CAT 2018 Question Paper | Verbal Slot 2

CAT Questions

This is a question based on general understanding of the passage. These types of questions could be both an easy-win and at the same time, time-consuming. But this question is somewhere between both that appeared in the CAT 2018 Slot 2 Meritocracy passage. Understanding the detail from the paragraph is uber-crucial to nail these types of questions in your CAT Exam. Keep practicing questions like these and see your Online CAT Preparation reach heights.

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The complexity of modern problems often precludes any one person from fully understanding them. Factors contributing to rising obesity levels, for example, include transportation systems and infrastructure, media, convenience foods, changing social norms, human biology and psychological factors. The multidimensional or layered character of complex problems also undermines the principle of meritocracy: the idea that the ‘best person’ should be hired. There is no best person. When putting together an oncological research team, a biotech company such as Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire people whose resumes score highest according to some performance criteria. Instead, they would seek diversity. They would build a team of people who bring diverse knowledge bases, tools and analytic skills.

Believers in a meritocracy might grant that teams ought to be diverse but then argue that meritocratic principles should apply within each category. Thus the team should consist of the ‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool. That position suffers from a similar flaw.

Even with a knowledge domain, no test or criteria applied to individuals will produce the best team. Each of these domains possesses such depth and breadth, that no test can exist. Consider the field of neuroscience. Upwards of 50,000 papers were published last year covering various techniques, domains of enquiry and levels of analysis, ranging from molecules and synapses up through networks of neurons. Given that complexity, any attempt to rank a collection of neuroscientists from best to worst, as if they were competitors in the 50-metre butterfly, must fail. What could be true is that given a specific task and the composition of a particular team, one scientist would be more likely to contribute than another. Optimal hiring depends on context. Optimal teams will be diverse.

Evidence for this claim can be seen in the way that papers and patents that combine diverse ideas tend to rank as high-impact. It can also be found in the structure of the so-called random decision forest, a state-of-the-art machine-learning algorithm.

Random forests consist of ensembles of decision trees. If classifying pictures, each tree makes a vote: is that a picture of a fox or a dog? A weighted majority rules. Random forests can serve many ends. They can identify bank fraud and diseases, recommend ceiling fans and predict online dating behaviour. When building a forest, you do not select the best trees as they tend to make similar classifications. You want diversity. Programmers achieve that diversity by training each tree on different data, a technique known as bagging. They also boost the forest ‘cognitively’ by training trees on the hardest cases – those that the current forest gets wrong. This ensures even more diversity and accurate forests."

Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and even preschools test, score and hire the ‘best’. This all but guarantees not creating the best team. Ranking people by common criteria produces homogeneity. That’s not likely to lead to breakthroughs.

Question 13 : On the basis of the passage, which of the following teams is likely to be most effective in solving the problem of rising obesity levels?

1. A team comprised of nutritionists, psychologists, urban planners and media personnel, who have each scored a distinction in their respective subject tests.
2. A team comprised of nutritionists, psychologists, urban planners and media personnel, who have each performed well in their respective subject tests.
3. A specialised team of nutritionists from various countries, who are also trained in the machine-learning algorithm of random decision forest.
4. A specialised team of top nutritionists from various countries, who also possess some knowledge of psychology.

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Video Explanation

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Explanatory Answer

The author argues in the first paragraph that a problem like rising obesity levels can be best tackled by ‘a team of people who bring diverse knowledge bases, tools and analytic skills’. So options 3 and 4, which talk of ‘a specialised team of nutritionists’, are ruled out. Option 1 talks of a diverse team in which each member has ‘scored a distinction in their respective subject tests’. The author argues against this in paragraph 2 : ‘Even with a knowledge domain, no test or criteria applied to individuals will produce the best team.’

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The question is "On the basis of the passage, which of the following teams is likely to be most effective in solving the problem of rising obesity levels?"

Hence, the answer is A team comprised of nutritionists, psychologists, urban planners and media personnel, who have each performed well in their respective subject tests.

Choice B is the correct answer.

 

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