A new research paper introduces a Bayesian rational model for search engine users, detailing how individuals sequentially decide whether to inspect more items or stop with their best find. The model proposes an optimal policy where users cease searching when their best discovered item surpasses a threshold relative to their updated belief about the page's average item quality. This framework identifies three underlying user behaviors: trust, commitment, and loss-cutting, and offers testable predictions, including a novel learning-to-rank likelihood function. AI
RANK_REASON The cluster contains a research paper detailing a new model for user behavior in search engines. [lever_c_demoted from research: ic=1 ai=0.4]
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