Abstract
To be effective, governance structures in rapidly changing environments must not only have the capacity to govern, but also the resilience to respond to that changing environment. In disaster management, resilience in response networks is a product of identifying and exchanging information among participating actors regarding on-going changes in the system. Monitoring changes on the ground, coordinating actions within your own organization and with other organizations, and adapting to changing conditions with available resources produces resilience. These are overlapping concepts that often correlate, inhibiting easy combination. This research proposes a measure built from four components of resilience: presence on the ground, internal coordination, external coordination, and adaptation, using ordinal scales in a network analytic framework. Each component is scored in network interactions, and a factor analysis examines correlations among the components and calculates factor loadings that can aggregate to a measure of resilience. Data come from two events of post-conflict reconstruction: the UN interventions in Bosnia-Herzegovina, which began in 1995, and in Haiti, which began in 2004. The research proposes a measure of resilience that is based in a complex adaptive systems approach and applies that measure to these two cases to verify its effectiveness.
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Notes
- 1.
Portions of this section of text, including Robust Physical Systems and Resilience as Adaptation, were first prepared for an internally-published doctoral dissertation (Scheinert 2012). Used with the author’s permission.
- 2.
As opposed to computer networks, which, while not dissimilar, are fundamentally different.
- 3.
These actions were sufficiently detailed to code for network analysis, although those analyses are beyond the scope of this chapter. See Scheinert (2012), for these analyses.
- 4.
These factors are produced internally to the factor analysis method and do not have a separate, conceptual definition. Such a definition can sometimes be proposed when the variables assigned to one or more factors show a pattern in what they measure, but this is not based in the method and was not possible in this analysis (see Table 11.4).
- 5.
All statistical analyses are run using Intercooled Stata 11.
- 6.
Stata reports uniqueness (U) rather than communality (C) scores for its factor analysis results. Uniqueness and communalities are related measurements such that: U = 1 − C.
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Acknowledgments
This research was done with support from the University of Pittsburgh’s Center for Disaster Management (CDM). This document was prepared with support from Vermont EPSCoR and funds from the National Science Foundation (NSF) Grant EPS-1101317. Any opinions, findings, and conclusions or recommendations expressed in this material are those the authors and do not necessarily represent the views of the NSF, Vermont EPSCoR, CDM, the University of Vermont or the University of Pittsburgh.
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Scheinert, S., Comfort, L. (2014). Finding Resilient Networks: Measuring Resilience in Post-Extreme Event Reconstruction Missions. In: Kapucu, N., Liou, K. (eds) Disaster and Development. Environmental Hazards. Springer, Cham. https://doi.org/10.1007/978-3-319-04468-2_11
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