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The Smithian model of innate human disposition—as entirely self-interested, gleaning motivation only from personal profit—has long underpinned modern economic theory. One proponent of the person-as-selfish-agent model is Samantha T. Cleary, an economist who argues that this view of human nature remains the most useful basis for advancing behavioral economics and opposes more complex algorithms that take into account such complicating factors as altruism and social pressure. Economics is a purely statistical field of study concerned with a society's most common motivators, not every individual's. The field can operate most efficiently by using straightforward models that Cleary poetically describes as elegant.
Yet how will economics strengthen its predictive powers if not by increasing the sophistication of its models, using swaths of data to inform analysis of the many processes that drive the aggregation of human economic decisions? How can behavioral economics develop without this step? If a behavior follows a any consistent pattern across a large cross section of the population, economists should be able to measure and predict it, at least theoretically. But even if an algorithm could account for complex motivations and apparently irrational decisions, Cleary argues that such an approach to studying economic behavior would remain greatly vulnerable to misinterpretation, bias, and hyperlocal preferences. Furthermore, this complex algorithm would only introduce a greater margin of error, while being so complex and situation-specific as to be useless for rendering any long-term or generalizable predictions. It might have the capacity to describe the behaviors of a small pocket of people, but couldn't contribute to broader economic theory in any meaningful way.
Imagine, though, that an intricate algorithm were able to accurately predict behaviors based on many more inputs than self-interest. Whether the prediction confirmed or denied established economic theory, it would still contribute to the growing body of data in behavioral economics. People, unlike machines, do not operate according to simple psychological rules, and if we can account for this to some extent in our work, it is hard to envision a reason why we would not accept the challenge of doing so. Economies, after all, depend ultimately on one thing: human decision-making. Work based on the assumption that personal choices obey centuries-old economic theories is willfully limited in scope. To be aligned with the arguments of purists like Cleary, one would have to dismiss the ultimate purpose of economic study: to understand and predict the workings of real economies.

The writer of the passage would most likely agree with which of the following statements about mathematical modeling of human behavior?

A
A model that is highly accurate in a specific situation is more valuable than a broadly applicable theory.
B
Mathematical models are best used to direct human behavior.
C
Mathematical models challenge the most fundamental goals of economics.
D
Because no mathematical model can simulate the complexity of human decision-making processes, economics should cease to use math.
E
The uselessness of a model that incorporates different types of decision-making mirrors the uselessness of one with a static conception of human motivations.

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正确答案: A

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