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3.3.1 Representativeness Heuristics :
 
Have a look at following example give by Tversky and 
Kahneman: Linda is 31 yrs, single, outspoken, and very bright. She 
majored in philosophy in college. As a student, she was deeply 
concerned with discrimination and other social issues, and 
participated in anti-nuclear demonstrations. Which statement is 
more likely? 
a. Linda is a bank teller. 
b. Linda is a bank teller and active in the feminist movement. 
Most of you would answer ‘B’, that is ‘Linda is a bank teller 
and active in the feminist movement.’ In reality, ‘A’ is more likely 
than ‘B’. Because ‘B’ (bank tellers who are feminist activist) is a 
subset of set ‘A’ (bank tellers). We know that probability of subset is 
always less than (or at the most equal to) the probability of set.
Why did we make this error? It happened because we used 
something called as ‘representativeness heuristics’. When 
likelihood of an event is judged on the basis of the extent that it 
represents the essential features of the parent population or of its 
generating process is called as representativeness heuristics. 
When an individual is similar to a typical member of a given group, 
then he/she is judged to be more likely a member of that group. 
The heuristic is useful in inductive reasoning.
Representativeness heuristics is a strategy for making judgments 
and decisions on the basis of the extent to which current stimulus 
or event resembles to other stimuli or category. 
The use of this heuristic can systematically lead to make 
errors in judgements. One such example is ‘base rate fallacy’. In 
an experiment by Tversky and Kahneman, subjects were told that a 

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profile of Jack is picked up from 100 profiles in which 30 are 
engineers and 70 are lawyers. Jack is 30 yr old man. He is married 
and had no children. He is man of high ability and high motivation 
and promises to be quite successful in his field. He is liked by his 
colleagues. What is more likely occupation of Jack? Many 
responded Engineer. While doing so they ignored very important 
information regarding base rate. The base rate of engineers is 30% 
and so the probability of Jack being an engineer can not be more 
than .30. This is called as base rate fallacy. Subjects ignored base 
rates because they focused on representativeness. Hence, 
representativeness heuristics can also lead to errors.