Dr. Yan Liu

1
Introduction

• Dr. Yan Liu
• Department of Biomedical, Industrial Human
  Factors Engineering
• Wright State University

2
Making Hard Decisions

• Decision Making
• Cognitive process of reaching a decision
• We occasionally need to make hard decisions
• What is the HARDEST decision that you have ever
  had to make?
• Decision Domains
• Personal domain
• e.g. which career to pursue, where to live, etc
• Business domain
• e.g. which product to invest, where to locate
• Government domain
• e.g. how to cope with social problems, how to
  deal with international conflicts

3
Key Terms and Concepts

• Decision
• A conscious irrevocable allocation of resources
  with the purpose of achieving a desired objective
• Objective
• Something specific that the decision maker wants
  to achieve
• e.g. Maximize payoffs of investment in stocks
• Uncertainty
• Something that is unknown or not perfectly known
• e.g. Performance of stock market during the next
  five years

4
Key Terms and Concepts (Cont.)

• Outcomes (States of Nature)
• The possible things that can happen in the
  resolution of an uncertain event
• e.g. The stock market is stable during the next
  five years
• Values
• Things that matter to the decision maker
• e.g. payoffs of investment
• Decision maker should use values to compare one
  alterative versus another
• Decision Context
• The particular decision situation which
  determines what objectives are considered
• e.g. personal financial status, economic status
  of the nation
• Decision context and objectives go hand in hand

5
Requisite Decision Models

• A model is considered requisite if contains
  everything that is essential for solving the
  problem and only those that are essential
• It captures the essence of a decision modeling
  process
• It requires a fully development of the decision
  makers thoughts about the problem, beliefs
  regarding uncertainty, and preferences

6
Why Are Decisions Hard

• Structural Reasons
• uncertainty, trade-offs, complexity
• Emotional Reasons
• anxiety, pressure
• Organizational Reasons
• different perspectives, consensus

7
Why Study Decision Analysis

• Decision Analysis
• A prescriptive approach designed for normally
  intelligent people who want to think hard and
  systematically about some important real problems
• A tool to offer guidance to normal people making
  hard decisions based on fundamental principles
  and knowledge about human frailties in judgment
  and decision making
• Studying Decision Analysis Leads to Better
  Decisions
• Performance of decision making is better on
  average
• Decisions are consistent
• The same decision will be made given the same
  information
• No surprises due to thorough study of the problem

8
Good Decisions

• What Is a Good Decision
• Emerges as a result of careful consideration of
  the available information and thorough
  deliberation about the goals and possible
  outcomes
• Good Decision ? Good Outcome
• An outcome can be good because of good luck
• Decision analysis cannot improve our luck, but it
  can certainly help us understand our problems
  better and thus make better decisions in general

9
Origins of Decision Analysis

• Bernoulli (1738)
• Proposed the expected utility model with a
  logarithmic utility function
• Explain deviations from the expected value model
• Bayes (1763)
• Proposed Bayes Theorem
• The revision of probability based on observations
  
• von Neumann and Morgenstern (1947)
• Theory of Games and Economic Behavior, in which
  the expected utility (EU) model was proposed
• People choose among bets that maximize expected
  utility
• Savage (1954)
• The Foundations of Statistics proposed subjective
  expected utility model (SEU)
• Combine the ideas of utility theory and
  subjective probability

10
Origins of Decision Analysis (Cont.)

• Schlaifers (1959)
• Probability and Statistics for Business Decisions
  espoused Bayesian and decision analytic
  principles for business decisions
• Raiffa and Schlaifers (1961)
• Applied Statistical Decision Theory provided a
  detailed mathematical treatment of decision
  analysis, with focus on Bayesian Statistical
  Models
• Pratt (1964)
• Article Risk Aversion in the Small and in the
  Large made significant contributions to the
  theory of utility for money, formalizing a
  measure of risk aversion
• Howard (1966)
• First coined the term decision analysis
• Raiffa (1968)
• Decision Analysis established the decision
  analysis as a methodology in real applications

11
Process of Decision Analysis
(Figure 1.1 in the textbook)

• Objectives
• e.g. Min. cost, Max. profit

• Alternatives
• e.g. Invest or not invest, Choose A or B

• Decompose and Model
• Divide Conquer
• modeling techniques, mathematical and
  statistical tools

• Sensitivity
• Whether a slight change in one or more aspects
  of the model would affect the optimal decision

Such a process provides not only a structured way
of thinking about decisions but also a structure
in which the decision maker can develop beliefs
and feelings
12
Hartsfield Airport Example

• Hartsfield International Airport in Atlanta,
  Georgia, is one of the busiest airports in the
  world. Commercial development around the airport
  prevents it from building additional runways to
  handle the future air traffic demands.
  Therefore, plans are being developed to build
  another airport outside the city limits. Two
  possible locations (A and B) for the new airport
  have been identified, but a final decision is not
  expected for another year.
• The Magnolia Inns hotel chain intends to build a
  new facility near the new airport. Land values
  around the two possible sites for the new airport
  are increasing as investors speculate that
  property values will increase greatly in the
  vicinity of the new airport.

13

• Objective
• To maximize the profit

• Alternatives
• Purchase a land at location A
• Purchase a land at location B
• Purchase a land at both locations A and B
• Do nothing and wait till more information is
  obtained

• Decomposing and Modeling
• Two possible outcomes i) the airport is built
  at A, ii) the airport is built at B
• Information available the payoffs of all
  combinations of alternatives and outcomes, the
  probability of each outcome
• Develop a decision tree model for the decision

14

• Select the best alternative
• Purchasing a land at A has the highest expected
  payoff

• Sensitive analysis
• The decision is not sensitive to small changes of
  model parameters

Implement the chosen plan of purchasing a land at
A