2020考研英語沖刺押題：閱讀理解預(yù)測（二）

　　眾所周知，在考研英語中，得閱讀者，得英語，考研英語閱讀是分值最高的一考題，所以考生需要掌握大量的詞匯，同時(shí)要有豐富的閱讀經(jīng)驗(yàn)，尤其是外刊雜志。所以今天給大家整理了來自于英美國家的報(bào)刊雜志文章，進(jìn)行2020考研英語閱讀預(yù)測，希望對(duì)大家有幫助。

　　原文期刊：哈佛商業(yè)評(píng)論

　　原文標(biāo)題：What Good Are Experts?

　　文章分析了在計(jì)算能力日益提高和網(wǎng)絡(luò)進(jìn)一步釋放群體智慧的環(huán)境下專家的優(yōu)勢所在，并就各機(jī)構(gòu)應(yīng)如何充分利用專家優(yōu)勢提出建議。它屬于現(xiàn)象分析型文章，按照“指出現(xiàn)象——分析現(xiàn)象——提出建議” 的脈絡(luò)展開論述。

　　PART 1

　　原文

　?、馧ot long ago, recommendations from experts, even if imperfect, were the best ones available. So people relied on them to address challenges across the entire spectrum of complexity. At one end of that spectrum are the problems with unchanging causes and effects that can be confidently solved using rules-based processes. Today, computers increasingly solve such problems more cheaply and reliably than experts can. At the other end of the spectrum are probabilistic problems whose causes and effects are not clear and whose outcomes are significantly governed by chance. The collective wisdom of ordinary people，which is being released by the network, often proves to be better than experts at addressing such problems.

　?、騈onetheless, research across many fields, from complex systems to psychology, suggests there is a sweet spot where experts still have a unique edge. They're well equipped to solve problems that have rules-based solutions but that allow a high degree of freedom in arriving at them. When avenues for solutions are relatively few, such as in tic-tac-toe, the degree of freedom is limited. When the potential avenues are many, the degree of freedom is high. The greater the degree of freedom in solving a problem, whether rules-based or probabilistic, the more complex the challenge is.

　?、驝omputers are exceedingly adept at rules-based problems with a limited degree of freedom, like tic-tac-toe. However, they're often clumsy at high-freedom problems because, unlike people, computers cannot quickly eliminate unproductive avenues of inquiry and make creative connections among bits of information. Experts are likely to continue to outperform computers in rules-based areas that require deep, domain-specific knowledge, such as innovation and design. Success in these domains requires the efficient, creative recombination of a vast array of building blocks in novel, productive ways.

　　ⅣCrowds have proved to be skilled at solving certain probabilistic problems, but they often fare poorly if they lack sufficient domain-specific knowledge. Just as an expert coach will probably create a better game plan than a crowd because he can draw on unique knowledge of his team and the competition, experts within a company can be expected to outperform a nonexpert collective in shaping the company's strategy.

　?、鮂or problems where experts still prevail, psychologist Philip Tetlock offers an insight about how to nurture desired expertise. Using a metaphor borrowed from the Greek poet Archilochus, Tetlock segregated experts into hedgehogs and foxes. Hedgehogs, who deeply know one big thing, extend its explanatory reach to everything they encounter. Foxes, in contrast, tend to know a little about many aspects of their field and do not stick to a single approach in solving complex problems. Work by Tetlock suggests that organizations should find and nurture foxes, who have a “crowdlike” cognitive diversity. Compared with hedgehogs, foxes have a broader set of tools in their cognitive toolboxes, allowing them to effectively match solutions to problems.

　　PART 2

　　詞匯短語

　　1.recommendation [?rek?men?de??n] n.建議

　　2.available [??ve?l?bl] a.可用的

　　3.spectrum [?spektr?m] n.光譜，文中借指范圍

　　4.complexity [k?m?pleks?ti] n.復(fù)雜度

　　5.probabilistic [?pr?b?b??l?st?k] a. 基于概率的，或然的

　　6.edge [ed?] n.優(yōu)勢

　　7.avenue [?æv?nju:] n. 渠道，方式

　　8.exceedingly [?k?si:d??li] ad. 非常，極其地(P3L1)

　　9.clumsy [?kl?mzi] a.笨拙的

　　10.fare [fe?(r)] v.進(jìn)展

　　11.prevail [pr??ve?l] v.普遍發(fā)生，盛行

　　12.cognitive [?k?gn?t?v] a.認(rèn)知的

　　13.diversity [da??v?:s?ti] n. 多樣性

　　14.rely on 依靠

　　15.sweet spot n.甜點(diǎn)，指(平衡各種因素的)最有效點(diǎn), 最有效位置

　　16.adept at 擅長

　　17.an array of 一系列

　　18.match...to... 將……和……相匹配

　　19.*hedgehog [?hed?h?g] n. 刺猬

　　(標(biāo)*的為超綱詞)

　　PART 3

　　翻譯點(diǎn)評(píng)

　?、馧ot long ago, recommendations from experts, even if imperfect, were the best ones available. So people relied on them to address challenges across the entire spectrum of complexity. At one end of that spectrum are the problems with unchanging causes and effects that can be confidently solved using rules-based processes. Today, computers increasingly solve such problems more cheaply and reliably than experts can. At the other end of the spectrum are probabilistic problems whose causes and effects are not clear and whose outcomes are significantly governed by chance. The collective wisdom of ordinary people，which is being released by the network, often proves to be better than experts at addressing such problems.

　　翻譯：不久前，專家的建議就算不完美也會(huì)被認(rèn)為是現(xiàn)有最佳方案。因此，無論問題繁簡，人們一并倚賴專家意見。在復(fù)雜范圍的一端是永恒不變的因果關(guān)系，這些問題完全可以通過使用基于規(guī)律的程序得以解決。如今，較之依靠專家，計(jì)算機(jī)解決這類問題變得日益成本低廉，可靠性也在提高。在復(fù)雜范圍的另一端是概率性問題，這些問題的因果關(guān)系不明顯，其結(jié)果很大程度上受幾率影響。對(duì)于此類問題的解決，事實(shí)證明網(wǎng)絡(luò)正在釋放的普通人的群體智慧通常優(yōu)于專家意見。

　　點(diǎn)評(píng)：第一段通對(duì)比過去指出現(xiàn)狀：專家意見的優(yōu)勢范圍在變窄。段落前兩句指出過去情況：專家意見被認(rèn)為是一切問題的最佳解決方案。后四句指出現(xiàn)狀：計(jì)算機(jī)比專家更擅長處理存在明顯因果關(guān)系(即，復(fù)雜度低)的問題(第三、四句);網(wǎng)絡(luò)釋放的群體智慧通常比專家更擅長處理概率性(即，復(fù)雜度高的)問題(第五、六句)。

　?、騈onetheless, research across many fields, from complex systems to psychology, suggests there is a sweet spot where experts still have a unique edge. They're well equipped to solve problems that have rules-based solutions but that allow a high degree of freedom in arriving at them. When avenues for solutions are relatively few, such as in tic-tac-toe, the degree of freedom is limited. When the potential avenues are many, the degree of freedom is high. The greater the degree of freedom in solving a problem, whether rules-based or probabilistic, the more complex the challenge is.

　　翻譯：然而，從復(fù)雜系統(tǒng)到心理學(xué)等多個(gè)領(lǐng)域的研究都表明，專家獨(dú)具優(yōu)勢的“佳點(diǎn)”依然存在。他們強(qiáng)于處理那些“基于規(guī)律但處理過程存在較高自由度的”問題。當(dāng)解決方案相對(duì)較少時(shí)，如井字游戲，自由度也就有限。當(dāng)可能方式有多種時(shí)，自由度也就較高。隨著解決問題自由度的增高，挑戰(zhàn)的復(fù)雜性會(huì)增強(qiáng)，無論是基于規(guī)律的問題還是概率性問題都是如此。

　　點(diǎn)評(píng)：第二至四段分析現(xiàn)象，指出專家依然有獨(dú)具優(yōu)勢之處。第二段首句轉(zhuǎn)而提出全文主旨：專家依然有獨(dú)具優(yōu)勢之處。第二句具體指出專家優(yōu)勢所在：處理自由度高的、基于規(guī)律的問題。第三、四句補(bǔ)充指出自由度高低的決定因素：問題潛在解決途徑的多少(自由度隨潛在解決途徑的增多而增高)。末句聯(lián)系第一段末句進(jìn)一步指出，自由度決定復(fù)雜度(復(fù)雜度隨自由度的升高而增加)。

　?、驝omputers are exceedingly adept at rules-based problems with a limited degree of freedom, like tic-tac-toe. However, they're often clumsy at high-freedom problems because, unlike people, computers cannot quickly eliminate unproductive avenues of inquiry and make creative connections among bits of information. Experts are likely to continue to outperform computers in rules-based areas that require deep, domain-specific knowledge, such as innovation and design. Success in these domains requires the efficient, creative recombination of a vast array of building blocks in novel, productive ways.

　　翻譯：計(jì)算機(jī)極其擅長處理自由度有限的、以規(guī)律為基礎(chǔ)的問題，如井字游戲。然而，它們往往拙于解決高自由度的問題，因?yàn)橛?jì)算機(jī)不同于人，它們無法快速排除無效詢問途徑而在各信息單位之間建立創(chuàng)造性聯(lián)系。在需要深入、專門知識(shí)的、以規(guī)律為基礎(chǔ)的領(lǐng)域，如，創(chuàng)新和設(shè)計(jì)，專家往往依然勝過計(jì)算機(jī)。這些領(lǐng)域的成功需要將大量“砌塊”以新穎、有效的方式進(jìn)行快速、創(chuàng)造性的重新組合。

　　點(diǎn)評(píng)：第三段指出專家較計(jì)算機(jī)的優(yōu)勢所在。第一、二句分別指出計(jì)算機(jī)的強(qiáng)項(xiàng)(處理基于規(guī)律的問題)以及處理此類問題時(shí)依然存在的不足(處理需要?jiǎng)?chuàng)造性的高自由度問題)。第三、四句就計(jì)算機(jī)的特點(diǎn)指出專家的相對(duì)優(yōu)勢：處理需要利用專業(yè)知識(shí)對(duì)可用信息進(jìn)行創(chuàng)造性聯(lián)系的問題。

　　ⅣCrowds have proved to be skilled at solving certain probabilistic problems, but they often fare poorly if they lack sufficient domain-specific knowledge. Just as an expert coach will probably create a better game plan than a crowd because he can draw on unique knowledge of his team and the competition, experts within a company can be expected to outperform a nonexpert collective in shaping the company's strategy.

　　翻譯：已有證據(jù)表明，群體精于解決某些概率性問題，但若缺乏足夠的專業(yè)知識(shí)，則會(huì)進(jìn)展緩慢。專家教練所形成的比賽計(jì)劃可能會(huì)優(yōu)于群體，因?yàn)樗梢岳闷潢P(guān)于團(tuán)隊(duì)和競賽的獨(dú)特知識(shí)。同樣，我們可以期望公司內(nèi)的專家在制定公司策略方面會(huì)優(yōu)于非專家群體。

　　點(diǎn)評(píng)：第四段指出專家較群體智慧的優(yōu)勢所在。第一句指出群體智慧的擅長之處(處理概率性問題)，以及處理此類問題時(shí)依然存在的不足(欠缺專業(yè)知識(shí))。第二句就群體智慧的特點(diǎn)指出專家的相對(duì)優(yōu)勢：利用其專業(yè)知識(shí)制定公司發(fā)展策略。

　?、鮂or problems where experts still prevail, psychologist Philip Tetlock offers an insight about how to nurture desired expertise. Using a metaphor borrowed from the Greek poet Archilochus, Tetlock segregated experts into hedgehogs and foxes. Hedgehogs, who deeply know one big thing, extend its explanatory reach to everything they encounter. Foxes, in contrast, tend to know a little about many aspects of their field and do not stick to a single approach in solving complex problems. Work by Tetlock suggests that organizations should find and nurture foxes, who have a “crowdlike” cognitive diversity. Compared with hedgehogs, foxes have a broader set of tools in their cognitive toolboxes, allowing them to effectively match solutions to problems.

　　翻譯：對(duì)于專家依然普遍存在優(yōu)勢的問題，心理學(xué)家菲利普.泰特勞克就如何培養(yǎng)出所需專家意見提供了深刻的見解。借用希臘詩人阿克羅科斯的比喻，泰特勞克將專家分為“刺猬”和“狐貍”。對(duì)某件大事知之甚深的刺猬會(huì)用其解釋它所碰到的所有事情。相反，對(duì)其領(lǐng)域的許多方面都稍有了解的狐貍在處理復(fù)雜問題時(shí)不會(huì)固執(zhí)于單一的方法。泰特勞克建議：各機(jī)構(gòu)應(yīng)該發(fā)現(xiàn)并培養(yǎng)具有“類群體”的認(rèn)知多樣性的“狐貍”。較之刺猬，狐貍的認(rèn)知箱中裝有更多的工具，這使得他們能夠有效地找到與問題相匹配的解決方案。

　　點(diǎn)評(píng)：第五段提出建議：培養(yǎng)“類群體”的狐貍型專家。第五段首句提出段落討論的主題——如何培養(yǎng)需要的專家意見。第二至四句借用心理學(xué)家Tetlock的比喻將專家分為兩類：“只知一件大事，且緊緊抓住不放”的刺猬型專家，和“對(duì)很多領(lǐng)域都有所了解”的狐貍型專家。第五句指出各機(jī)構(gòu)應(yīng)培養(yǎng)的專家類型：狐貍型。末句說明理由：狐貍型專家的認(rèn)知多樣性使得他們能夠有效地將問題和答案相匹配。

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