Na ïve information aggregation in human social learning

We report on three multi-player experiments examining the dynamics of both mixed human-artificial and all-human social networks. Our analyses suggest that most human inferences are best described by a naïve learning account that is insensitive to known or inferred dependencies between network peers. Consequently, we find that simulated social learners that assume their peers behave rationally make systematic judgment errors when reasoning on the basis of actual human communications. We suggest human groups learn collectively through naïve signaling and aggregation that is computationally efficient and surprisingly robust. Overall, our results challenge the idea that everyday social inference is well captured by idealized rational accounts and provide insight into the conditions under which collective wisdom can emerge from social interactions.PMID:37897881 | DOI:10.1016/j.cognition.2023.105633

Source: Cognition - October 28, 2023 Category: Neurology Authors: J-Philipp Fr änken Simon Valentin Christopher G Lucas Neil R Bramley Source Type: research