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: Which of the following conditions, if true, would invalidate the passage’s main argument?
- 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.
Question: The author critiques meritocracy for all the following reasons EXCEPT that:
- 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.
Question: Which of the following conditions would weaken the efficacy of a random decision forest?
- 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.
Question: 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?
- 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.
Question: Which of the following best describes the purpose of the example of neuroscience?
- 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.