B3T3101 Behavioral Management Science
Lecture 1: Heuristics and biases
Video 1: System 1 and system 2
A rational decision
1. Define the problem
2. Identify all decision criteria
3. Allocate weights to the criteria
4. Identify all alternatives
5. Evaluate the alternatives
6. Choose the best alternative
Bounded rationality: resources (time, cognitive, etc.) are limited. We often don’t optimize, but “satisfice” (e.g.,
consider only few alternatives).
Predictable irrationality: we are not just often “wrong”. We are systematically wrong. That means we can study how we
decide.
System 1 and 2
- System 1: automatic, fast, not cognitively demanding -> intuitive answer
Heuristics: mental shortcuts to satisfactory solutions
o Efficient! No resources required and they work most of the time
Biases: systematic deviations from rationality
o Systematic deviations from rationally (systematic = predictable. Most people who don’t say 5 cents tend
to say 10 cents)
- System 2: controlled, slow, cognitively demanding -> rational answer
Two notes of caution
- What is rational is a debated question
- Not all biases are due to heuristics/ system 1
Video 2: Heuristics in probability estimation
Probability estimation
Representativeness heuristic:
- “The more X resembles Y, the more likely X is to be Y”
1. How confident are you in the resemblance?
Stereotypes
o Accurate but misleading
o Accurate stereotypes are still stereotypes
Information is often not enough
2. How likely is Y in the first place?
Representativeness ignores base rates
o Conjunction fallacy
o False positives
Video 3: Heuristics in probability
estimation: availability
, Availability heuristic
- “The more examples of X come to mind, the more likely X is”
- What makes examples easier to recall:
Familiarity
Recency
Salience
Many others…
Video 4: more bias
Anchoring and (insufficient) adjustment
Not only system 1….
Choice context effects
- Attraction effect
- Compromise effect
, - All effects together:
- Many more biases: foto?
Lecture 2: Decisions under risk
Video 1: risky decisions
Expected value (EV): the sum of all possible values each multiplied by the probability of its occurrence
Video 2: expected utility
Risk aversion
- Certainty equivalent: the certain amount that people find equivalent to the risky option
- Risk aversion: the CE is lower than the EV of the risky option
Example: a dace game that pays based on the roll? Better to get $3,50 for sure (example 1). Getting
$100 with 50% probability? Better to get $50 for sure (example 2)
- People don’t like taking risk: given options with similar EV, they would choose the less risky one. EV is not a
good description of people’s preferences.
Expected utility
Lecture 1: Heuristics and biases
Video 1: System 1 and system 2
A rational decision
1. Define the problem
2. Identify all decision criteria
3. Allocate weights to the criteria
4. Identify all alternatives
5. Evaluate the alternatives
6. Choose the best alternative
Bounded rationality: resources (time, cognitive, etc.) are limited. We often don’t optimize, but “satisfice” (e.g.,
consider only few alternatives).
Predictable irrationality: we are not just often “wrong”. We are systematically wrong. That means we can study how we
decide.
System 1 and 2
- System 1: automatic, fast, not cognitively demanding -> intuitive answer
Heuristics: mental shortcuts to satisfactory solutions
o Efficient! No resources required and they work most of the time
Biases: systematic deviations from rationality
o Systematic deviations from rationally (systematic = predictable. Most people who don’t say 5 cents tend
to say 10 cents)
- System 2: controlled, slow, cognitively demanding -> rational answer
Two notes of caution
- What is rational is a debated question
- Not all biases are due to heuristics/ system 1
Video 2: Heuristics in probability estimation
Probability estimation
Representativeness heuristic:
- “The more X resembles Y, the more likely X is to be Y”
1. How confident are you in the resemblance?
Stereotypes
o Accurate but misleading
o Accurate stereotypes are still stereotypes
Information is often not enough
2. How likely is Y in the first place?
Representativeness ignores base rates
o Conjunction fallacy
o False positives
Video 3: Heuristics in probability
estimation: availability
, Availability heuristic
- “The more examples of X come to mind, the more likely X is”
- What makes examples easier to recall:
Familiarity
Recency
Salience
Many others…
Video 4: more bias
Anchoring and (insufficient) adjustment
Not only system 1….
Choice context effects
- Attraction effect
- Compromise effect
, - All effects together:
- Many more biases: foto?
Lecture 2: Decisions under risk
Video 1: risky decisions
Expected value (EV): the sum of all possible values each multiplied by the probability of its occurrence
Video 2: expected utility
Risk aversion
- Certainty equivalent: the certain amount that people find equivalent to the risky option
- Risk aversion: the CE is lower than the EV of the risky option
Example: a dace game that pays based on the roll? Better to get $3,50 for sure (example 1). Getting
$100 with 50% probability? Better to get $50 for sure (example 2)
- People don’t like taking risk: given options with similar EV, they would choose the less risky one. EV is not a
good description of people’s preferences.
Expected utility
