{"title":"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","authors":"Tobias R. Rebholz, Marco Biella, Mandy Hütter","doi":"10.1002/bdm.2369","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":48112,"journal":{"name":"Journal of Behavioral Decision Making","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bdm.2369","citationCount":"0","resultStr":"{\"title\":\"Mixed-effects regression weights for advice taking and related phenomena of information sampling and utilization\",\"authors\":\"Tobias R. Rebholz, Marco Biella, Mandy Hütter\",\"doi\":\"10.1002/bdm.2369\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":48112,\"journal\":{\"name\":\"Journal of Behavioral Decision Making\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bdm.2369\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Behavioral Decision Making\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/bdm.2369\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PSYCHOLOGY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Behavioral Decision Making","FirstCategoryId":"102","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/bdm.2369","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PSYCHOLOGY, APPLIED","Score":null,"Total":0}
Mixed-effects regression weights for advice taking and related phenomena of information sampling and utilization
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.
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.