TY - JOUR
T1 - Resilience as Anticipation in Organizational Systems
T2 - An Agent-based Computational Approach
AU - Garcia-Diaz, Cesar
N1 - Publisher Copyright:
© 2024 Society for Chaos Theory in Psychology & Life Sciences
PY - 2024/7
Y1 - 2024/7
N2 - The literature on organizational resilience explores various viewpoints, ranging from strategies to recover after disruptions to proactive anticipation of threats. Formal models primarily focus on the ability to recover from shocks, analyzing factors like deviation from performance targets, recovery time, and potential adaptation in function and structure. However, incorporating anticipation into such models remains scarce. Additionally, existing anticipatory systems models often neglect key aspects of organizational behavior. This work addresses these gaps by introducing an agent-based modeling approach that integrates anticipation into organizational decision-making. Our computational model features agents embedded in different organizational structures who make decisions based on projected market states (levels and trends). These decisions are subject to delays in perceiving market conditions and vary depending on the organization's adaptive capacity to update its offering. We analyze different organizational structures and market behaviors (trend direction and volatility). Our results indicate that full connectivity among agents can be detrimental to organizational resilience, as it may reduce the diversity of anticipation strategies for forecasting the market. Conversely, either sparse or highly clustered networks demonstrate a greater ability, on average, to keep up with changing market levels and trends.
AB - The literature on organizational resilience explores various viewpoints, ranging from strategies to recover after disruptions to proactive anticipation of threats. Formal models primarily focus on the ability to recover from shocks, analyzing factors like deviation from performance targets, recovery time, and potential adaptation in function and structure. However, incorporating anticipation into such models remains scarce. Additionally, existing anticipatory systems models often neglect key aspects of organizational behavior. This work addresses these gaps by introducing an agent-based modeling approach that integrates anticipation into organizational decision-making. Our computational model features agents embedded in different organizational structures who make decisions based on projected market states (levels and trends). These decisions are subject to delays in perceiving market conditions and vary depending on the organization's adaptive capacity to update its offering. We analyze different organizational structures and market behaviors (trend direction and volatility). Our results indicate that full connectivity among agents can be detrimental to organizational resilience, as it may reduce the diversity of anticipation strategies for forecasting the market. Conversely, either sparse or highly clustered networks demonstrate a greater ability, on average, to keep up with changing market levels and trends.
KW - agent-based modeling
KW - anticipatory systems
KW - networks
KW - organizational resilience
UR - http://www.scopus.com/inward/record.url?scp=85196437859&partnerID=8YFLogxK
M3 - Article
C2 - 38880502
SN - 1090-0578
VL - 28
SP - 409
EP - 429
JO - Nonlinear Dynamics, Psychology, and Life Sciences
JF - Nonlinear Dynamics, Psychology, and Life Sciences
IS - 3
ER -