Learning in Complex Systems
Date
2019
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Abstract
This dissertation examines learning in disaster response networks from an intercrisis perspective. Using a complex systems framework to analyze disaster response networks and the distinct properties of their constituent parts, the author identifies relationships and processes that characterize multi-organizational responses to disasters. Starting with a traditional organizational learning model, the author observes and assesses rational-analytic learning efforts conducted by government and government-sponsored agents, following major catastrophes. The findings of this research suggest needed modifications in both theoretical models and processes to promote more effective learning in emergency response organizational systems, such as disaster response networks.