Comprehension
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: 1

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

Updated On: Jul 29, 2025
  • If top-scorers possessed multidisciplinary knowledge that enabled them to look at a problem from several perspectives.
  • If assessment tests were made more extensive and rigorous.
  • 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.
  • If a new machine-learning algorithm were developed that proved to be more effective than the random decision forest.
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The Correct Option is A

Solution and Explanation

The main argument of the passage is that the principle of meritocracy, which suggests that the 'best person' should be hired, is flawed when dealing with complex problems that require a diverse team with varied knowledge bases and skills. Here's why the correct answer is the statement that would invalidate this argument: 

  1. The passage suggests that complex problems require diverse perspectives and skills, thereby undermining the principle of selecting the top scorers based solely on meritocratic criteria.
  2. The correct answer option states: "If top-scorers possessed multidisciplinary knowledge that enabled them to look at a problem from several perspectives." This condition directly challenges the main argument's claim that no single individual can fully understand complex problems, necessitating diverse teams.
  3. If top scorers indeed possessed multidisciplinary knowledge, it would mean that a single person could potentially offer diverse perspectives and skills, countering the need for diverse teams and supporting the meritocratic approach.
  4. This would align with the meritocracy principle that hiring the 'best' based on individual merit can create effective teams, thus invalidating the passage's argument against meritocracy.
  5. Therefore, given the context of the comprehension, the most critical condition that invalidates the passage's main argument is the one where top-scorers have multidisciplinary abilities that enable them to independently view problems from several perspectives.
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Question: 2

The author critiques meritocracy for all the following reasons EXCEPT that:

Updated On: Jul 21, 2025
  • an ideal team comprises of best individuals from diverse fields of knowledge.
  • modern problems are multifaceted and require varied skill-sets to be solved.
  • criteria designed to assess merit are insufficient to test expertise in any field of knowledge.
  • diversity and context-specificity are important for making major advances in any field.
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The Correct Option is A

Solution and Explanation

The correct answer is (A):
The author in the passage discusses meritocracy from all the above perspectives except choice 1. Choice 1 speaks of what an ideal team comprises of, but the idea of ‘ideal team’ has not even come in the passage. To critique something means to evaluate that thing. The author evaluates meritocracy from different perspectives. Choice 2 can be seen in the first para of the passage where the author says: The multidimensional or layered character of complex problems also undermines the principle of meritocracy. Choice 3 is substantiated from the sentences that come in the second para where the author says: Even with a knowledge domain, no test or criteria applied to individuals will produce the best team. In other words, there cannot be a test to assess merit in any field of knowledge. Choice 4 can be found in the first sentence of the second paragraph: Believers in a meritocracy might grant that teams ought to be diverse but then argue that meritocratic principles should apply within each category.
Thus we see that meritocracy has been discussed from all of the above perspectives except 1. The composition of an ideal team has not been discussed anywhere in the passage.

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Question: 3

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

Updated On: Jul 29, 2025
  • If a large number of decision trees in the ensemble were trained on data derived from easy cases.
  • If the types of decision trees in each ensemble of the forest were doubled.
  • If a large number of decision trees in the ensemble were trained on data derived from easy and hard cases.
  • If the types of ensembles of decision trees in the forest were doubled.
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The Correct Option is A

Solution and Explanation

The question relates to the efficacy of a random decision forest, where diversity among decision trees is pivotal. According to the comprehension passage, the strength of a random decision forest lies in the diversity and challenge of the data used to train decision trees. Trees trained on identical or easy cases tend to make similar classifications, leading to a lack of diversity which diminishes performance. Therefore, the given condition that could weaken the efficacy of a random decision forest is: If a large number of decision trees in the ensemble were trained on data derived from easy cases. When decision trees in an ensemble are exposed to easy cases only, they fail to capture the complexity and variability needed for robust decision-making, leading to a homogenized and less effective model.
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Question: 4

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?

Updated On: Jul 29, 2025
  • A team comprised of nutritionists, psychologists, urban planners and media personnel, who have each scored a distinction in their respective subject tests.
  • A team comprised of nutritionists, psychologists, urban planners and media personnel, who have each performed well in their respective subject tests.
  • A specialised team of nutritionists from various countries, who are also trained in the machine-learning algorithm of random decision forest.
  • A specialised team of top nutritionists from various countries, who also possess some knowledge of psychology.
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The Correct Option is A

Solution and Explanation

The passage discusses the complexity of modern problems and emphasizes the importance of diverse teams rather than meritocratic principles when addressing such issues. Rising obesity levels are cited as a problem affected by numerous factors, and solving such a problem requires a team with diverse expertise. The passage highlights that successful teams comprise members with varied knowledge bases and skills. It critiques the idea of hiring the 'best person' based on scores, as it leads to homogeneity, which is not conducive to innovation or breakthroughs.

Based on the passage, the most effective team for solving the problem of rising obesity levels should be diverse with expertise in various relevant fields. This aligns with the description of "A team comprised of nutritionists, psychologists, urban planners and media personnel, who have each scored a distinction in their respective subject tests." This composition not only provides the necessary diversity across different domains but also ensures that individuals are distinguished in their respective fields, thus combining depth of expertise with breadth of perspective to tackle the multifaceted issue of obesity. 

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Question: 5

Which of the following best describes the purpose of the example of neuroscience?

Updated On: Jul 29, 2025
  • Unlike other fields of knowledge, neuroscience is an exceptionally complex field, making a meaningful assessment of neuroscientists impossible.
  • In narrow fields of knowledge, a meaningful assessment of expertise has always been possible.
  • Neuroscience is an advanced field of science because of its connections with other branches of science like oncology and biostatistics.
  • In the modern age, every field of knowledge is so vast that a meaningful assessment of merit is impossible.
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The Correct Option is D

Solution and Explanation

The passage emphasizes the complexity of modern fields like neuroscience and the challenge in assessing merit due to their vastness. It highlights that each field is multi-dimensional, making it impossible to develop a standard criterion to rank experts effectively in such domains. With neuroscience, for instance, due to the large number of publications and the variety of research areas, it's impossible to rank neuroscientists objectively. Instead, the passage argues that optimal teams should be diverse and context-dependent, rather than based purely on conventional meritocratic principles. Therefore, the best option that describes the purpose of the example of neuroscience from the passage is: In the modern age, every field of knowledge is so vast that a meaningful assessment of merit is impossible.
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