Management and Business Knowledge Representation for Decision Making: Applying Artificial Intelligence, Machine Learning, Data Science, and Advanced Quantitative Decision Analytics for Making Better-Informed Decisions
Abstract
How were the decisions made in the past, what were the drivers, strategy, and rationale? The old adage holds true on how organizations should learn from the past to help make better decisions in the future. This current first-phase research looks at how the Department of Defense (DOD) can inculcate institutional corporate memory. Specifically, the research tests and develops recommendations about how a transparent Decisions Options Register (DOR) integrated intelligent database system can be developed, where the DOR helps capture all historical decisions (assumptions, data inputs, constraints, limitations, competing objectives, and decision rules) for programs within the Department of Defense (DOD). Information in this DOR will be compatible with meta-semantic searches and data science analytical engines. The DOR is used for modeling future decision options to enable making decisions under uncertainty while leaning on past best practices and allow senior leadership to make defensible and practical decisions. The current first phase research uses stylized data and examples to illustrate the recommended methodologies. This research implements industry best-in-class decision analytics using advanced quantitative modeling methods (stochastic simulation, portfolio optimization) coupled with Artificial Intelligence (AI) and Machine Learning (ML) algorithms (data scraping, text mining, sentiment analysis) and Enterprise Risk Management (ERM) procedures.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 20, 2022
- Accession Number
- AD1174813
Entities
People
- Johnathan C. Mun
Organizations
- Naval Postgraduate School