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。

Journal of Behavioral Decision Making

ABSTRACT People are increasingly worried about untruthfulness in news reporting. We distinguish between two types of untruthfulness: apparent untruthfulness (containing false information) and consequential untruthfulness (giving readers a wrong impression of the truth). Consequential untruthfulness can be caused by both the presence of false information and cherry‐picking (reporting only parts of the truth). Despite this, we find that people's perception of untruthfulness depends largely on apparent untruthfulness. Consequently, they treat news that cherry‐picks information less negatively (e.g., less likely to criticize it and more likely to share it with others) than they treat news that contains false information, when the former is more consequentially untruthful than the latter. We dub this phenomenon as cherry‐picking tolerance . We also find that prompting people to think about the consequence of the news report (i.e., the impressions people form after they read the news reports) will mitigate the cherry‐picking tolerance. This research draws attention to the widespread practice of cherry‐picking in news reporting and calls for a new look at what constitutes fake news.

Xilin Li

Christopher K. Hsee

Shu Wang

Cherry‐Picking Tolerance About Untruthful News

关于不实新闻的选择性容忍

ABSTRACT Algorithms are capable of advising human decision‐makers in an increasing number of management accounting tasks such as business forecasts. Due to expected potential of these (intelligent) algorithms, there are growing research efforts to explore ways how to boost algorithmic advice usage in forecasting tasks. However, algorithmic advice can also be erroneous. Yet, the risk of using relatively bad advice is largely ignored in this research stream. Therefore, we conduct two online experiments to examine this risk of using relatively bad advice in a forecasting task. In Experiment 1, we examine the influence of performance feedback (revealing previous relative advice quality) and source of advice on advice usage in business forecasts. The results indicate that the provision of performance feedback increases subsequent advice usage but also the usage of subsequent relatively bad advice. In Experiment 2, we investigate whether advice representation, that is, displaying forecast intervals instead of a point estimate, helps to calibrate advice usage towards relative advice quality. The results suggest that advice representation might be a potential countermeasure to the usage of relatively bad advice. However, the effect of this antidote weakens when forecast intervals become less informative.

Stefan Daschner

Robert Obermaier

Do We Use Relatively Bad (Algorithmic) Advice? The Effects of Performance Feedback and Advice Representation on Advice Usage

我们是否使用了相对糟糕的（算法）建议？绩效反馈和建议呈现方式对建议使用的影响

Abstract Despite abundant literature theorizing societal implications of algorithmic decision making, relatively little is known about the conditions that lead to the acceptance or rejection of algorithmically generated insights by individual users of decision aids. More specifically, recent findings of algorithm aversion—the reluctance of human forecasters to use superior but imperfect algorithms—raise questions about whether joint human‐algorithm decision making is feasible in practice. In this paper, we systematically review the topic of algorithm aversion as it appears in 61 peer‐reviewed articles between 1950 and 2018 and follow its conceptual trail across disciplines. We categorize and report on the proposed causes and solutions of algorithm aversion in five themes: expectations and expertise, decision autonomy, incentivization, cognitive compatibility, and divergent rationalities. Although each of the presented themes addresses distinct features of an algorithmic decision aid, human users of the decision aid, and/or the decision making environment, apparent interdependencies are highlighted. We conclude that resolving algorithm aversion requires an updated research program with an emphasis on theory integration. We provide a number of empirical questions that can be immediately carried forth by the behavioral decision making community.

Jason W. Burton

Mari‐Klara Stein

Tina Blegind Jensen

J Behav Dec Making

A systematic review of algorithm aversion in augmented decision making

增强决策中的算法厌恶的系统综述

Abstract The claim that a person can use different strategies or processes to solve the same task is pervasive in decision making, categorization, estimation, reasoning, and other research fields. Yet such multi‐strategy approaches differ widely in how they envision that the different strategies are coordinated and therefore do not represent one unitary approach. Toolbox models, for example, assume that people shift from one strategy to another as they adapt to specific task environments based on past experience. Unlike such multi‐strategy selection approaches, multi‐strategy blending approaches assume that the outputs of different strategies are blended into a joint, hybrid response (i.e., “wisdom of strategies” in one mind). The goal of this article is twofold. First, we discuss strategy blending as a conceptual alternative to strategy selection for modeling human judgment. Second, we investigate the predictive performance of the different approaches in synthetic and real‐world environments. Taking a normative perspective , we study the coordination of rule‐based and exemplar‐based processes in estimation tasks. Our simulations using synthetic and real‐world environments indicate that, for medium‐sized samples, multi‐strategy blending approaches lead to more accurate estimates than relying on a single strategy or selecting a strategy based on past experience—possibly because neither rule‐ nor exemplar‐based processes in isolation are sufficient to capture statistical regularities that enable accurate estimates. This suggests that multi‐strategy blending approaches can be advantageous to the degree that they rely on qualitatively different strategies. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Stefan M. Herzog

Bettina von Helversen

Strategy Selection Versus Strategy Blending: A Predictive Perspective on Single‐ and Multi‐Strategy Accounts in Multiple‐Cue Estimation

策略选择与策略融合：多线索估计中单策略与多策略账户的预测视角

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

Dynamics of Reliance on Algorithmic Advice

算法建议依赖性的动态变化

ABSTRACT Learning the probabilities of multiple events from the environment is an important core competency of any organism. In our within‐participant experiment, participants experienced samples from two distributions, or prospects, each comprised of two to four events, and were required to provide simultaneous, rather than sequential, judgment of the likelihood of the complete set of observed events. Empirical calibration curves that map experienced probabilities to subjective probabilities reveal that the degree of underextremity (overestimation of low likelihood events and underestimation of high likelihood events) is strongly conditional on the number of judged events. We uncover two regularities conditional on the number of events that modify (a) the crossover points of the calibration curves with the identity line and (b) the gradient or sensitivity of probability judgments. We present a process model of elicited (subjective) probabilities that captures these empirical regularities. Experienced events recalled from memory may be erroneously attributed to the wrong events based on the similarity of event outcomes. We conclude that the observed miscalibration of probability judgments can be attributed to the noisy retrieval component of a rational process‐based decision model. We discuss the implications of our model for the conflicting empirical findings of overweighting and underweighting in the decisions from experience literature. Finally, we show that reliance on small samples can be an ecologically rational strategy for a bounded rational decision‐maker (subject to noisy recall), as aggregated subjective probabilities are closer to the ecological probabilities than the experienced (or sampled) probabilities are.

Leonidas Spiliopoulos

Ralph Hertwig

Noisy Retrieval of Experienced Probabilities Underlies Rational Judgment of Uncertain Multiple Events

经验概率的噪声检索是不确定多重事件理性判断的基础

ABSTRACT We investigated whether prescribing agreement improves the quality of judgments and decisions. Participants were first asked to provide judgments or decisions individually. Then, they either revised their initial judgments and decisions based on a partners' response, or they provided a joint judgment agreed upon with their partner. In the latter condition, we allowed for a minimal communication protocol restricted to acceptance and rejection responses to each other's proposals. In the Agreement condition, participants improved both in a cognitive (Experiment 1a) and a perceptual decision task (Experiment 1b). The cognitive task agreement allowed participants to improve above the level of accuracy achieved with revision. Surprisingly, the prescribing agreement improved the quality of the initial independent decisions. In a judgment task (Experiment 2), the prescribing agreement led to more accurate judgments because partners weighed each other's judgments more equally than in the Revision condition where they gave higher weight to their own judgments. We conclude that prescribing agreement reduces egocentric discounting bias and motivates individuals to be more accurate. These results not only demonstrate that collective benefits in judgment and decision making can be accrued without verbal communication but also suggest potential limitations of this approach.

Pavel V. Voinov

Günther Knoblich

Prescribing Agreement Improves Judgments and Decisions

处方协议改善判断与决策

ABSTRACT Some decision outcomes consist of sequences of single experiences. The aim is to propose a conceptualization of how such sequences are evaluated if affective evaluations of single experiences evoke transient emotional responses with lasting changes in current mood. The conceptualization implies three modes in which the sequences of single experiences are evaluated: (i) Aggregation of affective evaluations of the single experiences retrieved from memory; (ii) Aggregation of current moods associated with emotional responses to the single experiences retrieved or reconstructed from memory; and (iii) Updating of current mood. Simulations of parametrized models are used to compare the different evaluation modes to each other and to show to which extent the simulation results are consistent with some common findings in previous research. The previous research has primarily investigated different rules for aggregation of affective evaluations of single experiences. The simulation results motivate research comparing this mode to the other proposed modes.

Abbreviation	Behavioral Decision Making
Journal Impact	1.98
Quartiles(Global)	PSYCHOLOGY, APPLIED(Q3)

ISSN	0894-3257, 1099-0771
h-index	92

Publication Information	Publisher: John Wiley and Sons Ltd，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1988，Proportion of original research papers: 100.00%，Self Citation Rate:5.60%， Gold OA Rate: 42.28%

Journal of Behavioral Decision Making

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Cherry‐Picking Tolerance About Untruthful News

Do We Use Relatively Bad (Algorithmic) Advice? The Effects of Performance Feedback and Advice Representation on Advice Usage

A systematic review of algorithm aversion in augmented decision making

Strategy Selection Versus Strategy Blending: A Predictive Perspective on Single‐ and Multi‐Strategy Accounts in Multiple‐Cue Estimation

Dynamics of Reliance on Algorithmic Advice

Noisy Retrieval of Experienced Probabilities Underlies Rational Judgment of Uncertain Multiple Events

Cherry‐Picking Tolerance About Untruthful News

Prescribing Agreement Improves Judgments and Decisions

Do We Use Relatively Bad (Algorithmic) Advice? The Effects of Performance Feedback and Advice Representation on Advice Usage

Evaluation of Extended Decision Outcomes