The narrow search effect and how broadening search promotes belief updating

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 122; no. 13; p. e2408175122
• Main Authors: Leung, Eugina, Urminsky, Oleg
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 01.04.2025
• Subjects: Algorithms; Artificial Intelligence; Caffeine; Chatbots; Coronaviruses; Customization; Humans; Information Seeking Behavior; Interfaces; Nuclear energy; Nuclear reactor components; Psychological and Cognitive Sciences; Search algorithms; Search Engine; Search engines; Search strategies; algorithmic search; belief updating; artificial intelligence; confirmation bias
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2408175122

SignificanceIn a time of societal polarization, the combination of people’s search habits and the search tools they use being optimized for relevance may perpetuate echo chambers. We document this across various diverse studies spanning health, finance, societal, and political topics on platforms like Google, ChatGPT, AI-powered Bing, and our custom-designed search engine and AI chatbot platforms. Users’ biased search behaviors and the narrow optimization of search algorithms can combine to reinforce existing beliefs. We find that algorithm-based interventions are more effective than user-based interventions to mitigate these effects. Our findings demonstrate the potential for behaviorally informed search algorithms to be a better tool for retrieving information, promoting the shared factual understanding necessary for social cohesion. Information search platforms, from Google to AI-assisted search engines, have transformed information access but may fail to promote a shared factual foundation. We demonstrate that the combination of users’ prior beliefs influencing their search terms and the narrow scope of search algorithms can limit belief updating from search. We test this “narrow search effect” across 21 studies (14 preregistered) using various topics (e.g., health, financial, societal, political) and platforms (e.g., Google, ChatGPT, AI-powered Bing, our custom-designed search engine and AI chatbot interfaces). We then test user-based and algorithm-based interventions to counter the “narrow search effect” and promote belief updating. Studies 1 to 5 show that users’ prior beliefs influence the direction of the search terms, thereby generating narrow search results that limit belief updating. This effect persists across various domains (e.g., beliefs related to coronavirus, nuclear energy, gas prices, crime rates, bitcoin, caffeine, and general food or beverage health concerns; Studies 1a to 1b, 2a to 2g, 3, 4), platforms (e.g., Google—Studies 1a to 1b, 2a to 2g, 4, 5; ChatGPT, Study 3), and extends to consequential choices (Study 5). Studies 6 and 7 demonstrate the limited efficacy of prompting users to correct for the impact of narrow searches on their beliefs themselves. Using our custom-designed search engine and AI chatbot interfaces, Studies 8 and 9 show that modifying algorithms to provide broader results can encourage belief updating. These findings highlight the need for a behaviorally informed approach to the design of search algorithms.