Open AccessTask complexity moderates group synergy
Author(s) -
Abdullah Almaatouq,
Mohammed Alsobay,
Ming Yin,
Duncan J. Watts
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2101062118
Subject(s) - task (project management) , computer science , class (philosophy) , group (periodic table) , process (computing) , interdependence , computational complexity theory , quality (philosophy) , complexity class , theoretical computer science , artificial intelligence , algorithm , philosophy , chemistry , management , organic chemistry , epistemology , political science , law , economics , operating system
Significance Scientists and managers alike have been preoccupied with the question of whether and, if so, under what conditions groups of interacting problem solvers outperform autonomous individuals. Here we describe an experiment in which individuals and groups were evaluated on a series of tasks of varying complexity. We find that groups are as fast as the fastest individual and more efficient than the most efficient individual when the task is complex but not when the task is simple. We then precisely quantify synergistic gains and process losses associated with interacting groups, finding that the balance between the two depends on complexity. Our study has the potential to reconcile conflicting findings about group synergy in previous work.