Igor Douven

According to so-called epistemic theories of conditionals, the assertability/acceptability/acceptance of a conditional requires the existence of an epistemically significant relation between the conditional’s antecedent and its consequent. This paper points to some linguistic data that our current best theories of the foregoing type appear unable to explain. Further, it presents a new theory of the same type that does not have that shortcoming. The theory is then defended against some seemingly obvious objections.

Some of the information we receive comes to us in an explicitly conditional form. It is an open question how to model the accommodation of such information in a Bayesian framework. This paper presents data suggesting that there may be no strictly Bayesian account of updating on conditionals. Specifically, the data seem to indicate that such updating at least sometimes proceeds on the basis of explanatory considerations, which famously have no home in standard Bayesian epistemology. The paper also proposes a still broadly Bayesian model of updating on conditionals that explicitly factors in explanation. The model is shown to have clear empirical content and thus to be open to empirical testing

This article compares inference to the best explanation with Bayes’s rule in a social setting, specifically, in the context of a variant of the Hegselmann–Krause model in which agents not only update their belief states on the basis of evidence they receive directly from the world, but also take into account the belief states of their fellow agents. So far, the update rules mentioned have been studied only in an individualistic setting, and it is known that in such a setting both have their strengths as well as their weaknesses. It is shown here that in a social setting, inference to the best explanation outperforms Bayes’s rule according to every desirable criterion. 1 What Is Inference to the Best Explanation?2 Judging the Rules—By Which Lights?3 From an Individualistic to a Social Perspective 3.1 The Hegselmann–Krause model 3.2 A probabilistic extension of the Hegselmann–Krause model 3.3 Simulations4 Results and Discussion5 Interpretation6 Conclusion.

Over recent decades, computer simulations have become a common tool among practitioners of the social sciences. They have been utilized to study such diverse phenomena as the integration and segregation of different racial groups, the emergence and evolution of friendship networks, the spread of gossip, fluctuations of housing prices in an area, the transmission of social norms, and many more. Philosophers of science and others interested in the methodological status of these studies have identified a number of distinctive virtues of the use of computer simulations. For instance, it has been generally appreciated that as simulations require the formulation of an explicit algorithm, they foster precision and clarity about whatever conceptual issues are involved in the study. The value of computer simulations as a heuristic tool for developing hypotheses, models, and theories has also been recognized, as has been the fact that they can serve as a substitute for real experiments. This is especially useful in the social domain, given that human beings cannot be freely manipulated at the discretion of the experimenter . However, the main virtue of computer simulations is generally believed to be that they are able to deal with the complexities that arise when many elements interact in a highly dynamic system and which often evade an exact formal analysis

Inference to the Best Explanation has become the subject of a livelydebate in the philosophy of science. Scientific realists maintain, while scientificantirealists deny, that it is a compelling rule of inference. It seems that anyattempt to settle this debate empirically must beg the question against theantirealist. The present paper argues that this impression is misleading. A methodis described that, by combining Glymour's theory of bootstrapping and Hacking'sarguments from microscopy, allows us to test IBE without begging any antirealistissues.

Theories of personal identity purport to specify truth conditions for sentences of the form 'x-at-ti is the same person as y-at-tj. Most philosophers nowadays agree that such truth conditions are to be stated in terms of psychological continuity. However; opinions vary as to how the notion of psychological continuity is to be understood. In a recent contribution to this journal, Slors offers an account in which psychological continuity is spelled out in terms of narrative connectedness between mental states.The present paper argues that Slors' theory either is no theory of personal identity at all or is too weak.Towards the end of the paper, it is indicated how the problem uncovered for Slors' theory may be avoided.

We talk and think about our beliefs both in a categorical and in a graded way. How do the two kinds of belief hang together? The most straightforward answer is that we believe something categorically if we believe it to a high enough degree. But this seemingly obvious, near-platitudinous claim is known to give rise to a paradox commonly known as the 'lottery paradox' – at least when it is coupled with some further seeming near-platitudes about belief. How to resolve that paradox has been a matter of intense philosophical debate for over fifty years. This volume offers a collection of newly commissioned essays on the subject, all of which provide compelling reasons for rethinking many of the fundamentals of the debate.

It has been claimed that epistemic peers, upon discovering that they disagree on some issue, should give up their opposing views and ‘split the difference’. The present paper challenges this claim by showing, with the help of computer simulations, that what the rational response to the discovery of peer disagreement is—whether it is sticking to one’s belief or splitting the difference—depends on factors that are contingent and highly context-sensitive.Keywords: Peer disagreement; Computer simulations; Opinion dynamics; Hegselmann–Krause model; Social epistemology

This paper aims to contribute to our understanding of the notion of coherence by explicating in probabilistic terms, step by step, what seem to be our most basic intuitions about that notion, to wit, that coherence is a matter of hanging or fitting together, and that coherence is a matter of degree. A qualitative theory of coherence will serve as a stepping stone to formulate a set of quantitative measures of coherence, each of which seems to capture well the aforementioned intuitions. Subsequently it will be argued that one of those measures does better than the others in light of some more specific intuitions about coherence. This measure will be defended against two seemingly obvious objections.

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.