Decision Analysis: Fundamentals and Applications

By Fouad Sabry

What Is Decision Analysis

The term "decision analysis" (DA) refers to the academic field that encompasses the theory, technique, and professional practice that are required to tackle significant decisions in an organized fashion. It is possible to prescribe a recommended course of action by applying the maximum expected-utility axiom to a well-formed representation of the decision. Additionally, decision analysis includes many procedures, methods, and tools for translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker, as well as for other corporate and non-corporate stakeholders.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Decision Analysis

Chapter 2: Decision Theory

Chapter 3: Multiple-criteria Decision Analysis

Chapter 4: Expected Value of Sample Information

Chapter 5: Decision-making Software

Chapter 6: Robust Decision-making

Chapter 7: Expected Value of Including Uncertainty

Chapter 8: Decision Quality

Chapter 9: Value Tree Analysis

Chapter 10: Bayesian Inference in Marketing

(II) Answering the public top questions about decision analysis.

(III) Real world examples for the usage of decision analysis in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of decision analysis' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of decision analysis.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jun 27, 2023

Book preview

Chapter 1: Decision analysis

The study and application of the theory, technique, and professional practice of making and analyzing significant choices is known as decision analysis (DA). When applied to a well-formed representation of a decision, the maximum expected-utility axiom can be used to prescribe a recommended course of action; and the formal representation of a decision and its corresponding recommendation can be translated into insight for the decision maker, as well as other corporate and non-corporate st.

In 1931, mathematical philosopher Frank Ramsey pioneered the idea of subjective probability as a representation of an individual’s beliefs or uncertainties.

Then, around 1940, mathematician John von Neumann and economist Oskar Morgenstern developed an axiomatic basis for utility theory as a way of expressing an individual’s preferences over uncertain outcomes.

(This goes against the grain of the social-choice, It deals with the difficulty of extrapolating individual preferences to a collective setting. An alternative axiomatic framework for decision analysis was established by statistician Leonard Jimmie Savage in the early 1950s.

A thorough axiomatic framework for decision making in the face of uncertainty is provided by the ensuing expected-utility theory.

The techniques of decision analysis were further standardized and popularized after these foundational theoretical discoveries were made (e.g., in business schools and departments of industrial engineering). In 1968, Harvard Business School professor and decision theorist Howard Raiffa wrote a concise and easily digestible introductory work. Following is a list of references to subsequent textbooks and advances. More Reading Needed.

Decision analysis has traditionally been seen as part of the field of operations research, despite its multidisciplinary nature (which includes contributions from mathematicians, philosophers, economists, statisticians, and cognitive psychologists). The Decision Analysis Society began as a subset of the Operations Research Society of America (ORSA) in 1980; ORSA subsequently combined with TMS to establish the Institute for Operations Research and the Management Sciences (INFORMS). Decision Analysis, an INFORMS journal covering such research since 2004,.

Decision analysis has grown into a fully formed professional field in tandem with these scholarly advances.

Opportunity statement (what and why), boundary conditions, success measurements, a decision hierarchy, a strategy table, and action items are all developed during the framing phase of decision analysis. It's a common misconception that numerical techniques must always be used when doing decision analysis. Many judgments, however, may be made using just qualitative tools, such as value-focused thinking, from the decision-analysis toolkit, and not quantitative ones.

A decision tree or influence diagram might be created as a result of the framing process. These diagrams are often used in the practice of decision analysis. These visual aids help the decision-maker see the range of possible outcomes, the risks associated with each option, and the degree to which each result meets the decision-goals. maker's They may also serve as the backbone of a quantitative model. In the 1980s, for instance, software was created to use quantitative ways of doing Bayesian inference and discovering optimum options using impact diagrams.

Probabilities, and more especially subjective probabilities, are used to describe uncertainty in quantitative decision-analysis models. Utility functions indicate the decision maker's tolerance for risk, while multi-attribute value functions and multi-attribute utility functions express the decision maker's tolerance for trade-offs between competing goals (if there is risk involved). (The likelihood of reaching an undefined goal may stand in for a utility function in certain circumstances.) The optimal choice, according to the principles of decision analysis, is the one with the most predicted utility (or that maximizes the probability of achieving the uncertain aspiration level).

Assumptions are frequently made that only measurable elements may be used in quantitative decision analysis (e.g., in natural units such as dollars). However, even apparently intangible elements may be analyzed using quantitative decision analysis and associated methodologies like applied information economics.

Different from descriptive decision-making research, which seeks to explain how individuals really make choices, prescriptive decision-making research focuses on how to make best judgments (based on the axioms of rationality) (regardless of whether their decisions are good or optimal). Thus, it should come as no surprise that there are many instances in which people's choices differ significantly from what would be advised by decision analysis.

Formal techniques of choice analysis have been criticized for enabling decision makers to shirk responsibility, with advocates instead praising the use of intuition or gut instincts. Quantitative algorithms for decision making have been shown to be more effective than unaided intuition when time allowed.

At the moment, quantitative approaches to decision making are receiving a lot of attention. Many of these approaches, however, stray from the axioms of decision analysis and, as a