Individual Bias and Organizational Objectivity: An Agent-Based Simulation
Authored by Bo Xu, Renjing Liu, Weijiao Liu
Date Published: 2014-03-31
Sponsors:
Chinese National Natural Science Foundation
Doctoral Fund of Ministry of Education of China
Platforms:
NetLogo
Model Documentation:
Other Narrative
Model Code URLs:
https://www.comses.net/codebases/3742/
Abstract
We introduce individual bias to the simulation model of exploration and exploitation and examine the joint effects of individual bias and other parameters, aiming to answer two questions: First, whether reducing individual bias can increase organizational objectivity? Second, whether measures, such as increasing organization size, can increase organizational objectivity in the presence of individual bias? Our results show that individual bias has both positive and negative effects, and reducing individual bias may be not beneficial when organization size is large or environment is turbulent. Diverse knowledge resulting from large organization size can help avoid the negative effects of individual bias when the bias is strong enough so that the individuals who are less limited by bias can be distinguished as the source of learning. Our results also suggest that increasing interpersonal learning, decreasing learning from the organization, task complexity, and environmental turbulence, and maintaining personnel turnover can improve organizational objectivity in the presence of individual bias.
Tags
Agent-based modeling
Performance
Diversity
Network Structure
Decision-Making
Exploitation
Exploration
Individual Bias
Consensus
information
Computer-simulation
Empirical-test
Evolutionary perspective