Scientific Models of Human Health Risk Analysis in Legal and Policy Decisions

The quality of scientific predictions of risk in the courtroom and policy arena rests in large measure on how the two differences between normal practice and the legal/policy practice of science are reconciled. This article considers a variety of issues that arise in reconciling these differences, and the problems that remain with scientific estimates of risk when these are used in decisions

Alternative psychotherapies: Conceptual elucidation and epidemiological framework

This article elucidates and defines alternative psychotherapies, as well as describes the variables that explain why some professional psychologists are prone to endorse these practices. First, the novel concept of “complementary and alternative psychotherapies” (CAP) is defined within the framework of the established hierarchy of clinical evidence. Second, we report a literature review to aid understanding of the main variables explaining why some clinicians prefer CAP. We review rejection of scientific reasoning, misconceptions about human nature, and pragmatic limitations of evidence-based practice

What Makes Delusions Pathological?

Bortolotti argues that we cannot distinguish delusions from other irrational beliefs in virtue of their epistemic features alone. Although her arguments are convincing, her analysis leaves an important question unanswered: What makes delusions pathological? In this paper I set out to answer this question by arguing that the pathological character of delusions arises from an executive dysfunction in a subject’s ability to detect relevance in the environment. I further suggest that this dysfunction derives from an underlying emotional imbalance—one that leads delusional subjects to regard some contextual elements as deeply puzzling or highly significant

Social Epistemology as a New Paradigm for Journalism and Media Studies

Journalism and media studies lack robust theoretical concepts for studying journalistic knowledge ‎generation. More specifically, conceptual challenges attend the emergence of big data and ‎algorithmic sources of journalistic knowledge. A family of frameworks apt to this challenge is ‎provided by “social epistemology”: a young philosophical field which regards society’s participation ‎in knowledge generation as inevitable. Social epistemology offers the best of both worlds for ‎journalists and media scholars: a thorough familiarity with biases and failures of obtaining ‎knowledge, and a strong orientation toward best practices in the realm of knowledge-acquisition ‎and truth-seeking. This paper articulates the lessons of social epistemology for two central nodes of ‎knowledge-acquisition in contemporary journalism: human-mediated knowledge and technology-‎mediated knowledge.

Objective Styles in Northern Field Science

Social studies of science have often treated natural field sites as extensions of the laboratory. But this overlooks the unique specificities of field sites. While lab sites are usually private spaces with carefully controlled borders, field sites are more typically public spaces with fluid boundaries and diverse inhabitants. Field scientists must therefore often adapt their work to the demands and interests of local agents. I propose to address the difference between lab and field in sociological terms, as a difference in style. A field style treats epistemic alterity as a resource rather than an obstacle for objective knowledge production. A sociological stylistics of the field should thus explain how objective science can co-exist with radical conceptual difference. I discuss examples from the Canadian North, focussing on collaborations between state wildlife biologists and managers, on the one hand, and local Aboriginal Elders and hunters, on the other. I argue that a sociological stylistics of the field can help us to better understand how radically diverse agents may collaborate across cultures in the successful production of reliable natural knowledge

What else justification could be

According to a captivating picture, epistemic justification is essentially a matter of epistemic or evidential likelihood. While certain problems for this view are well known, it is motivated by a very natural thought—if justification can fall short of epistemic certainty, then what else could it possibly be? In this paper I shall develop an alternative way of thinking about epistemic justification. On this conception, the difference between justification and likelihood turns out to be akin to the more widely recognised difference between ceteris paribus laws and brute statistical generalisations. I go on to discuss, in light of this suggestion, issues such as classical and lottery-driven scepticism as well as the lottery and preface paradoxes

The Role of Deontic Logic in the Specification of Information Systems

In this paper we discuss the role that deontic logic plays in the specification of information systems, either because constraints on the systems directly concern norms or, and even more importantly, system constraints are considered ideal but violable (so-called `soft¿ constraints).\ud To overcome the traditional problems with deontic logic (the so-called paradoxes), we first state the importance of distinguishing between ought-to-be and ought-to-do constraints and next focus on the most severe paradox, the so-called Chisholm paradox, involving contrary-to-duty norms. We present a multi-modal extension of standard deontic logic (SDL) to represent the ought-to-be version of the Chisholm set properly. For the ought-to-do variant we employ a reduction to dynamic logic, and show how the Chisholm set can be treated adequately in this setting. Finally we discuss a way of integrating both ought-to-be and ought-to-do reasoning, enabling one to draw conclusions from ought-to-be constraints to ought-to-do ones, and show by an example the use(fulness) of this

Numerical Representations of Acceptance

Accepting a proposition means that our confidence in this proposition is strictly greater than the confidence in its negation. This paper investigates the subclass of uncertainty measures, expressing confidence, that capture the idea of acceptance, what we call acceptance functions. Due to the monotonicity property of confidence measures, the acceptance of a proposition entails the acceptance of any of its logical consequences. In agreement with the idea that a belief set (in the sense of Gardenfors) must be closed under logical consequence, it is also required that the separate acceptance o two propositions entail the acceptance of their conjunction. Necessity (and possibility) measures agree with this view of acceptance while probability and belief functions generally do not. General properties of acceptance functions are estabilished. The motivation behind this work is the investigation of a setting for belief revision more general than the one proposed by Alchourron, Gardenfors and Makinson, in connection with the notion of conditioning.Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995