CAT 2018 Question Paper | Verbal Slot 2

CAT Questions

Critical Reasoning type questions have become common in the CAT Exam. The trickiest part comes when phrases like ‘IF TRUE' or 'INVALIDATES’ arise in the question. Read and solve tons of such questions during your CAT Online Preparation to crack the CAT Exam with a kick-ass percentile. Start with this question from CAT 2018 Question Paper right away!

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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 12 : Which of the following conditions, if true, would invalidate the passage’s main argument?

1. If assessment tests were made more extensive and rigorous.
2. If top-scorers possessed multidisciplinary knowledge that enabled them to look at a problem from several perspectives.
3. If it were proven that teams characterised by diversity end up being conflicted about problems and take a long time to arrive at a solution.
4. If a new machine-learning algorithm were developed that proved to be more effective than the random decision forest.

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Video Explanation

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Explanatory Answer

Tricky question. The main argument of the passage is that meritocracy is a flawed principle, because (a) complex problems are multidimensional, and (b) even within a domain, no test or criteria can be applied to choose the ‘best’, as expertise is context-specific. Option 2 touches upon both these aspects. If top scorers possessed multi-disciplinary knowledge and could look at a problem from multiple perspectives, then the idea of meritocracy will hold good i.e it would deliver the results. This goes against the main argument of the passage.

Option 1 calls for assessment tests to be made ‘more extensive and rigorous’. This neither addresses the multidimensional aspect nor the point about context-specificity. So, it does not invalidate the main idea of the passage.

Option 3 talks of diverse teams that are conflicted and take a long time to arrive at solutions. This brings in two totally different aspects—conflict and decision time—that are not discussed in the passage. This does not invalidate the author’s argument.

Option 4 is not relevant. The author only talks of random decision forests in to illustrate the point that diverse ideas have high impact.

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The question is "Which of the following conditions, if true, would invalidate the passage’s main argument?
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Hence, the answer is If top-scorers possessed multidisciplinary knowledge that enabled them to look at a problem from several perspectives.

Choice B is the correct answer.

 

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