Diversity and Social Network Structure in Collective Decision Making:
Evolutionary Perspectives with Agent-Based Simulations
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by
Shelley D. Dionne, Hiroki Sayama, Francis J. Yammarino
2019
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 non-trivial
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 multi-level
decision making are discussed.
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