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Lecture 2 Summary B&M CH 3 Summary Tversky & Kahneman 1974 Lecture notes
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Behavioural FinanceWeek 2
Monday & Wednesday: B&M Ch 3 + Tversky & Kahneman 1974
Chapter 3:
Availability
Bias 1: Ease of Recall (Based on Vividness and Recency)
When something has happened recently or has been mentioned more often in the media,
people are more likely to think it is happening more often.
Bias 2: Retrievability (Based on Memory Structures)
The degree to which you can remember something well (nine letters, eight letters n vs. nine
letters, ending in -ing) determines choices. In addition, people rely too much on their social
network, which often means that the same kind of people are hired.
Representativeness
Bias 3: Insensitivity to Base Rates
People ignore base rates (percentages), which can have major consequences:
- Entrepreneurs are too concerned with the success of their business instead of the
percentage that fails when starting a business
- When admitting graduates, they often look at lenient grading. They assume that this figure
is achieved by high achievement, but this is based on an average
- People punish others for bad outcomes of behavior while this is purely a chance.
Bias 4: Insensitivity to Sample Size
People ignore the effect of sample size on percentages. In a smaller sample it is more likely
that 60% boys are born instead of 50% because this already happens with an extra boy. In a
large sample size this is less likely, even if it is the same percentage.
Bias 5: Misconceptions of Chance
When it comes to odds, people often think they see a pattern (after having 3 girls, small
chance that you will have another girl). However, these chances are still separated from
each other. This has also been associated with the 'gambler's fallacy' in which it is assumed
that there is a dependence between independent events. Also called the 'hot hand', in
which people that had a a successful outcome will be followed by a success.
Bias 6: Regression tot he Mean
People ignore the fact that everything will probably end up being average. Extreme events
will probably not be followed by another extreme event. If an employee does very well
once, over-ambitious plans may be made and too much is expected, while after that it will
likely return to average.
Bias 7: The Conjunction Fallacy
,The chance that two events occur together is smaller than these two events separately.
However, many people often see these conjunctions as more likely (Linda is a bank teller
and feminist is more probable than Linda is a bank teller? Not possible ..)
Confirmation
Bias 8: The Confirmation Trap
We will always quickly accept or agree with things that confirm our beliefs (look on the
internet for confirmations). For this purpose, a business opportunity was devised by Dick
Thaler: Two consulting firms, one 'yes person' (all ideas are great) and one 'devil's advocate'
(disapprove any plans). The latter will be more relevant to the customer, but in reality, many
will choose the 'yes person'.
Bias 9: Anchoring
When making choices, we pay attention to irrelevant 'anchors'. First of all, we will make
estimates based on an initial anchor (no sufficient adjustments away from anchor). Also,
people think of information that is consistent with the anchor rather than inconsistent.
Bias 10: Conjunctive and Disjunctive Event Bias
If multiple events all need to occur, we overestimate the true likelihood of this. However, if
only one of many events need to occur, we underestimate the likeliness of the event
Bias 11: Hindsight and the Curse of Knowledge
‘Hindsight bias’ – Not good at recalling or reconstructing the way an uncertain situation
appeared to us before finding out the results of the decision. Knowledge of an event’s
outcome works as an anchor.
‘Curse of knowledge’ – When assessing others’ knowledge, people are unable to ignore
knowledge that they have that others do not have (clear directions of your home, still lost,
you know where it is).
Bias 12: Overconfidence
These heuristics simplify decision making and more than one can operate at the same time.
Article 2: Judgment under Uncertainty: Heuristics and Biases (Tversky & Kahneman, 1974)
Representativeness (usually employed when people are asked to judge the probability that
an object or event A belongs to class or process)
- Insensitivity to prior probability of outcomes
- Insensitivity to sample size
- Misconceptions of chance
- Insensitivity to predictability
- The illusion of validity
- Misconceptions of regression
Availability (Often employed when people are asked to assess the frequency of a class or th
plausibility of a particular development)
- Biases due to the retrievability of instances
, - Biases due to effectiveness of a search set
- Biases of imaginability
- Illusory correlation
Adjustment and Anchoring (Usually employed in numerical prediction when a relevant value
is available)
- Insufficient adjustment
- Biases in the evaluation of conjunctive and disjunctive events
- Anchoring in the assessment of subjective probability distribution
Highly economical, usually effective but lead to systematic and predictable errors. Improve
judgments and decision in situations of uncertainties by understanding these heuristics.
Lecture 3 – Representativeness Biases:
Question – Tom W. is of high intelligence, although lacking in true creativity. He has a need
for order and clarity, and for neat and tidy systems in which every detail finds its
appropriate place. His writing is rather dull and mechanical, occasionally enlived by
somewhat corny puns and flashes of imagination of the sci-fi type. He has a strong drive for
competence. He seems to have little feel and little sympathy for other people and does not
enjoy interacting with others. Self-centered, he nonetheless has a deep moral sense.
The preceding personality sketch of Tom W. was written during Tom’s senior year in high
school by a psychologist, on the basis of projective tests. Tom W. is currently a graduate
student.
Rank the following nine areas in terms of the likelihood that Tom W. is now a graduate
student in each of these fields:
- Business Administration
- Computer Science
- Engineering
- Humanities and Education
- Law
- Library Science
- Medicine
- Physical and Life Science
- Social Science and Social Work
Representativeness Heuristic: Rule of thumb in which…
…the likelihood of a hypothesis is evaluated by considering the degree to which available
info is representative of the hypothesis
…the probability that X (Tom) belongs to set Y (engineers) is judged on the basis of how
similar X is to the stereotype of Y (alternative formulation).
You may also think of it as the Similarity Heuristic
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