PMID- 33721120
OWN - NLM
STAT- MEDLINE
DCOM- 20211025
LR  - 20211025
IS  - 2365-7464 (Electronic)
IS  - 2365-7464 (Linking)
VI  - 6
IP  - 1
DP  - 2021 Mar 15
TI  - Group decisions based on confidence weighted majority voting.
PG  - 18
LID - 10.1186/s41235-021-00279-0 [doi]
LID - 18
AB  - BACKGROUND: It has repeatedly been reported that, when making decisions under 
      uncertainty, groups outperform individuals. Real groups are often replaced by 
      simulated groups: Instead of performing an actual group discussion, individual 
      responses are aggregated by a numerical computation. While studies have typically 
      used unweighted majority voting (MV) for this aggregation, the theoretically 
      optimal method is confidence weighted majority voting (CWMV)-if independent and 
      accurate confidence ratings from the individual group members are available. To 
      determine which simulations (MV vs. CWMV) reflect real group processes better, we 
      applied formal cognitive modeling and compared simulated group responses to real 
      group responses. RESULTS: Simulated group decisions based on CWMV matched the 
      accuracy of real group decisions, while simulated group decisions based on MV 
      showed lower accuracy. CWMV predicted the confidence that groups put into their 
      group decisions well. However, real groups treated individual votes to some 
      extent more equally weighted than suggested by CWMV. Additionally, real groups 
      tend to put lower confidence into their decisions compared to CWMV simulations. 
      CONCLUSION: Our results highlight the importance of taking individual confidences 
      into account when simulating group decisions: We found that real groups can 
      aggregate individual confidences in a way that matches statistical aggregations 
      given by CWMV to some extent. This implies that research using simulated group 
      decisions should use CWMV instead of MV as a benchmark to compare real groups to.
FAU - Meyen, Sascha
AU  - Meyen S
AUID- ORCID: 0000-0001-6928-4126
AD  - Experimental Cognitive Science, Department of Computer Science, University of 
      Tubingen, Tubingen, Germany. sascha.meyen@uni-tuebingen.de.
FAU - Sigg, Dorothee M B
AU  - Sigg DMB
AD  - Experimental Cognitive Science, Department of Computer Science, University of 
      Tubingen, Tubingen, Germany.
FAU - Luxburg, Ulrike von
AU  - Luxburg UV
AD  - Theory of Machine Learning, Department of Computer Science, University of 
      Tubingen, Tubingen, Germany.
AD  - Max Planck Institute for Intelligent Systems, Tubingen, Germany.
FAU - Franz, Volker H
AU  - Franz VH
AD  - Experimental Cognitive Science, Department of Computer Science, University of 
      Tubingen, Tubingen, Germany.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210315
PL  - England
TA  - Cogn Res Princ Implic
JT  - Cognitive research: principles and implications
JID - 101697632
SB  - IM
MH  - *Decision Making
MH  - Group Processes
MH  - Humans
MH  - *Politics
PMC - PMC7960862
OTO - NOTNLM
OT  - Confidence weighted majority vote
OT  - Group decision
OT  - Group discussion
OT  - Wisdom of the crowd
COIS- The authors declare no competing interests.
EDAT- 2021/03/16 06:00
MHDA- 2021/10/26 06:00
PMCR- 2021/03/15
CRDT- 2021/03/15 17:33
PHST- 2019/10/31 00:00 [received]
PHST- 2021/02/12 00:00 [accepted]
PHST- 2021/03/15 17:33 [entrez]
PHST- 2021/03/16 06:00 [pubmed]
PHST- 2021/10/26 06:00 [medline]
PHST- 2021/03/15 00:00 [pmc-release]
AID - 10.1186/s41235-021-00279-0 [pii]
AID - 279 [pii]
AID - 10.1186/s41235-021-00279-0 [doi]
PST - epublish
SO  - Cogn Res Princ Implic. 2021 Mar 15;6(1):18. doi: 10.1186/s41235-021-00279-0.