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DBIASiNG! 
Presented by: 
Amir MohammadShirazi 
Hamid Darzi 
Mehran Azimi 
1
 Introduction(Rationality & Debiasing) 
 Nature of Biases 
 Motivational Strategies 
 Cognitive Strategies 
 Technological Strategies 
 Adoption & Diffusion 
 Future of Debiasing 
2
Debiasing 
Strategies 
Bridge The Gap 
Descriptive 
Behavior 
Normative Ideal 
3
1.People posses normative strategies 
2.Intuitive and normative strategies 
4
1.By Status Quo and Environment 
2.Increasing Decision Making Skills 
Robustness 
1. Meliorists 
2. Apologists 
5
1.Recognition 
2.Correction 
1. Feed back problem & Source of Error 
2. Uncertainty of Environment(Self Serving attributions 
of ability) 
3. Compensation of Cultural Mechanism 
6
• Specific Bias 
• Source of Bias 
7
8
Psychophysical based errors 
Shifting reference 
point 
Inconsistent judge 
9
Association based errors 
Activation of associated cognitions 
Inhibition of unassociated cognitions 
Narrow band information 
10
Strategy based errors 
intuitive repertoire 
Benefit-Cost 
calculation 
11
12
13
 incentive 
 accountability 
14
15
16
17
Greater effort 
Accuracy 
Better performance 
Preventing biases 
Need to look consistent 
18
Potential Problems 
Give the people 
what they wants 
Justification-base 
Decision Bias 
19
 Consider the opposite 
Training in rules 
Training in representation 
Training in biases 
20
ASK: 
What are some reasons that my initial judgment might be wrong? 
What about simply listing reasons? 
Overconfidence 
Anchoring 
Hindsight 
21
How inferior strategies can be 
replaced by the better one? 
Experience 
Training 
22
The rules can be thought in the 
form of mathematics and logic 
23
The rules can be applied in everyday 
life decisions 
24
People reason better about 
“frequencies” than about “probabilities” 
Frequencies Probabilities 
25
• Information as “Probabilities” 
 %0.8 of women have breast cancer 
 The probability of “positive result” for 
those who have cancer is %90 
The probability of “positive result” for 
those who don’t have cancer is %7 
26
• Information as “Frequencies” 
‘Eight’ out of every 1000 women have 
breast cancer 
Of this ‘eight’ women ‘seven’ will have 
a positive result 
Of the ‘992’ women who don’t have 
cancer ‘70’ will still have a positive 
result 
27
Is “Awareness” 
Enough? 
28
29 
TECHNOLOGICAL 
STRATEGIES
Disadvantages 
 Increase sample size of experience 
Synergy 
Error-checking system 
30 
 Social cost of rejection 
 Free riding 
Anchoring 
Benefits
31 
LINEAR MODELS 
& STATISTICAL TECHNIQUES 
Experts can be replaced by models based on their judgments 
Theses models can make better prediction than experts . 
U(A) = w1 X1 + w2 X2 + w3 X3 
Proper & Improper 
Linear Models
32 
MUA (Multiattribute Utility) Analysis 
Decision Analysis 
Decision support system (DSS)
The main question is: 
How to encourage 
people to adopt 
better decision 
strategies? 
33
People resist being debiased 
Me?! 
Biased?! 
IMPOSSIBLE!!! 
34
People resist being Debiased 
“Doing it Wrong” for all these years?! 
Relinquish control over a decision process 
Techniques are alien and complex and the 
benefits are noisy, delayed or small 
35
Promote Adoption 
Compliance to 
behavioral norm 
Compliance to 
behavioral norm 
InternValisza.tion of 
beliefs 
Internalization 
of beliefs 
36
Management Tools 
TQM Six Sigma 
37
Defect? 
Where is it? 
38
• Senior management do not practice 
endorsement 
• Doubtfulness of outside consultants 
• Separation from coworkers 
39
H“Leoarwn it ,c Teaanch iitt, U bsee it, fInisxpeecdt it?” 
40
Simple 
Domain-Specific 
Social 
Top-Down 
Complex 
Domain-General 
Indivisual 
Bottom-Uop 
Vs. 
41
The Most Successful 
Simple 
Domain-Specific 
Social 
Top-Down 
Complex 
Domain-General 
Individual 
Bottom-Up 
42
Informal Debiasing techniques 
43
44 
The higher you are, the 
harder you fall !f
45

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