依赖算法建议的动态变化

IF 1.8 3区 心理学 Q3 PSYCHOLOGY, APPLIED Journal of Behavioral Decision Making Pub Date : 2024-10-08 DOI:10.1002/bdm.2414
Andrej Gill, Robert M. Gillenkirch, Julia Ortner, Louis Velthuis
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引用次数: 0

摘要

本研究探讨了人类在战略互动情况下依赖算法建议的动态变化。参与者在不同条件下进行了 "石头剪刀布"(RPS)策略游戏,在面对人类或算法对手时接受算法决策支持。结果表明,参与者往往对算法建议利用不足,尤其是在早期失误之后,但在早期预测成功之后,参与者会越来越依赖算法。这种行为表现出了对决策结果的敏感性,而且具有不对称性:拒绝建议会不断强化再次拒绝建议的行为,而接受建议则会导致基于结果的不同反应。我们还研究了算法熟悉程度和领域经验等个人特征如何影响对算法建议的依赖。这两个因素都与依赖性的增加呈正相关,而算法熟悉程度在很大程度上调节了结果反馈与依赖性之间的关系。面对算法对手会增加建议被拒绝的频率,而信任和互动动态的决定因素与面对人类对手时不同。我们的研究结果加深了人们对算法厌恶和依赖人工智能的理解,表明提高对算法的熟悉程度可以改善算法与决策过程的融合。
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Dynamics of Reliance on Algorithmic Advice

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.

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来源期刊
CiteScore
4.40
自引率
5.00%
发文量
40
期刊介绍: 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.
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