BUS143 1 Introduction and Preferences BUS143: Judgment and Decision Making Ye Li All rights reserved ® Why you decided to take this class “Decisions are the essence of management. They’re what managers do—sit around all day making (or avoiding) decisions. Managers are judged on the outcomes, and most of them—most of us—have only the foggiest idea how we do what we do.” Thomas Stewart Former editor (2002-2008), Harvard Business Review BUS143 2 Decision Making: Two Questions • Why is decision making difficult? • What constitutes a good decision? Decision Making: Good Process • What is a decision? – A costly commitment to a course of action. • Outcomes versus Process Outcomes Good Bad Process Good Bad Bad “luck” Good “luck” BUS143 3 Components of a Good Decision • I have considered my ABCs – Alternatives – Beliefs – Consequences • I am devoting an appropriate amount of resources • I have avoided major decision traps Decision Making Components: The ABCs • Alternatives – Identification and articulation – Construction/refinement • Beliefs – Identification and quantification of uncertainties – Information collection/gathering • Consequences – Identification of consequences (and objectives addressed by consequences) – When possible, quantification of tradeoffs among objectives BUS143 4 Decision Making: Good Process • Putting it all together (for now)… Good decision making is choosing the alternative that best meets your objectives in the face of uncertainty about what consequences will ensue. 3 Perspectives on Decision Making • Normative – How should people make decisions?  Related concepts: rational; optimizing; forward-looking • Descriptive – How do people make decisions?  Related concepts: boundedly rational; limited cognitive capacity; heuristics or rule-based; myopic • Prescriptive – How can we help people make better decisions? – Prescriptive advice via practical applications, in…  Management  Marketing  Finance  HR  Life! BUS143 5 Example • Problem – Imagine two 1-mile-long (1.61km) pieces of railroad track, put end to end, and attached to the ground at the extremes. When it gets hot, each piece of track expands by 1 inch (2.54cm), forcing the pieces to rise above the ground where they meet in the middle. How high will the track be in the middle? • Normative rule: – Pythagorean Theorem: • Descriptive reality: – Most people underestimate x. (We anchor on 1 inch.) • Prescription: – Use normative rule (geometry). Don’t rely on intuition. More Examples • Normative rule: – Lighter objects should be judged as lighter. • Descriptive reality: – Sometimes our vision tricks us. • Prescription: – Use an outside reference or instrument – Note: Pilots have specific strategies for counteracting visual illusions Which box looks lighter? BUS143 6 Class Philosophy • Overarching goal: – Help you to.

1. BUS143
1
Introduction and Preferences
BUS143: Judgment and Decision Making
Ye Li
All rights reserved ®
Why you decided to take this class
“Decisions are the essence of
management. They’re what
managers do—sit around all
day making (or avoiding)
decisions. Managers are judged
on the outcomes, and most of
them—most of us—have only
the foggiest idea how we do
what we do.”
Thomas Stewart
Former editor (2002-2008),
Harvard Business Review
BUS143

2. 2
Decision Making: Two Questions
• Why is decision making difficult?
• What constitutes a good decision?
Decision Making: Good Process
• What is a decision?
– A costly commitment to a course of action.
• Outcomes versus Process
Outcomes
Good Bad
Process
Good
Bad
Bad “luck”
Good “luck”
BUS143
3
Components of a Good Decision

3. • I have considered my ABCs
– Alternatives
– Beliefs
– Consequences
• I am devoting an appropriate amount of
resources
• I have avoided major decision traps
Decision Making Components: The ABCs
• Alternatives
– Identification and articulation
– Construction/refinement
• Beliefs
– Identification and quantification of uncertainties
– Information collection/gathering
• Consequences
– Identification of consequences (and objectives
addressed by consequences)
– When possible, quantification of tradeoffs among
objectives
BUS143
4
Decision Making: Good Process

4. • Putting it all together (for now)…
Good decision making is choosing the
alternative that best meets your objectives
in the face of uncertainty about what
consequences will ensue.
3 Perspectives on Decision Making
• Normative
– How should people make decisions?
-looking
• Descriptive
– How do people make decisions?
Related concepts: boundedly rational; limited cognitive
capacity;
heuristics or rule-based; myopic
• Prescriptive
– How can we help people make better decisions?
– Prescriptive advice via practical applications, in…
BUS143

5. 5
Example
• Problem
– Imagine two 1-mile-long (1.61km) pieces of railroad track,
put
end to end, and attached to the ground at the extremes.
When it gets hot, each piece of track expands by 1 inch
(2.54cm), forcing the pieces to rise above the ground where
they meet in the middle.
How high will the track be in the middle?
• Normative rule:
– Pythagorean Theorem:
• Descriptive reality:
– Most people underestimate x. (We anchor on 1 inch.)
• Prescription:
– Use normative rule (geometry). Don’t rely on intuition.
More Examples
• Normative rule:
– Lighter objects should
be judged as lighter.
• Descriptive reality:
– Sometimes our vision
tricks us.
• Prescription:

6. – Use an outside reference
or instrument
– Note: Pilots have specific
strategies for
counteracting visual
illusions
Which box looks lighter?
BUS143
6
Class Philosophy
• Overarching goal:
– Help you to think differently and better about
how you and others arrive at judgments and
choices
• Premise 1: Effective management requires a good
understanding of how people make decisions (so
does effective marketing, investing, etc.)
• Goal 1: Understand what drives decisions.
– Traditional view: Informed decision-maker who
decides based on “value” (= preferences × probabilities)
– Behavioral view: People evolved to solve problems of
survival and reproduction, not complex business

7. problems
Class Philosophy
• Premise 2: “Flaws” in decision processes are
everywhere, and in some cases unavoidable
In the land of the blind, the one eyed man is king
– Erasmus
• Goal 2: What are the implications of human
decision “anomalies” for…
– Managers, investors, consumers, policy makers, etc.
– A self-defense guide
BUS143
7
Logistics, Format and Target Audience
• I run this class like an MBA class
– Primary source readings (mostly academic papers)
– In-class and online demonstrations
– Want to maximize YOUR engagement in the class
• Target market
– The intellectually curious
– Future management consultants, brand managers,
financial professionals, entrepreneurs, policy makers,

8. lawyers, etc.
My Teaching Philosophy
• I’m here to help you succeed—
in class and in life!
• I will submit the best grades I can!
– BUT, maximum allowable average GPA: 3.25
• The focus is on learning and not testing
– I will not give you any busywork. Everything has a
purpose.
BUS143
8
Administrative Stuff
• Coordination
– Class will start on time, will not end late
-loaded: Basic concepts in first 20 mins
– Lecture slides will be posted 9pm day before class
– Please check your email (forward it to your phone!)
• What you will be graded on
20% - Participation
40% - Four 600-word write-ups
10% - Weekly web assignments
30% - Take-home final exam

9. 20%: Participation
• Purpose: 1) Engage students and get different perspectives to
improve
learning (and to know when students are confused). 2) Get
practice for jobs
and MBA classes (participation % is sometimes as high as
50%!). 3) 80min of
lecture is boring!
– Attendance is required for learning but not enough!
– Participation means raising your hand and contributing to
hear from at least one-third of students every class!
–
is useful for learning
and should not make you afraid!
– You should also ask ME questions (there are no bad
questions)
– Doing readings before class makes it easier to contribute
-5 hours a week.
– Last quarter, average participation score was 17/20! (so do not
stress)
• I will use a seating chart to learn names but you pick your own
seat
– Your seat in class will be permanent for the rest of the quarter
• Electronics policy: Absolute no phones! (I can see you using
them)
– Leave them at home if you don’t have the self-control to not
use them.
– Computers and tablets for note-

10. section
BUS143
9
40%: Four Write-Ups
• Purpose: Encourage deeper thought on the readings to
stimulate
better class discussion.
• 600-word limit (penalties for going over) each write-up
– Business memos must be concise and to the point. People are
busy and
won’t read anything beyond first page!
– Shorter write-ups (<450 words) tend to lack detail. Aim for
500-600.
– Do NOT include an introduction, background, or conclusion!
understanding by
correctly applying the concepts and provide concrete examples
• NO need for outside research
– Should be based on class concepts and readings!
• Clarifying a possible misconception: This is not a writing
class
– Yes there is a lot of writing, but the purpose is to
communicate your

11. understanding and ability to apply class concepts.
– Style, grammar, etc., only important in terms of me being able
to
understand you
Write-Up Logistics
• Due on Tuesday at 9pm each week on iLearn
– 1 point bonus if turned in by 9pm Sunday, and feedback
guaranteed by the weekend
– 2 point penalty after 9pm Tuesday
• First write-up is to be done in randomly-assigned groups
• Pick any 3 of the remaining 6 weekly topics (no topics weeks
9 or 10)
– Can only submit current week’s topic
– Weighted by how well you do (WARK: Weighting after
results known)
10%
– Optional: Submit a 5th write-up and the lowest score will be
dropped
• Scores out of 25 (but they are not percentages!)
– Most scores will be in the 19-22 range. 20 and 21 are most
common
– - - th
some errors,
22- -
examples
– Individualized feedback to improve for next time (via iLearn)

12. – Please read the syllabus for more details on what we’re
looking for!
BUS143
10
10%: Web assignments
• Purpose: Demonstrate ideas we will cover in class.
Seeing is believing.
– Short web assignments due each Friday at 3pm
included in the class data
(and no
chance for extra credit)
– Complete BEFORE doing readings.
– No formal grades (no right or wrong answers)
arly just rushed through the survey
without any
thought.
– At least one chance for extra credit each week, sometimes
more!
30%: Take-Home Final Exam

13. • Purpose: Test your understanding of ALL class materials.
No midterm!
• Take-home case final
– Available last class and due June 10th
– 2000-word write-up analyzing a real business case using
concepts from this course
– May complete the final in pairs or alone (90% in pairs)
– More details in last few weeks of class
• Note: If you don’t know anyone in this class, get to know
your groupmates!
– I also want to meet each group! Groups 1 and 2: please see
me after class today!
BUS143
11
Up to 5%: Extra credit (see syllabus)
• Purpose: Encourage you to see the world in terms of class
concepts.
• Find a recent (within 1 month) example from current news,
ads, etc.,
that illustrates an idea or ideas from the course
– One paragraph explanation of why this is a good example of
something we
discussed in class.
– Submit example by email (see syllabus for format)

14. • Great examples earn up to 1% extra credit (~50% past success
rate)
– Maximum of 5 submissions. Maximum of 5% total extra credit
in class
• HINTS
1. Articles about research or that use class concepts directly are
not good
examples. The idea is that I might use the example in my slides!
2. The example should very clearly illustrate the class concept
even before I
read your explanation.
3. If you specifically go looking for examples/articles, you
probably won’t find
good ones. The best examples are just things you happen upon
in everyday
life.
Progression for each week
Wednesday
Web assignment
link posted
Additional videos
posted (if any)
Can start readings
after completing
web assignment
Friday 3pm

15. Web assignment due
(before doing
reading)
Plan 2‐5 hours per
week for readings
Plan 3‐6 hours per
600‐word write‐up
Sunday 9pm
Write‐up early
deadline
(Tuesday 9pm
is normal
deadline)
Lecture slides
posted (for
help in taking
notes)
Monday &
Wednesday
Class
9:30‐10:50;
11:00‐12:20
Office Hours
MW 12:30‐1:30,

16. Th 9:30‐10:30
BUS143
12
My blunt view on you liking this class
• (I think) MOST of you will enjoy this class, and
(I hope) many will love it
• However, some of you may not, especially if…
– You do not like doing potentially difficult (non-textbook)
readings before learning the topic in class
– You do not think you can write concise write-ups based on
those readings and open-ended prompts
– You do not enjoy class discussions
– You are not here to actively learn (or are just here because
you heard the class is easy)
• This class is designed to challenge you but it will be very
rewarding to the intellectually curious!
Outline of Topics
Domain-General,
Foundational Stuff
• Two Brains
• Preferences (Loss Aversion,
Prospect Theory)

17. • Uncertainty, Heuristics, Biases
• Overconfidence
• Mental Accounting
• Choice Context
• Choice over Time
• Memory, Emotions, Social
heuristics
Bringing It All Together
and Reinforcement
• Prediction: Models vs.
Experts
• Organizational Decision
Making
– “Into Thin Air” Case
• Happiness
• Nudging (AKA choice
architecture)
• Behavioral Finance
• Deciding Better
BUS143
13
The Balance of Good Decision Making
“Decision-making is a struggle
between emotion and intellect.

18. You must use intuition and
analysis, not intuition or
analysis. When you use both
together, you achieve the
optimal result.”
-Andy Grove
Address to Harvard Business
School Class of 2000
Introduction:
Two types of decision makers
• Homo sapiens (humans)
– Limited processing
capacity: memory,
attention, will
– Satisfices: Aims for
“acceptable” level of
performance, defined over
limited problems
– Has social concerns,
positive and negative
• Homo economicus (econs)
– Infinitely sensitive,
incredibly smart
• Know precisely what they
want
• Know exact tradeoffs

19. – Maximizes across all
decisions
– Purely selfish and greedy,
pursues wealth
BUS143
14
Introduction: Your two brains
• System 1: Intuitive
– Fast
– Automatic
– Effortless
– Associative
– Difficult to modify
Introduction: Your two brains
• System 2: Deliberative
– Slow
– Needs to be learned
– Effortful
– Non-emotional
– Deliberately controlled
– Easy to modify
– Rule-governed

20. BUS143
15
The Modular Brain
• System 1 evolved over millions of years to solve
simple decisions quickly.
– e.g., Food, Fight, Flight, Mate?
• The more “effortful” System 2 is a much more recent
evolution
– Prefrontal cortex deals with planning, personality, social
“control”, etc.
– What happens with more complex decisions: choosing a
mortgage, stock, or employee?
System 2 as an integrator
and editor of System 1 output
• Keep this in mind whenever you have questions:
– About rationality
– “Why are people so stupid?”
• The big question: Do people always figure out
when System 1 makes mistakes? Does System 2
always dominate?
BUS143

21. 16
Agenda: Preferences
• Review: normative theory vs. behavioral theory
• Applications of loss aversion
– Endowment effect
– Pricing
– Sunk costs
– Defaults
– Persuasion
– Multi-attribute reference levels
– Targets
Normative Theory:
Subjective Expected Utility
• People assess options
relative to their net
worth
• People pick the best
option that maximizes
utility across all
possible actions, times.
• Diminishing marginal
utility of wealth
explains why people
are risk averse
Total Wealth
U

22. ti
li
ty
$0
Utility of gamble:
50% of winning $140
50% of losing $100
(assume you have $1,000 saved)
u($1,000)
.5*u($1,140)+.5*u($900)
$900 $1000 $1140
>
BUS143
17
Are you an Econ?
Rabin’s calibration theorem
• A person who turns down this gamble
– 50% chance of +$10.10
– 50% chance of -$10
• Must turn down
– 50% chance of -$800
– 50% chance of +$3,494

23. • And also must refuse
– 50% chance of -$1,000
– 50% chance of +$10,000,000,000
(or more)
(Rabin 2000, Econometrica)
Behavioral Theory:
Prospect Theory (Kahneman & Tversky 1979)
1. Relative Evaluation:
Value is judged relative
to a reference point (xr),
NOT total wealth.
2. Loss Aversion: Losses
loom larger than gains.
– V(x) = xi–xr if x >r
– V(x) = λ(xi-xr) if x < r
3. Diminishing sensitivity.
– V(x) = xα where α < 1
λ = how much
steeper slope
is for losses
GainsLosses
reference point
Value

24. Change in
wealth
BUS143
18
Loss aversion: Defined
• Reactions to losses are more intense than
reactions to gains of the same magnitude
• Potential costs, efforts, and sacrifices are
weighted more heavily than potential benefits,
rewards, and opportunities
Measuring Loss Aversion
You indicated
whether you would
play a series of
gambles involving
potential losses
• If the coin turns up heads, then you
lose $15; if the coin turns up tails,
you win $90
• If the coin turns up heads, then you
lose $30; if the coin turns up tails,
you win $90
…

25. • If the coin turns up heads, then you
lose $90; if the coin turns up tails,
you win $90
• If the coin turns up heads, then you
lose $105; if the coin turns up tails,
you win $90
BUS143
19
Framing Matters: Mortality Rate
McNeil, Pauker, Sox, & Tversky (1982)
• Surgery: Of 100 people having surgery for this
condition, 10 die in surgery or in the postoperative
period, 32 have died within a year, and 66 have died
by the end of 5 years.
• Radiation: Of 100 people having radiation therapy
for this condition, 0 die in treatment, 23 have died
within a year, and 78 have died by the end of 5
years.
Framing Matters: Survival Rate
McNeil, Pauker, Sox, & Tversky (1982)
• Surgery: Of 100 people having surgery for this
condition, 90 live through the postoperative period,
68 are alive at the end of the first year, and 34 are
alive at the end of 5 years.

26. • Radiation: Of 100 people having radiation therapy
for this condition, 100 live through treatment, 77
are alive at the end of the first year, and 22 are alive
at the end of 5 years.
BUS143
20
Loss Aversion in Marketing:
Endowment Effect (Thaler)
• Why do businesses offer free trials?
• Loss aversion when trial ends!
– Also, laziness/forgetting to cancel (inertia)
– One reason stores offer free returns
• Endowment effect: People overvalue what they own
– People won’t trade random lottery tickets with each other
– Your examples?
• Rocksbox $21 membership credit or StitchFix $20 styling fee
credit
(credits are use it or lose it)
– This is also why Marie Kondo is a thing (hard to give up
stuff)
What are some other examples of
setting reference points strategically?
• HINT: This is what part of Write-up Topic 1 is
about

27. • Some examples:
– MSRP’s
– Company earnings expectations (profits $/share)
– Negotiating
BUS143
21
General Strategies
for Reference-Setting
• Provide people with frames that look good
– But be careful of high expectations
• Discover customers’ reference points, which
may differ by segments
– Your current customers
– Competition’s current customers (switchers)
– New customers (to the product category)
• Be sensitive to when reference points are
updated
Multi-dimension Reference Dependence
• Each dimension has its own λ and reference point
• Example:
– How much would you pay for one more year of life
expectancy?
– How much would I have to pay you to have one less

28. year of life expectancy?
• More loss aversion for attributes that are:
– Important
– Hedonic (related to happiness)
– Difficult to tradeoff for money
BUS143
22
Loss aversion across attributes:
Car buying
1. You are about to buy a new car and are considering a model
that does not have/has side-impact airbags. These are bags
which will deploy in case of a collision, lowering the chance of
injury. Another model of the same car, identical in every other
respect, has/does not have side-impact airbags.
2. You are about to buy a new car and are considering a model
that gets 25/33 miles per gallon. Another model of the same
car, identical in every other respect, gets 33/25 miles per
gallon.
• How much more would you be willing to pay for that car?
• How much cheaper would that car have to be, for you to
switch?
Loss aversion across attributes:
Your data (medians)
• Price to buy side-impact airbags: $700

29. • Price to give them up: $2500
• λ(air bags) = 2500/700 = 3.6
• λ(gas mileage) = 1.66
• Assuming $3/gallon, 10k miles/year, $291
difference a year
BUS143
23
Applying Prospect Theory:
Approaches to Risk Attitudes
• In expected utility theory, it is
all about the shape of the
utility function
– Convex: risk-seeking
– Concave: risk-aversion
• In Prospect Theory, it depends
on the reference point
– For gains, risk-aversion
– For losses, risk-seeking
Total Wealth
U
ti
li
ty

30. GainsLosses
People hate losses and try to avoid them
• Risk attitude depends on
reference point
– For gains, risk-averse
– For losses, risk-seeking
GainsLosses
BUS143
24
Risk Attitudes for Gains and Losses
Example 2: Risky gambles
A: A sure gain of $240
B: A 25% chance to gain $1000, and a 75% chance to gain
nothing
C: A sure loss of $750
D: A 75% chance to lose $1000, and a 25% chance to lose
nothing
E: A 75% chance of losing $760 and a 25% chance of gaining
$240
F: A 75% chance of losing $750 and a 25% chance of gaining
$250

31. $240 $240
‐$1000 $0
‐$760 $240
$0 $1000
‐$750 ‐$750
‐$750 $250
75% chance 25% chance
A
D
E: A & D
B
C
F: B & C
75% chance 25% chance
Why this is inconsistent
BUS143
25

32. Sunk Cost Fallacy
• Normative: Econs only includes future costs
and benefits in computing net present value
(NPV)
• Descriptive: Humans pay attention to sunk
costs
– Related to loss aversion: we hate to “close” a
mental account at a loss
– Leads to throwing good money after bad
Sunk Cost Fallacy: Your answers
As the president of an airline company, you have
invested $100 million of the company’s money into a
research project. The purpose was to build a plane
that would not be detected by conventional
radar. When the project is 95% completed, another
firm begins marketing a plane that cannot be
detected by radar. Also, it is apparent that their plane
is much faster and more economical than the plane
your company is building—pretty much better in
every important way.
The question is: Should you invest the last $5 million
of research funds to finish your plane?
BUS143
26

33. Sunk Costs in Professional Sports
• Do sunk costs affect play time?
• Case study: Kwame Brown (2001-2013), 6’11 C
– Drafted by Washington Wizards 1st pick in 2001
(under team president Michael Jordan)
– Average 4.5ppg, 3.5rpg as a rookie
– Played for Lakers from 2005-2008, averaging 7.4ppg
– SIX other teams gave him a shot!
– Career averages: 6.6ppg, 5.5rpg, 0.6bpg
• Camerer & Weber (1999) found that highly
drafted players (who are paid a lot of money) get
more playing time, controlling for talent
• Any of your own examples of sunk
cost fallacy? (sports or otherwise)
Why do these countries differ so much?
Proportion of people classified as organ donors
4.25
27.5
17.17
12
99.98 98 99.91 99.997 99.5 99.64
85.9
0

34. 10
20
30
40
50
60
70
80
90
100
BUS143
27
Manage defaults wisely…
• iPhone and AT&T’s billing
– Many people were surprised
to see bills dozens of pages
long.
– One user received a large

35. box with over 300 pages.
– Most entries: “1 kb download
(time) $0.00.”
• Why? The default at signup
was for itemized bills!
Loss Aversion, Compensation and Targets
• Many people work to a target.
• Sometimes these are explicit:
– Sales targets
– Productivity quotas
• Sometimes these are self
imposed:
– Income or savings goals
– Effort goals
BUS143
28
How will targets affect behavior?
• Hint #1: The target is a reference point
• Hint #2: Not meeting the target is a loss
• So, first, people will work much harder until the
target is met
– But then they’ll slack off

36. • Question: Why is so hard to get a cab when it
rains in New York?
A general point about
goal-setting and goal-striving
– Targets above current
performance…
– Motivate people (more)
to reach them
– Motivate them less when
they are reached
– Should be aware of both
effects on motivation
– Note: Extremely high
targets, not met, can be
demotivating
BUS143
29
Summary: Loss Aversion Matters
• The power of the status quo as a reference point
– Endowment effect
– Defaults
– Sunk costs
• Framing effects (changing reference points)

37. – Targets/aspirations
– Risk preferences
• Marketing and persuasion
– Market segmentation
– Product positioning
Ye’s Keys: Topic 1
1. Fast, automatic System 1 is audited by
slower, analytic System 2, but not always
that successfully.
2. Loss aversion makes people reluctant to
switch from the status quo (e.g.,
endowment effect, trial pricing, defaults)
and makes them do things they otherwise
wouldn’t (e.g., sunk cost effect, increased
risk taking)
3. Losses are in the eye of the beholder, and
(largely) in the hands of the reference-
setter. You can use framing to change
reference points and therefore behavior.
BUS143 Topic 2
1
Uncertainty, Risks,
and Heuristics
BUS143: Judgment and Decision Making

38. Ye Li
All rights reserved ®
Please download Moblab if you don’t have it already!
How do we form these judgments?
1. In all low-income countries across the world today, how
many girls finish primary school?
• 20%, 40%, or 60%
2. Where does the majority of the world population live?
• Low, Middle or High countries
3. In the last 20 years the proportion of the world population
living in extreme poverty has?
• Almost doubled, Remained more or less the same, Almost
halved
4. What is the average life expectancy in the world today?
• 50, 60, or 70 years
5. There are 2 billion children in the world today aged 0-15
years old, how many children will there be in 2100 according
to the UN?
• 4 billion, 3 billion, or 2 billion
6. The UN predicts that by 2100 the world population will have
increased by another 4 billion people, what is the main reason?
• There will be more children aged below 15
• There will be more adults aged 15-74
• There will be more very old people aged 75 and older
7. How did the number of deaths per year from natural

39. disasters change over the last 100 years?
• More than doubled, Remained about the same, or
Decreased to less than half
8. There are about 7 billion people in the world today,
approximately
where do they live?
• 1 billion in Europe, 4 in Asia, 1 in Africa and 1 in Americas
• 1 billion in Europe, 3 in Asia, 2 in Africa and 1 in Americas
• 1 billion in Europe, 3 in Asia, 1 in Africa and 2 in Americas?
9. How many of the world's 1 year old children today have been
vaccinated against some diseases?
• 20%, 50%, or 80%
10. Worldwide, 30 year old men have spent 10 years in school
on
average. How many years have women of the same age spent in
school?
• 9 years, 6 years, or 3 years
11. In 1996 tigers, giant pandas, and Black Rhinos were all
endangered.
How many of these species are critically endangered today?
• 2 of them, 1 of them, or none of them
12. How many people in the world have some access to
electricity?
• 20%, 50%, 80%
13. Global climate experts believe that over the next 100 years
the
average temperature will on average...?
• get warmer, remain the same, or get colder

40. BUS143 Topic 2
2
Decisions require uncertainty judgments
• Uncertainty: Uncontrollable events that decision-makers
do not have total information about.
• Probability: Quantified beliefs about uncertain events.
• How is uncertainty different from risk?
– A risk has a known probability distribution. E.g., coin flip
• Who uses probability estimates in making choices?
• Nearly every business (and other) decision involves some
estimate of likelihood
– Alternatives
– Beliefs
– Consequences
Charlie Munger
“If you don’t get this elementary, but mildly unnatural
mathematics of probability into your repertoire, then
you go through a long life like a one-legged man in an
ass-kicking contest.
One of the advantages of a fellow like Buffett, whom
I’ve worked with all these years, is that he
automatically thinks in terms of decision trees and the

41. elementary math of permutations and
combinations...”
Address to USC Marshall Business School
BUS143 Topic 2
3
Why should we quantify uncertainty?
• MobLab: What probability would you assign to the
following verbal probability statements? (0 to 100%)
–“Usually” _____%
–“Possible” _____%
–“Somewhat likely” _____%
–“Probably” _____%
–“Fairly unlikely” _____%
Normative: Econs
Subjective expected utility =
Value(outcome) × Probability(outcome)
How do Econs use probabilities?
– Stated probabilities: 20% = 20%
– Estimated probabilities:

42. BUS143 Topic 2
4
Bayes’ Rule Primer
Suppose that your friend has been feeling quite sick and
thinks he or she has the new swine flu going around.
Fortunately, there is a new quick diagnosis test for swine flu
available. This test will make a positive diagnosis if you have
swine flu 99.99% of the time. Your friend gets tested and it
comes back positive…
• What is the probability that your friend has the
swine flu?
• What additional info do you need to know?
– If you do not have swine flu, there is still a 1% chance that
the test will be positive (false positive).
– 1% of Americans have swine flu
Flu diagnosis: Normative Analysis (blank)
Pr(Positive)=
Pr(Flu|Positive)= Pr(Positive|Flu)∙Pr(Flu)/Pr(Positive)
Normative answer depends on:
The base rate (1%)
Ex. 1: Pr(Flu) = 0.1% =

43. Ex. 2: Pr(Flu) = 10% =
The quality of the information (1% false positive rate)
Ex. 3: Pr(Positive|No Flu) = 10%
Ex. 4: Pr(Positive|No Flu) = .01%
1%
No Flu
99%
Positive|Flu
Negative|Flu
Flu
Positive|No Flu
Negative|No Flu
BUS143 Topic 2
5
Descriptive: Humans
• How do humans actually think about risk and
uncertainty?
– How do we actually use stated probabilities? (risk)
– How do we estimate probabilities that we do not

44. know? (uncertainty)
• Answer: People use heuristics
– Heuristics can lead to biases
Homo sapiens: Probability distortions
• Very small probabilities
treated as larger than
they actually are (e.g.,
1/100)
• Almost certain events
less certain than they
actually are (e.g., 85%)
• 0% and 100% are special
cases
BUS143 Topic 2
6
Homo sapiens: Probability Estimation
• Definition: Heuristics
– Rules of thumb (shortcuts) that simplify judgments
and decisions
– System 1

45. • Definition: Biases
– When judgments and decisions deviate systematically
from what is considered optimal or appropriate
– Sometimes caused by usage of heuristics
Major Heuristics under Uncertainty
1. Availability (judging by familiarity)
2. Representativeness (judging by resemblance)
3. Anchoring (judging from starting values)
• Heuristics can lead to overconfidence, which
we’ll discuss next week
BUS143 Topic 2
7
Availability Example 1
The following 10 corporations were ranked by Fortune
magazine to be among the 150 largest United-States-based
firms according to revenue for 2019:
Group A: Starbucks, McDonald’s, Facebook,
American Express, Nike
Group B: Kroger, Fannie Mae, United Health,
McKesson, Amerisource Bergen
Compare these two groups to each other in terms of revenue

46. for 2018:
Your estimate: A =________ / B = _________
REMINDER: You should not feel the need to Google answers fo
r web assignments…
Cause of Death
Median
Estimate
(x000)
Actual
(2018;
x000)
Percent
(Est./Act.)
Google News
Search (2018)
Fire 5 2.60 192% 27,900,000
Lightning 100 total 26 total 384% 309,000
Motor vehicle accident 80 37.9 211% 11,400,000
Falls 5 33.0 15% 433,000
Homicide (murder) 40 15.8 253% 29,700,000
Suicide 50 42.8 122% 6,970,000
Terrorism 5000 total 80 total 6250% 3,860,000

47. Lung Cancer 75 155.6 48% 217,000
Breast Cancer 50 41.7 120% 2,340,000
Heart Disease 100 614.3 16% 942,000
Alzheimer’s Disease 50 93.5 53% 225,000
Availability Example 2:
Reasoning by Recall
Estimate the number of people living in the US who die annuall
y
from each of the following causes. ~2.5 million deaths in US / y
ear
BUS143 Topic 2
8
Availability
In forming a judgment, we tend to…
• Make predictions and evaluations based on the ease with
which objects or instances come to mind
– Rely heavily on readily available (recent, salient, vivid)
information
– Fail to discount the quality of the information
– Fail to consider other possible sources of information
• More simply…
– Things that are easy to “picture” are overestimated
– Things that are hard to “picture” are underestimated

48. Sources of Availability
• What creates easy recall?
– Frequent exposure
-of-mouth, day-to-day experience
– Recent exposure (e.g., Flu, shootings in the news)
– Linking to what we already know
– Vividness
onal impact
BUS143 Topic 2
9
Implications for Consumer Demand
Imagine you are about to take a 1-week trip to
Malaysia (Israel) as part of your UCR education. You
do not have any insurance for this trip. No
insurance is provided by the credit card company
through which the tickets were purchased or
through UCR. How much would you pay for a
policy which pays $100,000 in case of your death
due to terrorism / any cause?
• Your data, $100k policy for death due to:
– Terrorism:
– Any cause:

49. More on Unpacking
1. What is the probability that it will rain in Riverside
during finals week this quarter?
2. a. What is the probability that it will rain in Riverside
exactly one day during finals week this quarter?
b. What is the probability that it will rain in Riverside
more than one day during finals week this quarter?
Event Average Probability
Packed 36%
Unpacked
‐ One day
‐ Two or more days
75%
50%
25%
BUS143 Topic 2
10
Some marketing implications
To make things appear more likely or larger:
• Create familiarity, especially right before consumers
make decisions
– This is why Google search ads are so powerful

50. – And why Facebook ads are effective
• Link to pre-existing knowledge structures (the power
of stories)
• Use vivid imagery
– Encourage customers to mentally imagine the experience
• How might store music affect your purchasing
behavior?
Subtle uses of availability: Priming
0
5
10
15
20
25
30
35
40
45
Buy French Wine Buy German Wine

51. Hear French Music
Hear German Music
BUS143 Topic 2
11
Unintentional use of availability:
Mere Measurement (Morwitz, Johnson, & Schmittlein, 1993)
0
1
2
3
4
5
Computers Cars
%
B
u
y
in
g
P
ro
d

52. u
ct
A 33%
increase
An 18%
increase
Availability Wrap-Up
• Familiarity, recency, and vividness (or the lack
thereof) affect judgments and behavior
– Overestimate salient causes of death, earnings of
familiar companies
– Used extensively in advertising
• Availability can impact choice without awareness
– Priming: German vs. French wine
BUS143 Topic 2
12
Representativeness:
a.k.a. “the Similarity Heuristic”
In forming a judgment, we tend to…
• Make predictions and evaluations based on
similarity to salient examples and schemas (i.e,
mental frameworks of the world)

53. • More simply: People draw analogies to what they
think is a similar situation or a good example
Problems with representativeness
• Problem 1: It’s extremely sensitive to the
example(s) selected
– People are anecdote rather than data-
driven
– Favors recent and vivid examples
(i.e., availability), and stereotypes
– Many bad examples!
ckaging color
BUS143 Topic 2
13
Problems with representativeness
• Problem 2: Beliefs about causes of random
outcomes are often not quite right
– Base-rate neglect (remember: Flu problem)
– Traditional medicine (e.g., Chinese) uses for rhino
horn, tiger penis, bear testicles, etc.
– Misunderstanding how randomness works

54. where it is
Forecasting Problem (in MobLab)
Cox & Summers 1987
Enter as millions
(no need for the
0’s), make sure it
adds to 99!
BUS143 Topic 2
14
Even experts forget to be regressive
22 of 35 “excellent” companies
underperformed the S&P 500 over
next 10 years (e.g., Atari, Wang
all…
Only 8 of 18 “visionary” companies
outperformed the S&P 500 over
next 10 years
Representativeness:
Misperceptions of randomness
P(switch) = .37 P(switch) = .51 P(switch) = .63

55. Streaks don’t feel representative of randomness!
(think streaks of same answers on a multiple
choice test)
BUS143 Topic 2
15
Representativeness:
Streaks and gambler’s fallacy
• Suppose you’re playing roulette. The ball landed on red 5
times in a row. What do you want to bet on?
A. Bet it all on red!
B. Bet it all on black!
• This is the same principle that makes music playlists not
feel random enough on “shuffle”!
New algorithm that spaces out artists more evenly
Real World Implications
• Choose examples and analogies wisely…
• Shape people’s evaluations by influencing
associations
– Increase availability of beneficial examples
– Increase genuine or superficial similarity to certain
examples

56. • Don’t trust your intuitions about randomness
– Remember about regression to the mean!
BUS143 Topic 2
16
Aside: Why do heuristics persist?
• Big reason: Confirmation bias
• Types of confirmation bias
– Selective Search: Seeking information that confirms
(both purposely and non-purposely) hypothesis
you expect to agree with you, asking
leading questions (Don’t you love BUS143?)
– Interpret ambiguous info in line with hypothesis
– Biased memory
reality
Wason’s Card Task
Suppose each card has a number on one side and
a letter on the other. Which of these card(s) are
worth turning over if you want to know whether
the statement below is false? "If a card has a
vowel on one side, then it has an even
number on the other side."

57. BUS143 Topic 2
17
Wason’s Card Task – Now in context
Imagine you’re a bouncer at a bar. You must enforce
the rule that if a person is drinking beer, then he or
she must be over 21 years old. The four cards below
each represent one customer in your bar. One side
shows what the person is drinking, and the other side
shows the drinker’s age. Pick only the cards you
definitely need to turn over to see if any of these
people are breaking the law and need to be thrown
out.
Can a smell help avoid confirmation bias?
• Lee & Schwarz (2012) found that
exposing people to incidental fishy
smells made them more suspicious
triplets (the 4, 8, 12 game)!
• Example of embodied cognition
• Another ‘intuitive’ way to be
more disconfirming? Treat
everyday like April Fool’s Day!

58. BUS143 Topic 2
18
Disconfirmation Practice:
CEOs and their Pets
“Results of a recent survey of 74 chief executive officers
indicate that there may be a link between childhood pet
ownership and future career success.
Fully 94% of the CEOs, all of them employed within Fortune
500 companies, had possessed a dog, a cat, or both as
youngsters….
The respondents said that pet ownership helped them
develop many of the positive character traits that make them
good managers today, including responsibility, empathy,
generosity, and good communication skills.”
Management Focus Magazine
What do you think? Are pets important for CEOs?
Anchoring and Adjustment
In forming a judgment, we tend to…
• Use starting values (“anchors”) and adjust our
judgment in the direction that seems appropriate
• Anchoring works by ‘unconsciously’ increasing the
availability of some information
• Many problems:
– People are not aware of anchors

59. – People use even irrelevant anchors
– People do not adjust enough from the anchor
– (Like other heuristics) Can lead to overconfidence
BUS143 Topic 2
19
Anchoring Example:
Provided anchor
1. What is the probability that 2019 Toyota Camry passenger
car sales
(in the United States) were higher than 100,000 (1,000,000)?
2. What is your best guess (in thousands of cars) as to 2019
Toyota
Camry passenger car sales (in the United States)?
• 100,000 anchor: 200,000
• 1,000,000 anchor: 750,000
• Actual: 336,978
Bonus: Best selling cars of 2019 (USA)
1. Ford F-Series 896,526
2. Dodge Ram 633,694
3. Chevrolet Silverado 575,600
4. Toyota Rav 4 448,071
5. Honda CR-V 384,168
6. Nissan Rogue 350,447
7. Chevrolet Equinox 346,048
Anchoring Example:
Unit anchors

60. • Estimate the total U.S. egg production in 2019.
– in billions
– in millions
• Billions: 20 billion
• Millions: 300 million
• Actual: 95.3 billion
Bonus fact: Average American eats ~280 eggs a year!
BUS143 Topic 2
20
Anchoring Example:
Even totally uninformative anchors
• What are the last three digits of your cell phone number? 446
• Would you pay that much for an iPad Pro 64gb? 70% said yes
• What is the most you would pay?
r = .39
$407 vs. $600 (p < .001)
Real World Examples?
• Pricing
– Sales prices
• Suggested quantities
• Predictions of tastes

61. – “False consensus” effect
BUS143 Topic 2
21
Do credit card minimum payments anchor?
Stewart, 2009
• Minimum Payment
• For people making a partial payment, r = .75
correlation between minimum payment and
actual payment amount
• If minimum payment is removed, payments rose
by 70%!
Experts are NOT immune,
and the consequences can be huge
Listed Price
(Anchor)
Estimates by Real Estate Agents
Appraised
value
Recommended
Selling Price
Reasonable
Purchase Price

62. $129,900 $114,204 $117,745 $111,454
$139,900 $125,041 $128,530 $124,653
$149,000 $128,754 $130,981 $127,318
BUS143 Topic 2
22
Wrap-up of Heuristics
• Availability, representativeness, and anchoring
-weighing information
• Quality of the information (sample size; validity)
is under-weighed
Things to Remember
• Effective marketing (persuasion, PR) means
getting your ideas in people’s heads…
• And on careful selection of those ideas…
– Even superficial similarity to examples can
powerfully influence liking
– Even somewhat arbitrarily suggested numbers
(asking prices, suggested quantities, yesterday’s
trading value) influence prediction, valuation, and
choice

63. BUS143 Topic 2
23
Ye’s Keys
4. Recent, vivid, and/or familiar examples are
easy recalled and this feeling of availability
impacts judgments, often without awareness.
5. People draw analogies to representative
examples and fit data to patterns, leading to
biased judgments, especially of randomness.
6. Numbers—even completely irrelevant
ones—can anchor numerical judgments.
7. Confirmation bias—the tendency to focus on
information consistent with a favored
hypothesis and ignore information consistent
with other hypotheses—makes these biases
hard to avoid.
1
Choice Context
BUS143: Judgment and Decision Making
Ye Li

64. Repeating themes in this class
• People’s evaluations tied to the local, rather than
global context. For example:
– (Topic 1) We take choices as given (concreteness
principle), and evaluate outcomes relative to reference
points (prospect theory)
– (Topic 4a) We form narrow, “topical” accounts rather
than comprehensive mental accounts
– (Topic 5) We exhibit myopia in intertemporal choices
• Why?
– In most cases, people find relative evaluation easier than
absolute
evaluation
2
Choice in context
• Given a set of alternatives, how do people
select a preferred option?
• How about buying a new smartphone?
– What was your process like?
– How would you describe it?
• How about what school to attend (or apply to)?
• How about what to eat for breakfast?

65. Making choices:
What do Econs do?
• A value-maximizing decision-maker would…
– Take stock of goals (i.e., knows exactly what he or
she wants)
– Explore ALL alternatives
– Evaluate how well each alternative addresses their
goals
– Choose alternative that has greatest total utility
3
Value Maximization:
Prescriptions from Intro Economics
• Use a decision matrix (step 1 of 3)
– First, identify and set priorities among objectives
Attribute Importance 4 3 1 2 2 5 3 2 3
Attribute Weight 16% 12% 4% 8% 8% 20% 12% 8% 12%
Option Price Size Weight Display Camera Software Storage
Processor Battery
Value Maximization:
Prescriptions from Intro Economics
• Use a decision matrix (step 2 of 3)
– Second, determine how alternatives measure up

66. Option Price Size Weight Display Camera Software Storage
Processor Battery
iPhone X 0 40 0 75 100 100 0 100 100
iPhone 8 67 95 80 0 50 100 0 95 50
iPhone 7 78 100 100 0 0 100 75 0 0
Galaxy S8 100 0 60 50 40 0 100 50 45
Option Price Size Weight Display (" PPI type) Camera Software
Storage Processor Battery
iPhone X $1085 5.65×2.79×0.30 6.14oz 5.8" 458 OLED
12 dual/7 iOS 11 64gb A11 ~10:35
iPhone 8 $760 5.45×2.65×0.29 5.22oz 4.7" 326 IPS LCD 12/7
iOS 11 64gb A11 8:37
iPhone 7 $705 5.44×2.64×0.28 4.87oz 4.7" 326 IPS LCD 12/8
iOS 11 128gb A10 7:46
Galaxy S8 $600 5.85×2.68×0.31 5.36oz 5.8" 570 SAMOLED
12/8 Android 64gb+ Snap. 835 8:22
4
• Use a decision matrix (step 3 of 3)
– Calculate utility for each option

67. Attribute Weight 16% 12% 4% 8% 8% 20% 12% 8% 12%
iPhone X 58.8 0 40 0 75 100 100 0 100 100
iPhone 8 62.9 67 95 80 0 50 100 0 95 50
iPhone 7 57.5 78 100 100 0 0 100 75 0 0
Galaxy S8 51.0 100 0 60 100 40 0 100 50 45
Value Maximization:
Prescriptions from Intro Economics
Why not use this method for most choices you encounter in life?
Making Choices:
What Humans actually do
• Humans use shortcuts
– People often make “reason-based” choices (more
details later)
– Screening (removing options)
– Relative rank matters (not absolute goodness)
• Implications…
– For modeling people’s choice behavior
– For product positioning
5
X4=taste

68. X3=calories
Homo economicus:
How Econs maximize value
X2=sugar
X1=caffeine
“Conjoint analysis” (a major marketing tool) is based on
assumption that
utility of option is sum of component utilities (“purely additive
model”)
Economic Modeling of Choice I: 1950-1970s
Coke
60%
Pepsi
40%
Coke
48%
Pepsi
32%
TALLP
(ex-Pepsi)
8%
TALLP
(ex-Coke)

69. 12%
Assumption: Proportionality (“constant ratio rule”)
New offering will take in proportion to original shares.
TALLP
Suppose that, when added,
TALLP takes 20% share
6
Choice Modeling II:
Similarity Hypothesis
Coke
60%
Pepsi
40%
TALLP
Coke
55%
Pepsi
25%
TALLP
(ex-Pepsi)
17%

70. TALLP
(ex-Coke)
3%
Assumption: Similarity
New offering will take more share from those that are similar
(i.e., similar goods swap out for each other in the market)
Again, suppose that
TALLP takes 20% share
Choice Modeling III:
Regularity Assumption
x
z
y
A B
Pr(x;A) = ? Pr(x;B) = ?
Pr(x;A) ≤ Pr(x;B)
The entry of an additional alternative will either reduce the
share of existing alternatives or leave them unchanged
7

71. High-Stakes Violation of Regularity
Redelmeier & Shafir 1995
• Scenario presented to neurosurgeons:
Who has priority for surgery?
– Two options
– Three option
• Why do these surgeons violate regularity?
C
T
Quality
Price
70
50
$1.80$2.60
D
C = competitor
D = decoy

72. Violating choice principles: The Attraction Effect
Huber, Payne, & Puto 1982
T = target
8
B
A
Similarity
Distance (in miles)
80
70
3050
C
The Attraction Effect in Dating
60
35
Context Effect 2: Decoys without dominance?
C
T

73. Quality
Price
70
50
$1.80$2.60
D
Efficient Frontier
“Compromise Effect”
- Not just similarity effect
- Not necessarily a relatively
inferior alternative
9
B
A
Similarity
Distance (in miles)
80
70
50

74. C
The Compromise Effect in Dating
90
75 30
Why? Extremeness Aversion
(harder to defend)
A
Probability of Repair
40
16
B
C
32
24
8
9% 7% 5% 3% 1%
Shift from B to A
= loss of reliability
Shift from B to A
= gain of functionality

75. Shift from B to C
= loss of functionality
Shift from B to C
= gain of reliability
Number
of functions
10
Reason-Based Choice
Shafir, Simonson & Tversky 1993
• Basic idea: Individuals construct reasons to
resolve conflict and justify their choice
– “Choice is a search for a unique principle that covers the
decision at hand and is not dominated by another more
powerful principle.” (Prelec & Hernstein 1991)
– Reason-based choice seems more compelling than a
tradeoff-based choice
• Why is making tradeoffs difficult?
– Conflicting objectives (and loss aversion!)
– Optimizing/maximizing (pick the best) versus
Satisficing (pick something that is ‘good enough’)
Reason-based Choice II
Shafir, Simonson & Tversky 1993

76. • Reason-based choice is NOT normative because:
– “More important” attributes get too much weight in reason-
based choice
– Reasons are frame-dependent (see next slide)
• Reason-based choice occurs more often:
– In complicated situations (lots of information; many
alternatives)
– When value-based approaches are hard to defend
Would you expect context effects to be exacerbated or
diminished in organizational decisions?
• Reason-based choice increases with accountability!
11
Which to choose?
Shafir 1993
Imagine that you serve on the jury (one of 12 jurors) of an only-
child sole-custody case following a relatively messy divorce.
The
facts of the case are complicated by several ambiguous
economic, social, and emotional considerations, and you must
decide on the basis of the following few observations:
To which parent would you award sole custody of the child?
• Parent A

77. – average income, average health, average working hours,
reasonable
rapport with the child, relatively stable social life
• Parent B
– above-average income, very close relationship with the child,
extremely active social life, lots of work-related travel, minor
health problems
reject?
Give people a reason to choose you…
Iyengar & Lepper 2000
• Field experiment
• Two tasting booths
A. Few options: 6
jams
B. Many options: 24
jams
• Difficult to make
choice or choice
deferral
• Your examples?
12

78. Disjunction Effect
Tversky & Shafir 1992
Imagine that you have just taken a very tough final exam. It is
the
end of Winter quarter, you feel tired and run-down, and you are
not sure that you passed the exam (or class). In case you failed,
you have to take the class again in spring quarter—after Spring
Break. You now have an opportunity to buy a very attractive 5-
day vacation package in Hawaii at an exceptionally low price.
The
special offer expires tomorrow, while the exam grade will not
be
available until the day that.
Would you…?
A. Buy the vacation package.
B. Not buy the vacation package.
C. Pay a $5 non-refundable fee in order to retain the rights to
buy the vacation package at the same exceptional price the
day after tomorrow—after you find out whether or not
you passed the exam.
Context matters for evaluability
Hsee et al 1999
• Joint evaluation: Simultaneous consideration of
two or more options
• Separate evaluation: Consideration of each option
in isolation
• Example: How happy would you be with this
legal settlement?
A. You get paid $500 and other person gets paid $500

79. B. You get paid $600 and other person gets paid $800
• Evaluability of a continuous attribute depends
on knowledge of average, best and worst values
– Reflects the desirability of an attribute value in a given
decision context
13
Evaluability Hypothesis
Hsee et al 1999
• When an option is judged in isolation, the
judgment is influenced more by the attributes
that are easier to evaluate in isolation
– E.g., salary, beauty, height… What else?
– Give me examples of dimensions that are or are
NOT evaluable. (e.g., diamond 4C’s)
• In joint evaluation, each option serves as the
most salient reference for evaluating the other
valuability
– Can shift what attributes are important!
Joint evaluation increases evaluability
Hsee 1996
Assume you are a music major and are looking for a
music dictionary in a used book store and planned to
spend between $10 and $50.
$22.05

80. $20.20
$18.23
$25.25
$15.00
$17.00
$19.00
$21.00
$23.00
$25.00
$27.00
Separate Evaluation Joint Evaluation
Your Data
Dictionary A
Dictionary B
14
More Joint vs. Separate Reversals
Hsee 1998

81. Imagine that you are shopping for a dinnerware set. There is a
clearance sale in a
local store where dinnerware usually sells for between $40-$80
a set. Suppose
that you have found these two sets (this set) on clearance. They
are made by a
reputable manufacturer and are white and simple.
$35.33
$40.13
$41.13
$37.33
$25.00
$30.00
$35.00
$40.00
$45.00
Separate Evaluation Joint Evaluation
Your Data Set A Set BSet A includes
40 pcs
Set B includes
24 pcs
Dinner plates:
8, all in good
condition

82. 8, all in good
condition
Soup/salad
bowls:
8, all in good
condition
8, all in good
condition
Dessert plates:
8, all in good
condition
8, all in good
condition
Cups:
8, 2 of them
are broken
Saucers:
8, 4 of them
are broken
Which evaluation mode is better?
• Of course, neither is always better, so qualify
your answer with when.
• Most researchers would argue for joint
evaluation. Why?

83. • When and why might separate evaluation be
better?
15
Summary
• Choice depends on context
– Marketers should consider how to control the choice
environment, e.g. via product characteristics.
– Decisions in separate and joint evaluations may differ!
• People hate making tradeoffs (loss aversion) and
generate reasons to choose one alternative over
the other(s)
• This leads to violating basic choice principles
– The Attraction Effect
– The Compromise Effect
Ye’s Keys
14. Everything is relative. Context impacts choice
and evaluability.
Maximize your position by identifying
extremes and beating the heck out of the
weak competitor.
16. Easier to make choices we can explain to
others (and ourselves). So give people a
reason to choose your product!

84. 17. You only live one life, so some attributes are
hard to evaluate (e.g,. diamonds). Make sure
you are maximizing the ‘right’ attributes in
choices you make in joint evaluation.