Showing 1–2 of 2 results for author: Charness, G

The friendship paradox: Causal evidence of its behavioral consequences

Authors: Gary Charness, Francesco Feri, Matthew O. Jackson, Miguel A. Melendez-Jimenez, Matthias Sutter

Abstract: We provide a first causal analysis of the behavioral consequences of the friendship paradox-the fact that people's friends in a network have more connections than average. We find that people's behavior is biased by their network position: they do not best respond to what they should infer the average behavior of the population to be, but instead simply to the average behavior of their friends. Mo… ▽ More We provide a first causal analysis of the behavioral consequences of the friendship paradox-the fact that people's friends in a network have more connections than average. We find that people's behavior is biased by their network position: they do not best respond to what they should infer the average behavior of the population to be, but instead simply to the average behavior of their friends. Moreover, we find that they fail to learn to overcome such a bias when relocated within the network, varying their observational environment. In these games of complements, the friendship paradox generates a systematic upward distortion in actions, increases behavioral dispersion, and persists despite learning opportunities. △ Less

Submitted 7 August, 2026; originally announced August 2026.

On Prior Confidence and Belief Updating

Authors: Kenneth Chan, Gary Charness, Chetan Dave, J. Lucas Reddinger

Abstract: We experimentally investigate how confidence over multiple priors affects belief updating. Theory predicts that the average Bayesian posterior is unaffected by confidence over multiple priors if average priors are the same. We manipulate confidence by varying the time subjects view a black-and-white grid, the proportion representing the prior in a Bernoulli distribution. We find that when subjects… ▽ More We experimentally investigate how confidence over multiple priors affects belief updating. Theory predicts that the average Bayesian posterior is unaffected by confidence over multiple priors if average priors are the same. We manipulate confidence by varying the time subjects view a black-and-white grid, the proportion representing the prior in a Bernoulli distribution. We find that when subjects view the grid for a longer duration, they have more confidence, under-update more, and place more (less) weight on priors (signals). Overall, confidence over multiple priors matters when it should not, while confidence in prior beliefs does not matter when it should. △ Less

Submitted 14 May, 2025; v1 submitted 13 December, 2024; originally announced December 2024.