Final

Question 1

Narrative fallacy

Answer 1

addresses our limited ability to look at sequences of facts without weaving an explanation into them, or, equivalently forcing a logical link, an arrow of relationship upon them

Question 2

Representativeness:

Answer 2

focusing on similarity to stereotypes

System 1 is a machine for pattern matching and looking for similarity

Question 3

streaky v switchy example

Answer 3

• 50/50 Chance: the event is a fair independent coin flip
• Streaky: the event tends to have positive correlation over time. So a “hit” (outcome happens) is more likely if it happened that last round and less likely if it didn’t
• Switchy: the event tends to have negative correlation over time. So a “hit” (outcome happens) is less likely if it happened the last round and more likely if it didn’t

Question 4

How does the “chance” vs. “streaky” vs. “switchy” experiment we conducted in class relate to the idea of the representativeness heuristic?

Answer 4

• Switch seems more representative of randomness more so than actual 50/50 chance (we are really bad at faking randomness)
• Relates to representativeness because when say it is supposed to be 50/50 chance we assume it is following a pattern of switchy or streaky. If it is heads we think is should be tails to fit that 50/50 when it shouldn’t always work that way unless its switchy

Question 5

Over-belief in the hot hand

Answer 5

• In environments where we are not sure of the underlying random process
• We try to understand the process (infer ability) from what we see
• Underappreciated how easily “Average” processes (ability) can randomly generate strings of success or failure
• So over-infer (too much confidence) that we must be looking at an extreme process when we see extreme event

Question 6

Gambler’s fallacy

Answer 6

• In environments where we are sure of the underlying random process:
• We expect to see sequences that make sense to us
• Surprised when we see “extreme” sequences
• May believe that the sequence will “correct itself” to look representative

Question 7

Law of small numbers

Answer 7

• many people erroneously exaggerate the degree to which a small sample should resemble the population from which it is drawn
• We wrongly expect the law of large numbers to hold in small samples
• We wrongly expect many statistics other than the average that are true for a random process to hold in smaller samples

Question 8

Can you explain why the “law of small numbers” relates to the concept of represenativeness heuristic?

Answer 8

-Having multiple children example
BBBBG - people think having 4 boys in a row is unlikely when really there is a 50% chance each time
-However over the course of having more and more children the B/G ratio should even out
-The thinking is no matter the sample size, the outcome should be representative

Question 9

What is the evidence about performance persistence for actively traded mutual funds over time? What does that suggest about the relative importance of skill vs luck in determining relative returns across these funds? What (if anything) does this say fundamentally about whether picking stocks is a skill activity?

Answer 9

• All I really remember about this is mutual funds aren’t that great and I think the S&P almost always has a higher return than mutual funds
• If there is a good mutual fund - maybe some luck but also mostly survivorship bias

Question 10

Survivorship bias

Answer 10

• the logical error of concentrating on the people or things that made it past some selection process and overlooking those that did not, typically because of their lack of visibility.
• Put simply: only focusing on survivors, not everyone
• This can lead to false conclusions in several different ways.

Question 11

Reversion to the mean

Answer 11

• Tendency to move to the average overtime
• Success = skill/effort/ability + luck
• Skill is typically persistent
• But in many situations luck (randomness) is temporary
• Great success (or failure) usually followed by a “reversion the the mean”
• But people think if they had some success they will have a failure next instead of thinking it will be the reversion to the mean

Question 12

Base rate neglect

Answer 12

comes from ignoring the actual stats so in availability bias, when people think more sharks kill people than work accidents - they are neglecting the base rate that really more work accidents happen but it’s just not talked about as much

Question 13

why is base rate neglect important?

Answer 13

Important so people can make rational decisions instead of them being based off skewed stats/thinking

Question 14

bayes’ rule

Answer 14

true/(true + false

Question 15

steps for calculating probabilities using bayes’ rule

Answer 15

• Calculate expected rate of true positives
• Calculate expected rate of false positives
• Take ration of true/(true + false)

Question 16

System neglect

Answer 16

• another way to think about representativeness heuristic
• We pay primary attention to signals we see and not enough attention to what we could know about environment (base-rates, sample size, underlying variation). Notice signals; neglect system that created
• This means that we often make too much of observations we see in environments that are overall pretty stable but have a lot of noise
• We also undervalue info we get in environments that do not have a lot of noise but where there is a reasonable chance that the environment could change

Question 17

Reference Class Forecasting

Answer 17

• Identify an appropriate reference class
• Obtain statistics for the reference class
• Adjust cautiously away from those reference statistics by comparing how your specific situation compares to those situations

Question 18

Confirmation bias

Answer 18

we tend to look for (and prefer) info that confirms our beliefs

Question 19

The importance of confirmation bias

Answer 19

• The positive test strategy is our common instinct
• Start with a conjecture (ex: this is a good investment to make)
• Look for evidence consistent with that conjecture
• Stop and conclude depending on how easily that evidence is collected

Question 20

The problem with overconfident CEOs

Answer 20

• Higher tendency to take on costly mergers
  
  • Especially true if they have access to internal financing
  • Interestingly, markets seem to spot this overconfidence
  • Mergers announced by companies with overconfident CEO (per malmendier-tate measure) are met with more negative market reaction
• More likely to have earning misstatements
  
  • Overestimate future earnings
  • So more likely to borrow aggressively from future earnings using “earnings management” techniques to avoid missing current forecasts
  • Generally practice less-conservative accounting principles

Question 21

Availability bias

Answer 21

past success makes it harder to envision chnce of failure

Question 22

Narrative fallacy

Answer 22

once we have a compelling story, we quickly come to see it as truth and rarely challenge that narrative

Question 23

Confirmation bias`

Answer 23

we tend to look for (and prefer) info that confirms our beliefs

Question 24

Anchoring bias

Answer 24

once we have an idea in our head, it is difficult to think about alternatives (failure of our imaginations)

Question 25

Inside view

Answer 25

specific circumstances, own experience

Question 26

Outside view

Answer 26

reference similar cases, baseline, base-rates

Question 27

Planning fallacy

Answer 27

• Forecasts are often unrealistically close to best-case scenarios
• Stems from inside view, improved by outside view

Question 28

Irrational persistence (sunk-cost fallacy)

Answer 28

• Difficult to abandon a project once begun - effort feels “lost”
• Leads to escalation of commitment
• Heightens importance of realistic projections at the outset

Question 29

General approaches to take the “outside view”

Answer 29

• Adopting a skeptical mindset
• Asking probing questions about assumptions, point of view, sources
• Developing a culture of curiosity and acceptance of new view points
• Engaging system 2 - uncovering what system 1 influence is

Question 30

The premortem technique

Answer 30

A managerial strategy in which a project team imagines that a project or organization has failed, and then works backward to determine what potentially could lead to the failure of the project or organization.

Question 31

How do we address these problems of the inside view?

Answer 31

• take the outside view
• Reference class forecasting
• The premortem technique