# Decision Making

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```					Decision Making

Chapter 10
Decision Making

   Phases of Decision Making ►
   Basic Concepts of Probability ►
   Cognitive Illusions in Decision
Making ►
   Utility Models of Decision Making ▶
   Descriptive Models of Decision
Making ▶
Phases of Decision Making

   Setting Goals ►
   Gathering Information ►
   Structuring the Decision ►
   Making a Final Choice ►
   Evaluating ►

Back              A Schematic View of these Phases
Phases of Decision Making

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Setting Goals

   The goals for a decision
   What are you going to accomplish?

   The goals influence decision making
in various ways

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Gathering Information

   Consider the decision of
   Choosing a college major

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Structuring the Decision

   Decision structuring
   To manage various information
 When there are a great number of options
 When there are lots of considerations to
be used in making the decision

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Making a Final Choice

   Decide when to stop gathering
information

   Decide which information is more
relevant or reliable

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Evaluating

   To evaluate the whole process
 What went well?
 What didn’t go so well?

   To reflect and identify the areas of the
process that can stand improvement
and those that ought to be used again
in future, similar decision

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Basic Concepts of Probability

   The condition of uncertainty
   Probability
 A measurement of a degree of uncertainty
 Subjective probabilities
   Probabilities are influenced by the estimators
 Happy or sad; optimistic or pessimistic

   Objective probabilities
   Not influenced by the estimators

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Basic Concepts of Probability
   A 30-year-old woman discovers a lump in
her breast and goes to her physician. The
physician knows that only about 5 in 100
women of the patient’s age and health
have breast cancer. A mammogram
(breast X-ray) is taken. It indicates
cancer 80% of the time in women who
have breast cancer but falsely indicates
cancer in healthy patients 20% of the
time. The mammogram comes out
positive. What is the probability that the
patient has cancer?
   5*80%÷[(100-5)*20%]=4/23=0.17

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Cognitive Illusions in Decision Making

   How people gather and access the
relevance of different pieces of
information
   Cognitive illusions
   Certain systematic and common biases
under most conditions but can lead to
error when misapplied

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Cognitive Illusions in Decision Making

   Availability ►
   Representativeness ►
   Framing Effects ►
   Anchoring ►
   Sunk Cost Effects ►
   Illusory Correlation ►
   Hindsight Bias ►
   Confirmation Bias ►
   Overconfidence ►

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Availability
   Ten students from a nearby college have
indicated a willingness to serve on a
curriculum committee. Their names are
Ann, Bob, Dan, Elizabeth, Gary, Heidi,
Jennifer, Laura, Terri, and Valerie.
   The dean wants to form a two-person
committee. What is your estimate of the
number of distinct committees that could be
formed? (don’t use formulas; just respond
intuitively)
   The dean wants to form an eight-person
committee. What is your estimate of the
number of distinct committees that could be
formed? (don’t use formulas; just respond
intuitively)
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Availability
   Consider the two structures shown below:

   A path in a structure is a line that connects one “x”
from each row, starting with the top row and
finishing at the bottom row. How many paths do
you think each structure has?
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Availability

   Tversky & Kahneman, 1973
   When faced with the task of estimating
probability, frequency, or numerosity,
people rely on shortcuts or rules of
thumb (heuristics) to help make
judgments easier.
   Availability heuristic
   Assessing the ease with which the relevant
mental operation of retrieval, construction,
or association can be carried out.
   Formulas: 10!/{(x!)([10-x]!)} for problem 1
xy for problem 2
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Representativeness
   Two students, Linda and Joe, are having a
boring Saturday afternoon in the student
union. For lack of something better to do,
they each begin flipping a quarter,
keeping track of the way it lands over
time. Then they compare results. Linda
reports that her sequence of coin flips
Joe gets the following results: tails, tails,

   Which student has obtained a more
statistically probable series of results?
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Representativeness

   Kahneman & Tversky, 1973
   Representativeness heuristic
   A belief that outcomes will always reflect
characteristics of the process that
generated them
   Conducted an experiment
   Using three conditions
   Base rate ▶
   Similarity ▶
   Prediction ▶

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Representativeness
percentage now enrolled in each of the
following nine fields of specialization.
   Computer science
   Engineering
   Humanities and education
   Law
   Library science
   Medicine
   Physical and life sciences
   Social science and social work

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Representativeness
   How similar Tom W. is to the typical graduate
student in each of the following nine fields of
   Tom W. is of high intelligence, although lacking in
true creativity. He has a need for order and clarity,
and for neat and tidy systems in which every detail
finds its appropriate place. His writing is rather dull
and mechanical, occasionally enlivened by
somewhat corny puns and by flashes of imagination
of the sci-fi type. He has a strong drive for
competence. He seems to have little feel and little
sympathy for other people and does not enjoy
interacting with others. Self-centered, he
nonetheless has a deep moral sense.

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Representativeness

   Participants were given the
personality sketch but were told it
was written several years ago,
during Tom W.’s senior year of high
school, based on his response to
projective tests. They were asked to
predict the likelihood for each field
that Tom W. was currently a

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Representativeness

Gambler’s fallacy
Representativeness
   A random process will not always produce
results that look random, especially in the
short run.

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Representativeness

   Tversky & Kahneman, 1971
   Law of small numbers
 Misuse of representativeness
 “man who” argument (Nisbett & Ross,
1980)
   I know a man who smoked three packs a day
and lived to be 110.

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Framing Effects
and you see two service stations, both
advertising gasoline. Station A’s price is
\$1.00 per gallon; station B’s, \$0.95.
Station A’s sign also announces, “5
cents/gallon discount for cash!” Station
B’s sign announces, “5 cents/gallon
surcharge for credit cards.” All other
factors being equal, to which station
would you choose to go?

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Framing Effects

   Tversky & Kahneman, 1981
   People evaluate outcomes as changes
from a reference point--- their current
state
   Depending on how their current state is
described, they perceive certain outcomes
as gains or losses.
   The description “frame”s the decision or
provides a certain context
   Context effect

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Framing Effects

   Simply changing the description of a
different reference points and
therefore to see the same outcome
as a gain in one situation and a loss
in the other.

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Anchoring

   Estimate the population of
   Tim and Kim were given a starting
value respectively: 1 million & 2 million
 Tim: 1.25 million
 Kim: 1.75 million

   The starting point have huge effects
on their final estimates
   Correct value: 1.5 million

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Anchoring

   Definition
   A decision-making heuristic in which
final estimates are heavily influenced
by initial value estimates
   Estimate values
   8x7x6x5x4x3x2x1
   1x2x3x4x5x6x7x8
 2250
 512

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Sunk Cost Effects
   A major educational initiative is begun in
your hometown; \$3 million is invested to
help students stay away from cigarettes,
liquor, and other drugs. In the third of
four years, evidence begins to accumulate
that the program is not working. A local
legislator proposes ending funding to the
program before the scheduled date.
Howls of protest go up from some
individuals, who claim that to stop a
program after a large expenditure of
funds has been spent would be a waste.

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Sunk Cost Effects

   A bias in decision making in which
influence decision making to
continue.

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Illusory Correlation
   Hair twisting
   The person pinches a strand of hair between
thumb and forefinger and proceeds to twist
it around the forefinger.
   If you believe this behavior is especially
likely in people undergoing a great deal
of stress.
   Observe 150 students
   The results:


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Illusory Correlation

   People report seeing data
associations that seem plausible
even when associations are not
present

   Illusory correlation
   An association between factors that is
not supported by data but seems
plausible

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Hindsight Bias

   A tendency to exaggerate the
certainty of what could have been
   Once you know how a decision has
turned out, you look back on the
events leading up to the outcome as
being more inevitable than they really
were

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Confirmation Bias

   The tendency to search only for
information that will confirm one’s
initial hunch or hypothesis, and to
overlook or ignore any other
information

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Overconfidence
   Choose one answer for each question,
on a scale from .5 (just guessing) to 1.0
(complete certain)
   Which magazine had the largest circulation in
1978?
   Which city had the larger population in 1953?
 St. Paul, MN         New Orleans, LA
   Who was the 21st president of the United
States?
 Arthur              Cleveland
   Who began the profession of nursing?
 Nightingale         Barton

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Overconfidence

   Typical findings

Calibration
curve

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Overconfidence

   An overly positive judgment of one’s
own decision-making abilities and
performance
   Confidence ratings are higher than
actual accuracy

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Utility Models of Decision Making
   What people are doing when they
structure a decision and choose from
alternatives?
   Normative models
 Ideal performance under ideal circumstances

   Prescriptive models
 How we ought to make decisions under non-
ideal circumstances
   Descriptive models
 Detail what people actually do when they
make decisions

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Utility Models of Decision Making
   Expected-utility theory
   A normative model of decision making in which
the decision maker weights the personal
importance and the probabilities of different
outcomes in choosing among alternatives in
order to maximize overall satisfaction of
personal goals
   Expected value = ∑(Pi x Vi)
 P=probability of the ith outcome

   V=the monetary value of that outcome

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Utility Models of Decision Making

   Imaging a lottery with ten tickets
numbered 1 through 10. If the
ticket drawn is numbered 1, you
win \$10. If the ticket drawn is
numbered 2, 3, or 4, you win \$5.
Any other numbers drawn are worth
nothing. The EV of this lottery is
   (.1 x \$10) + (.3 x \$5) + (.6 x \$0)
=\$1.60

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Utility Models of Decision Making
   Expected utility (EU) = ∑(pi x ui)

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Utility Models of Decision Making
   Multiattribute Utility Theory
   A normative model of decision making that
provides a means of integrating different
dimensions and goals of a complex decision.
   It involves six steps
   Breaking a decision down into independent
dimensions
   Determining the relative weights of each of
those dimensions
   Listing all the alternatives
   Ranking all the alternatives along the
dimensions
   Multiplying the rankings by the weightings to
determine a final value for each alternative
   Choosing the alternative with the highest value

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Utility Models of Decision Making
Weightings of five dimensions in the decision “choosing” a major.

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Utility Models of Decision Making

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Utility Models of Decision Making

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Utility Models of Decision Making

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Utility Models of Decision Making

   Payne, 1976
   People do not always spontaneously
use MAUT.
   How people chose apartments when given
different numbers of alternatives.
   Participants were presented with an
“information board” carrying a number of
cards. ▶
Utility Models of Decision Making

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Utility Models of Decision Making
   Two factors were varied in the
experiment
   The number of alternatives presented
   The number of factors of information
available per alternative
   When participants had to decide
among 6 to 12 apartments, they
used another strategy.
   They eliminated some alternatives on the
basis of only one or a few dimensions.
   Elimination by aspects
   A descriptive model     Back
Descriptive Models of Decision Making

   Image Theory ▶

   Recognition-Primed Decision Making ▶
Descriptive Models of Decision Making

   Image theory
   A descriptive theory of decision making
that posits that the process consists of
two stages
   A noncompensatory screening of options
against the decision maker’s image of
values and future in which the number of
options is reduced to a very small set
   Prechoice screening of options
   If necessary, a compensatory choice
process

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Descriptive Models of Decision Making

   Three images
   Value image
   Containing the decision maker’s values,
morals, and principles
   Trajectory image
   Containing the decision maker’s goals and
aspirations for the future
   Strategic image
   The way in which the decision maker
plans to attain his or her goals

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Descriptive Models of Decision Making

   If there is more than one survivor of
the prechoice screening phase, the
decision maker may go on to use
compensatory or other decision
strategy to make the final choice

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Descriptive Models of Decision Making

   Gary Klein, 1998
   Studied special experts
   Firefighters, intensive care pediatric
nurses, military officers
   Experts most likely to rely on intuition,
mental simulation, making metaphors
or analogies, and recalling or creating
stories

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Descriptive Models of Decision Making

   Recognition-Primed Decision Making
   A theory of expert decision making that
holds that decision makers choose
options based on analogy of a given
situation with previously encountered
situations

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