Igor Douven

This paper studies the learnability of natural concepts in the context of the conceptual spaces framework. Previous work proposed that natural concepts are represented by the cells of optimally partitioned similarity spaces, where optimality was defined in terms of a number of constraints. Among these is the constraint that optimally partitioned similarity spaces result in easily learnable concepts. While there is evidence that systems of concepts generally regarded as natural satisfy a number of the proposed optimality constraints, the connection between naturalness and learnability has been less well studied. To fill this gap, we conduct a computational study employing two standard models of concept learning. Applying these models to the learning of color concepts, we examine whether natural color concepts are more readily learned than nonnatural ones. Our findings warrant a positive answer to this question for both models employed, thus lending empirical support to the notion that learnability is a distinctive characteristic of natural concepts.

Philosophers have recently questioned the methodological status of agent-based modeling. Meanwhile, this methodology has been central to various studies of the COVID-19 pandemic. Few agent-based COVID-19 models are accessible to philosophers for inspection or experimentation. We make available a package for modeling the COVID-19 pandemic and similar pandemics and give an impression of what can be achieved with it. In particular, it is shown that by coupling an agent-based model to a standard optimizer we are able to identify strategies for implementing non-pharmacological interventions that flexibly lower or raise social activity, depending on how the outbreak develops, while balancing various desiderata that cannot be fully satisfied together. The simulation outcomes to be presented testify to the power of agent-based modeling and thereby help to push back against the recent philosophical critique of this methodology.

Conditionals are sentences of the form 'If A, then B', and they play a central role in scientific, logical, and everyday reasoning. They have been in the philosophical limelight for centuries, and more recently, they have been receiving attention from psychologists, linguists, and computer scientists. In spite of this, many key questions concerning conditionals remain unanswered. While most of the work on conditionals has addressed semantical questions - questions about the truth conditions of conditionals - this book focuses on the main epistemological questions that conditionals give rise to, such as: what are the probabilities of conditionals? When is a conditional acceptable or assertable? What do we learn when we receive new conditional information? In answering these questions, this book combines the formal tools of logic and probability theory with the experimental approach of cognitive psychology. It will be of interest to students and researchers in logic, epistemology, and psychology of reasoning.

There is widespread agreement among philosophers about the Mens Rea Asymmetry (MRA), according to which praise requires intent, whereas blame does not. However, there is evidence showing that MRA is descriptively inadequate. We hypothesize that the violations of MRA found in the experimental literature are due to what we call “moral compositionality,” by which we mean that people evaluate the component parts of a moral problem separately and then reach an overall verdict by aggregating the verdicts on the component parts. We have subjected this hypothesis to the test and here report the results of our experiment. We explore several explanations of the experimental findings and conclude that they present a puzzle to moral theory.

In van Fraassen's The Scientific Image we are told that the scientific anti‐realist need not appeal to some special semantics for scientific language. He can allegedly hold – just like his direct opponents typically do – that truth‐conditional semantics is appropriate both for claims about the observable and claims about the unobservable. However, I shall point out that this kind of semantics goes badly with the anti‐realist's epistemic attitude vis‐his the unobservable. In this paper an alternative semantics shall be outlined that is perfectly compatible with the rest of the anti‐realist's position. Although this alternative semantics will be seen to fly in the face of the established doctrine in the field, I shall argue that there is currently no good reason not to adopt it. It will further be pointed out that this semantics has something to be recommended from a realist perspective as well

The idea that knowledge is conceptually related to practical reasoning is becoming increasingly popular. In defending this idea, philosophers have been relying on a conception of practical reasoning that drastically deviates from one which has been more traditionally advocated in analytic philosophy and which assigns no special role to knowledge. This paper argues that these philosophers have failed to give good reasons for thinking that the conception of practical reasoning they have been assuming is the right one, and that hence they have been rash to conclude that there is a conceptual relation between knowledge and practical reasoning.

According to van Fraassen, constructive empiricism yields a better account of science than does scientific realism. One particularly important advantage van Fraassen claims his position to have over scientific realism is that the former can make sense of science without invoking pre-Kantian metaphysics. In the present paper the consistency of van Fraassen's position is put in doubt. Specifically, it will be argued that van Fraassen faces the paradox that he cannot do with nor without the pre-Kantian metaphysics he abhors.

This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty ; and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in dynamic normative contexts.

This paper is concerned with a version of Kamp and Partee's account of graded membership that relies on the conceptual spaces framework. Three studies are reported, one to construct a particular shape space, one to detect which shapes representable in that space are typical for certain sorts of objects, and one to elicit degrees of category membership for the various shapes from which the shape space was constructed. Taking Kamp and Partee's proposal as given, the first two studies allowed us to predict the degrees to which people would judge shapes representable in the space to be members of certain categories. These predictions were compared with the degrees that were measured in the third study. The comparison yielded a test of the account of graded membership at issue. The outcome of this test was found to support the conceptual spaces version of Kamp and Partee's account of graded membership.

In _Suppose and Tell_, Williamson makes a new case for the material conditional account. He tries to explain away apparently countervailing data by arguing that these have been misinterpreted because researchers have overlooked the role of heuristics in the processing of conditionals. Cases involving the receipt of apparently conflicting conditionals play an important dialectical role in Williamson’s book: they are supposed to provide evidence for the material conditional account as well as for the defeasibility of a key procedure underlying our everyday assessments of conditionals. We argue that they can serve neither of these purposes and that Williamson overestimates the reach of heuristics. We specifically challenge Williamson’s assumption that, in the kind of cases centrally at issue in his book, the recipient of conflicting conditionals will typically accept those at face value, even granting Williamson that conditionals can be freely passed among speakers under normal conditions of testimony.

Conditionals have been studied by philosophers for over two thousand years. This should not be surprising, given how central conditionals are to human reasoning and decision making. This chapter argues that making progress in answering the questions about conditionals which have occupied so many philosophers, past and present, may require the use of methods that go beyond those traditionally used in analytic philosophy. The reasons some might have for believing that experimental methods have no place in, or are redundant for, the philosophical study of conditionals will be found wanting. The chapter provides positive reasons for holding that philosophers studying conditionals may greatly benefit from the use of such methods. These reasons will emerge from a discussion of relatively recent results concerning conditionals, most of which have been obtained by experimentally working psychologists as well as by teams consisting of both such psychologists and philosophers.