The final network workshop entitled “Formal Models of Scientific Inquiry” takes place from 29-31st of January, 2025. We will start on the 29th (Wednesday) in the afternoon.

Many factors contribute to whether juries reach right verdicts. Here we focus on the role of diversity. Direct empirical studies of the effect of altering factors in jury deliberation are severely limited for conceptual, practical, and ethical reasons. Using an agent-based model to avoid these difficulties, we argue that diversity can play at least four importantly different roles in affecting jury verdicts. We show that where different subgroups have access to different information, equal representation can strengthen epistemic jury success, and if one subgroup has access to particularly strong evidence, epistemic success may demand participation by that group. Diversity can also reduce the redundancy of the information on which a jury focuses, which can have a positive impact. Finally, and most surprisingly, we show that limiting communication between diverse groups in juries can favor epistemic success as well.

Confirmation bias has been widely studied for its role in failures of reasoning. Individuals exhibiting confirmation bias fail to engage with information that contradicts their current beliefs, and, as a result, can fail to abandon inaccurate beliefs. But although most investigations of confirmation bias focus on individual learning, human knowledge is typically developed within a social structure. We use network models to show that moderate confirmation bias often improves group learning. However, a downside is that a stronger form of confirmation bias can hurt the knowledge-producing capacity of the community.

This paper examines the fundamental problem of testimony. Much of what we believe to know we know in good part, or even entirely, through the testimony of others. The problem with testimony is that we often have very little on which to base estimates of the accuracy of our sources. Simulations with otherwise optimal agents examine the impact of this for the accuracy of our beliefs about the world. It is demonstrated both where social networks of information dissemination help and where they hinder. Most importantly, it is shown that both social networks and a common strategy for gauging the accuracy of our sources give rise to polarisation even for entirely accuracy motivated agents. Crucially these two factors interact, amplifying one another’s negative consequences, and this side effect of communication in a social network increases with network size. This suggests a new causal mechanism by which social media may have fostered the increase in polarisation currently observed in many parts of the world.

We model the impact of different incentives on journal behavior in undertaking peer review. Under one scheme, the journal aims to publish the highest-quality papers; under the second, the journal aims to maintain a high rejection rate. Under both schemes, journals prefer to set very high standards for acceptance despite allowing significant error in peer review. Under the second scheme, however, in order to encourage more submissions of mediocre papers, the journal is incentivized to make its editorial process less accurate. This leads to both worse peer review and lower-quality articles being published.

Explanations are social, and when people try to explain something, they usually seek input from others. We present a simple theory of how people use the explanations they encounteras clues to the broader landscape of possible explanations,informing their decision to exploit what has been found orexplore new possibilities. The challenge of coming up withnovel explanations draws people to exploit or imitate appealing ones (information cascades); this draw increases as lessappealing alternatives become more distant (the “strawman”effect). Conversely, pairs of low-quality explanations promoteexploratory behavior or long-leaps away from observed attempts, and pairs of divergent high-quality explanations canlead to merging and syncretism. We use a transmission-chainexperiment to test, and confirm, these predictions. Intriguingly,we also find that while people imitate good explanations, theirimitations often fall short in quality. Our work provides newinsight into how collective exploration can be promoted, orstalled, by implicit information about what is yet to be discovered.