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Building decision support for dynami...

Arora, Hina.

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Building decision support for dynamic decision making: A design science approach.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Building decision support for dynamic decision making: A design science approach./
作者:	Arora, Hina.
面頁冊數:	143 p.
附註:	Source: Dissertation Abstracts International, Volume: 70-04, Section: B, page: 2551.
Contained By:	Dissertation Abstracts International70-04B.
標題:	Engineering, System Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3353671
ISBN:	9781109102611

Building decision support for dynamic decision making: A design science approach.
Arora, Hina.

Building decision support for dynamic decision making: A design science approach. - 143 p.

Source: Dissertation Abstracts International, Volume: 70-04, Section: B, page: 2551.

Thesis (Ph.D.)--Arizona State University, 2009.

Dynamic decision-making problems require a series of interdependent decisions in real-time to maximize decision-making performance and minimize loss. The problem environment is constantly evolving as a consequence of the decision maker's actions and through the influence of events otherwise considered independent of the situation at hand. This dissertation develops a decision support mechanism to alleviate the cognitive burden that decision-makers face in dynamic decision-making environments, and leverages the influenza pandemic and associated factual and decisional drivers as a surrogate for dynamic decision making.

ISBN: 9781109102611Subjects--Topical Terms:

1018128
Engineering, System Science.

Building decision support for dynamic decision making: A design science approach.
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500 $a Source: Dissertation Abstracts International, Volume: 70-04, Section: B, page: 2551.
502 $a Thesis (Ph.D.)--Arizona State University, 2009.
520 $a Dynamic decision-making problems require a series of interdependent decisions in real-time to maximize decision-making performance and minimize loss. The problem environment is constantly evolving as a consequence of the decision maker's actions and through the influence of events otherwise considered independent of the situation at hand. This dissertation develops a decision support mechanism to alleviate the cognitive burden that decision-makers face in dynamic decision-making environments, and leverages the influenza pandemic and associated factual and decisional drivers as a surrogate for dynamic decision making.
520 $a A three study approach is followed using the design science paradigm as the guiding framework. The first study develops a cost-benefit analysis based optimization model for resource allocation across multiple regions in a static disease environment. The second study addresses the pandemic response problem in a dynamic disease environment. A six-stage disease propagation model is developed to interact with the resource allocation model from the first study. The third study develops an interactive multi-agent based decision-support environment that encompasses the resource allocation and disease propagation models developed in the first two studies.
520 $a This research produces three key design artifacts: a resource allocation model, a disease propagation model, and a decision-support tool instantiation for dynamic decision making. The results contribute to four key findings with important policy implications. The findings suggest, first, that an effective way of checking the progression of a pandemic is a multi-pronged approach that includes a combination of pharmaceutical and non-pharmaceutical interventions. Second, mutual aid should be recommended as a strategy only when the regions with lower population are hit before those with higher populations. Third, mutual aid is effective only when regions that have been affected by the pandemic are sufficiently isolated from other regions through non-pharmaceutical interventions. When regions are not sufficiently isolated, mutual aid can in fact be detrimental. And fourth, intra-region non-pharmaceutical interventions such as school closures are more effective than inter-region non-pharmaceutical interventions such as border closures. In addition to specific findings and policy suggestions, this research provides a framework for developing decision support tools for dynamic decision making problems.
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