Lecture 9 (End of Chapter Questions)

Question 1

How can you elicit probabilities?

Answer 1

1. Direct Method: directly ask for data points on the cumulative distribution function (CDF)
2. Indirect Method: Derive probabilities by asking the expert to compare the event of interest to a lottery

Question 2

Explain Availability Bias with an example.

Answer 2

• The easier (or faster) we remember an example of an event, the more likely we believe it is. (e.g. ..ing word vs ..n.. word)
• This heuristic is often suitable but sometimes the availability of an event has nothing to do with its probability
• Example: Which is a more common cause of death, homicide or diabetes -> most people say homicide because it is more often reported in newspapers

Question 3

Explain Hindsight Bias with an example.

Answer 3

• When some event occurred, people think in retrospect that they have evaluated the event to be more likely (ex-ante) than they really did.
• Particularly strong when small probabilities are involved
• Example: Respondents were asked to assign probabilities to scenario regarding Nixon’s diplomatic initiatives before he visited Russia and China in 1972. After the visit the respondents recalled their own predictions and assigned higher (lower) probabilities if the event occurred (did not occur)

Question 4

Explain Representativeness Heuristic with an example.

Answer 4

• The probability that some object belongs to some category is evaluated to be higher if the object looks representative for the category.
• Representativeness heuristic is used as rule of thumb but it can lead to several cognitive biases (e.g. base rate neglect, reversing conditional probabilities, conjunction fallacy)
• Example: Likelihood of the sequence H,T,T,G,HT,H is estimated to be more likely than T,T,T,T,T,H (coin)

Question 5

Explain Anchoring and adjustment with an example.

Answer 5

Making evaluations often turns out to be a two-step-procedure:

1. Anchoring: A near-at-hand first guess serves as a anchor point
2. Adjustment: further thinking causes adjustment of the original guess

Bias: Adjustment is often insufficient and the anchor exerts too much influence, which causes the estimates to stay too close to the anchor.

Example: 1 x 2 x 3 x 4 x 5 x 6 x 7 vs. 7 x 6 x 5 x 4 x 3 x 2 x 1 -> median is a lot higher for second one -> people compute a few steps and then estimate

Question 6

Explain Overconfidence Bias with an example.

Answer 6

• The tendency of a person to place too much confidence in his own abilities, knowledge or the quality of his forecast.
• When people take a guess about something that they are not certain of they tend to overestimate the probability they are correct (the stronger the more complicated the task)
• Example: Give a answer and say how confident you are in percent -> number is way to high