The Wisdom of the Small Crowd: Myside Bias and Group Discussion

Abstract

The my-side bias is a well-documented cognitive bias in the evaluation of arguments, in which reasoners in a discussion tend to overvalue arguments that confirm their prior beliefs, while undervaluing arguments that attack their prior beliefs. The first part of this paper develops and justifies a Bayesian model of myside bias at the level of individual reasoning. In the second part, this Bayesian model is implemented in an agent-based model of group discussion among myside-biased agents. The agent-based model is then used to perform a number of experiments with the objective to study whether the myside bias hinders or enhances the ability of groups to collectively track the truth, that is, to reach the correct answer to a given binary issue. An analysis of the results suggests the following: First, whether the truth-tracking ability of groups is helped or hindered by myside bias crucially depends on how the strength of myside bias is differentially distributed across subgroups of discussants holding different beliefs. Second, small groups are more likely to track the truth than larger groups, suggesting that increasing group size has a detrimental effect on collective truth-tracking through discussion.

Mandevillian vices

“…To better accommodate this fact, Baccini et al ( 2023 ) generated a Bayesian model in which myside bias is distributed heterogeneously over populations of deliberating agents. They found that group deliberation generally had a negative effect on a group’s chances of reaching an accurate consensus, except when myside bias is distributed asymmetrically across initially correct and incorrect reasoners.…”

“…In many contexts this is not the case. For instance, hearings in a court of law or in a parliament chamber are not random, and there is a clear protocol that determines who gets to speak at which time” (Baccini et al, 2023 ). As they point out, adversarial deliberation works best when it’s well structured, such that different agents have different roles to play and everyone follows the same procedural protocol.…”