Measuring Performance in Complex Adaptive Systems: An Agent-Based Approach to Managerial and Technological Change

In today's fast-paced and unpredictable world, organizations must constantly adapt to stay efficient, innovative, and competitive. This dissertation explores how organizational performance can be better understood and measured by viewing the organization as a Complex Adaptive System (CAS), a dynamic network of socio-technical systems including people, teams, and technologies that continuously evolve and respond to change. To measure how well these systems perform over time, this research uses advanced models from the efficiency measurement paradigm, such as the Malmquist Index (MI) and Hicks-Moorsteen Index (HMI). These models can track improvements in how resources are used (efficiency) and progress in innovation or technology (productivity shifts), considering both managerial and technological perspectives. This dissertation separates managerial and technological change efficiency analysis over multiple time periods for CASs, constituting a major contribution of this dissertation. Furthermore, the study creates a flexible and powerful system by linking a simulation platform with data analysis tools in testing ideas, performing sensitivity analyses, and validating results using real-world data. At the heart of this study is a computer simulation model called Complex Adaptive Productive Efficiency Model (CAPEM). This dissertation dives into model version 2.0 (CAPEM 2.0), which builds on previous versions of CAPEM. CAPEM 2.0 integrates new insights from complexity science and behavioral theory in simulating how diverse parts of an organization interact, learn, and make decisions, especially in decentralized environments. A guiding metaphor in this research is "flocking," which has inspired me through the way birds move as a group. Just as birds adjust their paths based on the movements of leaders and nearest neighbors, like peers around them, people and teams in organizations often appear to adapt their actions based on local information, collaboration, and feedback. A case study involving deregulated power plants (DPPs) demonstrates how CAPEM 2.0 can assist in uncovering which factors have the optimal impact on system-wide outcomes. For example, it reveals how certain roles, behaviors, or conditions can excessively affect an organization's success by providing crucial guidance for decision-makers who operate in dynamic environments. Overall, this research offers a new way to think about performance in complex systems. It emphasizes the importance of adaptive strategies, decentralized decision-making, and innovation-driven efficiency. CAPEM 2.0 gives analysts and policymakers an approach for designing systems that advance and innovate as they continue to evolve and improve over time.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Industrial and Systems Engineering

Department dc:contributor.department

Industrial and Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2026

Author dc:creator

• Lyddane, Glen Edward

Chairs dc:contributor.committeechair

• Triantis, Konstantinos P.
• Van Aken, Eileen Morton

Committee members dc:contributor.committeemember

• Ghaffarzadegan, Navid
• Herrera-Restrepo, Oscar A.

• socio-technical systems
• infrastructure systems engineering
• efficiency performance measurement
• Data Envelopment Analysis (DEA)
• Malmquist Index (MI)
• Malmquist Production Index (MPI)
• Hicks-Moorsteen Index (HMI)
• multivariate statistics
• robust pri

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:45630

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/140948