This paper examines the basic question of how we can come to form accurate beliefs about the world when we do not fully know how good or bad our evidence is. Here, we show, using simulations with otherwise optimal agents, the cost of misjudging the quality of our evidence. We compare different strategies for correctly estimating that quality, such as outcome‐ and expectation‐based updating. We also identify conditions under which misjudgment of evidence quality can nevertheless lead to accurate beliefs, as well as those conditions where no strategy will help. These results indicate both where people will nevertheless succeed and where they will fail when information quality is degraded.

The paper introduces, compares and contrasts formal models of source reliability proposed in the epistemology literature, in particular the prominent models of Bovens and Hartmann (Bayesian epistemology, Oxford University Press, Oxford, 2003) and Olsson (Episteme 8(02):127-143, 2011). All are Bayesian models seeking to provide normative guidance, yet they differ subtly in assumptions and resulting behavior. Models are evaluated both on conceptual grounds and through simulations, and the relationship between models is clarified. The simulations both show surprising similarities and highlight relevant differences between these models. Most importantly, however, our evaluations reveal that important normative concerns arguably remain unresolved. The philosophical implications of this for testimony are discussed.

With the advent of social media, the last decade has seen profound changes to the way people receive information. This has fueled a debate about the ways (if any) changes to the nature of our information networks might be affecting voters’ beliefs about the world, voting results, and, ultimately, democracy. At the same time, much discussion in the public arena in recent years has concerned the notion that ill‐informed voters have been voting against their own self‐interest. The research reported here brings these two strands together: simulations involving agent‐based models, interpreted through the formal framework of Condorcet's (1785) jury theorem, demonstrate how changes to information networks may make voter error more likely, even though individual competence has largely remained unchanged.

Unpopular norms are a pervasive and puzzling phenomenon of the social world. They are norms that are established and maintained against the interest of their subjects, but without external coercion. Pluralistic ignorance has been suggested as a potential explanation of unpopular norms. What is currently lacking is a formal model of this process that can be meaningfully compared with empirically known properties of pluralistic ignorance. An agent-based model of a growing social network can reproduce the most significant qualitative features, viz a deviation of the perceived norm from the preference distribution and the dynamical lag of the former behind the latter. In addition, the model is extended with a central influence representing for example central media or a powerful political elite.

Science is a social epistemic enterprise. The complexity of research requires the division of cognitive labor. As a consequence, scientists have to present results and incorporate the results of others into their body of knowledge. This creates the possibility of strategic behavior, leading to phenomena such as publication bias. To analyze the dynamics of strategic behavior in epistemic communities, agent-based modeling suggests itself as a method. The phenomena generated by the developed agent-based simulation model reveal a diverse set of possible dynamics in strategically heterogeneous groups and support the claim that there is a trade-off between a behavioral rule's efficacy to generate accurate beliefs under optimal conditions and its robustness to variation in the composition of the epistemic environment.