Diversity and Social Network Structure in Collective Decision Making

Evolutionary Perspectives with Agent-Based Simulations

Shelley D. Dionne, Hiroki Sayama, Francis J. Yammarino

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number7591072
JournalComplexity
Volume2019
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Evolutionary
Agent-based simulation
Simulation
Collective decision-making
Network structure
Social networks
Scale-free networks
Decision making
Evolutionary theory
Managerial decision making
Small-world network
Clustering
Network topology
Group size
Decision quality

ASJC Scopus subject areas

  • General

Cite this

Diversity and Social Network Structure in Collective Decision Making : Evolutionary Perspectives with Agent-Based Simulations. / Dionne, Shelley D.; Sayama, Hiroki; Yammarino, Francis J.

In: Complexity, Vol. 2019, 7591072, 01.01.2019.

Research output: Contribution to journalArticle

@article{e826d88f400747d7947f17d7b3feb6df,
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.",
author = "Dionne, {Shelley D.} and Hiroki Sayama and Yammarino, {Francis J.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1155/2019/7591072",
language = "English",
volume = "2019",
journal = "Complexity",
issn = "1076-2787",
publisher = "John Wiley and Sons Inc.",

}

TY - JOUR

T1 - Diversity and Social Network Structure in Collective Decision Making

T2 - Evolutionary Perspectives with Agent-Based Simulations

AU - Dionne, Shelley D.

AU - Sayama, Hiroki

AU - Yammarino, Francis J.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85063998837&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063998837&partnerID=8YFLogxK

U2 - 10.1155/2019/7591072

DO - 10.1155/2019/7591072

M3 - Article

VL - 2019

JO - Complexity

JF - Complexity

SN - 1076-2787

M1 - 7591072

ER -