Doctoral Dissertation Research in DRMS: Modeling the Dynamics of Agency-agency Partnerships before and following Extreme Events
Doctoral Dissertation Research in DRMS: Modeling the Dynamics of Agency-agency Partnerships before and following Extreme Events
批准号:
1261058
负责人:
Jun Zhuang
金额:
$1.25万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-02-15 至 2015-01-31
中文摘要
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英文摘要
The objective of this research is to model and analyze partnership creation, length, and conclusion in networks of agencies responding to extreme events. The researchers develop models to explore how characteristics of partnerships could be used to predict dynamics in agency investment, commitment length, partnership selection, and exit timing. The research collects and validates data by interviewing agencies active in responding to extreme events. This project compares agency behavior in two separate disaster response scenarios, mathematically models the life-cycle of agency partnerships during disaster operations, and conducts controlled experiments to analyze agency decision making. Extreme events have had a significant impact on the world over the last several years, including earthquakes, tsunamis, hurricanes, and tropical storms. The scale and scope of events like Hurricane Sandy, the earthquake and tsunami in Japan, and the tornados around the United States make it imperative to increase our understanding of how government, non-governmental, and business agencies interact with one another in the aftermath of such extreme events. This study builds on previous work in emergency management to provide an analysis of the partnership selection and resource sharing processes that occur following an extreme event. By documenting the dynamical change in roles and flow of resources following extreme events, this work tests hypotheses regarding agencies and how they are impacted by partnerships, goals, roles, and prior involvement. With the support of a host of agencies that are actively involved in relief operations in the U.S. and around the world, the model results are checked for relevance, accuracy, and correct representation of the agencies and individuals responding to extreme events.
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