People systematically discount or fail to listen to expert advice
While people sometimes discount expert advice due to biases, partisan divisions, or political pressures, research shows that under many conditions individuals readily use and appreciate expert recommendations.
The claim asserts that people systematically discount or fail to listen to expert advice. The retrieved literature offers mixed findings: paper [5] shows bias against counter-partisan advice, and paper [6] documents real-world political sidelining of scientific expertise. Conversely, papers [1] and [8] show that judges and decision-makers often utilize and appropriately weight advice from experts. Because the evidence demonstrates that discounting occurs conditionally (based on framing, partisanship, or stakes) rather than universally as a blanket systematic failure, the balance of evidence is categorized as CONTESTED.
Yunhao Zhang, David G. Rand. Sincere or motivated? Partisan bias in advice-taking. 2023. https://doi.org/10.1017/jdm.2023.28
Demonstrates that individuals systematically discount advice from sources they are biased against, such as counter-partisans.
Janet A. Sniezek, Gunnar E. Schrah, Reeshad S. Dalal. Improving judgement with prepaid expert advice. 2004. https://doi.org/10.1002/bdm.468
Shows that under certain conditions, such as committing money for advice, decision-makers readily utilize expert guidance.
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N. Marres, Matías Valderrama Barragán. Making expert advice public in a time of emergency: Independent SAGE and the contestation of science during the Covid pandemic in the UK. 2025. https://doi.org/10.1177/03063127241309071
Describes how public health expertise was ignored and sidelined during the COVID-19 pandemic, reflecting a dismissal of expert guidance.
Konstantin Boob-Engel. Is AI as trustworthy as human experts? The role of advice source, technology knowledge, and perceptions of AI’s impact in organizational decision-making. 2025. https://doi.org/10.1080/12460125.2025.2594620
Finds that individuals place substantial weight on advice, showing no inherent aversion to expert sources compared to algorithms.
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