The main issue in the epistemology of peer disagreement is whether known disagreement among those who are in symmetrical epistemic positions undermines the rationality of their maintaining their respective views. Douven and Kelp have argued convincingly that this problem is best understood as being about how to respond to peer disagreement repeatedly over time, and that this diachronic issue can be best approached through computer simulation. However, Douven and Kelp’s favored simulation framework cannot naturally handle Christensen’s famous Mental Math example. (...) As a remedy, I introduce an alternative simulation framework, Laputa, inspired by Alvin Goldman’s seminal work on veritistic social epistemology. I show that Christensen’s conciliatory response, reasonably reconstructed and supplemented, gives rise to an increase in epistemic value only if the peers continue to recheck their mental math; else the peers might as well be steadfast. On a meta-level, the study illustrates the power of Goldman’s approach when combined with simulation techniques for handling the computational issues involved. (shrink)

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. (shrink)

In this paper, we investigate the problem of truth approximation via belief merging, i.e., we ask whether, and under what conditions, a group of inquirers merging together their beliefs makes progress toward the truth about the underlying domain. We answer this question by proving some formal results on how belief merging operators perform with respect to the task of truth approximation, construed as increasing verisimilitude or truthlikeness. Our results shed new light on the issue of how rational (dis)agreement affects the (...) inquirers’ quest for truth. In particular, they vindicate the intuition that scientific inquiry, and rational discussion in general, benefits from some heterogeneity in opinion and interaction among different viewpoints. The links between our approach and related analyses of truth tracking, judgment aggregation, and opinion dynamics, are also highlighted. (shrink)