Speaker 1: This is Bloomberg Surveillance. I think there are some signs,
Speaker 1: some early signs that manufacturing is rebounding, including in the US.
Speaker 1: An investment outlook beyond three or four years, to me
Speaker 1: is more barry, like a religion or a philosophy than
Speaker 1: a forecast. This economy is just stuck there. Used to
Speaker 1: be stuck above two percent. Now it seems to be
Speaker 1: stuck in this one and three quarters to till in
Speaker 1: a quarter type growth, right. Bloomberg Surveillance your link to
Speaker 1: the world of economics, finance, and investment on Bloomberg Radio.
Speaker 1: Good morning everyone, Michael McKee and Tom Keane Bloomberg Surveillance
Speaker 1: barrier rid holds in for Mr McKee today. Maybe we'll
Speaker 1: get them tomorrow as well and under Friday. It would
Speaker 1: be an exciting week of very rid holes here if
Speaker 1: we can, you know, because people people in that uh
Speaker 1: Marcus on the move. Futures up, eleven down, futures up.
Speaker 1: N Bloomberg Surveillance This morning brought you by co Resnick
Speaker 1: Accounting Tax Advisory. To keep your business on top of
Speaker 1: issues in the evolving renewable energy market, it takes dedicated
Speaker 1: industry experts like Cone Resnick find out more at cone
Speaker 1: Resnick dot com, CEO H N R. E Z and
Speaker 1: I C. K. Cone Resnick uh dot com. Um, this
Speaker 1: is gonna be an important discussion. We do economics, finance, investment,
Speaker 1: and international relations, and what we really do in the
Speaker 1: word that we use is insight. Unfortunately, there is the
Speaker 1: illusion or I would even say the delusion of insight,
Speaker 1: and that you've got to be careful and how you
Speaker 1: interpret the insight. This, of course from the Daniel kahnman
Speaker 1: Uh in his iconic work common in divers Key out
Speaker 1: of Princeton with Warten Philip Tetlock, Edinburgh Professor on Democracy
Speaker 1: and Citizenship, and particularly Philip Tetlock on how we communicate
Speaker 1: in this digital overloaded world professor, good morning, good morning.
Speaker 1: Tell me about the illusion of insight. We think we're
Speaker 1: getting smarter listening to Bloomberg surveillance. Are we fooling ourselves? Well,
Speaker 1: you know, it's really hard to tell whether someone knows
Speaker 1: what they're talking about, So we use proxies. We say, well,
Speaker 1: does this person have high status? We say, is this
Speaker 1: person entertaining this personal equacious? Uh? There are a lot
Speaker 1: of proxy queues, but typically I think, what what your
Speaker 1: listeners care about the most is whether they have some
Speaker 1: insights into the future and that could guide their decisions,
Speaker 1: their business decisions. Uh. And the only way to ascertain
Speaker 1: that is to keep rigorous score. And that is something
Speaker 1: very two places do. We do it internally at Bloomberg.
Speaker 1: It was invented by myself and Emily has God Soul
Speaker 1: and Rachel Worstspan does it every day now in the
Speaker 1: basic idea for us is to work off the classic
Speaker 1: research of Edelman, Richard Edelman and the people there that
Speaker 1: look at expert oology in good experts in bad experts.
Speaker 1: We are drowning in that distinction right now in our
Speaker 1: political debate and some would say in our business media debate.
Speaker 1: How do you, as the pro discern between good experts
Speaker 1: and bad experts? Well, the work I've been doing with
Speaker 1: US intelligence community over the last several years involves um
Speaker 1: having people participate in forecasting tournaments and asking them very
Speaker 1: large numbers of questions over large spans of time, and
Speaker 1: keeping rigorous score. One of the big mistakes people make
Speaker 1: is they conclude that someone is a bad forecaster if
Speaker 1: they make one bad call, if they're on the wrong
Speaker 1: side of maybe on a big call Uh. And even
Speaker 1: the very best forecasters are, of course, often on the
Speaker 1: wrong side of maybe, so you need to keep it good.
Speaker 1: Judgment is an aggregate thing and recuse a lot of patients. Uh.
Speaker 1: And that doesn't lend itself to most media formats. So,
Speaker 1: Professor I learned from your earlier work on on expert
Speaker 1: political judgment that the average so called expert isn't an
Speaker 1: especially good forecaster. And then you updated that work in
Speaker 1: your most recent book, Super Forecasters, where you said, there
Speaker 1: are some basic things all forecasters can do to become
Speaker 1: better smarter prognosticators. Give us a few examples of what
Speaker 1: those of us who toil in the markets and the
Speaker 1: economy can do to think more intelligently about the future. Well,
Speaker 1: I think you had. You had an interesting quote from
Speaker 1: somebody at the very beginning of this piece in which
Speaker 1: somebody he was saying that an investment, uh, with whose
Speaker 1: claims they can see out four or five years, that's
Speaker 1: essentially philosophy, it's not forecasting. And that's more or less
Speaker 1: what my earlier work found. It was very, very hard
Speaker 1: for even the very smartest people to do appreciably better
Speaker 1: than chance. When you're looking at that farm, it is
Speaker 1: possible though, for smart people who get a lot of
Speaker 1: practice at it to make better probability judgments in the
Speaker 1: several months range. Okay, we've got a bad connection right
Speaker 1: now with Professor Tetlock. We're gonna reconnect. Ken Felio, who's
Speaker 1: so good at this, is going to try to reconnect
Speaker 1: and get a better signal. Barry, I think we need
Speaker 1: to say without question that the way to get smarter
Speaker 1: and this is a tough business, folks, It's not easy.
Speaker 1: I mentioned Daniel Kaneman and Connon and Traversky, which changed
Speaker 1: how all of us think day to day about behavioral
Speaker 1: finance and how wrong we are in what we trust
Speaker 1: and what we guess at. The modern book is Thinking
Speaker 1: Fast and Slow by Professor kenn Yeah. I discussed that
Speaker 1: with Professor Tetlock. He was our guest on Masters in
Speaker 1: Business last weekend, and he told the story of having
Speaker 1: met uh Danny Kahneman and discussing how when he began
Speaker 1: to quantify experts, he really wasn't impressed with what he saw,
Speaker 1: and Conman, just off the cuff said, I would imagine
Speaker 1: the average expert is no better than a well read
Speaker 1: New York Times person, and he turned out to be
Speaker 1: right as we as we try to get Professor on
Speaker 1: very one of the things that one of my gospel things.
Speaker 1: And we do this folks every day in surveillance. And
Speaker 1: thank you so much for your support. I love when
Speaker 1: experts are wrong. I learned more from somebody's smart I
Speaker 1: learned more from So well, let's bring professor lock back.
Speaker 1: We're talking to your professor. Thank you for coming back
Speaker 1: with us about the idea of somebody that I believe
Speaker 1: is a good guest, a good expert. I learn more
Speaker 1: when they're wrong than when they're right. Discussed that research. Oh,
Speaker 1: I think there's a deep truth to that. Uh. Very
Speaker 1: concrete example would be some of the controversy that surrounded
Speaker 1: Nate Silver, who had, of course a great reputation from
Speaker 1: some of the previous presidential and congressional races, and he
Speaker 1: made he made the comment in the late summer of
Speaker 1: that he thought the number of people who would take
Speaker 1: Donald Trump's candidacy seriously would be roughly comparable to the
Speaker 1: number of people who thought the moon landings were faked. Um,
Speaker 1: so you put the probability somewhere between zero and five percent,
Speaker 1: I believe, and a lot a lot of people would
Speaker 1: jump jumped on d silver for that UM. Now, even
Speaker 1: a perfectly calibrated forecaster, and I'll say in a minute,
Speaker 1: what a perfectly calibrated forecaster is. Even a perfectly calibrated
Speaker 1: forecaster is going to be wrong five percent of that
Speaker 1: UH is going to be wrong at least five percent
Speaker 1: of the time when when they when they say five
Speaker 1: percent UM. So it's a it's a difficult thing for
Speaker 1: people to appreciate. But you you can't judge the accuracy
Speaker 1: of probability judgments based on the individual forecast. You can
Speaker 1: only do that if the forecaster is really reckless and
Speaker 1: says hudd percent and it doesn't happen, or zero percent
Speaker 1: and it does. And in that case, you know the
Speaker 1: forecast a slat out wrong and forli forecast is taking
Speaker 1: a big reputational risk and it is lost um. But
Speaker 1: in the other cases, it's going to require some more nuance.
Speaker 1: And that's that's the tricky thing that people have our
Speaker 1: time wrapping their heads around. So so what should those
Speaker 1: of us who who think about the economy, you think
Speaker 1: about markets, and are occasionally called upon to make a
Speaker 1: prediction out six months or a year, what should we
Speaker 1: do to improve that process to become better forecasters. I
Speaker 1: think you want people to feel accountable. Uh. So insofar
Speaker 1: as the people who come on a shows use vague verbue,
Speaker 1: make vague verbuge forecast and they think that they say,
Speaker 1: I think there's a distinct possibility the market's going to
Speaker 1: go over Bible blup. Uh. People may who make those
Speaker 1: kinds of claims they sound meaningful, but in close inspection
Speaker 1: they're very close to meaning less because distinct possibility is
Speaker 1: one of those things you can you can you've got
Speaker 1: a perfect hedge. If it happens, you can say, hey,
Speaker 1: I told you distinct possibility. If it doesn't happen, you
Speaker 1: can say, hey, I just said it was possible. So
Speaker 1: it sounds meaningful. Uh. People not They think they've learned something,
Speaker 1: but on close inspection, there's absolutely no way to monitor
Speaker 1: keep track. So a better way to go is to
Speaker 1: pressure people to make more precise estimates. Even though it hurts,
Speaker 1: it's hard, it's potentially embarrassing, but best it's the best
Speaker 1: way to learn. Beware weasel words. Speaking speaking of potentially embarrassing,
Speaker 1: let's come back and talk to the professor about our
Speaker 1: presidential derby. It has been sports Philip tet Luck with
Speaker 1: this Anneburg professor university professor in Democracy and Citizenship at Wharton,
Speaker 1: And uh, somebody has written some very important books on
Speaker 1: thinking about really what do we do about certitude? And
Speaker 1: is Mark Mark Twain. John Tucker was one of our
Speaker 1: first guests and Bloombergunny Economy. It was a few years.
Speaker 1: It was a book tour. I was like, it wasn't
Speaker 1: Huncle Berry Finn, it was something else. But as Mr
Speaker 1: Twain would say, the certitude of the second rate, maybe
Speaker 1: that's a gospel of surveillance. As we listen with respected
Speaker 1: Professor Tetlock, will return with Philip Tetlock, and uh look
Speaker 1: forward to um. Some of the political certitude that is
Speaker 1: out there very reminds me of the rule of thirty
Speaker 1: which is abused every single day. We take a sample
Speaker 1: size of four and say we believe, and uh, it
Speaker 1: really doesn't work all that well. Futures up eleven down,
Speaker 1: features up one oh one. Janet Yellen still influence, influencing
Speaker 1: the market with yields set lower from twelve noon yesterday,
Speaker 1: Oil thirty eight seventy seven the American barrel And now
Speaker 1: to the news in New York. Here's Michael Barr, Tom Berry,
Speaker 1: thank you very much. The US is trying to give
Speaker 1: support to allies in Eastern Europe who are increasingly concerned
Speaker 1: about a more aggressive Russia. The Army plans to announce
Speaker 1: today the deployment of an armored combat brigade along with
Speaker 1: their equipment, to Eastern Europe next February, as part of
Speaker 1: the ongoing effort to rotate troops in and out of
Speaker 1: the region. The three Republican presidential candidates now saying they
Speaker 1: won't commit to support whoever the party nominates in September,
Speaker 1: Donald Trump, Ted Crews and John Kasi said they would
Speaker 1: pledge their support. A federal judge in Oakland, California, has
Speaker 1: scheduled to hear arguments today over whether to block a
Speaker 1: new federal law that requires sex offenders to have unique
Speaker 1: identifiers in their passports. Global News twenty four hours a day,
Speaker 1: powered by our twenty four hundred journalists more than one
Speaker 1: hundred fifty news bureaus from around the world. I am
Speaker 1: Michael Barr, Tom and Michael, thanks so much again. I
Speaker 1: remove the market. Finally, the bond market pauses, but the
Speaker 1: two year yield point seven eight four three Genet yell
Speaker 1: and are really moving uh markets yields lower, bond prices
Speaker 1: higher over the last eighteen hours. They rid Alds Toime
Speaker 1: Keene with Philip Block of Wharton Bloomberg Surveillance, Surveillance MUTI
Speaker 1: by n y c B asking about their my community
Speaker 1: interest checking but free n y c B online and
Speaker 1: mobile banking. Earn more, get more. Visit n y CP
Speaker 1: family dot com for details. Global business news twenty four
Speaker 1: hours a day at Bloomberg dot com, the Radio plus
Speaker 1: Mobile Act and on your radio, this is a Bloomberg
Speaker 1: Business Flash. And I'm Karen Moscow roiking economic news. Crossing
Speaker 1: the Bloomberg. Here's at Vinny down Judas with the latest
Speaker 1: Vinny ran on the Labor Park labor market. The ADP
Speaker 1: National Employment Report showing a gain of two hundred thousand,
Speaker 1: and March again at two hundred thousand, topping Wall Street forecast.
Speaker 1: That sets the stage for Friday's government employment report. Economists
Speaker 1: they're also looking for in the neighborhood at two hundred thousand.
Speaker 1: Again the a P report Private payrolls march up two
Speaker 1: hundred thousand. Karen, back to you, all right, thank you,
Speaker 1: Vinnie del Judas. Let's go to the first word Breaking
Speaker 1: News Desk Now for today's morning call, and here's Bill Maloney.
Speaker 1: Good morning, Bill, Good morning Karen. Futures are adding to
Speaker 1: yesterday's gains. Doubt youre just currently hired by a hundred
Speaker 1: one point says he's gain eleven and as a futures
Speaker 1: rise by thirty two. The U S tenual at one
Speaker 1: point eight three per cent. Up of markets are also rising,
Speaker 1: led by one point nine percent gains in France after
Speaker 1: develops Night Restoration Hardware Queen at JESSEPS and revenue trailed
Speaker 1: estimates and very que for jesse EPs missed estimates Shares
Speaker 1: a down fourteen percent pre market regarding earnings this morning.
Speaker 1: Lul Lemon Hire by five point seven percent pre market
Speaker 1: after its results, while Paychecks EPs was in line until
Speaker 1: New Stage Street degrees to buy g e St Management
Speaker 1: for up to four hundred eighty five million in cash.
Speaker 1: Also another news, Valiant launches credit line amendment repeats following
Speaker 1: plans and says comfortable with current liquidity position in family
Speaker 1: some of your wallsheet upgrades and downgrades. Compass Meneral cut
Speaker 1: the hold at BB and T, Apple raised out performer
Speaker 1: cown price target one thirty five Varian Systems cut down
Speaker 1: perform at Credit Suite, A I G raised to buy
Speaker 1: a Janny and all scripts raised overweight over at Morgan
Speaker 1: Stanley Live in the First Breaking News Desk on Bill Maloney.
Speaker 1: Karen thanks Bill to hear live breaking news over your
Speaker 1: Bloomberg type squawk go on your terminal. Let's ask you
Speaker 1: you a w K go and that's a Bloomberg business flash.
Speaker 1: Tom and Barry un thanks so much. Appreciate that. This
Speaker 1: morning Bloomberg Surveillance Bunch by Investco. Do the day's headlines
Speaker 1: have you searching for more investment views? Investco's experts can
Speaker 1: help find out the latest thought leadership and Investco blog.
Speaker 1: Visit investco dot com slash us to subscribe. At a
Speaker 1: wonderful time with Investco yesterday, learned a lot about value
Speaker 1: dividends and this this interesting phrase deep value from Keith
Speaker 1: Holding the Value team UH at Investo and thank them
Speaker 1: for their UH continued support of Bloomberg's surveillance and what
Speaker 1: Michael and I do every day. Very results with us, Phillips,
Speaker 1: luck with us who long ago and far away not
Speaker 1: fifty two years ago? Read the medium is the message
Speaker 1: understanding media the extensions of Van Marshall McLuhan. Have we
Speaker 1: jumped to shark Professor Tutlock? Are we are we at
Speaker 1: the point where the medium is the message and it
Speaker 1: completely reforms and restructures our presidential politics day to day? Well, Uh,
Speaker 1: that's a that's a big question. Um. So the conventional
Speaker 1: wisdom's obviously been really rattled by the Donald Trump phenomenon.
Speaker 1: I guess that's what you're probably referring to. Um. Yeah,
Speaker 1: I thinks things have changed a lot. Uh. Trump was
Speaker 1: able to tap into deep resentment. He was able to
Speaker 1: get away with really pretty primitive debating tactics and wasn't
Speaker 1: punished for them. U. Um. Donald Trump's style of debating
Speaker 1: is pretty much the opposite end of the continuum from
Speaker 1: the style of debating we try to encourage and forecasting tournaments, uh,
Speaker 1: which value precision questioning and precision estimates. And who's that
Speaker 1: you different universe? Who is the adult in the room
Speaker 1: that's going to get us back to a better discourse? Um?
Speaker 1: Who is he? Adntal boy boy? You asked tough questions, Tom.
Speaker 1: People in the GOP are pointing to Paul Ryan and
Speaker 1: John Kass as the presumptive adults in the room. Uh,
Speaker 1: in in in that political party right now? Well, clearly
Speaker 1: John kask at least on the debate stage, had has
Speaker 1: appeared like the adults. The problem is he's been rewarded
Speaker 1: for that with a third place finish everywhere outside his
Speaker 1: home state. Are we looking of politics? Are we looking
Speaker 1: in the wrong place for adult discussions about important issues?
Speaker 1: Probably perhaps so at the moment um. But when you
Speaker 1: look at the bigger picture, are the best forecasters, for example,
Speaker 1: in our tournaments are putting close to a seventy percent probability?
Speaker 1: Now and Hillary being the next president of the United States,
Speaker 1: so once she's sworn in, this will look this will
Speaker 1: be a distant memory. Uh, And the question will be
Speaker 1: what have what? What has the Republican Party in particular
Speaker 1: learned from its experience? Um? Now, of course, uh, the
Speaker 1: very best forecasters, like Nate Silver was was was somewhat
Speaker 1: off the mark early on, and then the and and
Speaker 1: the best forecasters in our tournament maybe off the mark. Also,
Speaker 1: when you say seventy percent probability, that means the percent
Speaker 1: probability of other things. Uh. So let's talk about that
Speaker 1: with more important issues than politics. What I what I
Speaker 1: find fast sitting about your work is how the intelligence
Speaker 1: community has sort of thrown aside their old way of
Speaker 1: doing things and approached you to say, we need better
Speaker 1: ways to think about uncertain events. So let's talk about that.
Speaker 1: What is the intelligence community learning from the mistakes that
Speaker 1: have been made in the past. Well, the intelligence community
Speaker 1: works at ground zero in the blame game political culture.
Speaker 1: So if they make a wrong call, they're gonna get
Speaker 1: They're gonna get blasted one way or the other. And
Speaker 1: the wrong calls can take one of two forms. They
Speaker 1: can have a catastrophic um false negative such as nine
Speaker 1: eleven UH, They'll be totally condemned. They'll be roundly condemned
Speaker 1: for it. There'll be presidential commission, will be congressional investigations.
Speaker 1: And the one lesson that an intell intelligence intelligence officer
Speaker 1: would learn from that experiences whatever mistake I'm going to make,
Speaker 1: don't make the mistake I just made, which means don't
Speaker 1: make a false negative. That sets you up for making
Speaker 1: the opposite mistake a false positive. Now, they made a
Speaker 1: false positive mistake with weapons of mass destruction in Iraq. Uh.
Speaker 1: They got blasted for that, and there are investigations and
Speaker 1: commissions and what does an intelligent, intelligent officer intelligence officer
Speaker 1: conclude from all that, Well, whatever mistake I'm gonna make,
Speaker 1: don't make the mistake I made last. So they get
Speaker 1: caught up in this kind of blame game ping pong um,
Speaker 1: which is not really learning. It's it's threshold shifting. It's
Speaker 1: it's shifting my tolerance from making one mistake or other
Speaker 1: rather than learning in a deep and a methodical way
Speaker 1: from history. The way to learn in a deep and
Speaker 1: methodical way from history is to make explicit judgments the
Speaker 1: domains in which are well calibrated not and proceed that way.
Speaker 1: And professor, thank you so much for being us with
Speaker 1: us today. Phil Tetlock is from Warton super Forecasters is
Speaker 1: a hugely successful book on the experts. We really appreciate
Speaker 1: his perspective as we try to find you the best
Speaker 1: insight across economics, finance and investment still hit the old
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