Mdm Finish Ba Film

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


Common Biases

435,000 Tobacco 400,000 Poor diet and physical inactivity 43000 Motor vehicle accidents 29000 Fire arms (guns) 17000 Drugs


Common Biases



Common Biases



Common Biases

Other examples:


Common Biases

Other examples:


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


Insensitivity to base rates Insensitivity to sample size

Representativeness

Common Biases

Larger hospital


Smaller hospital


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


Common Biases

1. H-T-H-T-T-H

2. H-H-H-T-T-T

3. H-H-H-H-T-H


Common Biases

Misconceptions of chance:

People expect a sequence of random events to “look” random!


Common Biases

Other examples:


Common Biases

Other examples:


Common Biases

Other examples:


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


Common Biases

Regression to mean

Regression to the mean:

extreme events tend to regret to the mean on subsequent trials


Common Biases

Regression to mean Conjunction fallacy

Answer the question about Miss. Linda!!!


Common Biases


A


Common Biases


A B


Common Biases


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


Common Biases




Common Biases




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%


Common Biases

Your chance to get a ticket : 30% 25% 15% 20% 25%What is your chance of getting on one flight?


Common Biases


1 - .7 x .75 x .85 x .8 x .75 = ?


Common Biases


1 - .7 x .75 x .85 x .8 x .75 = ?


Common Biases


1 - .7 x .75 x .85 x .8 x .75 = ?


Common Biases


1 - .7 x .75 x .85 x .8 x .75 = ?


Common Biases


1 - .7 x .75 x .85 x .8 x .75 = ?


Common Biases


1 - .7 x .75 x .85 x .8 x .75 = ?


Common Biases

73%


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:


Common Biases

Overconfidence: Individuals tend to be overconfident of the infallibility of their judgments when answering moderately to extremely difficult questions.


Common Biases



Common Biases



Common Biases

The confirmation trap: Yes person devil’s advocate

Confirmation trap


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


Common Biases

Example :

hirePositive informationNegative information

Confirmation trap


Common Biases

Example :

hire

Negative information

Confirmation trap


Common Biases

Hindsight

People are not very good at recalling the way anuncertain situation appeared to them!

Confirmation trap Hindsight


Common Biases

Hindsight

Knowledge of an event’s outcome becomes an anchor!



Common Biases

Hindsight

Knowledge of an event’s outcome becomes an anchor!



Common Biases

Hindsight

Individuals should be judged by the process and logic of their decisions ,



Common Biases

Hindsight

Individuals should be judged by the process and logic of their decisions , not on their results



Common Biases

Curse of knowledge

Confirmation trap Hindsight Curse of knowledge


Common Biases

Curse of knowledge

sending a message clear to us but ambiguous to other individual



Common Biases

Example:

“about the restaurant, it was marvelous, just marvelous”


Mdm Finish Ba Film

Documents

tobacco poor

extremely

conjunction

common biasesrepresentativene

sample size

base rates

common biasesavailability

confirmation