Mixed-effects regression weights for advice taking and related phenomena of information sampling and utilization

IF 1.8 3区 心理学 Q3 PSYCHOLOGY, APPLIED
Tobias R. Rebholz, Marco Biella, Mandy Hütter
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Abstract

Advice taking and related research is dominated by deterministic weighting indices, specifically ratio-of-differences-based formulas for investigating informational influence. Their arithmetic is intuitively simple, but they pose several measurement problems and restrict research to a particular paradigmatic approach. As a solution, we propose to specify how strongly peoples' judgments are influenced by externally provided evidence by fitting corresponding mixed-effects regression models. Our approach explicitly distinguishes between endogenous components, such as updated beliefs, and exogenous components, such as independent initial judgments and advice. Crucially, mixed-effects regression coefficients of various exogenous sources of information also reflect individual weighting but are based on a conceptually consistent representation of the endogenous judgment process. The formal derivation of the proposed weighting measures is accompanied by a detailed elaboration on their most important technical and statistical subtleties. We use this modeling approach to revisit empirical findings from several paradigms investigating algorithm aversion, sequential collaboration, and advice taking. In summary, we replicate and extend the original finding of algorithm appreciation and initially demonstrate a lack of evidence for both systematic order effects in sequential collaboration as well as differential weighting of multiple pieces of advice. In addition to opening new avenues for innovative research, appropriate modeling of information sampling and utilization has the potential to increase the reproducibility and replicability of behavioral science. Furthermore, the proposed method is relevant beyond advice taking, as mixed-effects regression weights can also inform research on related cognitive phenomena such as multidimensional belief updating, anchoring effects, hindsight bias, or attitude change.

Abstract Image

建议采纳与信息取样和利用相关现象的混合效应回归权重
建议采纳及相关研究主要采用确定性加权指数,特别是基于差异比率的公式来调查信息影响。它们的计算方法直观简单,但却带来了一些测量问题,并将研究限制在特定的范式方法中。作为一种解决方案,我们建议通过拟合相应的混合效应回归模型来明确人们的判断受外部证据影响的程度。我们的方法明确区分了内生成分(如更新的信念)和外生成分(如独立的初始判断和建议)。最重要的是,各种外生信息源的混合效应回归系数也反映了个人权重,但它们是基于概念上一致的内生判断过程。在正式推导所建议的加权措施的同时,我们还对其最重要的技术和统计微妙之处进行了详细阐述。我们利用这种建模方法重新审视了研究算法厌恶、顺序协作和建议采纳的几个范式的经验发现。总之,我们复制并扩展了算法鉴赏的原始发现,并初步证明了在顺序协作中缺乏系统顺序效应的证据,也缺乏对多个建议进行不同权重的证据。除了为创新研究开辟新途径外,对信息采样和利用进行适当建模还有可能提高行为科学的可重复性和可复制性。此外,所建议的方法不仅适用于建议的采纳,因为混合效应回归权重还可以为相关认知现象的研究提供信息,如多维信念更新、锚定效应、事后偏差或态度改变。
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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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