Post on 26-Oct-2014
description
transcript
Common Biases
Mohammad Hossein Tajvarpour Mohammad Reza Mousavizadeh
In the name of god the compassionate the merciful
Introduction
• Heuristics (rules of thumb)
Introduction
• Heuristics
Reduce information processing
Save time
Introduction
• Heuristics (rules of thumb):
sometimes make systematically biased mistakes
Predictable mistakes!
• Which group (A or B) had the larger total sales revenues?
A: Reebok Int., Starbucks, RadioShack, Hilton Hotels, Hershey Foods
B: Cocono, American Int. Group ,Mckesson, AmerisourceBergen ,The Altria Group
Introduction
Availability
Common Biases
Common Biases
Availability
Representativeness
Common Biases
Availability
Representativeness
Beyond availability and representativeness
Common Biases
Availability: Retrievability
Presumed associations
Ease of recall
Availability
Common Biases
----ing ?
0 1-2 3-4 5-7 8-10 11-15 16+
Retrievability
Availability
Common Biases
-----n- ?
0 1-2 3-4 5-7 8-10 11-15 16+
Retrievability
Availability Retrievability
Common Biases
Retrievability:
Based on memory structures
Mermory structure affects the search process
Availability
Common Biases
Is marijuana use related to delinquency?
Retrievability Presumed associations
Availability Retrievability Presumed associations
Common Biases
Presumed associations:
Overestimate the probability of two co-occurring events
Availability Retrievability Presumed associations
Common Biases
Presumed associations:
Overestimate the probability of two co-occurring events
Based on the number of similar associations we can easily recall
Availability Retrievability Presumed associations Ease of recall
Common Biases
Ease of recall:
Based on vividness or recency
Events that are easily recalled from memory seem to be more frequent
Availability Retrievability Presumed associations Ease of recall
Common Biases
435,000 Tobacco 400,000 Poor diet and physical inactivity 43000 Motor vehicle accidents 29000 Fire arms (guns) 17000 Drugs
Availability Retrievability Presumed associations Ease of recall
Common Biases
435,000 Tobacco 400,000 Poor diet and physical inactivity 43000 Motor vehicle accidents 29000 Fire arms (guns) 17000 Drugs
Availability Retrievability Presumed associations Ease of recall
Common Biases
435,000 Tobacco 400,000 Poor diet and physical inactivity 43000 Motor vehicle accidents 29000 Fire arms (guns) 17000 Drugs
Availability Retrievability Presumed associations Ease of recall
Common Biases
Other examples:
Availability Retrievability Presumed associations Ease of recall
Common Biases
Other examples:
Availability Retrievability Presumed associations Ease of recall
Common Biases
Other examples:
Common Biases
Availability: Ease of recall
Retrievability
Presumed associations
Common Biases
Representativeness
Common Biases
Representativeness: Insensitivity to base rates
Common Biases
Representativeness: Insensitivity to base rates Insensitivity to sample size
Common Biases
Representativeness: Insensitivity to base rates Insensitivity to sample size Misconceptions of chance
Common Biases
Representativeness: Insensitivity to base rates Insensitivity to sample size Misconceptions of chance Regression to the mean
Common Biases
Representativeness: Insensitivity to base rates Insensitivity to sample size Misconceptions of chance Regression to the mean The conjunction fallacy
Representativeness Insensitivity to base rates
Common Biases
Mark is finishing his MBA. He is very interested in the arts and at one time considered a career as a musician. Where is he more likely to take a job?
a. in arts management b. with a consulting firm
Representativeness Insensitivity to base rates
Common Biases
Insensitivity to base rates:
Individuals tend to ignore base rates if any descriptive information is provided
Representativeness
Common Biases
Larger hospital
• 45 babies are born every year
Smaller hospital
• 15 babies are born every year
Insensitivity to base rates Insensitivity to sample size
Representativeness
Common Biases
Larger hospital
• 45 babies are born every year
Smaller hospital
• 15 babies are born every year
Insensitivity to base rates Insensitivity to sample size
Representativeness Insensitivity to base rates Insensitivity to sample size Chance misconception
Common Biases
1. H-T-H-T-T-H
2. H-H-H-T-T-T
3. H-H-H-H-T-H
Representativeness Insensitivity to base rates Insensitivity to sample size Chance misconception
Common Biases
1. H-T-H-T-T-H
2. H-H-H-T-T-T
3. H-H-H-H-T-H
Representativeness Insensitivity to base rates Insensitivity to sample size Chance misconception
Common Biases
Misconceptions of chance:
People expect a sequence of random events to “look” random!
Representativeness Insensitivity to base rates Insensitivity to sample size Chance misconception
Common Biases
Other examples:
Representativeness Insensitivity to base rates Insensitivity to sample size Chance misconception
Common Biases
Other examples:
Representativeness Insensitivity to base rates Insensitivity to sample size Chance misconception
Common Biases
Other examples:
Representativeness Insensitivity to base rates Insensitivity to sample size Chance misconception
Common Biases
Regression to mean
Store 2004• 1. 12,000,000• 2. 11,500,000• 3. 11,000,000• 4. 10,500,000• 5. 10,000,000• 6. 9,500,000• 7. 9,000,000• 8. 8,500,000• 9. 8,000,000TOTAL = 90,000,000
2006
?
• TOTAL = 99,000,000
Representativeness Insensitivity to base rates Insensitivity to sample size Chance misconception
Common Biases
Regression to mean
Regression to the mean:
extreme events tend to regret to the mean on subsequent trials
Representativeness Insensitivity to base rates Insensitivity to sample size Chance misconception
Common Biases
Regression to mean Conjunction fallacy
Answer the question about Miss. Linda!!!
Representativeness Insensitivity to base rates Insensitivity to sample size Chance misconception
Common Biases
Regression to mean Conjunction fallacy
A
Representativeness Insensitivity to base rates Insensitivity to sample size Chance misconception
Common Biases
Regression to mean Conjunction fallacy
A B
Representativeness Insensitivity to base rates Insensitivity to sample size Chance misconception
Common Biases
Regression to mean Conjunction fallacy
A B
Conjunction is a subset!
Common Biases
Representativeness: Insensitivity to base rates Insensitivity to sample size Misconceptions of chance Regression to the mean The conjunction fallacy
Common Biases
Beyond availability and representativeness
Common Biases
Beyond availability and representativeness:Anchoring
Common Biases
Beyond availability and representativeness:AnchoringConjunctive and disjunctive events
Common Biases
Beyond availability and representativeness:AnchoringConjunctive and disjunctive events Over confidence
Common Biases
Beyond availability and representativeness:AnchoringConjunctive and disjunctive events Over confidenceThe confirmation trap
Common Biases
Beyond availability and representativeness:AnchoringConjunctive and disjunctive events Over confidenceThe confirmation trapHindsight
Common Biases
Beyond availability and representativeness:AnchoringConjunctive and disjunctive events Over confidenceThe confirmation trapHindsightThe curse of knowledge
Beyond them! Anchoring
Common Biases
Anchoring:1.External anchoring
2.Insufficient adjustment
Beyond them! Anchoring
Common Biases
Anchoring:1.External anchoring
2.Insufficient adjustment
Beyond them! Anchoring
Common Biases
Anchoring:1.External anchoring
2.Insufficient adjustment
Beyond them! Anchoring
Common Biases
Examples : First impression Salary negotiations
Beyond them! Anchoring Conjunctive, disjunctive events
Common Biases
Your chance to get a ticket : 30% 25% 15% 20% 25%
Beyond them! Anchoring Conjunctive, disjunctive events
Common Biases
Your chance to get a ticket : 30% 25% 15% 20% 25%What is your chance of getting on one flight?
Beyond them! Anchoring Conjunctive, disjunctive events
Common Biases
Your chance to get a ticket : 30% 25% 15% 20% 25%What is your chance of getting on one flight?
1 - .7 x .75 x .85 x .8 x .75 = ?
Beyond them! Anchoring Conjunctive, disjunctive events
Common Biases
Your chance to get a ticket : 30% 25% 15% 20% 25%What is your chance of getting on one flight?
1 - .7 x .75 x .85 x .8 x .75 = ?
Beyond them! Anchoring Conjunctive, disjunctive events
Common Biases
Your chance to get a ticket : 30% 25% 15% 20% 25%What is your chance of getting on one flight?
1 - .7 x .75 x .85 x .8 x .75 = ?
Beyond them! Anchoring Conjunctive, disjunctive events
Common Biases
Your chance to get a ticket : 30% 25% 15% 20% 25%What is your chance of getting on one flight?
1 - .7 x .75 x .85 x .8 x .75 = ?
Beyond them! Anchoring Conjunctive, disjunctive events
Common Biases
Your chance to get a ticket : 30% 25% 15% 20% 25%What is your chance of getting on one flight?
1 - .7 x .75 x .85 x .8 x .75 = ?
Beyond them! Anchoring Conjunctive, disjunctive events
Common Biases
Your chance to get a ticket : 30% 25% 15% 20% 25%What is your chance of getting on one flight?
1 - .7 x .75 x .85 x .8 x .75 = ?
Beyond them! Anchoring Conjunctive, disjunctive events
Common Biases
73%
Beyond them! Anchoring Conjunctive, disjunctive events
Common Biases
Conjunctive and disjunctive events bias:
.Overestimating the probability of conjunctive events
.Underestimating the probability of disjunctive events
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Overconfidence:
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Overconfidence: Individuals tend to be overconfident of the infallibility of their judgments when answering moderately to extremely difficult questions.
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Overconfidence: Individuals tend to be overconfident of the infallibility of their judgments when answering moderately to extremely difficult questions.
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Overconfidence: Individuals tend to be overconfident of the infallibility of their judgments when answering moderately to extremely difficult questions.
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
The confirmation trap: Yes person devil’s advocate
Confirmation trap
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
The confirmation trap: Individuals tend to seek confirmatory
information for what they think is true and fail to search for disconfirmatory evidence
Confirmation trap
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Example :
hirePositive informationNegative information
Confirmation trap
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Example :
hire
Negative information
Confirmation trap
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Hindsight
People are not very good at recalling the way anuncertain situation appeared to them!
Confirmation trap Hindsight
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Hindsight
Knowledge of an event’s outcome becomes an anchor!
Confirmation trap Hindsight
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Hindsight
Knowledge of an event’s outcome becomes an anchor!
Confirmation trap Hindsight
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Hindsight
Individuals should be judged by the process and logic of their decisions ,
Confirmation trap Hindsight
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Hindsight
Individuals should be judged by the process and logic of their decisions , not on their results
Confirmation trap Hindsight
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Curse of knowledge
Confirmation trap Hindsight Curse of knowledge
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Curse of knowledge
sending a message clear to us but ambiguous to other individual
Confirmation trap Hindsight Curse of knowledge
Beyond them! Anchoring Conjunctive, disjunctive events Overconfidence
Common Biases
Example:
“about the restaurant, it was marvelous, just marvelous”
Confirmation trap Hindsight Curse of knowledge