The Evolution of Intermediary Institutions in Europe: From Corporatism to Governance

This book investigates the consecutive shifts between three types of intermediary institutions in the European context: Corporatist, Neo-corporatist and Governance institutions. It develops a new conceptual framework for understanding the function and position of intermediary institutions in society, as well as a vocabulary capable of explaining the causes and consequences of these shifts for politics, economy and society at large. The book is designed to fill a gap in three rather distinct, yet also overlapping bodies of literature: European Political Economy, European Integration and governance studies, and socio-legal studies in the European context. Reviews: - Anne Guisset: Transfer: European Review of Labour and Research, 22, 3, 427-429, 2016. - Ian Bruff, Capital & Class, 40, 3, 555 – 57, 2016

Bayesian Argumentation and the Value of Logical Validity

According to the Bayesian paradigm in the psychology of reasoning, the norms by which everyday human cognition is best evaluated are probabilistic rather than logical in character. Recently, the Bayesian paradigm has been applied to the domain of argumentation, where the fundamental norms are traditionally assumed to be logical. Here, we present a major generalisation of extant Bayesian approaches to argumentation that (i)utilizes a new class of Bayesian learning methods that are better suited to modelling dynamic and conditional inferences than standard Bayesian conditionalization, (ii) is able to characterise the special value of logically valid argument schemes in uncertain reasoning contexts, (iii) greatly extends the range of inferences and argumentative phenomena that can be adequately described in a Bayesian framework, and (iv) undermines some influential theoretical motivations for dual function models of human cognition. We conclude that the probabilistic norms given by the Bayesian approach to rationality are not necessarily at odds with the norms given by classical logic. Rather, the Bayesian theory of argumentation can be seen as justifying and enriching the argumentative norms of classical logic

When No Reason For Is A Reason Against

We provide a Bayesian justification of the idea that, under certain conditions, the absence of an argument in favour of the truth of a hypothesis H constitutes a good argument against the truth of H

Imaging Uncertainty

The technique of imaging was first introduced by Lewis (1976), in order to provide a novel account of the probability of conditional propositions. In the intervening years, imaging has been the object of significant interest in both AI and philosophy, and has come to be seen as a philosophically important approach to probabilistic updating and belief revision. In this paper, we consider the possibility of generalising imaging to deal with uncertain evidence and partial belief revision. In particular, we introduce a new logical criterion that any update rule should satisfy, and use it to evaluate a range of different approaches to generalising imaging to situations involving uncertain evidence. We show that none of the currently prevalent approaches to imaging allow for such a generalisation, although a lesser known version of imaging, introduced by Joyce (2010), can be generalised in a way that mitigates these problems

On the Origins of Old Evidence

The problem of old evidence, first described by Glymour (1980), is still widely regarded as one of the most pressing foundational challenges to the Bayesian account of scientific reasoning. Many solutions have been proposed, but all of them have drawbacks and none of them is considered to be definitive. Here, we introduce and defend a new kind of solution, according to which hypotheses are confirmed when we become more confident that they provide the only way of accounting for the known evidence

The Logic of Partial Supposition

According to orthodoxy, there are two basic moods of supposition: indicative and subjunctive. The most popular formalisations of the corresponding norms of suppositional judgement are given by Bayesian conditionalisation and Lewisian imaging, respectively. It is well known that Bayesian conditionalisation can be generalised (via Jeffrey conditionalisation) to provide a model for the norms of partial indicative supposition. This raises the question of whether imaging can likewise be generalised to model the norms of `partial subjunctive supposition'. The present article casts doubt on whether the most natural generalisations of imaging are able to provide a plausible account of the norms of partial subjunctive supposition

Imaging Uncertainty

The technique of imaging was first introduced by Lewis (1976), in order to provide a novel account of the probability of conditional propositions. In the intervening years, imaging has been the object of significant interest in both AI and philosophy, and has come to be seen as a philosophically important approach to probabilistic updating and belief revision. In this paper, we consider the possibility of generalising imaging to deal with uncertain evidence and partial belief revision. In particular, we introduce a new logical criterion that any update rule should satisfy, and use it to evaluate a range of different approaches to generalising imaging to situations involving uncertain evidence. We show that none of the currently prevalent approaches to imaging allow for such a generalisation, although a lesser known version of imaging, introduced by Joyce (2010), can be generalised in a way that mitigates these problems

Bayesian Argumentation and the Value of Logical Validity

According to the Bayesian paradigm in the psychology of reasoning, the norms by which everyday human cognition is best evaluated are probabilistic rather than logical in character. Recently, the Bayesian paradigm has been applied to the domain of argumentation, where the fundamental norms are traditionally assumed to be logical. Here, we present a major generalisation of extant Bayesian approaches to argumentation that (i)utilizes a new class of Bayesian learning methods that are better suited to modelling dynamic and conditional inferences than standard Bayesian conditionalization, (ii) is able to characterise the special value of logically valid argument schemes in uncertain reasoning contexts, (iii) greatly extends the range of inferences and argumentative phenomena that can be adequately described in a Bayesian framework, and (iv) undermines some influential theoretical motivations for dual function models of human cognition. We conclude that the probabilistic norms given by the Bayesian approach to rationality are not necessarily at odds with the norms given by classical logic. Rather, the Bayesian theory of argumentation can be seen as justifying and enriching the argumentative norms of classical logic