Dynamic systems, Resilience, Social entropy, Network evolution, Disaster recovery,

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This paper introduces and tests a novel methodology for measuring networks. Rather than collecting data to observe a network or several networks in full, which is typically costly or impossible, we randomly sample a portion of individuals in the network and estimate the network based on the sampled individuals' perceptions on all possible ties. We find the methodology produces accurate estimates of social structure and network level indices in five different datasets. In order to illustrate the performance of our approach we compare its results with the traditional roster and ego network methods of data collection. Across all five datasets, our methodology outperforms these standard social network data collection methods. We offer ideas on applications of our methodology, and find it especially promising in cross-network settings. © 2012 Elsevier B.V

Whether and how organizations adapt to risk in changing contexts is a perennial problem in public administration. We explore this problem in a comparative analysis of four hurricanes that struck the Gulf Coast in 2005 and 2008: Hurricanes Katrina and Gustav in Louisiana and Hurricanes Rita and Ike in Texas. We use a framework of complex adaptive systems to assess what changes facilitate this transition in disaster contexts and what conditions inhibit adaptation. Methods include content and network analysis, including the calculation of E/I index scores. Findings suggest that investment in information technology and training in Louisiana following a perceived poor response to Katrina in 2005 led to adaptive performance in Gustav in 2008 in Louisiana, whereas minimal change following a perceived credible response to Rita in 2005 led to slower adaptation in response to Ike in 2008 in Texas.