Counteracting Anchoring Effects in Group Decision Making

Stettinger, Martin; Felfernig, Alexander; Leitner, Gerhard; Reiterer, Stefan

doi:10.1007/978-3-319-20267-9_10

Cited by 34 publications

(22 citation statements)

References 17 publications

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“…Yet, other forms of online group scenarios deserve attention, too. Algorithmic decision-making for groups, for instance, is an increasingly important topic (e.g., [21,34]).…”

Section: Introductionmentioning

confidence: 99%

Conformity Behavior in Group Playlist Creation

Bauer

Ferwerda

2020

Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

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A strong research record has evidenced that individuals tend to conform with a group's majority opinion. In contrast to existing literature that investigates conformity to a majority opinion against an objectively correct answer, the originality of our study lies in that we investigate conformity in a subjective context. The emphasis of our analysis lies on the "switching direction" in favor or against an item. In an online experiment, groups of five had to create a music playlist. A song was added to the playlist with an unanimous positive decision only. After seeing the other group members' ratings, participants had the opportunity to revise their own response. Results suggest different behavior for originally favored compared to disliked songs. For favored songs, one negative judgement by another group member was sufficient to induce participants to downvote the song. For disliked songs, in contrast, a majority of positive judgements was needed to induce participants to switch their vote.

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“…Yet, other forms of online group scenarios deserve attention, too. Algorithmic decision-making for groups, for instance, is an increasingly important topic (e.g., [21,34]).…”

Section: Introductionmentioning

confidence: 99%

Conformity Behavior in Group Playlist Creation

Bauer

Ferwerda

2020

Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

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“…In RE sessions, developers are tasked to determine and prioritize the requirements of the software system, trying to integrate end‐users needs 58 . Our results can be used to support collaborative decision‐making processes in requirements prioritization 60 . In particular, information about the criticality of user‐reported issues can provide a better picture of the specific needs of the users of a particular app category.…”

Section: Discussionmentioning

confidence: 99%

“…58 Our results can be used to support collaborative decision-making processes in requirements prioritization. 60 In particular, information about the criticality of user-reported issues can provide a better picture of the specific needs of the users of a particular app category. This information can be exploited during the requirements elicitation and prioritization phases for ensuring a higher perceived quality from the early stages of software development.…”

Section: Discussionmentioning

confidence: 99%

Investigating the criticality of user‐reported issues through their relations with app rating

Sorbo

Grano

Visaggio

et al. 2020

J Software Evolu Process

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App quality impacts user experience and satisfaction. As a consequence, both app ratings and user feedback reported in app reviews are directly influenced by the user-perceived app quality. Through an empirical study involving 210,517 reviews related to 317 Android apps, in this paper, we experiment with the combined usage of app rating and user reviews analysis (i) to investigate the most important factors influencing the perceived app quality, (ii) focusing on the topics discussed in user review that most relate with app rating. Besides, we investigate whether specific code quality metrics could be monitored to prevent the rising of negative user feedback (i.e., types of user review comments), connected with low ratings. Our study demonstrates that user comments reporting bugs are negatively correlated with the rating, while reviews reportingfeature requests do not. Interestingly, depending on the app category, we observed that different kinds of issues have rather different relationships with the rating and the user-perceived quality of the app. In particular, we observe that for specific app categories (e.g., communication), some code quality factors have significant relationships with the raising of certain types of feedback, which, in turn, are negatively connected with app ratings.

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“…There is considerable evidence that decision making by groups, either by reaching consensus or by amalgamation, can produce better outcomes than decision making by individuals (e.g., Salerno et al, 2017;Kugler et al, 2012;Charness and Sutter, 2012;Straus et al, 2011). However, there are also well-known problems that arise with group interactions, e.g., anchoring, groupthink, and psycho-social influences (for more details, see Kahneman et al, 1982;Mumford et al, 2006;Packer, 2009;Stettinger et al, 2015). Groups also have potential logistical advantages in that subtasks can be divided among members and/or performed by the most competent.…”

Section: Delphi Groups To Improve Reasoningmentioning

confidence: 99%

Individuals vs. BARD: Experimental Evaluation of an Online System for Structured, Collaborative Bayesian Reasoning

et al. 2020

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US intelligence analysts must weigh up relevant evidence to assess the probability of their conclusions, and express this reasoning clearly in written reports for decision-makers. Typically, they work alone with no special analytic tools, and sometimes succumb to common probabilistic and causal reasoning errors. So, the US government funded a major research program (CREATE) for four large academic teams to develop new structured, collaborative, software-based methods that might achieve better results. Our team's method (BARD) is the first to combine two key techniques: constructing causal Bayesian network models (BNs) to represent analyst knowledge, and small-group collaboration via the Delphi technique. BARD also incorporates compressed, high-quality online training allowing novices to use it, and checklist-inspired report templates with a rudimentary AI tool for generating text explanations from analysts' BNs. In two prior experiments, our team showed BARD's BN-building assists probabilistic reasoning when used by individuals, with a large effect (Glass' 0.8) (Cruz et al., 2020), and even minimal Delphi-style interactions improve the BN structures individuals produce, with medium to very large effects (Glass' 0.5-1.3) (Bolger et al., 2020). This experiment is the critical test of BARD as an integrated system and possible alternative to business-as-usual for intelligence analysis. Participants were asked to solve three probabilistic reasoning problems spread over 5 weeks, developed by our team to test both quantitative accuracy and susceptibility to tempting qualitative fallacies. Our 256 participants were randomly assigned to form 25 teams of 6-9 using BARD and 58 individuals using Google Suite and (if desired) the best pen-and-paper techniques. For each problem, BARD outperformed this control with very large to huge effects (Glass' 1.4-2.2), greatly exceeding CREATE's initial target. We conclude that, for suitable problems, BARD already offers significant advantages over both business-as-usual and existing BN software. Our effect sizes also suggest BARD's BN-building and collaboration combined beneficially and cumulatively, although implementation differences decreased performances compared to Cruz et al. (2020), so interaction may have contributed. BARD has enormous potential for further development and testing of specific components and on more complex problems, and many potential applications beyond intelligence analysis.

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Counteracting Anchoring Effects in Group Decision Making

Cited by 34 publications

References 17 publications

Conformity Behavior in Group Playlist Creation

Conformity Behavior in Group Playlist Creation

Investigating the criticality of user‐reported issues through their relations with app rating

Individuals vs. BARD: Experimental Evaluation of an Online System for Structured, Collaborative Bayesian Reasoning

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