Igor Douven

Many cognitive psychologists have come to regard graded belief as fundamental to our understanding of how humans reason and many have also come to think of probability theory as providing at least part of the norms of correct reasoning. David Over has characterized this development as the emergence of a new paradigm in the Kuhnian sense. The target article argues that the choice of this term was unwarranted and also that it has done more harm than good. This commentary argues that there is nothing in Thomas Kuhn’s work to suggest that he would object to Over’s terminological choice and that there is no evidence that the choice has caused any harm.

It is undeniable that inductive reasoning has brought us much good. At least since Hume, however, philosophers have wondered how to justify our reliance on induction. In important recent work, Schurz points out that philosophers have been wrongly assuming that justifying induction is tantamount to showing induction to be reliable. According to him, to justify our reliance on induction, it is enough to show that induction is optimal. His optimality approach consists of two steps: an analytic argument for meta-induction (that is, induction over inductive methods) and an application of meta-induction to empirical findings concerning the predictive accuracy of our actual inductive practices as compared to various non-inductive methods. The present article agrees with Schurz’s general conclusion but also argues that it leaves an important aspect of our inductive practices unaccounted for, to wit, that these practices have so far been highly successful; to satisfy the optimality requirement, induction only needs to be as successful as random guessing. To explain the success of induction, I turn to insights from social epistemology, also highlighted in Schurz’s work, and make these formally explicit in a framework that combines the Hegselmann–Krause model for opinion dynamics and Carnap’s system of λ rules. Within this framework, I use a standard evolutionary algorithm to show how communities of interacting agents may, through variation and selection, come to consist of highly successful inductive reasoners.

This position paper advocates combining formal epistemology and the new paradigm psychology of reasoning in the studies of conditionals and reasoning with uncertainty. The new paradigm psychology of reasoning is characterized by the use of probability theory as a rationality framework instead of classical logic, used by more traditional approaches to the psychology of reasoning. This paper presents a new interdisciplinary research program which involves both formal and experimental work. To illustrate the program, the paper discusses recent work on the paradoxes of the material conditional, nonmonotonic reasoning, and Adams’ Thesis. It also identifies the issue of updating on conditionals as an area which seems to call for a combined formal and empirical approach.

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.

There is an ongoing debate about which rule we ought to use for scoring probability estimates. Much of this debate has been premised on scoring-rule monism, according to which there is exactly one best scoring rule. In previous work, I have argued against this position. The argument given there was based on purely a priori considerations, notably the intuition that scoring rules should be sensitive to truthlikeness relations if, and only if, such relations are present among whichever hypotheses are at issue. The present paper offers a new, quasi-empirical argument against scoring-rule monism. This argument uses computational simulations to show that different scoring rules can have different economical consequences, depending on the context of use.

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.

The bounded confidence model has become a popular tool for studying communities of epistemically interacting agents. The model makes the idealizing assumption that all agents always have access to all other agents’ belief states. We draw on resources from network epistemology to do away with this assumption. In the model to be proposed, we impose an explicit communication network on a community, due to which each agent has access to the beliefs of only a selection of other agents. A much-discussed result from network epistemology shows that densely connected communication networks are not always preferable to sparser networks. The aim of this paper is to investigate whether there are any noteworthy network effects in a version of the bounded confidence model augmented with communication networks, and in particular whether the aforementioned result from network epistemology can be replicated in that version.

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.

In Suppose and Tell, Williamson makes a new and original attempt to defend the material conditional account of indicative conditionals. His overarching argument is that this account offers the best explanation of the data concerning how people evaluate and use such conditionals. We argue that Williamson overlooks several important alternative explanations, some of which appear to explain the relevant data at least as well as, or even better than, the material conditional account does. Along the way, we also show that Williamson errs at important junctures about what exactly the relevant data are.

Confirmation is a graded notion: evidence can confirm a hypothesis to a greater or lesser degree. There has been debate about how to measure degree of confirmation. Starting from the observation that we would like evidence to be a discriminating indicator of truth, we conduct computer simulations to determine how well the various known measures of confirmation predict the extent to which a given piece of evidence fulfills that role, given a hypothesis of interest. The outcomes show that some measures are markedly better indicators of truth than others.

Cope’s Computers and Musical Style describes a computer program that allegedly can represent and replicate musical styles solely on the basis of compositions that have been entered into it. If this claim is correct, then it must be that an oeuvre’s stylistic characteristics locally supervene on its textual features, which roughly means that its stylistic properties are entirely determined by its textual properties. In my paper I argue that stylistic properties do not locally supervene on textual properties, and thus that neither Cope’s program nor any other that essentially works like it can represent or replicate styles.

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.

The gift of life argument, the claim that suicide is immoral because our lives are not ours to dispose of as we are their guardians or stewards, is a persistent theme in debates about the morality of suicide, assisted-suicide, and euthanasia. I argue that this argument suffers from a fatal internal incoherence. The gift can either be interpreted literally or analogically. If it is interpreted literally there are serious problems in understanding who receives the gift. If it is understood analogically the question arises whether the gift is understood to be a finite or everlasting existence. If it is finite then it is hard to see how one can be punished for bringing that existence to an end for one will not be around to be punished. If the existence is infinite it is impossible to see how one can be punished for ending one’s life because one cannot end it. However, there is still ethical mileage to be gained from the description of life as a gift and in the concluding section I indicate one way in which this is so

According to what is now commonly referred to as “the Equation” in the literature on indicative conditionals, the probability of any indicative conditional equals the probability of its consequent of the conditional given the antecedent of the conditional. Philosophers widely agree in their assessment that the triviality arguments of Lewis and others have conclusively shown the Equation to be tenable only at the expense of the view that indicative conditionals express propositions. This study challenges the correctness of that assessment by presenting data that cast doubt on an assumption underlying all triviality arguments.

Many have the intuition that the right response to the Lottery Paradox is to deny that one can justifiably believe of even a single lottery ticket that it will lose. The paper shows that from any theory of justification that solves the paradox in accordance with this intuition, a theory not of that kind can be derived that also solves the paradox but is more conducive to our epistemic goal than the former. It is argued that currently there is no valid reason not to give preference to the derived accounts over the accounts from which they come.

Many philosophers deny that happiness can be equated with pleasurable experiences. Nozick introduced an experience machine thought experiment to support the idea that happiness requires pleasurable experiences that are “in contact with reality.” In this thought experiment, people can choose to plug into a machine that induces exclusively pleasurable experiences. We test Nozick’s hypothesis that people will reject this offer. We also contrast Nozick’s experience machine scenario with scenarios that are less artificial, and offer options which are less invasive or disruptive than being connected to a machine, specifically scenarios in which people are offered an experience pill or a pill that improves overall functioning.

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.