# Decision Making by M2H14i0

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Decision Making

chapter 8
Daniel Kahneman
• Dan Kahneman (e.g. Kahneman & Tversky, Kahneman
& Treisman) won the 2002 Nobel Prize in Economics
along with GMU Professor Vernon Smith.

“Kahneman has integrated insights from psychology into
economics, especially concerning human judgment and
decision-making under uncertainty, the Royal Swedish
Academy of Sciences said in its citation.” – CNN.com
What makes a decision good?
1. Maximize the expected value of the return

2. Good decisions produce good outcomes; bad decisions

3. Expertise -- Experts tend to produce better decisions that
novices.
What makes a decision good?
• Maximizing expected value of return?
– Over time, make sure the average payoff of a series of
decisions is as high as possible?

– Problems include:
• the value/cost of a payoff might be hard to estimate
• expected value is only realized over the long term;
value of individual decisions may vary
• it may be more important to avoid loss than to secure
a benefit (“loss aversion”)
What makes a decision good?
• Example
– An investor might believe the higher return of a stock
investment is less valuable than the security of a
savings account.

– The expected value of a series of roulette bets is
negative, but a single bet might return a positive value
What makes a decision good?
• Producing a good result for a given decision?
– problem: since outcomes are probabilistic, a single
good result might not reflect the value of average,
long-term results.

– Example: a single winning bet on red on a roulette
wheel does not indicate that betting red consistently is
a good strategy.
What makes a decision good?
• Domain experts say that it‟s good?
– Problem: experts’ opinions about what is a good
decision might conflict with those of first two criteria

– Example: circumstances might meet criteria for a
police officer to fire a weapon, even if suspect is not a
threat.
What Constitutes a Good Decision?
•   Maximizing Expected Value
•   Producing a „good‟ outcome
•   Meeting experts‟ criteria

•   Expected Value
– To calculate expected value:
1. List all potential outcomes, along with their value
2. Calculate probability of each outcome
3. Multiply probability of each outcome by value of
each outcome.
4. Sum the resulting values.
Assume that in a lottery, 6 numbers are randomly chosen from the
range 1-52. To win, a player must match all 6 numbers. The prize
is \$1 million.
Q: What is the expected value of a \$ 1 lottery ticket with 2 chances to
win?

• Probability of winning = .0000001
• Value of winning = \$999,999

• Probability of losing = .9999999
• Value of losing = -\$1

• Expected Value of \$1 bet = \$999,999  .0000001  \$1 .9999999
= -\$0.90

“A tax on people who 
aren‟t very good at math” – Garrison Keillor
Assume that two people agree to bet on tosses of a coin. For
every toss that lands heads, Person A wins \$1. For every toss
that lands tails, Person A loses \$1

Q: What is the expected value of a coin toss to Person A?

P(tails) = .5        Value(tails) = -\$1

E(coin toss) = (.5 x \$1)+(.5 x -\$1)
= \$0.5 - \$0.5
=0
Note that at after any given coin toss, one person or the other
may be momentarily ahead of the other
Perception of Cues
• To make a good decision, people need to be able to
properly assess the situation.

• That is, they need to look for clues that will guide them
in their decision making.

• However, humans being humans, there are some
inherent biases and weaknesses in their ability to
correctly perceive clues…
Perception of Cues
• Humans are good at
– estimating the mean of multiple values
– estimating proportions that aren’t too extreme

• Humans are poorer at:
– estimating extreme proportions
• If I have seen 99 normal parts, then detecting 1
abnormal part will have more of an impact than the
100th normal part
Perception of Cues
• Humans are poorer at:
– estimating extreme proportions
– extrapolating nonlinear trends

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0    1   2   3   4   5   6   7
Perception of Cues
• Humans are poorer at:
– estimating extreme proportions
– extrapolating nonlinear trends

– estimating variance
• estimations are affected overall
magnitude
• tend to estimate ratio of
variance to mean magnitude
Perception of Cues
• Humans are poorer at:
– estimating extreme proportions
– extrapolating nonlinear trends
– estimating variance

– estimating degree of correlation in scatter plots
• tend to overestimate small correlations and
underestimate large correlations
Cues and Cue Integration
• Observer must attend to & integrate cues to diagnose a
situation & establish an hypothesis about the state of the
world.
• Cues can be characterized by 3 properties:
– Diagnosticity
• How much evidence does cue provide for a given
hypothesis?
– Reliability
• How much can the cue be trusted?
• Information Value = Diagnosticity x Reliability
– Salience
• How conspicuous is the cue?
Cues and Cue Integration
Example: A financial website predicts that some company‟s
stock will rise. Should you consider investing in this stock?

– Diagnosticity
• Does prediction say that rise is almost certain, or is
only a bit more likely than not?
– Reliability
• Have this website‟s predictions been accurate in the
past?
– Salience
• Is prediction written in big letters at the top of the
page or in smaller letters near bottom of page?
Quality of situation assessment?
• How well a person assesses the situation is dependent
on:
– Comprehensiveness of Info
• Is important information missing?
– Quantity of information
• too little? too much?
– Relative Salience of cues
• Are some cues more salient than others
– Relative weighting (importance) given to cues
• Are some cues considered more important?
Quality of situation assessment?
• Comprehensiveness of info
– Important information might be lacking
– a good decision maker should know what info is
lacking

– Example
• A computer user calling for tech support might
neglect to report a valuable symptom of his
computer's problem. An experienced technician
should recognize that symptom has not been discussed
Quality of situation assessment?
• Quantity of information
– Cues rarely have an Information Value of 1.0, so we
need additional cues to help us make a decision.
– However, there might be too much info for decision
maker to attend to and remember.
declines
effectively utilize

– Example
• a financial web site might provide minutiae which
Quality of situation assessment?
• Relative Salience
– Some cues might be bigger/brighter/louder than other
info, regardless of relative value.
– Salient cues tend to be weighted higher in decision
making

– Example:
• website might place info at top of page, above other
info of equal value.
• brochure might place some info in small type at
bottom of page.
Quality of situation assessment?
• Relative weighting given to cues
– DM might fail to properly discount low value cues.
– Often do not give more reliable cues enough
weighting.
– People tend to weight cues of equal salience equally.

– Example:
• Nurses might make diagnoses based on number of
symptoms present, rather than on Diagnosticity of
symptoms
Cue Conclusions
• Humans good at estimating: mean, non-extreme proportions
–   extreme proportions
–   extrapolating non-linear trends
–   estimating variance
–   estimating degree of correlation in scatter plots
• Cues have 3 characteristics
– Diagnosticity
– Reliability
– Salience
• Cue Assessment (person side of things)
–   Comprehensiveness of info
–   Quantity of Info
–   Relative Salience
–   Relative Weighting
Expertise and Automaticity
• Recognition-Primed DM
– Rather than making a calculated decision, experts
sometimes employ pattern matching to make rapid
decisions.
– That is, “I’ve encountered this in the past, and here is
the solution I used”
– Because detailed analysis is not performed, matching
can sometimes occur in error and lead to incorrect or
suboptimal choices.
Heuristics in Decision Making
• Decision makers seem to behave as if guided by a
number of Heuristics or “rules of thumb”
– simplify decision making
– don’t always produce a correct decision, but might be
good enough most of the time
Example
•   If a coin is flipped 6 times, which of the following
outcomes is most likely?
1. H T T H T H
2. H H H T T T
3. H H H H H T

All are equally likely.
Gamblers fallacy: assumes independent events
are correlated.
Example 2
• A group of people contains 70 engineers and 30
lawyers. A person is drawn at random. This person is a
bit shy, and enjoys math and science. What is the
likelihood that this person is a lawyer?

• A group of people contains 30 engineers and 70
lawyers. A person is drawn at random. This person is a
bit shy, and enjoys math and science. What is the
likelihood that this person is a lawyer?
Heuristics & Biases in DM
• Representativeness Heuristic
– “If it walks like a duck, and quacks like a duck, it’s
probably a duck.”
– Assessments of situation based on similarity to mental
representation of hypothesized situation.
– Ignores base rate of events (e.g. what if ducks are
rare?)

– Example:
• If a patient has 4 symptoms that match disease A and
2 symptoms that match disease B, doctor might
diagnose A even though B is much more common.
Heuristics & Biases in DM
• Availability Heuristic
– judged likelihood of event might be based on the ease
with which event comes to mind.
– incorporates base-rate info, because common events
come to mind more easily
– might be biased by irrelevant characteristics (salience,
recency, or simplicity of diagnosis).

– Example:
• people tend to overestimate the frequency of deaths by
an exciting cause (e.g. airplane, sniper) and
underestimate frequency of death by mundane cause
(e.g. heart disease).
Heuristics & Biases in DM
• Anchoring Heuristic
– after forming a belief, people are biased not to
abandon it.
– in other words, primacy of info increases its weighting
in judgments

– Example:
• When surprised by reported earnings, analysts
naturally anchor to their old earnings until they are
convinced the earnings change is due to permanent
rather than temporary factors.
Heuristics & Biases in DM
• Anchoring Heuristic
– When a long series of simple cues must be integrated,
primacy effects are shown.
– When cues are complex (detailed, unfamiliar), recency
effects are more likely.

• Punchline:
• Beliefs might depend on order in which info is
presented.
• Where info should be equally weighted, should be
presented simultaneously.
Heuristics & Biases in DM
• Confirmation Bias
– After forming a belief, people tend to seek evidence
consistent with that belief, and discount inconsistent
evidence.

– Example:
• A person that believes in the abilities of a psychic will
tend to pay attention to the successes and ignore the
failures.
Heuristics & Biases in DM
• Overconfidence bias
– People tend to assume that their judgments are much
more accurate than is true

– Example:
• of all instances when a given stock picker estimates a
a stock is 90% likely to climb, it might actually climb
only 60% of the time.
Heuristics & Biases Conclusions

• Experts might rely on automaticity (time-stressed situations)

• Heuristics & Biases
–   Representativeness
–   Availability
–   Anchoring
–   Confirmation Bias
–   Overconfidence Bias
Choice of Action
•   Certain Choice
– Results of action are known with certainty
– A DM can optimize choice:
1. List attributes of potential choices, decide how
important each attribute is
2. For each potential choice, multiply value of
attributes by importance of attribute.
3. 3 Sum products, and choose option which
maximizes this sum.
optimize choice
BMW 325i     Yamaha
w eight     Wagon       YZF-R1      Miata
Room                    0.25          100          10            50
Gas mileage               0.2          30         100            50
insurance                 0.1          50          10            60
Sexiness                  0.2          60          80            90
crashw orthiness        0.25          100          10            70

BMW 325i     Yamaha
Wagon       YZF-R1      Miata
Room                                   25          2.5        12.5
Gas mileage                              6          20          10
insurance                                5           1           6
Sexiness                               12           16          18
crashw orthiness                       25          2.5        17.5
total              73           42          64
Choice of Action
Or more likely…
• Satisfice, or pick a choice which might not be optimal,
but is good enough.
– BMW dealer is 100 miles away, Mazda dealer is
down the street…
• or
• choose by heuristic elimination by aspects: choose a
single attribute, discard any choices which do not
meet criterion for that attribute.
• Example:
• I only have \$20k to spend on a vehicle. Therefore,
the BMW is out of my price range and the Miata is
iffy…
Choice of Action
• Uncertain Choice
– Consequences of choice are not certain
– That is, the consequences are probabilistic
– Maximize expected utility of outcome
Utility = subjective value
– Utility is not always the same as objective value
Distortions of Value and Cost
1. People underestimate gains in
value.
Utility
Gain

Value
Loss               Gain
2. Potential losses are perceived
as having greater subjective
consequences.
Loss
Uncertain Choice
• Biases in setting Utility
– people are loss aversive, prefer to avoid risk of loss
rather than gamble on a gain.
– Example:
• Imagine that you're a contestant on a TV game show.
You have just won \$10,000. The host offers you a
choice: You can quit now and keep the \$10,000, or
you can play again. If you play again, there is a 0.5
probability that you will win again, and wind up with
\$20,000. If you play again and lose, you lose your
\$10,000 and take home nothing. You quickly calculate
that the expected value of playing again is \$10,000,
the same as sticking with the \$10,000 you have won
so far. Which do you chose?
Distortions of Value and Cost
3. There are progressively
smaller gains in utility as
value increases.                        Utility
Gain
A gift of \$100 seems like a lot
if you are a poor graduate
student, but is a pittance of
Value
you are a movie star.         Loss               Gain

Loss
Perceptions of Probability
1. Probability of rare events
is overestimated
1.0
e.g. probability of needing

Subjective Probability
to file insurance claim is
low

0                          1.0
Stated Probability
Perceptions of Probability
• Example: Insurance
– The estimated value of purchasing insurance is
negative: on average, you will lose money (other wise
the insurer would not make money).
• Example:
– the stock market might appear more risking than a
savings account. However, with interest rate below
the rate of inflation, a savings account guarantees
losses
Perceptions of Probability
2. Reduced Sensitivity to
probability changes at low
P().
1.0

Subjective Probability
e.g. sluggish beta,
representativeness
heuristic, & ignorance
of base rates

0                          1.0
Stated Probability
Perceptions of Probability
3. Perceived probability is
less than real probability
1.0
e.g. the probability of a risky

Subjective Probability
but high-payoff outcome
might be
underestimated, and a
surefire (but low-paying)
outcome might be

0                          1.0
Stated Probability
Uncertain Choice
• Biases in setting Utility
– Perception of outcome as a gain or loss is determined
by perceived neutral point. This is the framing effect.
– Example:
• people see a treatment with 90% survival rate as being
preferable to a treatment with 10% mortality rate.
Biases in Uncertain Choice
• Direct Retrieval/automaticity
– Experts might automatically retrieve a solution when
the cues fit a given pattern, rather than analyzing the
situation.
– Error example:
• Policeman fires his gun when a person reaches into his
pocket.
Choice Conclusions
• Certain Choice
–   outcomes are known
–   Optimize choice
–   Satisfice
–   Elimination by aspect
• Uncertain Choice
–   outcomes are probabilistic
–   maximize expected utility
–   gain is underestimated
–   loss is overestimated
–   rare events are overestimated
–   if not rare, overall probability is underestimated
• Direct Retrieval/automaticity
Improving Decision Making
• Practice in DM doesn‟t necessarily make perfect.
• Expertise in a DM task doesn‟t make one immune from
the effects of biases and heuristics.
Improving Decision Making
In many domains, feedback is limited in several ways:
1. Feedback is ambiguous:
• poor choices sometimes reinforced, good choices
sometimes punished.
• Example: lottery sometimes pays off
Improving Decision Making
In many domains, feedback is limited in several ways:
1. Feedback is ambiguous

2. Feedback is often delayed:
Example: effects of smoking might not be felt for
Improving Decision Making
In many domains, feedback is limited in several ways:
1. Feedback is ambiguous:
2. Feedback is often delayed:

3. Feedback is provided selectively:
• some choices receive feedback and others don‟t.
•   Example: no feedback emerges from a decision to
not hire somebody.
Automation
• Expert Systems
– Statistical systems, that when fed clues (e.g.
symptoms), will recommend a choice of action

• Problem: Quality of decision making is only as good as
the quality of the model
– Long Term Capital Management (LCTM)
• Hedge fund company set up by 2 winners of Nobel
Prize in Economics.
• Decision model did not understand how to deal with
Russian default on bonds (1998).
• Result: \$100 billion in debt.
Fini

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