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RC Passage
Direction for the questions 1 to 5: The passage below is accompanied by a set of five questions. Choose the best answer to each question.
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 behavior. 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.
Full RC Video Analysis
RC Line-wise Explanation
Paragraph 1
"The complexity of modern problems often precludes any one person from fully understanding them."
Explanation: Modern problems are so complex that no single person can fully comprehend all aspects of 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."
Explanation: There are many factors that contribute to rising obesity rates, such as transportation, media, easy-to-get foods, changes in society, biology, and psychology.
"The multidimensional or layered character of complex problems also undermines the principle of meritocracy: the idea that the ‘best person’ should be hired."
Explanation: The complexity of these issues challenges the idea of meritocracy, which assumes there is a single best person for a task.
"There is no best person."
Explanation: The notion of a single "best person" is flawed in such complex scenarios.
"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."
Explanation: In assembling an oncological research team, companies like Gilead or Genentech wouldn't simply hire the top scorers from a test or the people with the best resumes.
"Instead, they would seek diversity."
Explanation: Instead, they would look for diversity in the team.
"They would build a team of people who bring diverse knowledge bases, tools and analytic skills."
Explanation: The team would be composed of individuals with different areas of knowledge, tools, and analytical approaches.
Paragraph 2
"Believers in a meritocracy might grant that teams ought to be diverse but then argue that meritocratic principles should apply within each category."
Explanation: People who believe in meritocracy might agree that teams should be diverse but still argue that the best person in each category should be selected.
"Thus the team should consist of the ‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool."
Explanation: They would argue that the team should consist of the best people in each field, such as mathematicians, oncologists, and biostatisticians.
"That position suffers from a similar flaw."
Explanation: However, this approach has a similar flaw to the meritocratic model.
"Even with a knowledge domain, no test or criteria applied to individuals will produce the best team."
Explanation: Even within a specific field of knowledge, no test or criteria will create the best team.
"Each of these domains possesses such depth and breadth, that no test can exist."
Explanation: These fields are so broad and deep that no test can properly rank individuals within them.
"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."
Explanation: For example, neuroscience is a vast field with over 50,000 published papers covering everything from molecular techniques to brain networks.
"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."
Explanation: Due to this complexity, trying to rank neuroscientists like athletes competing in a race is doomed to 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."
Explanation: However, it is possible that for a specific task and a certain team composition, one scientist might be more suited to contribute than another.
"Optimal hiring depends on context. Optimal teams will be diverse."
Explanation: The best hiring decisions depend on context, and the best teams are those that are diverse.
Paragraph 3
"Evidence for this claim can be seen in the way that papers and patents that combine diverse ideas tend to rank as high-impact."
Explanation: Research papers and patents that combine diverse ideas tend to be highly impactful, which supports the idea that diversity leads to better results.
"It can also be found in the structure of the so-called random decision forest, a state-of-the-art machine-learning algorithm."
Explanation: This idea of diversity can also be seen in the structure of a random decision forest, a type of machine-learning algorithm.
"Random forests consist of ensembles of decision trees."
Explanation: Random forests are made up of multiple decision trees working together.
"If classifying pictures, each tree makes a vote: is that a picture of a fox or a dog? A weighted majority rules."
Explanation: In image classification, each tree votes on whether an image is a fox or a dog, and the majority decision is used.
"Random forests can serve many ends. They can identify bank fraud and diseases, recommend ceiling fans and predict online dating behavior."
Explanation: Random forests have a wide range of applications, such as identifying fraud, diagnosing diseases, recommending products, and predicting behaviors.
"When building a forest, you do not select the best trees as they tend to make similar classifications."
Explanation: When creating a random forest, you don’t pick the best-performing trees, as they tend to make similar predictions.
"You want diversity."
Explanation: Instead, you seek diversity among the trees.
Paragraph 4
"Programmers achieve that diversity by training each tree on different data, a technique known as bagging."
Explanation: Diversity is achieved by training each tree on different data, a method called bagging.
"They also boost the forest ‘cognitively’ by training trees on the hardest cases – those that the current forest gets wrong."
Explanation: Programmers also make the forest more effective by training trees on the most difficult cases, the ones the forest struggles with.
"This ensures even more diversity and accurate forests."
Explanation: This process ensures even more diversity and improves the accuracy of the random forest.
Paragraph 5
"Yet the fallacy of meritocracy persists."
Explanation: Despite the evidence supporting diversity, the myth of meritocracy still persists.
"Corporations, non-profits, governments, universities and even preschools test, score and hire the ‘best’."
Explanation: Many organizations, from corporations to schools, continue to test, score, and hire based on the idea of selecting the "best."
"This all but guarantees not creating the best team."
Explanation: This approach almost guarantees that the best team won’t be created.
"Ranking people by common criteria produces homogeneity."
Explanation: Ranking people based on uniform criteria leads to a lack of diversity.
"That’s not likely to lead to breakthroughs."
Explanation: This lack of diversity is unlikely to result in innovative breakthroughs.
RC Paragraph Explanation
Paragraph 1 Summary
Modern complex problems, such as obesity, require diverse knowledge and skills, which challenges the principle of meritocracy. For example, research teams in specialized fields like oncology or biotech value diversity rather than hiring the top scorers or those with the best resumes.
Paragraph 2 Summary
Meritocratic beliefs might suggest diversity within fields, but this approach is flawed because the complexity of any knowledge domain, like neuroscience, makes it impossible to rank individuals effectively. Team composition and context are more important than individual ranking.
Paragraph 3 Summary
Evidence supports the importance of diversity in teams, as seen in high-impact papers, patents, and machine learning algorithms like random forests, which rely on diverse decision trees to make better predictions and solve complex problems.
Paragraph 4 Summary
Programmers ensure diversity in random forests by training trees on different data and focusing on challenging cases. This increases both diversity and accuracy in the system’s predictions.
Paragraph 5 Summary
Despite the evidence for diversity’s effectiveness, the fallacy of meritocracy persists, with many organizations continuing to rank and hire based on common criteria, which leads to homogeneity rather than breakthrough innovation.
RC Quick Table Summary
| Paragraph Number | Main Idea |
|---|---|
| Paragraph 1 | Complex problems require diverse knowledge, which challenges meritocratic hiring principles. |
| Paragraph 2 | Meritocracy within fields is flawed because the complexity of knowledge domains makes ranking individuals ineffective. |
| Paragraph 3 | Diversity leads to higher impact, as seen in papers, patents, and random forests, which use diversity to improve predictions. |
| Paragraph 4 | Diversity in random forests is achieved by training on different data and focusing on challenging cases to improve accuracy. |
| Paragraph 5 | Despite evidence for the effectiveness of diversity, meritocracy still prevails, leading to homogeneity and preventing breakthroughs. |
RC Questions
Ques 1. Which of the following conditions, if true, would invalidate the passage’s main argument?
Ques 2. The author critiques meritocracy for all the following reasons EXCEPT that:
Ques 3. Which of the following conditions would weaken the efficacy of a random decision forest?
Ques 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?
Ques 5. Which of the following best describes the purpose of the example of neuroscience?
