regulation has led to a growing debate about the role of values. This article contributes to the clarification of what specific functions cognitive and noncognitive values adopt in knowledge generation and decisions, and what consequences the operation of values has for policy making and regulation. For our analysis, we differentiate between three different types of decision approaches, each of which shows a particular constellation of cognitive and noncognitive values. Our (...)
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  • Evidence based methodology: a naturalistic analysis of epistemic policies in regulatory science.José Luis Luján & Oliver Todt - 2021 - European Journal for Philosophy of Science 11 (1):1-19.
    In this paper we argue for a naturalistic solution to some of the methodological controversies in regulatory science, on the basis of two case studies: toxicology and health claim regulation. We analyze the debates related to the scientific evidence that is considered necessary for regulatory decision making in each of those two fields, with a particular attention to the interactions between scientific and regulatory aspects. This analysis allows us to identify two general stances in the debate: a) one that argues (...)
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  • Mechanistic Information as Evidence in Decision-Oriented Science.José Luis Luján, Oliver Todt & Juan Bautista Bengoetxea - 2016 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 47 (2):293-306.
    Mechanistic information is used in the field of risk assessment in order to clarify two controversial methodological issues, the selection of inference guides and the definition of standards of evidence. In this paper we present an analysis of the concept of mechanistic information in risk assessment by recurring to previous philosophical analyses of mechanistic explanation. Our conclusion is that the conceptual analysis of mechanistic explanation facilitates a better characterization of the concept of mechanistic information. However, it also shows that the (...)
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