@article{Isch2026, 
author = {Calvin Isch and Damon Centola},
title = {Experimental Evidence That Social Learning in Structured Information-Sharing Networks Corrects Anchoring Bias},
year = {2026},
journal = {Journal of Social Computing},
volume = {7},
number = {1},
pages = {3-13},
keywords = {collective intelligence, anchoring effects, cognitive bias, networks, wisdom of the crowd, human-centered computing, collaborative and social computing},
url = {https://www.sciopen.com/article/10.23919/JSC.2026.0005},
doi = {10.23919/JSC.2026.0005},
abstract = {The anchoring effect is a cognitive bias in which exposure to arbitrary initial values disproportionately influences estimates. While this effect has been widely documented, its role in social contexts remains unclear. Laboratory studies of isolated individuals often show stronger anchoring effects than naturalistic settings where people interact in social networks. We propose that social networks can amplify or attenuate anchoring depending on whether anchors are “helpful” or “harmful” for accuracy. In a preregistered experiment with 1600 participants across 40 groups, we compared individuals working independently to those embedded in peer-to-peer information-sharing networks. Networked groups improved overall belief accuracy, showing truth-seeking reductions in bias from harmful anchors, while also exhibiting truth-seeking increases in anchoring bias from helpful anchors. By contrast, isolated individuals showed no endogenous change in anchoring in either case. Our analyses identify a psychological mechanism that may underlie these effects: “confidence in others”, rather than self-confidence.}
}