CAT 2018 Question Paper | Verbal Slot 2

CAT Questions

This is a negative Critical Reasoning type question from the Meritocracy passage that appeared in CAT 2018 Question Paper Slot 2. This is a type of question you can definitely expect in your CAT Exam.Even though the question is easy to comprehend, one catch that is there is the ‘weakening’ part. Try your hands at this question and see if you get to the correct answer. Reading and solving a gazillion number of questions during your CAT Online Preparation is uber-crucial to crack the CAT with an amazing percentile.

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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 11 : Which of the following conditions would weaken the efficacy of a random decision forest?

1. If a large number of decision trees in the ensemble were trained on data derived from easy cases.
2. If a large number of decision trees in the ensemble were trained on data derived from easy and hard cases.
3. If the types of ensembles of decision trees in the forest were doubled.
4. If the types of decision trees in each ensemble of the forest were doubled.

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Video Explanation

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Explanatory Answer

See paragraph 3. ‘Programmers achieve that diversity by training each tree on different data.....They also boost the forest cognitively by training trees on the hardest cases.’ So, the efficacy of a random decision forest would be reduced if a large number of decision trees in the ensemble were trained on data derived from easy cases.

Option 2 would strengthen the logic of the decision tree forest, as the data is derived from both easy and hard cases. If the types (option 3) or the numbers (option 4) of decision trees in the ensemble are doubled, it would train the system better as a weighted majority is used to take decisions.

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The question is "Which of the following conditions would weaken the efficacy of a random decision forest?
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Hence, the answer is If a large number of decision trees in the ensemble were trained on data derived from easy cases.

Choice A is the correct answer.