I am on p.56, Vol 2 of curriculum 2014.
From my understanding, representative bias means that people tend to overweigh new information because they think that the new information is representative of the whole population. Sample-size neglect seems consistent with this definition. However, I don’t understand why base-rate neglect (the base rate or probability of the categorization is not adequately considered) is a type of representative bias.
CFAI uses Company ABC as an example of base-rate neglect. But in that example, what is the ‘new’ information? It seems that this example is more consistent with the definition in Wikipedia:http://en.wikipedia.org/wiki/Base_rate_fallacy
Thanks for your help!
Representativeness bias is taking new information and putting into “buckets” because the information is similar or “representative” to what you have experienced in the past
Base-Rate Neglect is not adequately considering whether the logic for putting new information into “buckets” is correct. In the ABC case the FMP has “bucketed” Company ABC into a growth stock without adequately considering if it truly is a growth stock. The “new” information is simply the characteristics of Company ABC.
Sample-Size Neglect is taking a small sample size of new information and assuming that this is representative of the population as a whole.
… Or at least that’s my understanding.
Thank you Marathon_runner! I think your explanation of representative bias is much better than that in the curriculum (Perhaps because I am not a native speaker of English?).
Can you also help me understand base-rate neglect in mathematical terms? According to http://en.wikipedia.org/wiki/Base_rate_fallacy, base-rate neglect is wrongly equating P(A|B) = P(B|A) and neglecting P(A)/P(B) in the Bayes formula. Applying to Company ABC example, FMPs neglect P(growth) and are trying to estimate P(success|growth). Does that mean FMPs wrongly think that P(success|growth) = P(growth|sucess)?
I think it would go like this. The FMP would think like this:
P(ABC actually is growth stock | ABC looks like a growth stock) = 100% (if it looks like a growth stock then it IS a growth stock)… when in fact by Bayes:
P(ABC actually is growth stock | ABC looks like a growth stock) =
P(ABC looks like a growth stock | ABC actually is growth stock) x P(ABC actually is growth stock)/(ABC looks like a growth stock)
= 100% x (something less than 100%) / (100%) = something less than 100%
… but I could be wrong – I’m not an expert.
Thanks a lot! That makes more sense to me!
If applying Bayesian lanauge to Question 2, Example 2, p.57,
Verte is evaluating P(recover | -ve news).
P(recover | -ve news)
= P(-ve news | recover) P(recover) / P(-ve news)
= P(-ve news | recover) P(recover) (since P(-ve news) = 1 )
P(recover) is high but P(-ve news | recover) is low. And Verte neglects the base rate P(recover) and so wrongly concludes that P(recover | -ve news) is low.
Am I correct?
you should read Daniel Kahnemen’s book and paper. Sheds a lot of light on these topics
Is it worth? It seems CFAI does not recommend doing this. Can you share your experience?