Speaker 1: Pushkin. Welcome back to Risky Business, a show about making
Speaker 1: better decisions. I'm Maria Kanakova.
Speaker 2: And I'm Nate Silvery. Today on the show, we'll talk
Speaker 2: about my new presidential election forecast.
Speaker 1: Well also talk about insurance. Although Nate, I'm really excited
Speaker 1: to hear about your election forecasts.
Speaker 2: You might want to ensure against that feeling, Marie, you
Speaker 2: may not like what it has to say. I'm talking
Speaker 2: about our top three ways that people get risk wrong.
Speaker 1: Nate, let's talk about your election model for the twenty
Speaker 1: twenty four elections, which is out this week. You've been
Speaker 1: working on this election model, tweaking it, developing it for
Speaker 1: sixteen years, and it's been remarkably accurate. So I'm actually
Speaker 1: really excited to talk about it and get into it
Speaker 1: with you, even you told me that I really shouldn't
Speaker 1: be so, I mean, I'm a little scared, too.
Speaker 2: Careful, careful what you wish for. I want you to
Speaker 2: guess what number it has in the chance that trumpel
Speaker 2: with the election.
Speaker 1: Oh night, this is are you gonna break my heart?
Speaker 1: Is it okay? Fifty eight percent?
Speaker 2: Higher?
Speaker 1: Fuck?
Speaker 2: I don't feel the exact number because we're we chake
Speaker 2: a couple days in advance. By the time the model
Speaker 2: is released, there could be new polls and so forth.
Speaker 2: I mean, we'll see what it says when it comes out.
Speaker 2: But we're in the We're in the load of mid
Speaker 2: sixties is kind of where the where the forecast is landing.
Speaker 1: Fucking you fuck fuck fuck is my is my response
Speaker 1: to that night. You're usually the one dropping the f
Speaker 1: phones on the show, but I think it's my turn. Yeah.
Speaker 2: Look, my job here is not to tell you who
Speaker 2: you should vote for. It's to forecast the election as
Speaker 2: best I can. And I think it's disingenuous too to
Speaker 2: call the race a toss up. You can kind of
Speaker 2: squint and say, oh, if you look at national polls
Speaker 2: and Biden's maybe only down by half a pointer or so.
Speaker 2: But the problem in the US is that we don't
Speaker 2: determine elections by the popular vote. Right, if it was
Speaker 2: a popular vote, the model things it would be a
Speaker 2: toss up, or maybe Biden slightly favored. In the electoral college.
Speaker 2: There has been for the past two cycles with Trump
Speaker 2: on the ballot, a gap that favors a GOP. In
Speaker 2: twenty and twenty, Biden won the popular vote by four
Speaker 2: and a half points, which is a pretty good margin
Speaker 2: against an incumbent. But all these key states Georgia and
Speaker 2: Wisconsin and Pennsylvania and so forth, the only win by
Speaker 2: about one point. So if we're now in twenty twenty
Speaker 2: four and Biden's trailing by half a point instead of
Speaker 2: winning by four and a half points in the popular vote,
Speaker 2: that's a shift of five points. If you have a
Speaker 2: state like Pennsylvania that you win by one point and
Speaker 2: you shift it by five points, that's not very good. Now.
Speaker 2: What's happening in practice is that there's a bit of
Speaker 2: a bifurcation between the Midwestern swing states is kind of
Speaker 2: trio of rust Belt states Pennsylvania, maybe even maybe not
Speaker 2: at the Midwest per se, but Pennsylvania, Wisconsin, Michigan, where
Speaker 2: BIA's polling is closer usually within the point maybe two
Speaker 2: point Trump lead, versus the sun Belt states, the new
Speaker 2: fangled Georgia, Nevada, Arizona, where Biden's polling is very bad.
Speaker 2: He might be down five or six points. Now. If
Speaker 2: he wins Michigan and Pennsylvania and Wisconsin and holds all
Speaker 2: the other states so like New Hampshire, Virginia and so forth,
Speaker 2: then he would finish with exactly two hundred and seventy
Speaker 2: electoral votes when you need to sixty nine to win.
Speaker 2: So there is a path, but not a whole lot
Speaker 2: of margin. For aer.
Speaker 1: Nate, this is not the way that I wanted to
Speaker 1: start off my week. So I think I think we're
Speaker 1: done with the podcast, goodbye, good bye luck as I
Speaker 1: go and hide in a cave. But no, in all honesty,
Speaker 1: this is sobering news. I so don't want Trump to
Speaker 1: win because I know I think it's going to have
Speaker 1: incredibly dire consequences for democracy. So I want to kind
Speaker 1: of squint and say, yeah, but there's this path and
Speaker 1: look at these states, and we're going to do it.
Speaker 1: But I think that you need to be realistic and
Speaker 1: try to figure out, Okay, given this, there's still it's
Speaker 1: not like the election is tomorrow, right, So what can
Speaker 1: we do? What shifts in your election model would actually
Speaker 1: change that outcome and bring that percentage back down? Are
Speaker 1: there certain lovers? Are there certain things that are actually
Speaker 1: going to be kind of the most meaningful as you
Speaker 1: play around with the inputs and the numbers.
Speaker 2: Yeah, look, Biden is struggling with groups that are traditionally
Speaker 2: strong Democratic groups like younger black voters, younger Hispanic voters.
Speaker 2: That's why you see his numbers fading in states like Georgia,
Speaker 2: for instance, whereas he's actually doing okay with these older
Speaker 2: white voters. That seems to be the constituency. It's like
Speaker 2: holding up for him the best. There is a debate today,
Speaker 2: This episode's coming out on Thursday tonight. That's an opportunity
Speaker 2: for Biden to turn things around. You know, the conventions
Speaker 2: have get to happen. The concern for Biden is that
Speaker 2: the race has been extremely steady so far, the steadiest
Speaker 2: you've ever seen Poland. I think in a presidential election
Speaker 2: Biden game maybe half a point to a point in
Speaker 2: the polls following Trump's conviction on thirty However, many it
Speaker 2: was felony counts, you know. But the fact that like
Speaker 2: Trump was convicted of paying hush money payments to like
Speaker 2: at porn star adult film actress, excuse me. And that's
Speaker 2: like a real sentence I can say, And that like
Speaker 2: had half a point worth of difference. I mean, that's
Speaker 2: that's not great for Biden. I think there have been
Speaker 2: a lot of false hopes from Democrats. People say, oh,
Speaker 2: this is going to move the poles. It's going to
Speaker 2: move the polls when the economy has gotten a fair
Speaker 2: bit better over the past six months. Inflation has abate,
Speaker 2: the stock market is way up actually, but like the
Speaker 2: numbers haven't really changed very much. And I don't know.
Speaker 2: I'm sure I'll be called a fascist, biased, republican hack
Speaker 2: by every democratic strategist in the book. But the whole
Speaker 2: point of having a model is that you're applying structured
Speaker 2: thinking where where you don't go in and tweak the
Speaker 2: results once you see what it spits out. Right, The
Speaker 2: whole point of committing to a model is that like
Speaker 2: it does take the emotional component, which I have like
Speaker 2: everybody else, out of the process or reduces it. Let's say, right,
Speaker 2: I'm someone who thinks professionally, it would be great to
Speaker 2: just say, oh, it's a toss of fifty to fifty,
Speaker 2: who knows can't get in triple that way. Well, that's
Speaker 2: dishonest and it's not what the number says, and it's
Speaker 2: not what the process I followed for sixteen year now
Speaker 2: years now is two thousand and eight says right, this
Speaker 2: is not a year that looks terribly uncertain. You don't
Speaker 2: have COVID. You don't have these polls volleying up and
Speaker 2: down like you did in twenty sixteen with Clinton and Trump.
Speaker 2: They're very well known candidates, and so Trump's advantage is narrow,
Speaker 2: but it's been persistent, and he has the more robust paths.
Speaker 2: The fact that Biden has to win all three of
Speaker 2: those Michigan, Wisconsin, Pennsylvania trio, all where he probably trails
Speaker 2: by a point or so, but he has to win
Speaker 2: all three and hold these other states. The fact that
Speaker 2: like the next best wing states like Arizona for Biden,
Speaker 2: where he's down by like four or five points, that's
Speaker 2: a real deficit. I don't think you can sit here
Speaker 2: with a straight face and say you'd rather be by
Speaker 2: I think you can say you'd rather be Trump.
Speaker 1: You started off by saying that it seems that Biden
Speaker 1: is slipping amongst some of those voters who are kind
Speaker 1: of traditionally democratic, right, like the younger black voters. Are
Speaker 1: those voters actually switching to Trump or what's happening to them?
Speaker 1: So where's that loss for Biden evaporating? Two?
Speaker 2: I think there might be, you know, some permissions ructures
Speaker 2: to vote for Trump that didn't exist last time, but no,
Speaker 2: a lot of them are going to undecided, to RFK
Speaker 2: junior or out of the electorate. You know, our model
Speaker 2: projects turn out of something like one hundred and fifty
Speaker 2: million this year, which is which would be down from
Speaker 2: twenty to twenty in all the polls, the indications that
Speaker 2: there's less enthusiasm for this election than there had been.
Speaker 2: Now to Biden this credit, he actually does well. Democrats
Speaker 2: in general do well with these very reliable regular voters.
Speaker 2: So like, lower turnout might actually be beneficial for Biden.
Speaker 1: So Nate, Okay, since we since we are we are here,
Speaker 1: what are kind of if you are at the Biden camp,
Speaker 1: where should they focus their efforts between now and election time?
Speaker 1: Like give us like top top two, top three priorities,
Speaker 1: Like what should the campaign be doing to try to
Speaker 1: get those numbers back to where in more winnable territory?
Speaker 2: Maybe fire some people I don't know, certainly, fire anybody
Speaker 2: in the campaig who's telling you not to believe the polls, right,
Speaker 2: fire them and don't get them a new job. I mean, seriously,
Speaker 2: if you're down, if you know, again, if you're at
Speaker 2: a mid thirties, maybe maybe they'll got up to the
Speaker 2: low forties by the time we published the model, but
Speaker 2: probably mid thirties. If you're in that territory, then you
Speaker 2: know that's the point when again, when a baseball team
Speaker 2: is winning thirty some five six seven percent of its
Speaker 2: ballgames and it tends to fire the manager and there's
Speaker 2: been very little turnover, and like, their theory of the
Speaker 2: case is wrong. They thought that Trump would be relatively
Speaker 2: easy to defeat and they could run back the twenty
Speaker 2: twenty playbook. You're talking about democracy, democracy, democracy, over and
Speaker 2: over and over again, and that this would work. Their
Speaker 2: theory of the case is wrong. They did not expect
Speaker 2: to be down at this point, right. This is why
Speaker 2: they're still doing the RFK junior stuff, because they thought
Speaker 2: they'd be winning, and so you want to reduce variants.
Speaker 2: When you're ahead and getting RFK junior, who's a wild card?
Speaker 2: I guess you can say, off the b it is
Speaker 2: something to do that. You know. Look, I think some
Speaker 2: of this stuff that you've done tactically suggests improvement, but
Speaker 2: but I don't know. I mean, I mean, you know,
Speaker 2: for all the fucking talk about how important the election is,
Speaker 2: and I think some of that talk is we'll scrutinize
Speaker 2: that more. At some point. I think some of it's
Speaker 2: a little precious. But like if you really think that
Speaker 2: like this is the you know, the end all be
Speaker 2: all of elections, then I'm not sure really acting like
Speaker 2: it very much. You know, they're acting like people who
Speaker 2: have incentive. We're called principal agent problems. Right, you don't
Speaker 2: want to be the polster that gives the president bad news.
Speaker 2: You definitely don't want to be the polster or the
Speaker 2: advisor who tells the president, hey, maybe you should stand
Speaker 2: down to think about having someone else run, because then
Speaker 2: you lose your job. But you know, and then there's
Speaker 2: a whole Kamala Harris issue too, where I mean, I
Speaker 2: think if she were more popular, then there's some chance
Speaker 2: that Biden would have stood down and Democrats to be
Speaker 2: in better shape. So I don't know. Look, all you
Speaker 2: can say is like, if Biden loses, and there's still
Speaker 2: a pretty some chance he wins, it's not like you
Speaker 2: can't see this coming. He has been behind in the
Speaker 2: polls consistently for you know, since Midsummer last year. Voters
Speaker 2: consistently have said you are too old, you should not
Speaker 2: run again. The first term was fine, you should not
Speaker 2: run again. You know they have ignored that message at
Speaker 2: every turn, and that's because the data is ambiguous. Right again,
Speaker 2: you can squint in the right day and look at
Speaker 2: the right poles and see not just a path to
Speaker 2: it Biden victory, but like a case where his chances
Speaker 2: look pretty decent. Right, you can't look at things like
Speaker 2: the twenty twenty two midterms where Democrats did relatively well,
Speaker 2: and you can hope that the polls are biased in
Speaker 2: your favor because biased against you, rather to outperform them,
Speaker 2: because sometimes they are. But that's usually very hard to predict,
Speaker 2: and you don't need many reminders of cases where the
Speaker 2: polls were biased against Trump in stead.
Speaker 1: All right, so shake things up. I think we see
Speaker 1: the old status quo bias that we've talked about week
Speaker 1: after week after week, and firesome people focus on those
Speaker 1: key states and like, just get your ass and gear
Speaker 1: and realize that there's a very very good chance you'll lose.
Speaker 2: We'll be right back. Nay.
Speaker 1: Let's talk about some huge shifts in the home insurance industry.
Speaker 1: There has been a lot of news recently about homeowners
Speaker 1: insurance because insurers are actually getting out of multiple states
Speaker 1: multiple areas and refusing just flat out and refusing to
Speaker 1: ensure homes, which is obviously affecting homeowners, home buyers, people
Speaker 1: who need to get you know, their their home mortgage,
Speaker 1: anything like that, because they like physically cannot get their
Speaker 1: home insured. And Nate, last week, you and I talked
Speaker 1: a lot about climate change and all of those effects,
Speaker 1: and this is directly related to that. So I think
Speaker 1: that this is one way that consumers are feeling what
Speaker 1: it means to be living in a world where the
Speaker 1: risks of one off events are no longer one off events,
Speaker 1: where the storm of the century is no longer the
Speaker 1: storm of the century, where you can actually see these
Speaker 1: people who use models to try to model out the
Speaker 1: risks losing money and saying, oh shit, we don't want
Speaker 1: to ensure your home anymore. We no longer want to
Speaker 1: take this risk.
Speaker 2: So let me ask, Yeah, what might seem like a
Speaker 2: naive question, right, why can't you just price these risks
Speaker 2: in that you're in California and you know the risk
Speaker 2: of wildfires, you're in Florida, you know the risk of hurricanes.
Speaker 2: You can build a model and then it costs more
Speaker 2: than it might what's a safe state, you know, Wyoming there,
Speaker 2: like Bolcanos there and that anyway, but why can't you
Speaker 2: charge more instead of not offering the risk at all?
Speaker 1: Yeah, I mean, I think that that's a really really
Speaker 1: good point, and I think that some insurance are trying
Speaker 1: to do that and trying to charge more. But I
Speaker 1: think that some are using old data instead of new
Speaker 1: data for the models. And the problem is there are
Speaker 1: no good data, right. How do you model something that's
Speaker 1: changing so rapidly in an environment that's changing rapidly, where
Speaker 1: there's a lot of volatility, and volatility in the actual
Speaker 1: sense of the word, which means that you know, ups
Speaker 1: and downs are very extreme in a short period of time.
Speaker 1: How do I factor in the uncertainty of these risks
Speaker 1: given that we don't have any historical comps, right, given
Speaker 1: that we don't know how it's going to play out
Speaker 1: in the next ten years, because the last ten years
Speaker 1: are not representative of the years before that are not representative,
Speaker 1: and we no longer have this steady model where where
Speaker 1: we can go back and say, Okay, you know, risk
Speaker 1: of this x percent, risk of this experson. I mean,
Speaker 1: I know that one way of handling volatility is to
Speaker 1: price it even higher, but at some point you know
Speaker 1: you're pricing it so high because of the uncertainty that
Speaker 1: it might not make sense anymore.
Speaker 2: Yeah, if you go to the New York Times, they
Speaker 2: have a interactive graphic called the Home Insurance Crunch, see
Speaker 2: What's happening in your state, where it just shows every
Speaker 2: state whether home insurance has been profitable or losing for
Speaker 2: the insurers. And what you quickly discover is that you
Speaker 2: have kind of limited upside where nothing goes wrong you
Speaker 2: do make a profit, but kind of uncapped downside where
Speaker 2: one year with crazy hurricanes or flooding or wildfires or
Speaker 2: things like that can wipe out ten marginally profitable years.
Speaker 2: And in states like California and Colorado a lot of
Speaker 2: fires Florida, of course, with hurricanes like that happens pretty often,
Speaker 2: so it's hard to measure tail risks, especially if the
Speaker 2: tails are expanding. We talked on the last week's episode
Speaker 2: about how if there's even a small shift in the distribution,
Speaker 2: the part of the curve under the tail can really
Speaker 2: get shifted out where it becomes substantially more probable. Yeah,
Speaker 2: and there's also you know, there's also something called adverse
Speaker 2: selection where maybe the people who want to buy insurance
Speaker 2: are people that are worse risks for you to take.
Speaker 2: Maybe they know something about their home that it's vulnerable
Speaker 2: to landslides, for instance, And therefore you kind of have
Speaker 2: what economists we call like a market for lemons, where
Speaker 2: you don't you have information asymmetries between buyer and seller
Speaker 2: that reduce the overall size of the market. And in
Speaker 2: those cases it might be irrational to might be rational
Speaker 2: rather to not offer a product. There's not a kind
Speaker 2: of market clearing price.
Speaker 1: Wait Nate, one second, let's clarify what exactly a market
Speaker 1: for lemons means.
Speaker 2: Yeah, the term was coined, I believe by the economist
Speaker 2: George Akerlove, who I think is now at Berkeley won
Speaker 2: a Nobel Price. He talks about, I mean, Elemon is
Speaker 2: a defective used car, and it's hard to know everything
Speaker 2: wrong with the car if you take a test drive
Speaker 2: for a few minutes or something like that. But basically,
Speaker 2: it's like when buyers don't have any way to trust sellers.
Speaker 2: There's no third party vouching for the reliability of these
Speaker 2: used cars. What happens is just the volume of transactions
Speaker 2: goes way down. Right, You have dead weight loss when
Speaker 2: there's no credible way to achieve trust. So the theory
Speaker 2: here would be, like, you know, if we think that
Speaker 2: insurance is a win win transaction, right, someone's paying some
Speaker 2: expected value on average, you lose money if you buy insurance,
Speaker 2: but they protect their downside risk. Right, that's a win
Speaker 2: win on a risk adjusted basis. But if there's not
Speaker 2: reliable information you're not sure you can trust the insurer,
Speaker 2: or if you're insured, you're not sure you can trust
Speaker 2: the homeowner, then you have transactions not happening that would
Speaker 2: create utility for both sides and for society.
Speaker 1: Yeah, I think that something that I know about investing
Speaker 1: is if you're trying to kind of look at an
Speaker 1: opportunity and you're comparing upside and downside risk. Right, when
Speaker 1: you have unlimited downside risk and your upside risk is
Speaker 1: your upside is capped, that's not an investment you want
Speaker 1: to be making, right. You want it to be asymmetric
Speaker 1: the other way. You want your downside risk to be
Speaker 1: capped and your upside to actually be able to go
Speaker 1: up further, especially if you're leveraged. Right, If you're thinking
Speaker 1: about it that way, and if you're thinking about the
Speaker 1: fact that this is incredibly asymmetric risk, and this is
Speaker 1: something that they've you know, this is something they've been
Speaker 1: doing for years where it's been asymmetric in their favor,
Speaker 1: where it has been basically a capped downside because these
Speaker 1: kind of once in a lifetime events didn't happen, or
Speaker 1: they did happen once in a lifetime, and so all
Speaker 1: of your other years where you were making money paid
Speaker 1: for it. But now if you're making less money, because
Speaker 1: there's always something happening, right if you look at the
Speaker 1: news every year, there's something happening. There's you know, home
Speaker 1: destruction because of all sorts of different things, tornadoes, hurricanes, wildfires,
Speaker 1: you know, you name it, you know, we got it.
Speaker 1: And then if you have that, so in your up years,
Speaker 1: up years, you're not nearly as up and then you
Speaker 1: have these catastrophic events that are happening over and over
Speaker 1: and over. Then to me, like, as if you're a
Speaker 1: rational investors, it's a bad investment. So you end up
Speaker 1: pulling out.
Speaker 2: What's not neglect the role of dorm regulations. According to
Speaker 2: the Washington Post, in California's case, insurance companies must use
Speaker 2: historical data rather than forward looking models when they price
Speaker 2: insurance plans that means is supporting them. Their policies may
Speaker 2: not reflect the actual risk you're supposed to hedge against.
Speaker 2: That's stupid. I mean, there's no serious put laws in California,
Speaker 2: and like I happen to agree with that. Night if
Speaker 2: you can't actually like adjust for changes in the climate,
Speaker 2: which I think most people in California would care about,
Speaker 2: then you're just making the market less efficient. You know,
Speaker 2: how you optimize this function where on the one hand,
Speaker 2: you probably don't want insurers to have literally unlimited liability
Speaker 2: because they can't. No one has infinity infinity dollars, right,
Speaker 2: they won't be able to pay it. On the other hand,
Speaker 2: you know, you don't want insurance not to make people
Speaker 2: relatively whole when there is a disaster. Otherwise what's the point.
Speaker 2: But I would gather that one that like these state
Speaker 2: regulations are not always that well designed.
Speaker 1: Yeah, I think that that's absolutely right because and states
Speaker 1: are trying to step in, by the way, not just
Speaker 1: in California, right, because there are these gaps where they say,
Speaker 1: oh shit, you know, what do we do if people
Speaker 1: in our state can't buy houses because they can't get insurance,
Speaker 1: Like this is actually a big, big issue, Nate. I
Speaker 1: know that at some point point in the in the past,
Speaker 1: you were considering buying a place in Miami. Is you know,
Speaker 1: is that something where like, let's talk about this on
Speaker 1: a personal level, Like, would you do that right now,
Speaker 1: given what's happening with insurance, given what we know about
Speaker 1: the risks there, given that you probably would not be
Speaker 1: able to get insurance on the you know, on an
Speaker 1: apartment that you wanted.
Speaker 2: Honest answer, they probably wouldn't affect my decision that much
Speaker 2: because look, home insurance is negative expective value on a
Speaker 2: non risk adjusted basis, right, Otherwise it wouldn't be offered.
Speaker 2: By definition, insurance means that someone who has more capital
Speaker 2: and more risk tolerance than you makes money on average,
Speaker 2: and homeowners lose money on average. I mean, it wouldn't
Speaker 2: be great. I'll probably take the negative evy like safe bet.
Speaker 2: I mean, I'm probably not going to buy a place
Speaker 2: in Mimi for other reasons. But no, I mean, but
Speaker 2: I'm also conscious about how much of my net worth
Speaker 2: I'm tied it being real estate, right. I think it's
Speaker 2: generally a mistake, for many reasons to have a great
Speaker 2: deal of your net worth tied up in real estate
Speaker 2: as opposed to a broader portfolio of stuff. So I
Speaker 2: think I think I'm hedged enough in diverse fi it
Speaker 2: enough that, like, although not ideal, I I you know,
Speaker 2: it wouldn't be a deal breaker.
Speaker 1: So you would basically write it off, right, Like, as
Speaker 1: this is an investment that might go to zero, I
Speaker 1: don't care that. I'm not going to get insurance on it,
Speaker 1: and I understand that it might get destroyed, and I'm
Speaker 1: okay with that. I'm just going to make it a
Speaker 1: small enough investment that if it goes to zero, I'll
Speaker 1: be okay.
Speaker 2: That's right.
Speaker 1: Yeah, So this, I think. So this actually kind of
Speaker 1: illustrates that it's affecting different people differently. Where if you
Speaker 1: are in a position where you can do that and
Speaker 1: you can say I will buy this place and I'm
Speaker 1: just going to write it off, then you can buy it.
Speaker 1: But if you're someone who can't do that, then the
Speaker 1: calculus changes.
Speaker 2: Like with those things, people who are you know, better
Speaker 2: off financially or who have more robust ways of looking
Speaker 2: at risk benefit from it, and the people who are
Speaker 2: disadvantaged are harmed by him more because yeah, I mean, look,
Speaker 2: if you're like a multi billionaire in Silicon Valley or something, right,
Speaker 2: and you lose your beautiful seven million dollar home in
Speaker 2: a wildfire or an earthquake, you'll still be okay, Right,
Speaker 2: if most of your net worth is tied up in
Speaker 2: your home, then you won't be.
Speaker 1: So it seems like when we're talking about this, we
Speaker 1: basically have the absolute like shit show of factors, if
Speaker 1: I may be, if I may use a psychological term here,
Speaker 1: So we have huge correlations, right, we have no good
Speaker 1: historic data, we have a lot of things changing, and
Speaker 1: so it's incredibly difficult to model. And so it's basically like,
Speaker 1: you know, this is your worst nightmare when you're trying
Speaker 1: to build a model that predicts risks and tries to
Speaker 1: figure out what in the world do we do? And
Speaker 1: so I think that at least in the short term,
Speaker 1: what I'm taking away from this is that a lot
Speaker 1: of people, especially kind of the people who cannot afford
Speaker 1: to just write off these these homes, are kind of
Speaker 1: fucked and cannot buy homes, have to move out of
Speaker 1: homes that they've owned where the insurers have stepped out,
Speaker 1: and there's really no clear solution. And as you know,
Speaker 1: as someone who is making these decisions on a personal level.
Speaker 1: Where do I move, you know, where where do I
Speaker 1: buy a house, Where do I rent? Where do I
Speaker 1: look at real estate? Where do I build a community.
Speaker 1: I think these are all things that people need to
Speaker 1: keep in mind as they're thinking about the future. And
Speaker 1: sometimes that sucks because sometimes you know, you really want
Speaker 1: to move to beautiful wildfire wildfire California or lovely coastal Florida,
Speaker 1: and that's just kind of the emotional elements of that
Speaker 1: are not going to be worth it for you in
Speaker 1: the long term. We'll be right back after the break.
Speaker 2: All right, Maria, Let's talk about our top three ways
Speaker 2: and people get risk wrong.
Speaker 1: So on the show we often talk about kind of
Speaker 1: these meta risk concepts, you know, elections and those types
Speaker 1: of big questions. But right now I'd love to talk
Speaker 1: bring it back to kind of individual psychology and talk
Speaker 1: about how people, how individuals get risk assessments wrong, the
Speaker 1: biggest problems that they have when they're thinking about it
Speaker 1: on an individual level. And I think you and I
Speaker 1: both have some thoughts as to what the most important
Speaker 1: ones are. I can lead us off if you'd like.
Speaker 2: I think that one of the biggest a draft of
Speaker 2: like cognitive biases.
Speaker 1: Yeah, draft of cognitive biases, but specifically.
Speaker 2: The amplast fallacy. You're looking pretty good and picking them
Speaker 2: at two.
Speaker 1: Exactly exactly.
Speaker 2: It's rookie out of Providence College.
Speaker 1: You and I. You and I can rate each other
Speaker 1: on our on our propensity to uh to exhibit some
Speaker 1: of these risky tendencies. But uh, but but it's on
Speaker 1: a serious note. I think that the one of the
Speaker 1: things that people get wrong, and I'm not going to
Speaker 1: give it a name or not. It's part of a
Speaker 1: lot of different fallacies, but incorrectly waiting small percentages, both
Speaker 1: overweighting them and underwaiting them depending on their personal experience
Speaker 1: and their situation. By small percentages, I mean like you know,
Speaker 1: one percent risk, two percent risk, less than one percent risk.
Speaker 1: I think that people are really really horrible at those
Speaker 1: at those tiny percents. Is this one that you actually
Speaker 1: had in your in your top mistakes as well or no?
Speaker 2: No, but I have something related And by the way,
Speaker 2: I'm not sure somebody these kind of like the risk
Speaker 2: to oh one mistakes, right is are not things like
Speaker 2: the Gambler's fallacy. I mean people think that if you
Speaker 2: get you know, if you get the coin heads four
Speaker 2: times in a row, therefore you must get tails next time.
Speaker 2: That's not true if the coin's fair and independent. There's
Speaker 2: a little bit more advanced. No, what I do on
Speaker 2: my list is not understanding the notion of calibration or forecast.
Speaker 2: Maybe it's kind of like related to the gambler's fallacy. Yeah,
Speaker 2: I think if you have and if you have a forecast.
Speaker 2: We talked about the forecast model this week. Right, if
Speaker 2: Biden has whatever, like a thirty five percent chance, he's
Speaker 2: actually supposed to win the election thirty five percent of
Speaker 2: the time. If the model is right, you are supposed
Speaker 2: to lose your flushtraw hands and poker thirty five you know,
Speaker 2: against a flush straw thirty five percent of the time,
Speaker 2: or the deck is rigged, or you're like the luckiest
Speaker 2: sun runner on Earth. I asked chat Shept. This question
Speaker 2: is a way I used to like test large language models.
Speaker 2: Maybe version four or something has gotten it right. But
Speaker 2: you say, yeah, I know a forecast, are supposed to
Speaker 2: every forecast right, it's not who for a probabilistic forecast.
Speaker 2: And it's a long stand, long standing pet peeve of mine,
Speaker 2: I think is yeah.
Speaker 1: Well I actually have that on my list too. So
Speaker 1: I have this as two separate things. I have the
Speaker 1: small percentages, which I think we can talk about separately,
Speaker 1: but then what I have is rounding to absolutes, right
Speaker 1: zero on one hundred, And I think that that's kind
Speaker 1: of what this is on a fundamental level, where if
Speaker 1: where if anything is like above seventy percent, or you're like, Okay,
Speaker 1: this is going to happen, and if it's like, you know,
Speaker 1: below below twenty percent, you're like, this ain't going to happen,
Speaker 1: or you know that that's a little bit of a
Speaker 1: rough estimate. But I think that that's often how human
Speaker 1: minds work, right. We gravitate toward absolutes because we gravitate
Speaker 1: towards certainty and away from uncertainty. And so if you're
Speaker 1: talking about you know, you just mentioned the weather, if
Speaker 1: you're talking about weather forecasters, this is like the quintessential
Speaker 1: example of when people get it wrong. When you say,
Speaker 1: you know, seventy five percent chance of rain, you bring
Speaker 1: your umbrella and it doesn't rain, You're like, fuck, you know,
Speaker 1: I've been lugging this umbrella around all day, liked it
Speaker 1: or like ninety percent chance of sun and it starts raining.
Speaker 1: When you get to the beach, You're like, what the hell,
Speaker 1: Like it wasn't supposed to rain. Ten percent is really
Speaker 1: not zero, But your brain just like goes to the
Speaker 1: absolutes and the weather is something we've all experienced, and
Speaker 1: yet we still, you know, we still have that notion.
Speaker 1: So I do think that this rounding thing is incredibly,
Speaker 1: incredibly important. But I think that we should talk separately
Speaker 1: about the small percentages because that's also very important, and
Speaker 1: I think that's a slightly different issue, especially when we're
Speaker 1: talking about risks that are incredibly important, and we've talked
Speaker 1: about some of them, Like we talked about p doom, right,
Speaker 1: the risk of destruction in the past. We've talked about
Speaker 1: the risks of one off storms events when it comes
Speaker 1: to climate change. I mean, we have to deal with
Speaker 1: small percentages all the time, and the brain just breaks down.
Speaker 2: Yeah. I was narrating the Silicon Valley chapter in my
Speaker 2: book just last week, so was rethinking about some of
Speaker 2: the stuff. You know, Silicon Valley understands the importance of
Speaker 2: large payoffs. They live in fear of a Marcus Zuckerberg
Speaker 2: or Elon Musk or whatever exiting their office on sand
Speaker 2: Hill Road, turning down their offer or not having been
Speaker 2: given an offer right when they could result in the
Speaker 2: one hundred X or one thousand X or ten thousand
Speaker 2: X payoff. They understand that there's a whole culture around
Speaker 2: Silicon Valley that leads them to be very strange in
Speaker 2: certain ways to encourage that type of risk taking because
Speaker 2: it's like not normal for people. There is a second
Speaker 2: I guess this is my number two, which is also
Speaker 2: inspired by Silicon Valley though, which is understanding the value
Speaker 2: of a portfolio of risks that if you have a
Speaker 2: lot of these high upside, high risk bets, magically, if
Speaker 2: you have enough of them, then your risk can be
Speaker 2: actually quite low. The top decile Silicon Valley firms actually
Speaker 2: are almost guaranteed a profit based on the research I've
Speaker 2: done for my book, because they first of all, they
Speaker 2: have like selection of xs, they get the best talent
Speaker 2: in the door. But like you know, if you're making
Speaker 2: a new fund every year and the fund has twenty
Speaker 2: five companies, then you're hedging a lot. You might have
Speaker 2: two hundred investments. If you have several years worth of
Speaker 2: funds at any given time. Right, So so all of
Speaker 2: a sudden, what seems risky is not if you can
Speaker 2: aggregate a portfolio, which is the different options. And there
Speaker 2: are issues about correlation, like we talked about in the
Speaker 2: last segment. If you're only invested in crypto or AI
Speaker 2: companies for example, those companies are gonna be correlated in
Speaker 2: how well they do. But still it's a pretty good
Speaker 2: business to be able to take enough high upside bets that, like,
Speaker 2: you know, if you could enter Maria, for example, if
Speaker 2: ten thousand copies of yourself could enter the World Series
Speaker 2: of Poker main event, which is a profitable tournament, so
Speaker 2: half the fucking players are clones of you. I mean
Speaker 2: that we get a little weird, right, But yeah, but
Speaker 2: you do it, you know, if you have the capital
Speaker 2: for it.
Speaker 1: Yeah, oh absolutely, I would for sure. No, I think
Speaker 1: that that's actually that's a great point. And I think
Speaker 1: that diversification is something that we really do not think
Speaker 1: about enough. And it's so funny because I'm just as
Speaker 1: you're talking, I'm like, we have so many cliches in
Speaker 1: like popular culture about this, like don't put all your
Speaker 1: eggs in one basket, and what do we do over
Speaker 1: and over and over. We take all our fucking eggs
Speaker 1: and we put them in one basket, and we're like,
Speaker 1: I'm just gonna go all out. I'm gonna go both
Speaker 1: to the wall. I'm gonna like go all out on
Speaker 1: this bet. That's not the correct way of taking risk.
Speaker 2: And we should take popular cliches and parables and idiom
Speaker 2: to like rate them. We're like, oh, that's pretty smart,
Speaker 2: right for sure, familiar eggs in one baskets of top
Speaker 2: fucking five idiom. It absolutely difference.
Speaker 1: I mean it's saying yeah, I don't know, yeah, but
Speaker 1: but what you know, I was just thinking about what
Speaker 1: we talked about a few weeks ago about roaring kids,
Speaker 1: like that that is the opposite of diversification as far
Speaker 1: as we know, right, Like, that's just like putting all
Speaker 1: of your bets onto this one thing, and like if
Speaker 1: it ends up going south, like your millions disappear, right like,
Speaker 1: you're fucked. And I think that that's really really difficult
Speaker 1: for people to understand, especially when things are going well.
Speaker 1: So I think that's psychologically it's really important to distinguish
Speaker 1: moments when things are going well and moments when things
Speaker 1: aren't going well because our ability to make good. Risky
Speaker 1: decisions are really affected both ways. Right, So like if
Speaker 1: you made this huge bet and it's going up, you're like,
Speaker 1: I am a genius, right, Like I'm brilliant. This is amazing,
Speaker 1: And even though you should probably exit at some point,
Speaker 1: then you're not going to. And if things are going poorly,
Speaker 1: then sometimes you actually end up doubling down. You don't
Speaker 1: sell when you should because kind of you're emotionally invested,
Speaker 1: and so you end up compounding your losses and not
Speaker 1: thinking clearly once again. And so this is just such
Speaker 1: a you know, such a broken way of human thinking
Speaker 1: that then kind of compounds your mistakes. And this happens,
Speaker 1: by the way to professional traders as well. This isn't
Speaker 1: just you're You're just like someone sitting down on your couch.
Speaker 1: So what's your what's your number three? Or do you
Speaker 1: want me to do my number three?
Speaker 2: I can go all right. I'm trying to figure out
Speaker 2: like a cute way to frame this. We need like
Speaker 2: some something's law or something. I think people kind of
Speaker 2: conflate risky decision making and ill informed decision making. I
Speaker 2: don't think people understand that, like, oftentimes having more information
Speaker 2: enables you to take more risk or more intelligent risks. Right.
Speaker 2: So I was at dinner last night with my partner
Speaker 2: some Italian place in the West Village which we had
Speaker 2: not been to before, and he caught me looking at
Speaker 2: Yelp to decide what I wanted to order. He's like,
Speaker 2: why do you care about Carmen and dubukee Iowa? We
Speaker 2: always make this woman from Iowa? What does she know
Speaker 2: about dining in New York City?
Speaker 1: That's not what your person sounds like. It's a really
Speaker 1: bad impression.
Speaker 2: I only could do an impression without him being self
Speaker 2: conscious about it. He no, I'm not gonna I'm not gonna. Yeah,
Speaker 2: I'm not gonna do it. But anyway, but he's like,
Speaker 2: if you're such a risk taker, Nate, how can you
Speaker 2: won't even order if fucking played a pasta without looking
Speaker 2: at yelp reviews? And I'm like, I'm not a big
Speaker 2: fan of octopus, don't love, don't hate, but you usually
Speaker 2: wouldn't order an a menu with fifteen items or something.
Speaker 2: Right if the review says this is the best octopus
Speaker 2: you've ever tried, even if you're not an octopus like her,
Speaker 2: you gotta try this octopus right then, I might get
Speaker 2: the octopus. It can enable you to like to deviate
Speaker 2: from your own plans. I mean, I think oftentimes on
Speaker 2: restaurant used by Tyler Cowan, the economist on this, oftentimes
Speaker 2: the least appetizing item on a menu is the thing
Speaker 2: you should actually order, right, but it's only there because
Speaker 2: like it must be good if it seems unappealing. Is
Speaker 2: kind of the kind of Tyler's an economist, it's kind
Speaker 2: of his theory. But yeah, the idea that like, uh,
Speaker 2: you know, I know, the idea that like you should
Speaker 2: just fly blind and it's like a smart risk to
Speaker 2: take to just kind of wing it. I think that's
Speaker 2: the opposite you know, preparation. If you talk to mountain
Speaker 2: climbers I literally talk to in my book, they prepare
Speaker 2: very extensively. It does not mean that every risk you
Speaker 2: take it twenty nine thousand feet can be mitigated to zero,
Speaker 2: but like, but they are the most prepared people in
Speaker 2: the world pretty much now.
Speaker 1: I think when it comes to risk, like we do
Speaker 1: want to be prepared and we do want to do
Speaker 1: our homework. Like you're not going to just randomly fly
Speaker 1: on an airline that you've never heard of before, Like
Speaker 1: at least I'm not like I'm going to do some research.
Speaker 1: I'm going to look at some reviews. I'm going to
Speaker 1: see what safety record is. I'm going to see what
Speaker 1: kind of planes safely And I think preparation is really
Speaker 1: important in a lot of different moments. So so I'm
Speaker 1: with you on this one, and I think that you know,
Speaker 1: this is as I always say, and as as I
Speaker 1: say when it comes to poker, when it comes to
Speaker 1: all decision making, information is power, right, you want to
Speaker 1: have the information advantage. And so that's that's a really
Speaker 1: good way of looking at it. Now for my final thing,
Speaker 1: I'm going to bring us more back to kind of
Speaker 1: the economics realm and the realm of delay discounting, because
Speaker 1: I think that that's something that we haven't talked about
Speaker 1: and something that people have problems with. So delay discounting
Speaker 1: is kind of your humans inability to kind of correctly
Speaker 1: decide between smaller sooner rewards and larger later rewards. Right,
Speaker 1: So kind of thinking about future and trying to figure out, Okay,
Speaker 1: you know, what do I want now versus if I
Speaker 1: can delay and take that risk and kind of get
Speaker 1: this later, how do I want to think about it,
Speaker 1: and I think fundamentally one of the reasons that people
Speaker 1: are bad at it is we're really really horrible at
Speaker 1: picturing ourselves in the future and knowing what our future
Speaker 1: self is going to be like and what our future
Speaker 1: self is going to want. And so this leads to
Speaker 1: a lot of issues where like we just project our
Speaker 1: current self right, like the Maria of today, and we
Speaker 1: just imagine that the Maria in ten years from now
Speaker 1: is going to be identical. And that's not the way
Speaker 1: that we should be thinking about things. That's not the
Speaker 1: way we should be thinking about risk. That's not the
Speaker 1: way we should be planning for a risk, that's not
Speaker 1: the way we should be just thinking about these things
Speaker 1: in general. And that's kind of that's a big issue.
Speaker 1: And one of the reasons why it's so difficult to
Speaker 1: fix it is because it's not always irrational, because life
Speaker 1: is unpredictable, and you know, there are some people who
Speaker 1: would argue, like, take the reward right now, because you
Speaker 1: might be dead in ten years, and I think that's
Speaker 1: something that we also have to consider.
Speaker 2: If the AI doom for gusts or correct.
Speaker 1: Yeah, p do is correct, then take the reward now.
Speaker 2: I think for every ten people who err on that side, right,
Speaker 2: for every ten people who err on the side of
Speaker 2: being too impulsive in the moment or not necessarily impulse
Speaker 2: or something wrong with impulse per se. But like, yeah,
Speaker 2: applying to steep a discount to maximize their overall expected happiness.
Speaker 2: To put it in a very clinical sounding way, there
Speaker 2: have to be ten people who do that versus everyone
Speaker 2: who does the opposite. And by the way, it's also
Speaker 2: like the triad. Along with understanding expected value and understanding
Speaker 2: portfolio theory, the thirteen silicon valley does well is they
Speaker 2: have long time horizons. You know, even the best investments
Speaker 2: often aren't profitable for ten thirteen years something like that.
Speaker 2: It's just a huge advantage. I mean, even just investing
Speaker 2: in the stock market, if you're in your twenty thirty
Speaker 2: to forties, if your fifties early right us returns are
Speaker 2: so much higher over the long run, there are enough
Speaker 2: time for business cycles and there will be crashes eventually
Speaker 2: to even out long time horizons are are are you know?
Speaker 2: Evolutionarily you're probably not wired to do that, right, I mean,
Speaker 2: you used to live shorter lives, Humans used to live
Speaker 2: closer for this subsistence level, meaning that we didn't really
Speaker 2: have any time of access at all. You protect what
Speaker 2: you have, right, But you know a bird in the
Speaker 2: hand is worth two in the bush? Is that like
Speaker 2: another what's name mean? Yeah? Yeah, I love it?
Speaker 1: Yes, I got though, right it is. Yeah, it is
Speaker 1: a bad one. It is a bad one. So we
Speaker 1: have one good one. So so as our running ranking
Speaker 1: of idioms of sayings, we we like that don't put
Speaker 1: all your eggs in one basket. We don't like the
Speaker 1: bird in the hand nearly as much.
Speaker 2: But it's it's a scarcity mindset to write. Like, Actually,
Speaker 2: one thing I'll get annoyed by you've heard of, like
Speaker 2: the marshmallow experiment, where like, of.
Speaker 1: Course Nate was my thesis advisor. I was this final
Speaker 1: bad student.
Speaker 2: Well, I don't like the marshmallow experiment because I think
Speaker 2: it kind of reads some privilege, where like if some
Speaker 2: weird fucking graduate student was like, I'll give you two
Speaker 2: marshmallows and I was like a poor kid, I'd be like,
Speaker 2: I don't fucking trust you, right.
Speaker 1: No, no, See that's a very very silly criticism. We're not
Speaker 1: going to get into it. But all this was controlled for.
Speaker 2: All segment coming up on the marshmallow problem. Tune in
Speaker 2: next week.
Speaker 1: Yes, seriously, I will, I will come in. I know
Speaker 1: this literature better better than probably better than most people alive,
Speaker 1: and today so I will, I will, I will come
Speaker 1: out guns of blazing. It's a very good study and
Speaker 1: it was absolutely controlled for with the poor kids. That's
Speaker 1: actually how it originated in in uh Island communities of
Speaker 1: kids who had no money. So so quick recap my
Speaker 1: top three incorrectly waiting small percentages both over and underwaiting
Speaker 1: them rounding two absolutes to zero or one hundred, and
Speaker 1: problems with delayed discounting so preferring smaller sooner two larger
Speaker 1: later rewards, and how that might not always be irrational.
Speaker 2: And three. The first is the failure to understand the
Speaker 2: nature of probabilistic forecasts. I will admit that's a pet
Speaker 2: peeve number steen with three I think can be more
Speaker 2: useful in your everyday life. Two is the failure to
Speaker 2: understand the value of portfolio theory and diversification. And three
Speaker 2: is the failure to understand that more information can actually
Speaker 2: make you more risk taking and smarter atrist taking.
Speaker 1: Risky Business is hosted by me Maria Kanakova and me
Speaker 1: Mate Silver. The show is a co production of Pushkin
Speaker 1: Industries and iHeartMedia. This episode was produced by Isabel Carter.
Speaker 1: Our associate producer is Gabriel Hunter Chang. Our executive producer
Speaker 1: is Jacob Goldstein.
Speaker 2: And if you want to listen to an add free version,
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