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26 Cards in this Set

  • Front
  • Back
System 1:
Fast, automatic, effortless, implicit,
emotional and common.
System 2:
Slow, conscious, effortful, logical and
less common.
Can System 1 and System 2 Thinking operate at the same time?
Both can operate at the same time and be in conflict with each other
Normative Decision Making
Perfect (ideal) decision making is referred to as “normative” decision
We evaluate all of the information to correctly derive the best solution.
Rational Decision Making: 6 Step Process
1. Accurately define the problem
2. Identify the criteria (the qualities needed)
3. Weight the criteria in importance
4. Generate alternatives (list of people)
5. Rate each alternative (applicant) on each criterion
6. Compute an optimal decision
Bounded Rationality
Decisions that are influenced by factors not directly tied to consequences are said to be bounded.
Most of the time our judgments are bounded or limited to some extent.
Why Bounded Rationality?
We lack or ignore important information.
We operate under time and/or cost constraints
We have a limited memory system (STM)
We have difficulty knowing what the optimal choice is
It is easier to settle for an acceptable (satisficing) solution rather than the “best” solution.
Decision Making Models: Perscriptive Models
The goal is to give us the best methods for making optimal decisions.
Develop mathematical models (actuarial methods).
Decision Making Models: Descriptive Models
The goal is to identify our mistakes and help us understand them.
A heuristic is a simplified strategy for solving
They usually give us a “good”
solution, but not always the best.
Heuristics are automatic.
Descriptive model decision making research has identified a number of heuristics.
Availability Heuristic
We assess the probability of an event by the degree to which the event is available in memory.
Emotional and vivid events are more available.
Example: What is safer traveling by air
or by car?
Biases Emanating from Availability Heuristic
Ease of recall
We judge events that are easy to recall because they are more vivid or recent to be more numerous.
Example: Performance appraisal are often weighted towards the most recent
Biases Emanating from Availability Heuristic
Are there more words that end in ing than words that have n as the 7th letter?
Biased on how our memory structures are organized.
Biases Emanating from Availability Heuristic
Is marijuana use linked to delinquency?
Presumed Associations
We tend to over estimate the
probability of two events co-occurring based on the number of similar associations we can easily recall.
Representative Heuristic
We tend to look for traits an individual may have that correspond with previously formed stereotypes.
Example: Gender and racial
stereotypes in hiring and promotion
Biases Emanating from Representativeness Heuristic
Mark is finishing his MBA at a prestigious university. He is very interested in the arts.
Where is he more likely to take a job?
– A. In arts management
B. With a consulting firm
Insensitivity to base rates.
When assessing the likelihood of events, we tend to ignore base rates if any other
information is provided.
Biases Emanating from Representativeness Heuristic
A large hospital has 45 babies born each day a smaller hospital has 15 babies born each day. In a one year period, which will have more days in which 60% of the babies born
were boys?
Insensitivity to Sample Size
We frequently fail to appreciate the role of
sample size.
Larger samples are more likely to come closer to the average than small samples.
Biases Emanating from Representativeness Heuristic
Which sequence is more random?
Misconceptions of Chance
We expect a sequence of random
events to look random even when the sequence is too short.
Gambler’s fallacy: After some bad luck, I’m due.
Biases Emanating from Representativeness Heuristic
What are the odds the Bengals will make the playoffs next year?
Regression to the Mean
We ignore the fact that extreme events are likely to regress to the mean on
subsequent trials.
Biases Emanating from Representativeness Heuristic
Linda is 31, single, outspoken, and very smart. She majored in philosophy and is deeply concerned with issues of discrimination. Linda is..
– A. a bank teller
– B. a bank teller and active in a feminist movement.
The Conjunction Fallacy
We falsely judge that two or more events that co-occur are more probable than a more
global set of of occurrences.
Affect Heuristic
Emotional influences that are automatic and maybe out of our awareness.
More likely to be used when people are busy or under time constraints.
Example: I don’t like her for some reason (she looks like my first wife).
Other Biases
We make estimates for values based upon an initial value and make insufficient
adjustments from that anchor.
We also access information that is consistent with the anchor.
The candidates resume was outstanding, his
interview was only average, but he is still my
best candidate.
Other Biases
Which is more likely to occur?
– A. Drawing a red marble from a bag with 50% red
and 50% white marbles..,,
– B. Drawing a red marble seven times in a row
from a bag with 90% red and 10% white marbles…,
– C. Drawing at least one red marble in seven tries from a bag with 10% red and 90% white
Conjunctive and Disjunctive Event Bias
We exhibit a bias towards overestimating the
probability of conjunctive events and under estimate the probability of disjunctive events.
Other Biases
Over Confidence
We tend to be overly confident of the infallibility of our judgments when
answering moderate to difficult questions.
Other Biases
The Confirmation Trap
We tend to seek confirmatory
information for what we think is true and fail to search for nonconfirmatory
Example: WMD’s in Iraq?
Other Biases
Hindsight and Curse of Knowledge
After finding out whether or not an event occurred, we tend to overestimate the degree to which we
would have predicted the correct outcome.