research-article

Polarization Effects in Group Decisions

Authors:

Martin StettingerAuthors Info & Claims

UMAP '18: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

Pages 305 - 310

https://doi.org/10.1145/3213586.3225242

Published: 02 July 2018 Publication History

Get Access

Abstract

Group Recommender Systems aim to support the identification of items that best fit individual preferences of group members. However, decision making behavior of group members can be affected by decision biases which can deteriorate group decision quality. In this paper, we analyze the existence of Group Polarization Effects in two different domains and present a way to counteract these effects. Group Polarization is the tendency of a group to make decisions that are more extreme than the average of individual group members' preferences. We analyze Group Polarization in the context of risk analysis and cost estimation. In risk related group decisions, we figured out that if individual group members tend to make cautious decisions, then the group decision will be more cautious. However, in decisions related to cost estimation, the group estimations are lower than the average of group members' estimations (i.e., cautious shift). Furthermore, our results show that individual group members with diverse preferences are not influenced by Group Polarization Effects. The diversity in preferences of individual group members helps to counteract Group Polarization Effects.

References

[1]

Thomas Abeel, Yves Van de Peer, and Yvan Saeys. 2009. Java-ML: A Machine Learning Library. The Journal of Machine Learning Research 10, 931--934 (April 2009). http://www.jmlr.org/papers/volume10/abeel09a/abeel09a.pdf

Digital Library

Google Scholar

[2]

Gediminas Adomavicius, Jesse Bockstedt, Shawn Curley, and Jingjing Zhang. 2011. Recommender systems, consumer preferences, and anchoring effects. Vol. 811. 35--42.

Google Scholar

[3]

JK Esser. 1998. Alive and Well after 25 Years: A Review of Groupthink Research. 73 (03 1998), 116--41.

Google Scholar

[4]

Alexander Felfernig. 2014. Biases in Decision Making. In International Workshop on Decision Making and Recommender Systems, Vol. 1278. 32--37.

Google Scholar

[5]

Alexander Felfernig, Muesluem Atas, Thi Ngoc Trang Tran, Martin Stettinger, Seda Polat Erdeniz, and Gerhard Leitner. 2017. An Analysis of Group Recommendation Heuristics for High- and Low-Involvement Items. In Advances in Artificial Intelligence: From Theory to Practice, Salem Benferhat, Karim Tabia, and Moonis Ali (Eds.). Springer International Publishing, Cham, 335--344.

Google Scholar

[6]

Alexander Felfernig, Ludovico Boratto, Martin Stettinger, and Marko Tkalcic. 2018. Group Recommender Systems -- An Introduction. Springer.

Google Scholar

[7]

Robert T. Futrell, Linda I. Shafer, and Donald F. Shafer. 2001. Quality Software Project Management. Prentice Hall PTR, Upper Saddle River, NJ, USA.

Digital Library

Google Scholar

[8]

Karen E. Jacowitz and Daniel Kahneman. 1995. Measures of Anchoring in Estimation Tasks. Personality and Social Psychology Bulletin 21, 11 (1995), 1161--1166. arXiv:https://doi.org/10.1177/01461672952111004

Crossref

Google Scholar

[9]

Anthony Jameson. 2004. More Than the Sum of Its Members: Challenges for Group Recommender Systems. In Proceedings of the Working Conference on Advanced Visual Interfaces (AVI '04). ACM, New York, NY, USA, 48--54.

Digital Library

Google Scholar

[10]

Irving Janis. 1972. Victims of groupthink: A psychological study of foreign decisions and fiascoes. (1972).

Google Scholar

[11]

Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. 2010. Recommender Systems: An Introduction (1st ed.). Cambridge University Press, New York, NY, USA.

Digital Library

Google Scholar

[12]

Helmut Lamm and Claudius Sauer. 1974. Discussion-induced shift toward higher demands in negotiation. European Journal of Social Psychology 4, 1 (1974), 85--88.

Crossref

Google Scholar

[13]

Monika Mandl, Alexander Felfernig, Erich Teppan, and Monika Schubert. 2011. Consumer Decision Making in Knowledge-based Recommendation. J. Intell. Inf.Syst. 37, 1 (Aug. 2011), 1--22.

Digital Library

Google Scholar

Cited By

View all

Felfernig AAtas MStettinger MTran TLeitner G(2023)Biases in Group DecisionsGroup Recommender Systems10.1007/978-3-031-44943-7_8(153-163)Online publication date: 23-Sep-2023
https://doi.org/10.1007/978-3-031-44943-7_8
Pinatti de Carvalho AReichel SSanchez Martin MAllen ESchweitzer M(2022)Group Effect Aspects in Digitalisation Production ContextsProceedings of the ACM on Human-Computer Interaction10.1145/35675607:GROUP(1-28)Online publication date: 29-Dec-2022
https://dl.acm.org/doi/10.1145/3567560
Matsubara PGadelha BSteinmacher IConte T(2022)SEXTAMTJournal of Systems and Software10.1016/j.jss.2021.111148185:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.jss.2021.111148
Show More Cited By

Index Terms

Polarization Effects in Group Decisions
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Decision support systems

Recommendations

Investigating Serial Position Effects in Sequential Group Decision Making
UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization

Group decision making is performed in real life for the purpose of selecting an optimal solution for the whole group. Decision making behavior of group members could be impacted by item domains and the chronological order in which decision tasks are ...
Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings

Prior research has shown a robust effect of personalized product recommendations on user preference judgments for items. Specifically, the display of system-predicted preference ratings as item recommendations has been shown in multiple studies to bias ...
Counteracting Serial Position Effects in the CHOICLA Group Decision Support Environment
IUI '15: Proceedings of the 20th International Conference on Intelligent User Interfaces

Decisions are often suboptimal due to the fact that humans apply simple heuristics which cause different types of decision biases. CHOICLA is an environment that supports decision making for groups of users. It supports the determination of ...

Comments

Information & Contributors

Information

Published In

UMAP '18: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

July 2018

349 pages

ISBN:9781450357845

DOI:10.1145/3213586

General Chairs:
Tanja Mitrovic
University of Canterbury, New Zealand
,
Jie Zhang
Nanyang Technological University, Singapore
,
Program Chairs:
Li Chen
Hong Kong Baptist University, China
,
David Chin
University of Hawaii, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 July 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

UMAP '18

Sponsor:

UMAP '18: 26th Conference on User Modeling, Adaptation and Personalization

July 8 - 11, 2018

Singapore, Singapore

Acceptance Rates

UMAP '18 Paper Acceptance Rate 26 of 93 submissions, 28%;

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
309
Total Downloads

Downloads (Last 12 months)61
Downloads (Last 6 weeks)4

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Felfernig AAtas MStettinger MTran TLeitner G(2023)Biases in Group DecisionsGroup Recommender Systems10.1007/978-3-031-44943-7_8(153-163)Online publication date: 23-Sep-2023
https://doi.org/10.1007/978-3-031-44943-7_8
Pinatti de Carvalho AReichel SSanchez Martin MAllen ESchweitzer M(2022)Group Effect Aspects in Digitalisation Production ContextsProceedings of the ACM on Human-Computer Interaction10.1145/35675607:GROUP(1-28)Online publication date: 29-Dec-2022
https://dl.acm.org/doi/10.1145/3567560
Matsubara PGadelha BSteinmacher IConte T(2022)SEXTAMTJournal of Systems and Software10.1016/j.jss.2021.111148185:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.jss.2021.111148
Tran TFelfernig ATintarev N(2021)Humanized Recommender Systems: State-of-the-art and Research IssuesACM Transactions on Interactive Intelligent Systems10.1145/344690611:2(1-41)Online publication date: 21-Jul-2021
https://dl.acm.org/doi/10.1145/3446906

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Investigating Serial Position Effects in Sequential Group Decision Making

Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings

Counteracting Serial Position Effects in the CHOICLA Group Decision Support Environment

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations