COURSE DESCRIPTION & OBJECTIVES
Catalog Description
ENVIRON 385. Environmental Decision Analysis. Quantitative methods for analyzing problems involving uncertainty and multiple, conflicting objectives. Topics include subjective probability, utility, value of information, and multiple attribute methods. Students will apply these tools to an environmental management decision in a group project. Prerequisite: A course in statistics.
Course Objectives
The purpose of this course is to make you a thoughtful practitioner of decision analysis techniques applied to environmental decisions. To become a thoughtful practitioner, you must learn (1) the tools of decision analysis and (2) enough of the theory to be aware of what could go wrong and how to avoid trouble.
When you have completed
this course, you should be able to:
1. Recognize the
kinds
of problems where decision analysis is likely to be useful.
2. Identify decision
points, alternatives, sources of uncertainty and possible outcomes and
represent these graphically using decision trees and influence diagrams.
3. Help clients
develop an objectives hierarchy and a set of measures that capture the
goals of a decision problem.
4. Elicit, and use
for analysis, decision makers’ and experts’ opinions on probabilities,
measures of outcomes, preferences among various outcomes and weights on
various objectives.
5. Use probability
calculations, including Bayes’ rule, to update probabilities with new information
and to combine objective and subjective information.
6. Evaluate trade-offs
among conflicting objectives.
7. Incorporate disparate
views of multiple parties in a decision analysis.
8. Recognize where
resolving uncertainty is critical to making a good decision, and where
it isn’t, and evaluate when it is likely to be worth gathering additional
information before making a decision.
9. Recognize the
limitations of decision analysis.
Format
We will achieve the goals of the course through a combination of in-class and out-of-class activities. In-class time will be used to introduce decision analysis tools and illustrate them with examples from environmental, health and business decision problems. We will also use class time for short presentations from students on study questions and exercises designed to reinforce learning of both concepts and tools of decision analysis and for progress reports on student projects. Out-of-class activities include (1) homework problems, which you may work on individually or in groups (but each student must turn in an individual write-up, except for group project questions); (2) study questions for each topic in the course; (3) exercises to practice interacting with clients; (4) readings in the text and supplementary materials, and (5) a group project applying decision analysis to an environmental decision.
There will be occasional
optionalhelp
sessions during the regular class time on Fridays, also in A247.
LECTURE SCHEDULE
AND READINGS
|
Date
|
Lecture
Topic
|
Reading
|
Exercise
|
|
|
I.
Structuring Decisions
|
|
|
|
January
8, Wed.
|
Introduction
to decision analysis, problem structuring, decision trees
|
Clemen
(C): ch.1, 2;Suppl.:
Howard (1988)
|
Designing
a decision tree
|
|
January
13, Mon.
|
Decision
trees
|
C:
ch.3, 4; Maguire 1987
|
|
|
January
15, Wed.
|
Influence
diagrams
|
C:
ch.3, 4; Borsuk et al. 2001
|
Designing
an influence diagram
|
|
TBA
|
optional
probability review
|
C:
ch.7; Behn and Vaupel, ch. 4 Appendix
|
|
|
January
20, Mon.
|
No
Class - MLK, Jr. Day
|
|
|
|
|
II.
Modeling Uncertainty
|
|
|
|
January
22, Wed.
|
Probability
theory
|
C:
ch.7; Behn and Vaupel, ch. 4 Appendix
|
|
|
January
27, Mon.
|
Subjective
probability; eliciting expert judgment
**Project
abstracts due**
|
C:
ch.8
|
|
|
January
29, Wed.
|
Uncertain
subjective information and decision heuristics
|
C:
ch.8, Suppl.: Morgan & Henrion or Meyer & Booker
|
Eliciting
probabilities
|
|
|
III.
Modeling Preferences
|
|
|
|
February
3, Mon.
|
Risk
attitudes, utility theory
|
C:
ch.13, 14; Raiffa, wildcatter problem from ch. 2 and 4
|
|
|
February
5, Wed.
|
Utility
axioms, pitfalls
**Homework
1 Due**
|
C:
ch. 14
|
Eliciting
utilities
|
|
|
IV.
Balancing conflicting objectives
|
|
|
|
February
10, Mon.
|
Values
structuring; objectives hierarchies
**Homework
2 out**
|
C:
ch.3, 6; (also, C: ch.2, pp. 21-25; ch.3, pp. 43-52, 54-5, 71-2; ch.4,
pp.137-141; ch. 6, pp. 230-233;); Gregory and Keeney 1994
|
|
|
February
12 Wed.
|
Attributes
and scales
|
C:
ch. 3, pp. 79-85, ch.15, pp.598-602
|
Objectives
hierarchy, attributes and scales
|
|
February
17, Mon.
|
Multiattribute
utility theory (MAUT)
|
C:
ch.15 and 16
|
|
|
February
19, Wed.
|
MAUT
|
C:
ch. 15 and 16; ch. 4, 137-145; vW&E,ch.
8; Behn and Vaupel, pp.311-322.
|
Checking
MAUT assumptions
|
|
February
24, Mon.
|
MAUT
|
McDaniels,
1995
|
|
|
February
26, Wed.
|
MAUT
Wrap-up
|
TBA
|
Assigning
scores and assessing weights
|
|
March
3, Mon.
|
Review
**Preliminary
results due**
|
|
|
|
March
5, Wed.
|
**Midterm
exam I**
|
|
|
|
|
SPRING BREAK
|
|
|
|
|
V.
Handling uncertain information
|
|
|
|
March
17, Mon.
|
Uncertain
objective information
|
C:
ch.10, 11
|
|
|
March
19, Wed.
|
Uncertainty,
value of information, using data
|
C:
ch.12, Raiffa, end of ch.2
|
Constructing
probability distributions
|
|
March
24, Mon.
|
Value
of information, poster methods
|
C:
ch.12
|
|
|
March
26, Wed.
|
Sensitivity
analysis, analyzing assessments
|
C:
ch.5, Maguire and Boiney 1994
|
Checking
sensitivity
|
|
|
VI.
Multi-party decisions
|
|
|
|
March
31, Mon
|
Decision
analysis & environmental dispute resolution
|
Maguire
and Boiney 1994;Maguire
and Sondak 1998
|
|
|
April
2, Wed.
|
MP
Symposium, No Class
|
|
|
|
April
7, Mon.
|
Multi-party
MAUT
|
Merkhofer
et al. 1997, Brown 1984, Dunning et al. 2000
|
|
|
April
9, Wed.
|
**Midterm
exam II**
|
|
|
|
April
14, Mon.
|
**Poster
presentations**
|
|
|
|
April
16, Wed.
|
Poster
evaluations; course evaluations; wrap-up
|
|
|
|
April
23, Wed.
|
**Written
Project Due**
|
|
|