ABSTRACT This study examines the dynamics of human reliance on algorithmic advice in a situation with strategic interaction. Participants played the strategic game of Rock–Paper–Scissors (RPS) under various conditions, receiving algorithmic decision support while facing human or algorithmic opponents. Results indicate that participants often underutilize algorithmic recommendations, particularly after early errors, but increasingly rely on the algorithm following successful early predictions. This behavior demonstrates a sensitivity to decision outcomes, with asymmetry: rejecting advice consistently reinforces rejecting advice again while accepting advice leads to varied reactions based on outcomes. We also investigate how personal characteristics, such as algorithm familiarity and domain experience, influence reliance on algorithmic advice. Both factors positively correlate with increased reliance, and algorithm familiarity significantly moderates the relationship between outcome feedback and reliance. Facing an algorithmic opponent increases advice rejection frequencies, and the determinants of trust and interaction dynamics differ from those with human opponents. Our findings enhance the understanding of algorithm aversion and reliance on AI, suggesting that increasing familiarity with algorithms can improve their integration into decision‐making processes.

Andrej Gill

Robert M. Gillenkirch

Julia Ortner

Louis Velthuis

Journal of Behavioral Decision Making

The Journal of Behavioral Decision Making is a multidisciplinary journal with a broad base of content and style. It publishes original empirical reports, critical review papers, theoretical analyses and methodological contributions. The Journal also features book, software and decision aiding technique reviews, abstracts of important articles published elsewhere and teaching suggestions.    The objective of the Journal is to present and stimulate behavioral research on decision making and to provide a forum for the evaluation of complementary, contrasting and conflicting perspectives. These perspectives include psychology, management science, sociology, political science and economics. Studies of behavioral decision making in naturalistic and applied settings are encouraged.

The Journal of Behavioral Decision Making is a multidisciplinary journal with a wide range of content and styles. It publishes original empirical reports, critical review papers, theoretical analyses, and methodological contributions. The journal also features reviews of books, software, and decision-aiding techniques, abstracts of significant articles published elsewhere, and teaching suggestions. The objective of the journal is to present and stimulate behavioral research on decision making and to provide a forum for evaluating complementary, contrasting, and conflicting perspectives. These perspectives include psychology, management science, sociology, political science, and economics. Studies of behavioral decision making in naturalistic and applied settings are encouraged.

《Journal of Behavioral Decision Making》是一本多学科期刊，内容广泛，风格多样。期刊发表原创的实证研究报告、批判性综述论文、理论分析和方法论贡献。此外，期刊还包括书籍、软件和决策辅助技术的评审，重要文章的摘要，以及教学建议。期刊的目标是展示和激励关于决策制定的行为研究，并为评估互补、对立和冲突的观点提供一个论坛。这些观点涵盖心理学、管理科学、社会学、政治学和经济学。鼓励在自然和应用环境中进行的行为决策研究。

Category	Quartile
心理学	3
心理学, 心理学应用	4

Dynamics of Reliance on Algorithmic Advice

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Journal Info

Category	Quartile
PSYCHOLOGY, APPLIED	3