Which one of the following best describes the term ‘Negative Demand’ ? ?->(Show Answer!)

1. Which one of the following best describes the term ‘Negative Demand’ ?

Write Comment

Comments

Tags

Show Similar Question And Answers

QA->After great change in the price of a commodity, there is no change in demand. The demand is known as ?....

QA->WHICH IS THE MOST ELECTRO NEGATIVE ELEMENT....

QA->In which plan was the growth rate of agricultural production negative?....

QA->Which countries have achieved a negative growth rate of population?....

QA->Which was the first Asian country to introduce the negative voting (NOTA) ?....

MCQ-> Analyse the following passage and provide appropriate answers for the follow. Popper claimed, scientific beliefs are universal in character, and have to be so if they are to serve us in explanation and prediction. For the universality of a scientific belief implies that, no matter how many instances we have found positive, there will always be an indefinite number of unexamined instances which may or may not also be positive. We have no good reason for supposing that any of these unexamined instances will be positive, or will be negative, so we must refrain from drawing any conclusions. On the other hand, a single negative instance is sufficient to prove that the belief is false, for such an instance is logically incompatible with the universal truth of the belief. Provided, therefore, that the instance is accepted as negative we must conclude that the scientific belief is false. In short, we can sometimes deduce that a universal scientific belief is false but we can never induce that a universal scientific belief is true. It is sometimes argued that this 'asymmetry' between verification and falsification is not nearly as pronounced as Popper declared it to be. Thus, there is no inconsistency in holding that a universal scientific belief is false despite any number of positive instances; and there is no inconsistency either in holding that a universal scientific belief is true despite the evidence of a negative instance. For the belief that an instance is negative is itself a scientific belief and may be falsified by experimental evidence which we accept and which is inconsistent with it. When, for example, we draw a right-angled triangle on the surface of a sphere using parts of three great circles for its sides, and discover that for this triangle Pythagoras' Theorem does not hold, we may decide that this apparently negative instance is not really negative because it is not a genuine instance at all. Triangles drawn on the surfaces of spheres are not the sort of triangles which fall within the scope of Pythagoras' Theorem. Falsification, that is to say, is no more capable of yielding conclusive rejections of scientific belief than verification is of yielding conclusive acceptances of scientific beliefs. The asymmetry between falsification and verification, therefore, has less logical significance than Popper supposed. We should, though, resist this reasoning. Falsifications may not be conclusive, for the acceptances on which rejections are based are always provisional acceptances. But, nevertheless, it remains the case that, in falsification, if we accept falsifying claims then, to remain consistent, we must reject falsified claims. On the other hand, although verifications are also not conclusive, our acceptance or rejection of verifying instances has no implications concerning the acceptance or rejection of verified claims. Falsifying claims sometimes give us a good reason for rejecting a scientific belief, namely when the claims are accepted. But verifying claims, even when accepted, give us no good and appropriate reason for accepting any scientific belief, because any such reason would have to be inductive to be appropriate and there are no good inductive reasons.According to Popper, the statement "Scientific beliefs are universal in character" implies that....

MCQ-> Read the passage carefully and answer the questions givenMore and more companies, government agencies, educational institutions and philanthropic organisations are today in the grip of a new phenomenon: ‘metric fixation’. The key components of metric fixation are the belief that it is possible - and desirable - to replace professional judgment (acquired through personal experience and talent) with numerical indicators of comparative performance based upon standardised data (metrics); and that the best way to motivate people within these organisations is by attaching rewards and penalties to their measured performance. The rewards can be monetary, in the form of pay for performance, say, or reputational, in the form of college rankings, hospital ratings, surgical report cards and so on. But the most dramatic negative effect of metric fixation is its propensity to incentivise gaming: that is, encouraging professionals to maximise the metrics in ways that are at odds with the larger purpose of the organisation. If the rate of major crimes in a district becomes the metric according to which police officers are promoted, then some officers will respond by simply not recording crimes or downgrading them from major offences to misdemeanours. Or take the case of surgeons. When the metrics of success and failure are made public - affecting their reputation and income - some surgeons will improve their metric scores by refusing to operate on patients with more complex problems, whose surgical outcomes are more likely to be negative. Who suffers? The patients who don’t get operated upon.When reward is tied to measured performance, metric fixation invites just this sort of gaming. But metric fixation also leads to a variety of more subtle unintended negative consequences. These include goal displacement, which comes in many varieties: when performance is judged by a few measures, and the stakes are high (keeping one’s job, getting a pay rise or raising the stock price at the time that stock options are vested), people focus on satisfying those measures - often at the expense of other, more important organisational goals that are not measured. The best-known example is ‘teaching to the test’, a widespread phenomenon that has distorted primary and secondary education in the United States since the adoption of the No Child Left Behind Act of 2001.Short-termism is another negative. Measured performance encourages what the US sociologist Robert K Merton in 1936 called ‘the imperious immediacy of interests … where the actor’s paramount concern with the foreseen immediate consequences excludes consideration of further or other consequences’. In short, advancing short-term goals at the expense of long-range considerations. This problem is endemic to publicly traded corporations that sacrifice long-term research and development, and the development of their staff, to the perceived imperatives of the quarterly report.To the debit side of the ledger must also be added the transactional costs of metrics: the expenditure of employee time by those tasked with compiling and processing the metrics in the first place - not to mention the time required to actually read them. . . .All of the following can be a possible feature of the No Child Left Behind Act of 2001, EXCEPT:
....

MCQ-> Read the passage carefully and answer the given questionsThe 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.Which of the following conditions, if true, would invalidate the passage’s main argument?
....

MCQ-> Answer the questions based on the following information. A series $$S_{1}$$ of five positive integers is such that the third term is half the first term and the fifth term is 20 more than the first term. In series $$S_{2}$$, the nth term is defined as the difference between the (n+1)th term and the nth term of series $$S_{1}$$, $$S_{2}$$ is an arithmetic progression with a common difference of 30.First term of $$S_{1}$$ is
....

MCQ->Which one of the following best describes the term ‘Negative Demand’ ?....