Whether you like it or not, radical transparency and algorithmicdecision-making is coming at you fast, and it's going to change your life.
That's because it's now easyto take algorithms and embed them into computers and gather all that datathat you're leaving on yourself all over the place, and know what you're like, and then direct the computersto interact with you in ways that are betterthan most people can.
Well, that might sound scary.
I've been doing this for a long timeand I have found it to be wonderful.
My objective has beento have meaningful work and meaningful relationshipswith the people I work with, and I've learned that I couldn't have that unless I had that radical transparencyand 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 thingsthat 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 outhow things worked for myself.
When I was 12, I hated school but I fell in lovewith 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 becausethe stock market was hot at the time.
And the first company I bought was a company by the nameof Northeast Airlines.
Northeast Airlines wasthe only company I heard of that was selling for lessthan 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 moneybecause 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 betagainst the consensus and be right.
One has to bet againstthe consensus and be right because the consensusis built into the price.
And in order to be an entrepreneur, a successful entrepreneur, one has to bet againstthe consensus and be right.
I had to be an entrepreneurand an investor — and what goes along with thatis making a lot of painful mistakes.
So I made a lot of painful mistakes, and with time, my attitude about those mistakesbegan 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 futureso I wouldn't make that painful mistake? And the gems were principles that I would then write downso 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 algorithmswould be embedded in computers, and the computers wouldmake decisions along with me; and so in parallel, we would make these decisions.
And I could see how those decisionsthen compared with my own decisions, and I could see thatthose decisions were a lot better.
And that was because the computercould make decisions much faster, it could process a lot more information and it can process decisions much more — less emotionally.
So it radically improvedmy 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 moneyto emerging countries than those countrieswere going to be able to pay back and that we would havethe 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 waskind 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 crisissince the Great Depression.
And because I had anticipated that, I was asked to testify to Congressand 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 honorto be able to appear before you in examination with whatis going wrong with our economy.
The economy is now flat — teetering on the brink of failure.
Martin Zweig: You were recentlyquoted in an article.
You said, “I can say thiswith absolute certainty because I know how markets work.
” Ray Dalio: I can saywith absolute certainty that if you look at the liquidity base in the corporationsand the world as a whole, that there's such reducedlevel of liquidity that you can't returnto 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 economywent up rather than going down, and I lost so much moneyfor myself and for my clients that I had to shut downmy 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 borrow4, 000 dollars from my dad to help to pay my family bills.
It was one of the most painfulexperiences of my life .
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but it turned out to beone of the greatest experiences of my life because it changed my attitudeabout 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 smartestpeople who would disagree with me to try to understand their perspective or to have themstress test my perspective.
I wanted to make an idea meritocracy.
In other words, not an autocracy in whichI would lead and others would follow and not a democracy in which everybody'spoints of view were equally valued, but I wanted to have an idea meritocracyin which the best ideas would win out.
And in order to do that, I realized that we would needradical truthfulness and radical transparency.
What I mean by radical truthfulnessand radical transparency is people needed to saywhat they really believed and to see everything.
And we literallytape almost all conversations and let everybody see everything, because if we didn't do that, we couldn't really havean idea meritocracy.
In order to have an idea meritocracy, we have let people speakand 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 availableto everybody in the company.
“Ray, you deserve a 'D-' for your performancetoday in the meeting .
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you did not prepare at all well because there is no wayyou 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 publicfor everybody to see, we wouldn't have an idea meritocracy.
So for that last 25 yearsthat's how we've been operating.
We've been operatingwith this radical transparency and then collecting these principles, largely from making mistakes, and then embeddingthose principles into algorithms.
And then those algorithms provide — we're following the algorithms in parallel with our thinking.
That has been how we've runthe investment business, and it's how we also dealwith the people management.
In order to give you a glimmerinto what this looks like, I'd like to take you into a meeting and introduce you to a tool of ourscalled 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 presidencywould mean for the US economy.
Naturally, people haddifferent opinions on the matter and how we wereapproaching the discussion.
The “Dot Collector” collects these views.
It has a list of a few dozen attributes, so whenever somebody thinks somethingabout another person's thinking, it's easy for themto convey their assessment; they simply note the attributeand 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 balanceof open-mindedness and assertiveness.
As the meeting transpired, Jen's assessments of peopleadded up like this.
Others in the roomhave different opinions.
That's normal.
Different people are alwaysgoing to have different opinions.
And who knows who's right? Let's look at just what people thoughtabout how I was doing.
Some people thought I did well, others, poorly.
With each of these views, we can explore the thinkingbehind the numbers.
Here's what Jen and Larry said.
Note that everyonegets to express their thinking, including their critical thinking, regardless of their positionin the company.
Jen, who's 24 years oldand right out of college, can tell me, the CEO, that I'm approaching things terribly.
This tool helps peopleboth express their opinions and then separate themselvesfrom their opinions to see things from a higher level.
When Jen and others shift their attentionsfrom inputting their own opinions to looking down on the whole screen, their perspective changes.
They see their own opinionsas just one of many and naturally start asking themselves, “How do I know my opinion is right?” That shift in perspective is like goingfrom seeing in one dimension to seeing in multiple dimensions.
And it shifts the conversationfrom arguing over our opinions to figuring out objective criteriafor determining which opinions are best.
Behind the “Dot Collector”is a computer that is watching.
It watches what allthese people are thinking and it correlates thatwith how they think.
And it communicates adviceback to each of them based on that.
Then it draws the datafrom all the meetings to create a pointilist paintingof what people are like and how they think.
And it does that guided by algorithms.
Knowing what people are like helpsto match them better with their jobs.
For example, a creative thinker who is unreliable might be matched up with someonewho's reliable but not creative.
Knowing what people are likealso allows us to decide what responsibilities to give them and to weigh our decisionsbased on people's merits.
We call it their believability.
Here's an example of a vote that we took where the majorityof people felt one way .
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but when we weighed the viewsbased on people's merits, the answer was completely different.
This process allows us to make decisionsnot based on democracy, not based on autocracy, but based on algorithms that takepeople's believability into consideration.
Yup, we really do this.
(Laughter) We do it because it eliminates what I believe to beone of the greatest tragedies of mankind, and that is people arrogantly, naïvely holding opinionsin their minds that are wrong, and acting on them, and not putting them out thereto stress test them.
And that's a tragedy.
And we do it because it elevates ourselvesabove our own opinions so that we start to see thingsthrough everybody's eyes, and we see things collectively.
Collective decision-making is so muchbetter than individual decision-making if it's done well.
It's been the secret saucebehind our success.
It's why we've mademore money for our clients than any other hedge fund in existence and made money23 out of the last 26 years.
So what's the problemwith being radically truthful and radically transparent with each other? People say it's emotionally difficult.
Critics say it's a formulafor a brutal work environment.
Neuroscientists tell me it has to dowith how are brains are prewired.
There's a part of our brainthat would like to know our mistakes and like to look at our weaknessesso we could do better.
I'm told that that'sthe prefrontal cortex.
And then there's a part of our brainwhich 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 experiencethat we can win this battle.
We win it as a group.
It takes about 18 months typically to find that most peopleprefer operating this way, with this radical transparency than to be operatingin 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 meritocracywhere people can speak up.
And that's been great.
It's given us more effective work, and it's given usmore effective relationships.
But it's not for everybody.
We found something like25 or 30 percent of the population it's just not for.
And by the way, when I say radical transparency, I'm not saying transparencyabout everything.
I mean, you don't have to tell somebodythat 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 yourselfin conversations with others.
Imagine if you knewwhat they were really thinking, and imagine if you knewwhat they were really like .
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and imagine if they knewwhat you were really thinking and what were really like.
It would certainly clear things up a lot and make your operationstogether more effective.
I think it will improveyour relationships.
Now imagine that you can have algorithms that will help you gatherall of that information and even help you make decisionsin an idea-meritocratic way.
This sort of radical transparencyis 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).
