자료유형  

 학위논문

Control Number  

0017162942

International Standard Book Number  

9798384344650

Dewey Decimal Classification Number  

150

Main Entry-Personal Name  

Eskew, Marc Antonio.

Publication, Distribution, etc. (Imprint  

[S.l.] : Stanford University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

190 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

General Note  

Advisor: Pate-Cornell, Elisabeth;Mancilla, Jose Blanchet;Shachter, Ross.

Dissertation Note  

Thesis (Ph.D.)--Stanford University, 2024.

Summary, Etc.  

요약In domains where a high-level strategy must be developed to achieve a vision of a future state, there is risk inherent in the selection and implementation of a strategy. Within domains such as business enterprise, financial investment, or national defense, a thorough risk assessment and risk mitigation plan are critical components of the strategy implementation process. However, within these areas there are different methods of analyzing the risk with no widely accepted general approach to a probabilistic management of the risk. This dissertation employs probabilistic risk analysis (PRA) to model strategic decision-making, focusing on how decisions and uncertainties affect tactical actions and outcomes. To analyze a set of potential strategies, one needs to examine the tactical sequences that can possibly follow in the chosen time horizon. Each of those contextual paths can lead to a spectrum of outcomes that determine the success or the failure of the corresponding strategy. By modeling tactics conditional on strategy, distributions of outcomes can be simulated utilizing decision-making augmented with artificial intelligence (AI) agents. Assuming rational actors, the decisions involved (overarching goal, choice of strategy, choice of tactical options) can be analyzed using a PRA to give the decision makers a quantitative understanding of the failure risk, the value of information of additional knowledge, sensitivity analysis to model parameters, and utility estimates to make informed decisions based on a risk attitude.

Subject Added Entry-Topical Term  

Success.

Subject Added Entry-Topical Term  

Decision making.

Subject Added Entry-Topical Term  

Probability.

Subject Added Entry-Topical Term  

Probability distribution.

Subject Added Entry-Topical Term  

Statistics.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 86-03B.

Electronic Location and Access  

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Control Number  

joongbu:657425