Human Cognitive
Processes: psyc 345
Ch. 13 Reasoning and Decision
Making
Takashi Yamauchi
© Takashi Yamauchi (Dept. of Psychology, Texas A&M University)
(Q1) what mental strategy do people often
employ when they make probabilistic
reasoning?
(Q2) what decision strategies do people
commonly use when they make a choice?
What factor influences our choice behavior
above and beyond our own rational
judgments?
Reasoning
• People start with information and come to
conclusions that go beyond that
information.
– You are incredibly nice today. What’s going
on?
Even understanding a joke requires some form of
reasoning (or inference)
Deductive reasoning vs. inductive
reasoning
• Deductive reasoning
– When the information you have is correct, you
can necessarily reach a conclusion.
• Inductive reasoning
– You can arrive at conclusions about what is
probably true.
Deductive reasoning
– Obama is the president of the US.
– Only natural-born citizens of the US can serve
as a president of the United States.
– Conclusion: Obama is a natural-born US
citizen.
– Arnold Schwarzenegger is not a natural-born
US citizen.
– Conclusion: Arnold S. cannot be a president
of the US.
Syllogism
• Premises and categorical syllogisms
• Premise 1: All birds are animals.
• Premise 2: All animals eat food.
• Conclusion: All birds eat food.
• Premise 1: All people in Texas love the
Spurs.
• Conclusion: All Texans hate the Lakers.
• Is this true?
• Is this valid?
• Premise 1: All Texans love the Spurs.
• Premise 2: All people who love the Spurs
hate the Lakers.
• Conclusion: All Texans hate the Lakers.
• Is this valid?
• Is this true?
– Logical validity:
• A syllogism is valid when its conclusion follows
logically from its premises.
(Q1) what mental strategy do people often
employ when they make probabilistic
reasoning?
Watson four-card problem
There is a letter on one side of each card and a number
on the other side.
Indicate the minimum number of cards you need to turn
over to test the following rule:
If there is a vowel on one side, then there is an even
number on the other side.
• Real-life concrete information helps your
reasoning
Each card has an age on one side and the name of a
beverage on the other side.
Indicate the minimum number of cards you need to turn
over to test the following rule:
If a person is drinking beer, then he or she must be over
19 years old.
Fig. 12-8, p. 447
• Answer
• The answers for the letter-number problem
and the beer-age problem are identical.
• You have to turn E and 7
– Only 7 % of the participants were correct.
• You have to turn beer and 16 years old.
– 73 % of the participants were correct.
Why is there such a big difference in the two problems?
People use a real-life schema to solve a reasoning
problems.
Inductive Reasoning
• Conclusions do not definitely follow from
premises.
• Conclusions are only probably true.
• E.g.,
– (premise) Many people in Texas love football.
– (conclusion) Many people in Oklahoma like
football.
Inductive reasoning
•
•
•
•
•
•
Examples:
Will the stock market go up or down next year ?
Which movie will win the Oscar?
Does she say “yes” if I ask her to go out?
How likely am I accepted to a medical school?
How much money can I make if I choose carrier
A?
• Is this plan likely to succeed?
• Is this guy a good fit for this job?
Inductive reasoning
• People draw a conclusion based on
“observations”.
• But when do they feel more / less certain
about our conclusion?
• What are the better ways to persuade
others?
(Q1) what mental strategy do people often
employ when they make probabilistic
reasoning?
– By and large, people tend to use quick and
easy heuristics.
– Availability, representativeness,
• Demo 1:
• Which is more prevalent, words that begin
with the letter r, or words in which r is the
third letter?
• Demo 2:
For each pair of cases, which cause of death you
consider more likely for people in the US?
Homicide
Auto-train collision
Appendicitis
Drowning
Measles
Smallpox
Botulism
Asthma
Asthma
Tornado
Appendicitis
Pregnancy
Availability heuristics
Demo 1: Which is more prevalent, words that
begin with the letter r, or words in which r is the
third letter?
About 70% of the respondents tend to respond
that there are more words with r in the first
position than in the third position.
(In reality, there are three times more words that
have r in the 3rd position).
The bars in the graph
indicate the number of
people who judged the
least likely alternative
in each pair as causing
the most deaths.
Pairs of “causes of
death” are listed below
the graph, with the
least likely cause on
the left. The number in
parentheses on the
right indicates how
many more times
more people were
actually killed by the
cause on the right.
Availability Heuristics
• We use our memory of actual instances
for our judgment. So, when we make a
judgment, things that are available in our
mind determine our judgment.
• E.g.,
– Think of words that begin with r.
– Think of words that have r in the third
position?
– Which is easier to think of?
• 90% of success is just showing up.
– Woody Allen
• http://blog.bplans.com/2008/02/22/90-of-successis-just-showing-up/
• http://en.wikiquote.org/wiki/Woody_Allen
Murder rate
• Finland vs. Dominica vs. Italy
• The murder rate of Finland is twice higher than that
of Italy.
• The murder rates of Finland and Dominica are
about the same.
• http://www.nationmaster.com/graph/cri_mur_perca
p-crime-murders-per-capita
Crime rate
• El Paso vs. Colorado Springs vs. Denver
vs. Boston
– El Paso (4.57 / 1000)
– Colorado Spring (4.9 / 1000)
– Denver (5.78 / 1000)
– Boston (9.92 / 1000)
– http://en.wikipedia.org/wiki/United_States_citi
es_by_crime_rate
Representative heuristics
• We tend to make judgments based on
resemblance.
– The probability that an event A comes from
class B is determined by how well A
resembles the properties of class B.
– Is A likely to be B?
–  we say yes, to the extent that A resembles
B.
Representative heuristics
• Demo 1
• We randomly pick one male from the
population of the US. That male, Robert,
wears glasses, speak quietly, and reads a
lot. Is it more likely that Robert is a
librarian or a farmer?
Representative heuristics
• The description of Robert as wearing
glasses, speaking quietly, and reading a
lot matched people’s image of a typical
librarian.
•  people make a judgment based on how
closely Robert resembles a typical
“librarian” or a “farmer.”
• Demo 2:
• Linda is 31 years old, single, outspoken,
and very bright. She majored in
philosophy. As a student, she was deeply
concerned with issues of discrimination
and social justice, and also participated in
antinuclear demonstrations. Which of the
following alternatives is more probable?
• 1. Linda is a bank teller.
• 2. Linda is a bank teller and is active in the
feminist movement.
• Many people choose 2, but 2 cannot be
more likely than 1. Why?
• People used
representative
heuristics, and
they are
incorrectly
assuming that
the description
of Linda fits an
image of
feminists.
• (Q2) what decision strategies do people
commonly use when they make a choice?
What factor influences our choice behavior
above and beyond our own rational
judgments?
Decision making and choosing
among alternatives
The utility approach to decisions
• Basic idea:
– People make decisions in order to maximize
the utility associated with the choice.
buying a car
• Honda Accord vs. Mercury Cougar
• You have $20,000 to spend.
• Honda Accord
Mercury Cougar
Compare the features of the two
cars
•
•
•
•
Accord is slightly more expensive than Cougar.
Cougar has better horse power than Accord.
Accord has better fuel economy.
Cougar is more stylish than Accord.
• Expected utility theory
• you choose the one that gives you the maximum
value (the maximum satisfaction you get).
• Sum(U(A))> Sum(U(B)) --> select A
• what is “valuable” is subjective.
• Expected utility theory
•  mental arithmetic
• Choose Honda Accord if
• Sum(Accord) > Sum(Cougar)
willingness
to buy / sell
Selling / Buying a car
seller
buyer
$price
Probability to say
“Yes, go for it!!”
Dating scene
girl
Christopher’s
Café Excel
Square one
Mr. G’s
KFC
McDonald
boy
$ cost
• Basic assumption in Economics
– People’s choice behavior is driven to
maximize their self-interest.
• Rational analysis
• Milton Friedman (2:24)
• http://www.youtube.com/watch?v=RWsx1X8PV_A
• But you see many irrational behaviors.
– Dot-com bubble, housing bubble
– Why do we make so many irrational
decisions?
• People may be rational, but their decision is
very much influenced by many psychological
factors (such as emotion, attention, and
memory)
• Behavioral economics
– Behavioral economics examines people’s
economic choice behavior from a vantage point of
cognitive psychology.
• Behavioral economics. 4:16
– http://www.youtube.com/watch?v=Fa-mIosWOK8
• Interview: NPR Behavioral economics (9:00)
Amos Tversky (1937 – 1996)
Daniel Kahneman (1934 –)
Problems with the utility approach
• People are not good at predicting their
emotional utility.
– Predicting the utility (how much satisfaction
you would get) of one choice over the other
isn’t that easy.
– Many psychological factors influence your
perception of utility
• Focusing illusion
– People focus on one aspect of a situation and
ignore other aspects.
• 1. How happy are you?
• 2. How many dates did you have last
month?
– Correlation = 0.12
• 1. How many dates did you have last
month?
• 2. How happy are you?
– Correlation = 0. 66
Your decisions depend on how
choices are presented
• Demo
• Coglab
• Risky decisions
• Imagine that the US is preparing for the
outbreak of an unusual Asian disease that is
expected to kill 600 people. Two alternatives
programs to combat the disease have been
proposed. Assume that the exact scientific
estimates of the consequences of the programs
are as follows.
•
– If Program A is adopted, 200 people will be saved.
– If Program B is adopted, there is a 1 / 3 probability
that 600 people will be saved, and a 2 / 3 probability
that no people will be saved.
• Which program would you choose?
• Imagine that the US is preparing for the
outbreak of an unusual Asian disease that is
expected to kill 600 people. Two alternatives
programs to combat the disease have been
proposed. Assume that the exact scientific
estimates of the consequences of the programs
are as follows.
•
– If Program C is adopted, 400 people will die.
– If Program D is adopted, there is a 1 / 3 probability
that nobody will die, and a 2 / 3 probability that 600
people will die.
• Which program would you choose?
Fig. 12-15, p. 470
• Risk-aversion strategy
– The idea of saving 200 lives with certainty is
more attractive than the risk that no one will
be saved.
• Risk-taking strategy
– The idea of losing 400 lives with certainty is
less attractive than the risk that a 2 in 3
chance that 600 people will die.
• When a problem is framed in terms of
gain, we tend to choose sure things (riskaversion strategy).
• When a problem is framed in terms of
loss, we tend to choose risky things (risktaking strategy)
• If you are lucky, you have a chance to win
$1000. Which game do you choose?
– Game A. a sure gain of $240
– Game B. 25% chance to gain $1000 and 75%
chance to gain nothing
– Game A  84%
– Game B  16%
• You are given $1000, provided that you
will play either one of the following games.
Which game do you choose?
– Game C. a sure loss of $750
– Game D. 75% chance to lose $1000 and 25%
chance to lose nothing.
– Game C.  13%
– Game D.  87%
• Imagine that the US is preparing for the
outbreak of an unusual Asian disease that is
expected to kill 600 people. Two alternatives
programs to combat the disease have been
proposed. Assume that the exact scientific
estimates of the consequences of the programs
are as follows.
•
– If Program A is adopted, 200 people will be saved.
– If Program B is adopted, there is a 1 / 3 probability
that 600 people will be saved, and a 2 / 3 probability
that no people will be saved.
• an unusual Asian disease that is expected to kill
600 people.
– Program A  200 people will be saved
– Program B  1 / 3 probability  600 saved
And 2 / 3 probability  no people will be saved.
200 saved
400 die
400 die
• an unusual Asian disease that is expected to kill
600 people.
– Program C  400 people will die.
– Program D  1 / 3 probability nobody will die,
and 2 / 3 probability  600 people will die.
200 saved
400 die
200 saved
400 die
200 saved
400 die
200 saved
400 die
200 saved
• If you are lucky, you have a chance to win
$1000. Which game do you choose?
– Game A. a sure gain of $240
• Expected Utility = $240
– Game B. 25% chance to gain $1000 and 75%
chance to gain nothing
• Expected Utility = $1000 * 0.25 + $0 * 0.75 = $250
– Game A  84%
– Game B  16%
• You are given $1000, provided that you will play
either one of the following games. Which game
do you choose?
– Game C. a sure loss of $750
• Expected Utility = $1000- $750 = $250
– Game D. 75% chance to lose $1000 and 25% chance to
lose nothing.
• Expected Utility = $1000 - $1000 * 0.75 - $0 * 0.25 = $250
– Game C.  13%
– Game D.  87%