我認(rèn)識一位投資者總喜歡將股市擬人化，就好像道指是個活生生的野獸。“感覺像不像市場把投資者聚在一起然后迎頭給你一棒？”他會這么說。仿佛“市場”是在時代廣場擺攤玩三張撲克專門騙游客的壞人。

聽起來不合邏輯，但現(xiàn)在有種投資策略越發(fā)流行，叫“技術(shù)分析”。其實是占星術(shù)的分支，即讀圖術(shù)，據(jù)說能“解讀”市場，盡管從走勢圖上根本看不到任何跟公司有關(guān)的信息。人們光是看曲折的走勢圖就能編出個故事，例如市場“動能衰竭”，或是“觸底”之類。

很多投資者癡迷于這種毫無作用的技術(shù)。行為經(jīng)濟學(xué)專門研究心理學(xué)在財務(wù)決策方面的作用，解釋了其中原因。作者納西姆·尼古拉斯·塔勒布稱之為“敘事謬誤”。從隨機事件中尋找規(guī)律，事情發(fā)生后將意料之外解讀為“意料之中”是人類本能傾向，也是人類的需要。

敘事謬誤可以解釋很多金融領(lǐng)域常見的錯誤，但理解當(dāng)今世界種種政治陰謀時用處不大。這就是為何人們很難相信刺殺約翰·F·肯尼迪是一個單獨行動的槍手隨機行為，會想象出很多陰謀理論，其實就是不想承認(rèn)混亂才是宇宙的主流，秩序沒那么強大。

塔勒布的《黑天鵝》一書出版于2007年，很快變成交易員案頭必備書。書中認(rèn)為面對經(jīng)濟危機之類隨機極端事件，再好的計劃也沒用。個人也難免受到隨機事件打擊，尤其是一些大災(zāi)難中恰好出現(xiàn)在錯誤地方的人，或是在曾經(jīng)輝煌的行業(yè)工作，例如新聞從業(yè)者因為新科技出現(xiàn)失去工作。

對正常行業(yè)來說隨機性的影響沒那么大，只會對部分公司和個人產(chǎn)生巨大影響，其他人感受不到。人壽保險和退休金之所以容易測算，是因為隨機性的影響不大，畢竟人類壽命不會突然大幅變化。但颶風(fēng)非常難測，南弗羅里達(dá)州經(jīng)營財產(chǎn)事故保險的公司就應(yīng)該時刻準(zhǔn)備好應(yīng)付大額賠付。

許多人都很難接受其實很多領(lǐng)域無法預(yù)測。回顧歷史也能發(fā)現(xiàn)很多事沒法解釋。總體來說，新聞事件和歷史進(jìn)程并沒有想象中容易解讀。沒人能準(zhǔn)確預(yù)測2011年911事件，也沒有經(jīng)濟學(xué)家能在1970年預(yù)測財富分配要開始傾斜，富人變得更富，中產(chǎn)階級陷入停滯。變量實在太多了，而且一些重要的變量（例如新生軟件行業(yè)的潛力）當(dāng)時很難意識到。

不過想避免落入敘事謬誤還是有可能的。如果是經(jīng)濟學(xué)家，就不要把上個經(jīng)濟周期的規(guī)律強加在未來走勢上。如果在商業(yè)界，要將分散的數(shù)據(jù)匯集起來想清楚要不要繼續(xù)投資，還要避免從數(shù)據(jù)中找出連貫故事的傾向。現(xiàn)實很混亂。投資成功也是要在未來成功，得挺過各種復(fù)雜環(huán)境，例如經(jīng)濟衰退，出現(xiàn)新競爭者或是戰(zhàn)爭。

投資者尤其要小心過于相信股市里的故事，一旦陷入錯誤邏輯，任何漲跌都只會印證之前的判斷。證券分析可不是尋求真相，說白了就是想盡可能低價買入。但判斷很可能出錯，一些隨機事件可能導(dǎo)致判斷錯誤。

在更廣闊的的世界里，如果不愿意接受現(xiàn)實，即所有事很容易出錯，就會導(dǎo)致挫敗感然后開始指責(zé)。還會拼命找原因。

可以看兩個正面例子。第一個來自《黑天鵝》一書。塔勒布提到“國王死了，接著王后也死了。”這句話沒什么特別。但只要稍微改一下：“國王死了，然后王后也傷心致死。”看！故事出來了。人們還從中找到了因果。難怪王后也死了，原來是因為傷心。

第二個例子來自邁克爾·劉易斯的新書《失敗的計劃》。劉易斯描寫了一個以色列心理學(xué)家丹尼爾·卡尼曼和亞摩斯·特沃斯基做的實驗，在行為經(jīng)濟學(xué)領(lǐng)域比較超前。為了迷惑參與測試者，他們假想出一個“琳達(dá)”，而且描述為“31歲，單身，性格直爽而且非常聰明……非常關(guān)心社會公平。”

參與者收到的問題是下面哪個選項可能性更大——（a）“琳達(dá)是個銀行柜員”或（b）“琳達(dá)是個銀行柜員而且很熱衷女權(quán)運動。”其實女權(quán)主義柜員琳達(dá)是柜員琳達(dá)的子集，所以正確答案應(yīng)該是（a）。但85%的人都選了（b）！因為人們更愿意接受琳達(dá)是個女權(quán)主義者，所以關(guān)注社會公平。這樣的琳達(dá)比較符合預(yù)先假設(shè)，也就是說對琳達(dá)的判斷陷入了敘事謬誤。

用劉易斯的話說，卡尼曼和特沃斯基其實在問“人們?nèi)绾卫斫怆S環(huán)境變化的事物。”塔勒布提供了答案，當(dāng)然兩位心理學(xué)家也推測到了：人們需要聽故事。

人們會發(fā)現(xiàn)偏執(zhí)狂與陰謀論邏輯出奇地相似。原本“偏執(zhí)”就是由歷史學(xué)家理查德·霍夫斯塔德提出用來描述政治傾向的。肯尼迪遇刺一年后，《哈珀》雜志發(fā)表了一篇題為《美國政治中的偏執(zhí)類型》，作者霍夫斯塔德在文中梳理了美國陰謀論的歷史。霍夫斯塔德寫道，政治上偏執(zhí)的人“將種種艱難困苦歸咎于人為，”仿佛總在跟（至少他自己相信）什么神奇又兇狠的“邪惡超人”生死搏斗：“就是這個惡人制造危機，弄得銀行擠兌，導(dǎo)致衰退，誘發(fā)災(zāi)難。”

雖然霍夫斯塔德寫的是麥卡錫主義，但個中描述完全適用當(dāng)代很多事件，辛辛那提牛頓校園槍擊案后拒絕承認(rèn)事實的人，盲目抵制疫苗的人，還有唐納德·特朗普演講中各種各樣的虛構(gòu)說法（質(zhì)疑奧巴馬出生地，還有數(shù)百萬非法投票）。半個世紀(jì)前書中的描述躍然紙上：“比起現(xiàn)實世界，偏執(zhí)狂的邏輯縝密得多。”

《波士頓環(huán)球報》最近在俄亥俄州貝爾維尤采訪了一位特朗普的支持者，他正感慨周圍的家庭雜貨店關(guān)門。“都怪政府不好，”他說。注意他找的指責(zé)對象，其實很有邏輯：一個簡單的解釋就能應(yīng)付極為復(fù)雜的問題。

霍夫斯塔德描述的“邏輯連貫”其實就是敘事謬誤，主要用來排解政治上的焦慮。行為經(jīng)濟學(xué)家已經(jīng)解釋為何人類這么想的根源。其實不只涉及經(jīng)濟學(xué)，跟政治和日常生活都有關(guān)系。講故事讓人相信和滿足，符合人類的需求。王后傷心至死。呵呵，說得好像親眼見到一樣。（財富中文網(wǎng)）

作者：Roger Lowenstein

譯者：Pessy

審稿：夏林

本文另一版本發(fā)表于2017年3月15日出版的《財富》雜志上，標(biāo)題為《那些我們信以為真的故事》

I know an investor who anthropomorphizes the stock market, as if the Dow were a living beast. “Wouldn’t it be just like the market to draw us in with a rally, then deliver a sucker punch?” he will say, as if “the market” were a canny three-card monte player, entrapping tourists in Times Square.

Irrational as that may sound, it’s no more so than the ever-popular investing method known as “technical analysis.” This is a branch of astrology—?chart -interpretation—that purportedly “reads” the market, even though it tells you nothing about the under? lying companies. You see a chart of zigs and zags and impute to it a story—the market is “exhausted,” or “bottoming,” or whatever.

Many investors are addicted to such fruitless techniques. Behavioral economics, which studies the impact of psychology on financial decision-making, explains why. It reflects what author Nassim Nicholas Taleb called the “narrative fallacy.” It’s the human tendency, actually the human need, to impose order on random events and to make events we didn’t anticipate seem “predictable” after the fact.

The narrative fallacy explains many common financial errors. The news is that it’s equally useful for understanding today’s conspiracy politics. It’s why, rather than accept that John F. Kennedy was killed by a lone, random gunman, we invented conspiracy theories—to spare us the ontological despair of acknowledging that chaos, not order, rules the universe.

Taleb’s The Black Swan, published in 2007, became a handbook for market traders—a reminder that random, extreme events like economic crashes overtake the best of plans. They also overtake individuals—those who happened to be in the wrong place during a natural disaster or who worked in a once-healthy industry, such as journalism, that was undercut by a new technology.

Randomness has a subtler significance for ordinary business. It delivers extreme results for some firms and human endeavors but not for others. Life insurance and pensions are predictable businesses because the randomness is contained—average life spans aren’t going to suddenly change by much. But given the unpredictability of hurricanes, a property and casualty insurer concentrated in South Florida should prepare for the possibility of very big variations in claims.

People have a tough time accepting that many realms are beyond the power to forecast. Even retrospectively, many occurrences cannot be explained. In general, news events and historical developments are less predictable than people suppose. No one could have forecast Sept. 11, 2001, with any useful specificity—nor could economists foresee in 1970 that wealth distribution was about to skew, the rich were about to get richer, and middle America was going to stagnate. The number of variables is simply too great. The important variables (say, the potential of a new industry called software) might not even be recognized.

It’s possible to avoid falling for the narrative fallacy. If you’re an economist, this means trying not to superimpose, say, a pattern from the last economic cycle on the future. If you’re in business, assessing diffuse data to figure out whether to go ahead with an investment, it means resisting the temptation to see a coherent story in that data. Reality is messy. If the investment works, it will have to work in a future that abides a wide range of ?environments—recessions, new competitors, wars.

Investors, in particular, should be wary of falling in love with a stock’s story, after which every data point is seen as confirmation. Securities analysis is not an exercise in metaphysical truth seeking. It’s a prosaic business of buying stocks at a discount. But judgments can be wrong, or random stuff can make them wrong.

In the broader world, refusing to accept that stuff can go randomly wrong can lead to frustration, then to blame. A reason is sought.

Consider a pair of benign examples. The first is from The Black Swan. Taleb offers the sentence “The king died, and the queen died.” It is unmemorable. Then a slight refinement: “The king died, and then the queen died of grief.” Voilà! We have narrative; what’s more, we have the satisfaction of causation. No need to wonder why the queen died—it was from grief.

The second is from Michael Lewis’s new book, The Undoing Project. Lewis describes an experiment conducted by the Israeli psychologists Daniel Kahneman and Amos Tversky, pioneers in behavioral economics. For the purpose of quizzing unknowing test subjects, the two invented ?“Linda.” They cast her as a type: “31 years old, single, outspoken and very bright?…?deeply concerned with issues of social justice.”

They then asked subjects which was more likely—(a) “Linda is a bank teller” or (b) “Linda is a bank teller and is active in the feminist movement.” Since feminist bank-teller Lindas are a subset of all bank-teller Lindas, the correct answer is (a). But 85% of the subjects said (b)! People find it easier to engage with a feminist Linda who cares about social justice. This Linda adheres to a preconceived type; she bows to the narrative fallacy.

Kahneman and Tversky, in Lewis’s words, were asking how “a person’s understanding of what he sees change(s) with the context in which he sees it.” Taleb supplies an answer, also anticipated by the psychologists: People need narrative.

It’s striking how similar this is to the language of paranoia and conspiracy. The idea of “paranoia” as a political tendency was introduced by historian Richard Hofstadter. In “The Paranoid Style in American Politics,” an essay that appeared in Harper’s Magazine a year after the Kennedy assassination, Hofstadter traced the history of conspiracy theory in American life. A politically paranoid person, Hofstadter wrote, “is always manning the barricades of civilization,” engaged (so he believes) in a life-and-death struggle against an “amoral superman” endowed with magical, malevolent powers: “He makes crises, starts runs on banks, causes depressions, manufactures disasters.”

Although Hofstadter was writing about McCarthyism, his words apply equally to contemporary Newtown school-shooting deniers, vaccine paranoids, and the sundry confabulations (birtherism, millions of illegal voters) echoed in Donald Trump’s speeches. From the distance of half a century, one phrase leaps off the page: “The paranoid mind is far more coherent than the real world.”

The Boston Globe recently talked to a supporter of the President in Bellevue, Ohio, who lamented the disappearance of mom-and-pop stores. “You can thank the government for that,” he said. Notice how his chosen culprit supplies coherence: a simple explanation for a numbingly complex issue.

Hofstadter’s “coherence” is the narrative fallacy, harnessed to the saddle of political anxiety. Behavioral economists have shown us why we’re prone to think that way. Their insights go far beyond economics, to politics and ordinary life. Narrative fulfills and satisfies. It answers a human need. The queen died of grief. If only we knew it.

A version of this article appears in the March 15, 2017 issue of Fortune with the headline "The Stories We Fall For."