中英文稿

不管你是否喜歡，?絕對透明和利用算法的 決策時代正在快速到來，?并且將改變你的生活。?因為現(xiàn)在把算法嵌入到計算機(jī)中?十分容易，?它能收集你所有的?私自保留的信息，?明白哪些是你需要的，?然后引導(dǎo)計算機(jī)與你互動，?這比大部分人跟你 互動的效果好多了。?這聽上去似乎有點(diǎn)嚇人。?不過我這么做已經(jīng)很久了， 發(fā)現(xiàn)其實很管用。?我的目標(biāo)是做有意義的工作，?與同事建立有意義的工作關(guān)系，?我知道，如果沒有?絕對透明和算法決策的支持， 這一切都不會發(fā)生。?我想告訴你們?yōu)槭裁磿@樣，?并展示它是如何運(yùn)作的。?

接下來向你們展示的?也許有點(diǎn)不同尋常。?當(dāng)我還是小孩子時， 我憎惡死記硬背。?不喜歡按部就班，?也不擅長墨守成規(guī)。?我喜歡用自己的方式來理解事物。?我12歲的時候，?討厭上學(xué)，但愛上了市場交易。?我當(dāng)時是個小球童，?服務(wù)一包球得5美元。?我把自己的球童收入投入了股市。?因為那個時候股市很火爆。?我買的第一個公司股票是?一家叫做東北航空的公司。?東北航空是當(dāng)時我所知道?唯一的單股價格低于 5 美元的。?（笑）?我計劃買更多的股票，?如果上漲，就可以賺更多的錢。?這個策略聽上去挺傻的吧？?不過我賺了3倍的錢，?因為當(dāng)時運(yùn)氣不錯。?那家公司快破產(chǎn)了，?最后被其他公司收購，?我就翻了3倍。?從此就一發(fā)不可收拾。?我在想： “這個游戲真簡單。”?后來，?我才明白這個游戲一點(diǎn)也不簡單。?要成為一個成功的投資人，?要和市場對賭，?還得成功。?要做到這一點(diǎn)很難。?之所以難，?是因為股票價格是由市場決定的。?不過要成為一個?成功的企業(yè)家，?你必須與市場對賭并且贏得勝利。

?我是個企業(yè)家，也是一個投資家，?扮演這種角色的代價就是 獲得了很多慘痛的教訓(xùn)。?我的確得到了很多教訓(xùn)，?隨后，?我對于犯錯的態(tài)度轉(zhuǎn)變了。?我開始意識到它們是謎團(tuán)。?如果我能解開這些謎團(tuán)，?就會獲得巨大的回報。?這個謎團(tuán)就是：?如何防止我在將來不犯大錯？?而要獲得回報的原則就是?記錄并回憶它們，?這對將來的決策很有幫助。?因為我清楚地做了詳細(xì)的記錄，?最終——?我發(fā)現(xiàn)了——?我能將這些方法嵌入算法中。?再把算法嵌入計算機(jī)中，?計算機(jī)就能協(xié)助我做決定；?同時，我們也能制定出最后的決策。?和我自己做的決策對比之后，?我發(fā)現(xiàn)這些決策要好很多。?因為計算機(jī)做決策更快，?能處理更多信息，?幾乎不帶有任何情緒地?處理更多決策。?于是它徹底提高了 我的決策正確率。?

在創(chuàng)建了Bridgewater 8年后，?我犯下了人生中最大的錯誤，?經(jīng)歷了最大的一次失敗。?那是1970年代末，?我當(dāng)時34歲，?計算出美國銀行?出借了太多資金給新興國家，?而不是那些有能力還款的國家。?這會導(dǎo)致自大蕭條后?最大的債務(wù)危機(jī)。?隨后，就會出現(xiàn)經(jīng)濟(jì)危機(jī)，?股市走熊。?這個觀點(diǎn)當(dāng)時是有爭議的，?人們認(rèn)為我的想法太瘋狂。?但是在1982年8月，?墨西哥違約，?其他一系列國家跟進(jìn)，?的確出現(xiàn)了自大蕭條后 最嚴(yán)重的債務(wù)危機(jī)。?因為我早先的預(yù)測，?我被國會要求出席 “華爾街之周”的聽證會，?當(dāng)時備受關(guān)注。?我裁剪了一小短現(xiàn)場錄像，?大家可以看到我。?（錄像）主席先生，米切爾先生，?很榮幸出席今天的會議，?檢查我們的經(jīng)濟(jì)哪里出了問題。?經(jīng)濟(jì)增長已經(jīng)停滯——?在失敗的邊緣搖搖欲墜。?Martin Zweig: 最近有一篇文章提到了你，?你說過：“我對此深信不疑，?我知道市場是如何運(yùn)作的?！?Ray Dalio： 我能毫不猶豫的說，?如果你們在公司和整個世界范圍內(nèi)?觀察流動性基礎(chǔ)，?流動性大幅度減少，?你不可能回到一個滯脹的時代?！?現(xiàn)在回過頭來看， 我想的是：“多么自負(fù)的傻瓜！”?（笑）?我太驕傲了，而且大錯特錯。?我的意思是，盡管債務(wù)危機(jī)發(fā)生了，?股市和經(jīng)濟(jì)卻都在上漲，而非下降，?我損失了自己和客戶的大量資金，?不得不關(guān)閉整個公司，?遣散所有的員工。?這是一個延伸的大家庭，?我的心都要碎了。?我本人也損失慘重，?不得不向我父親借了4000美元?來支付家庭的日常開支。

?這是我人生經(jīng)歷中 最慘痛的一次經(jīng)歷。。。?同時也成為我人生經(jīng)歷中 最珍貴的一次經(jīng)歷，?因為它改變了我對決策的態(tài)度。?不再去想，“我是正確的”，?我現(xiàn)在開始詢問自己?“你如何才知道自己是正確的？”?為了平衡我的魯莽，?我學(xué)會了什么是謙卑。?我需要找到最聰明的人來反駁我，?以此來理解他們的觀點(diǎn)，?或者來測試我的觀點(diǎn)。?我稱之為“精英對話”。?換個說法是，?不利用自己的權(quán)利來帶領(lǐng)其他人，?也不是每個人都能 平等說出自己觀點(diǎn)的民主氛圍，?而是用精英對話的方式， 來得到最好的點(diǎn)子。?為了實現(xiàn)這一點(diǎn)，?我意識到公司需要完全的信任，?完全的透明，?我指的絕對誠實和絕對透明，?是指人們能表達(dá)出自己的真實想法，?能看得更加全面。?我們會記錄下所有的對話，?讓每個人都能看清一切，?如果我們不這么做，?就不能實現(xiàn)真正的精英管理。?為了達(dá)到這個目的，?我們需要讓人們自由表達(dá)。

?這里有個例子，?這是一封Jim Haskel 寫給我的電子郵件——?Jim 是我的員工——?公司中任何人都可以這樣做。?“Ray，針對你今天在會上的表現(xiàn)，?只能給你評個D-，?你根本沒有做好準(zhǔn)備，?從來沒有哪次會議 組織得這么混亂?！?是不是很棒？?（笑）?的確很棒。?因為首先，我需要這樣的反饋。?我需要像這樣的反饋。?如果我不支持Jim或者其他人?自由表達(dá)自己的觀點(diǎn)，?我們的關(guān)系就會變得不同了。?如果我不讓每個人都能看見，?這就不是精英對話。?我們這樣做了25年，?保持絕對透明，?憑著這樣的原則，?避免了重大錯誤，?于是我們把這個原則植入了算法中。

?算法向我們證明了——?我們可以跟隨算法做決策，?同時也運(yùn)用我們自己的思考。?這就是我們?nèi)绾谓?jīng)營投資公司，?以及如何管理員工。?為了讓你們更好的理解我的意思，?我想邀請大家參加一個會議，?看看我們的工具“網(wǎng)絡(luò)收集者”?是如何幫助我們完成目標(biāo)的。?在美國大選一周后，?我們的調(diào)查團(tuán)隊開了個會，?討論特朗普當(dāng)選 對美國經(jīng)濟(jì)意味著什么。?很自然的，人們對此有很多不同觀點(diǎn)，?我們?nèi)绾芜M(jìn)行這次討論呢？?“網(wǎng)絡(luò)收集器”收集觀點(diǎn)?其中列舉了幾十個屬性，?只要思考其他人的想法?就很容易交換評價。?屬性提供從1-10的評價系統(tǒng)。?比如，會議開始時，?調(diào)查者Jen給我的評分是3——?換句話說，很糟——?（笑）?在開放和自信的 平衡方面做得不好。?

隨著會議繼續(xù)進(jìn)行，?Jen也對其他人也會做出評價，?屋里其他人有各自不同的觀點(diǎn)。?這很正常。?不同人總有不同觀點(diǎn)，?怎么分辨誰對誰錯呢？?只關(guān)注一下人們對我的評價。?一些人認(rèn)為我做的很好，?另外一些人認(rèn)為很糟。?依據(jù)不同的觀點(diǎn)，?我們能探索數(shù)字背后的原因。?這是Jen和Larry說的。?每人都有機(jī)會表達(dá)想法，?包括他們的評判式思考，?無論他們在公司里的地位如何。?Jen，24歲，剛從學(xué)校畢業(yè)，?她能告訴我，他們的CEO， 在解決問題方面很糟。?這個工具幫助人們自由表達(dá)，?把觀點(diǎn)和個人分離開，?從更高層次看問題。?當(dāng)Jen和其他人交換觀點(diǎn)?并縱覽整個屏幕，?他們的觀念就會發(fā)生轉(zhuǎn)變。?他們看出自己代表的 僅僅是其中的一部分觀點(diǎn)，?自然會開始自問，?“我怎么知道我的觀點(diǎn)是正確的呢？”?觀點(diǎn)的轉(zhuǎn)變就像只從一個維度?看多維空間。?

談話從爭論各種觀點(diǎn)，轉(zhuǎn)變成了?找到客觀標(biāo)準(zhǔn)來決定 哪個觀點(diǎn)最好。?在“網(wǎng)絡(luò)收集器”的背后， 計算機(jī)正在觀察?所有這些人在想什么，?并與他們?nèi)绾嗡伎枷嚓P(guān)聯(lián)。?基于這些談話反饋，?從所有會議中抽取數(shù)據(jù)，?對人們的特點(diǎn)和他們的想法?做出數(shù)據(jù)點(diǎn)圖。?這個過程由算法引導(dǎo)，?了解人們的特點(diǎn)可以 幫助他們更好的與工作進(jìn)行匹配。?例如，?創(chuàng)造性思想家不太靠譜，?也許可以匹配一個靠譜 但是無創(chuàng)造性的人。?知道其他人的想法， 也讓我們能夠決定?應(yīng)該賦予他們怎樣的責(zé)任，?并基于人們的優(yōu)勢 來權(quán)衡我們的決策。?我們稱之為他們的“可依賴度”。?

這里有個我們投票的例子，?大部分人同意一個方案——?但當(dāng)我們根據(jù)各自優(yōu)勢來權(quán)衡時，?答案就不同了。?這個過程讓我們做決定的時候 不再基于民主，?不基于專制，?僅基于考慮人們可依賴度的算法。?沒錯，我們就是這樣做的。?（笑）?因為這消除了?人性中最大的悲劇，?就是人類的自大、?天真，自以為是的 持有錯誤的觀點(diǎn)，?并執(zhí)行它，?而不放在壓力環(huán)境中進(jìn)行測試，?就會產(chǎn)生悲劇。?我們相信這樣做會提升自己，?開始從別人眼中看問題，?用集體方式的看待問題。?如果運(yùn)用良好，集體決策比個人決策?要好很多。?這就是我們成功背后的秘密武器。

?正因為如此，對比其他對沖基金公司，?我們可以為客戶賺更多錢。?26年中有23年都是如此。?那么絕對信任和絕對透明的?問題是什么呢？?人們認(rèn)為從感情上很難接受。?有批評家說這是 嚴(yán)酷工作環(huán)境的模式。?神經(jīng)科學(xué)家告訴我， 這與大腦如何進(jìn)行預(yù)先假設(shè)有關(guān)。?一部分大腦知道我們會犯錯，?直面缺點(diǎn)會讓后我們變得更好。?這是前額皮層做的事情。?大腦的另外一部分 會將這些當(dāng)成抨擊的觀點(diǎn)。?這是在大腦杏仁核區(qū)域發(fā)生的。?換句話說，有2個人在你體內(nèi)，?一個極富情緒，?另一個很理智，?他們彼此常?；ゲ幌嗳荩?時常對抗你。?我們的經(jīng)驗表明， 我們可以贏得這場戰(zhàn)爭，?以團(tuán)隊的方式。?通常需要18個月?去發(fā)現(xiàn)大部分人喜歡的方式，?以完全透明，?而非隱蔽的環(huán)境中 進(jìn)行公司改造。?沒有政治，沒有野蠻——?也就是沒有任何暗箱操作——?人們可以暢所欲言的表達(dá)。?這樣很好。?給我們更有效的工作，?以及更有效的人際關(guān)系。?但這并不適用于任何人。?我們發(fā)現(xiàn)有25%-30%的人?不適合這樣做。?

順便提一句，?當(dāng)我說絕對透明，?不是指對任何事情都絕對透明。?你不需要告訴別人 他們禿頂越來越嚴(yán)重，?或者他們的孩子很難看。?我的意思是——?（笑）?重要的事情。?所以——?（笑）?當(dāng)你離開這間屋子，?我希望你在與他人交流時 能仔細(xì)觀察自己，?想象如果你能真正理解他人的思想，?真正理解他人的特質(zhì)，?想象他們能真正理解你的思想，?理解你的特質(zhì)。?這樣一來一定會 幫你理清很多事情，?使得你們在一起合作更加高效。?我想這樣也會促進(jìn)你們的關(guān)系。?現(xiàn)在想象，你有一些算法?幫助你收集信息，?甚至幫助你做出優(yōu)選決策。?這樣絕對透明的時代馬上就要到來，?影響你的生活。?依我的拙見，?前途是光明的。?我希望這個方法對你有幫助，?就如同我們所收獲的一樣。?十分感謝。?（鼓掌）

Whether you like it or not,?radical transparency and algorithmic decision-making is coming at you fast,?and it's going to change your life.?That's because it's now easy to take algorithms?and embed them into computers?and gather all that data that you're leaving on yourself?all over the place,?and know what you're like,?and then direct the computers to interact with you?in ways that are better than most people can.

Well, that might sound scary.?I've been doing this for a long time and I have found it to be wonderful.?My objective has been to have meaningful work?and meaningful relationships with the people I work with,?and I've learned that I couldn't have that?unless I had that radical transparency and that algorithmic decision-making.?I want to show you why that is,?I want to show you how it works.?And I warn you that some of the things that I'm going to show you?probably are a little bit shocking.

Since I was a kid, I've had a terrible rote memory.?And I didn't like following instructions,?I was no good at following instructions.?But I loved to figure out how things worked for myself.?When I was 12,?I hated school but I fell in love with trading the markets.?I caddied at the time,?earned about five dollars a bag.?And I took my caddying money, and I put it in the stock market.?And that was just because the stock market was hot at the time.?And the first company I bought?was a company by the name of Northeast Airlines.?Northeast Airlines was the only company I heard of?that was selling for less than five dollars a share.

(Laughter)

And I figured I could buy more shares,?and if it went up, I'd make more money.?So, it was a dumb strategy, right??But I tripled my money,?and I tripled my money because I got lucky.?The company was about to go bankrupt,?but some other company acquired it,?and I tripled my money.?And I was hooked.?And I thought, "This game is easy."?With time,?I learned this game is anything but easy.

In order to be an effective investor,?one has to bet against the consensus?and be right.?And it's not easy to bet against the consensus and be right.?One has to bet against the consensus and be right?because the consensus is built into the price.?And in order to be an entrepreneur,?a successful entrepreneur,?one has to bet against the consensus and be right.?I had to be an entrepreneur and an investor --?and what goes along with that is making a lot of painful mistakes.?So I made a lot of painful mistakes,?and with time,?my attitude about those mistakes began to change.?I began to think of them as puzzles.?That if I could solve the puzzles,?they would give me gems.?And the puzzles were:?What would I do differently in the future so I wouldn't make that painful mistake??And the gems were principles?that I would then write down so I would remember them?that would help me in the future.?And because I wrote them down so clearly,?I could then --?eventually discovered --?I could then embed them into algorithms.?And those algorithms would be embedded in computers,?and the computers would make decisions along with me;?and so in parallel, we would make these decisions.?And I could see how those decisions then compared with my own decisions,?and I could see that those decisions were a lot better.?And that was because the computer could make decisions much faster,?it could process a lot more information?and it can process decisions much more --?less emotionally.?So it radically improved my decision-making.

Eight years after I started Bridgewater,?I had my greatest failure,?my greatest mistake.?It was late 1970s,?I was 34 years old,?and I had calculated that American banks?had lent much more money to emerging countries?than those countries were going to be able to pay back?and that we would have the greatest debt crisis?since the Great Depression.?And with it, an economic crisis?and a big bear market in stocks.?It was a controversial view at the time.?People thought it was kind of a crazy point of view.?But in August 1982,?Mexico defaulted on its debt,?and a number of other countries followed.?And we had the greatest debt crisis since the Great Depression.?And because I had anticipated that,?I was asked to testify to Congress and appear on "Wall Street Week,"?which was the show of the time.?Just to give you a flavor of that, I've got a clip here,?and you'll see me in there.

(Video) Mr. Chairman, Mr. Mitchell,?it's a great pleasure and a great honor to be able to appear before you?in examination with what is going wrong with our economy.?The economy is now flat --?teetering on the brink of failure.

Martin Zweig: You were recently quoted in an article.?You said, "I can say this with absolute certainty?because I know how markets work."

Ray Dalio: I can say with absolute certainty?that if you look at the liquidity base?in the corporations and the world as a whole,?that there's such reduced level of liquidity?that you can't return to an era of stagflation."

I look at that now, I think, "What an arrogant jerk!"

(Laughter)

I was so arrogant, and I was so wrong.?I mean, while the debt crisis happened,?the stock market and the economy went up rather than going down,?and I lost so much money for myself and for my clients?that I had to shut down my operation pretty much,?I had to let almost everybody go.?And these were like extended family,?I was heartbroken.?And I had lost so much money?that I had to borrow 4,000 dollars from my dad?to help to pay my family bills.

It was one of the most painful experiences of my life ...?but it turned out to be one of the greatest experiences of my life?because it changed my attitude about decision-making.?Rather than thinking, "I'm right,"?I started to ask myself,?"How do I know I'm right?"?I gained a humility that I needed?in order to balance my audacity.?I wanted to find the smartest people who would disagree with me?to try to understand their perspective?or to have them stress test my perspective.?I wanted to make an idea meritocracy.?In other words,?not an autocracy in which I would lead and others would follow?and not a democracy in which everybody's points of view were equally valued,?but I wanted to have an idea meritocracy in which the best ideas would win out.?And in order to do that,?I realized that we would need radical truthfulness?and radical transparency.

What I mean by radical truthfulness and radical transparency?is people needed to say what they really believed?and to see everything.?And we literally tape almost all conversations?and let everybody see everything,?because if we didn't do that,?we couldn't really have an idea meritocracy.?In order to have an idea meritocracy,?we have let people speak and say what they want.?Just to give you an example,?this is an email from Jim Haskel --?somebody who works for me --?and this was available to everybody in the company.?"Ray, you deserve a 'D-'?for your performance today in the meeting ...?you did not prepare at all well?because there is no way you could have been that disorganized."?Isn't that great?

(Laughter)

That's great.?It's great because, first of all, I needed feedback like that.?I need feedback like that.?And it's great because if I don't let Jim, and people like Jim,?to express their points of view,?our relationship wouldn't be the same.?And if I didn't make that public for everybody to see,?we wouldn't have an idea meritocracy.

So for that last 25 years that's how we've been operating.?We've been operating with this radical transparency?and then collecting these principles,?largely from making mistakes,?and then embedding those principles into algorithms.?And then those algorithms provide --?we're following the algorithms?in parallel with our thinking.?That has been how we've run the investment business,?and it's how we also deal with the people management.

In order to give you a glimmer into what this looks like,?I'd like to take you into a meeting?and introduce you to a tool of ours called the "Dot Collector"?that helps us do this.?A week after the US election,?our research team held a meeting?to discuss what a Trump presidency would mean for the US economy.?Naturally, people had different opinions on the matter?and how we were approaching the discussion.?The "Dot Collector" collects these views.?It has a list of a few dozen attributes,?so whenever somebody thinks something about another person's thinking,?it's easy for them to convey their assessment;?they simply note the attribute and provide a rating from one to 10.?For example, as the meeting began,?a researcher named Jen rated me a three --?in other words, badly --

(Laughter)

for not showing a good balance of open-mindedness and assertiveness.?As the meeting transpired,?Jen's assessments of people added up like this.?Others in the room have different opinions.?That's normal.?Different people are always going to have different opinions.?And who knows who's right??Let's look at just what people thought about how I was doing.?Some people thought I did well,?others, poorly.?With each of these views,?we can explore the thinking behind the numbers.?Here's what Jen and Larry said.?Note that everyone gets to express their thinking,?including their critical thinking,?regardless of their position in the company.?Jen, who's 24 years old and right out of college,?can tell me, the CEO, that I'm approaching things terribly.

This tool helps people both express their opinions?and then separate themselves from their opinions?to see things from a higher level.?When Jen and others shift their attentions from inputting their own opinions?to looking down on the whole screen,?their perspective changes.?They see their own opinions as just one of many?and naturally start asking themselves,?"How do I know my opinion is right?"?That shift in perspective is like going from seeing in one dimension?to seeing in multiple dimensions.?And it shifts the conversation from arguing over our opinions?to figuring out objective criteria for determining which opinions are best.

Behind the "Dot Collector" is a computer that is watching.?It watches what all these people are thinking?and it correlates that with how they think.?And it communicates advice back to each of them based on that.?Then it draws the data from all the meetings?to create a pointilist painting of what people are like?and how they think.?And it does that guided by algorithms.?Knowing what people are like helps to match them better with their jobs.?For example,?a creative thinker who is unreliable?might be matched up with someone who's reliable but not creative.?Knowing what people are like also allows us to decide?what responsibilities to give them?and to weigh our decisions based on people's merits.?We call it their believability.?Here's an example of a vote that we took?where the majority of people felt one way ...?but when we weighed the views based on people's merits,?the answer was completely different.?This process allows us to make decisions not based on democracy,?not based on autocracy,?but based on algorithms that take people's believability into consideration.

Yup, we really do this.

(Laughter)

We do it because it eliminates?what I believe to be one of the greatest tragedies of mankind,?and that is people arrogantly,******** holding opinions in their minds that are wrong,?and acting on them,?and not putting them out there to stress test them.?And that's a tragedy.?And we do it because it elevates ourselves above our own opinions?so that we start to see things through everybody's eyes,?and we see things collectively.?Collective decision-making is so much better than individual decision-making?if it's done well.?It's been the secret sauce behind our success.?It's why we've made more money for our clients?than any other hedge fund in existence?and made money 23 out of the last 26 years.

So what's the problem with being radically truthful?and radically transparent with each other??People say it's emotionally difficult.?Critics say it's a formula for a brutal work environment.?Neuroscientists tell me it has to do with how are brains are prewired.?There's a part of our brain that would like to know our mistakes?and like to look at our weaknesses so we could do better.?I'm told that that's the prefrontal cortex.?And then there's a part of our brain which views all of this as attacks.?I'm told that that's the amygdala.?In other words, there are two you's inside you:?there's an emotional you?and there's an intellectual you,?and often they're at odds,?and often they work against you.?It's been our experience that we can win this battle.?We win it as a group.?It takes about 18 months typically?to find that most people prefer operating this way,?with this radical transparency?than to be operating in a more opaque environment.?There's not politics, there's not the brutality of --?you know, all of that hidden, behind-the-scenes --?there's an idea meritocracy where people can speak up.?And that's been great.?It's given us more effective work,?and it's given us more effective relationships.?But it's not for everybody.?We found something like 25 or 30 percent of the population?it's just not for.?And by the way,?when I say radical transparency,?I'm not saying transparency about everything.?I mean, you don't have to tell somebody that their bald spot is growing?or their baby's ugly.?So, I'm just talking about --

(Laughter)

talking about the important things.?So --

(Laughter)

So when you leave this room,?I'd like you to observe yourself in conversations with others.?Imagine if you knew what they were really thinking,?and imagine if you knew what they were really like ...?and imagine if they knew what you were really thinking?and what were really like.?It would certainly clear things up a lot?and make your operations together more effective.?I think it will improve your relationships.?Now imagine that you can have algorithms?that will help you gather all of that information?and even help you make decisions in an idea-meritocratic way.?This sort of radical transparency is coming at you?and it is going to affect your life.?And in my opinion,?it's going to be wonderful.?So I hope it is as wonderful for you?as it is for me.

Thank you very much.

(Applause)