Paper Info
Title
Diversity and Social Network Structure in Collective Decision Making: Evolutionary Perspectives with Agent-Based Simulations.
Abstract
Collective, especially group-based, managerial decision making is crucial in organizations. Using an evolutionary theoretic approach to collective decision making, agent-based simulations were conducted to investigate how human collective decision making would be affected by the agents' diversity in problem understanding and/or behavior in discussion, as well as by their social network structure. Simulation results indicated that groups with consistent problem understanding tended to produce higher utility values of ideas and displayed better decision convergence, but only if there was no group-level bias in collective problem understanding. Simulation results also indicated the importance of balance between selection-oriented (i.e., exploitative) and variation-oriented (i.e., explorative) behaviors in discussion to achieve quality final decisions. Expanding the group size and introducing nontrivial social network structure generally improved the quality of ideas at the cost of decision convergence. Simulations with different social network topologies revealed collective decision making on small-world networks with high local clustering tended to achieve highest decision quality more often than on random or scale-free networks. Implications of this evolutionary theory and simulation approach for future managerial research on collective, group, and multilevel decision making are discussed.
Year
DOI
Venue
2019
10.1155/2019/7591072
COMPLEXITY
Field
DocType
Volume
Convergence (routing),Social network,Evolutionary theory,Network topology,Artificial intelligence,If and only if,Decision quality,Cluster analysis,Mathematics,Management science,Machine learning,Group decision-making
Journal
2019
ISSN
Citations 
PageRank 
1076-2787
0
0.34
References 
Authors
9
3
Name
Order
Citations
PageRank
Shelley D. Dionne1193.33
Hiroki Sayama231949.14
Francis J. Yammarino322.16