Speaker 1: Pushkin, Hey, Risky Business listeners. This weekend, Nate and I
Speaker 1: took a break from the World Series of Poker to
Speaker 1: go to the Aspen Ideas Festival. Sunday night, we got
Speaker 1: to tape a live episode of the Risky Business podcast
Speaker 1: and we're so happy to share that with you today.
Speaker 1: We hope you enjoy it. Welcome back to Risky Business,
Speaker 1: a show about making better decisions. I'm Maria Kondakova.
Speaker 2: And I'm Nate Silver.
Speaker 1: So today on the show, we're going to be starting
Speaker 1: with a little bit of poker. So Nate and I
Speaker 1: are both here from lovely Las Vegas because it is
Speaker 1: currently the middle of the World Series of Poker, but
Speaker 1: we love Aspen and the Aspen Ideas Festival so much
Speaker 1: that we're taking time off to be here.
Speaker 3: With only one straight days of poker, we can spare
Speaker 3: one day for the Aspen Night Festivals.
Speaker 1: We're going to be talking about a recent controversy at
Speaker 1: the World Series of Poker and what happened there. But
Speaker 1: after that we're going to be getting into something much metior.
Speaker 2: Yeah, I think the election of z around.
Speaker 3: I'm Donnie in New York or not, excuse me, The
Speaker 3: nomination he's still has to win. The general election is
Speaker 3: one of the most interesting and seismic events in American politics,
Speaker 3: at least since the election, you know, one of the
Speaker 3: most successful opportunities for like a explicitly socialist left wing
Speaker 3: candidate to Governess said, it's actually very pro financed. New
Speaker 3: York is not left in the way that Portland is
Speaker 3: or Espen might be, or things like us. We're going
Speaker 3: to talk about both the strategy behind rank choice voting
Speaker 3: and how that might have affected things, but also just
Speaker 3: you know, a big political moment, how this might affect
Speaker 3: the party's decisions going forward.
Speaker 1: And after that we will play a little game called
Speaker 1: how likely is It where we will test our knowledge
Speaker 1: and your knowledge of probabilistic thinking. So let's get into it, Nate. Yeah, So,
Speaker 1: first of all, we have a controversy at the World
Speaker 1: Series of Poker last week was one of the biggest
Speaker 1: tournaments of the summer. It's called the millionaire Maker because
Speaker 1: first place is always guaranteed to be at least a
Speaker 1: million dollars. This was a record breaking millionaire Maker, so
Speaker 1: second place was also over a million dollars. This was
Speaker 1: complicated by the fact that there was also a promotion
Speaker 1: being run by a rival brand, the World Poker Tour,
Speaker 1: which one of the two players who ended up getting
Speaker 1: heads up, which is one of the final two players,
Speaker 1: was eligible for which meant that if he won the tournament,
Speaker 1: and only if he won the bracelet, he would get
Speaker 1: an extra million dollars. So think about for a second, right,
Speaker 1: in the context of decision making, in the context of
Speaker 1: incentives exactly, incentives matter. What that does they get heads up?
Speaker 1: So two people left, only one.
Speaker 2: Time wines final feo.
Speaker 3: Happens to be the guy who can win this additional
Speaker 3: million dollar bonus on top of the million dollars has
Speaker 3: already won.
Speaker 1: Yes, Unfortunately, this guy has a chip deficit of nine
Speaker 1: to one. So that means that the other player, right,
Speaker 1: the one who is not eligible for the million dollars,
Speaker 1: has him out chipped nine times. So what that means
Speaker 1: is that usually nine times out of ten, that guy's
Speaker 1: going to win the tournament. And what happens to the
Speaker 1: million dollar bonus, It disappears incentives matter. So what ended
Speaker 1: up happening was a very strange heads up match that
Speaker 1: looked an awful lot. This is just allegations, you know,
Speaker 1: nothing proven, an awful lot like collusion. There was a
Speaker 1: lleged chip dumping where one of the players dumped chips
Speaker 1: to the other players and made what was supposed to
Speaker 1: be an incredibly competitive, televised by the way this was
Speaker 1: all televised match into something that looked very strange into
Speaker 1: people outside of poker. They were like, what in the
Speaker 1: world was going on? The player who had the million
Speaker 1: dollar incentive won the bracelet, won the tournament, and they
Speaker 1: are now currently both under investigation.
Speaker 2: Yeah. No, I mean there are a couple of themes here.
Speaker 3: I mean one is kind of how so it's not
Speaker 3: just that the tournament is televised, it's that in televised poker,
Speaker 3: the audience gets to see the player's private cards.
Speaker 2: Right, So to think that you could have.
Speaker 3: This live streamed on TV and everybody can like see
Speaker 3: the hands face up, basically accept the two opponents that's
Speaker 3: taped to lay fifteen minutes. Right, If one player what's
Speaker 3: his name, Jesse, Jesse, If Jesse had been way ahead.
Speaker 2: Then maybe, like you know what, maybe we're gonna be
Speaker 2: a little loose.
Speaker 3: But instead, every single decision he plays as though he
Speaker 3: has knowledge of what his opponent has.
Speaker 2: Right.
Speaker 3: Almost that exception to the point where the hosts are
Speaker 3: commenting on the real time. If you go and look
Speaker 3: at computers, computers now have solved poker at least heads
Speaker 3: up pick a poke in particular to a high degree.
Speaker 3: These are all plays that are done zero percent of
Speaker 3: the time in op and we'll play that are done
Speaker 3: almost your percent of the time by two very experienced players. Right,
Speaker 3: things like folding a pair to a tiny bit, which
Speaker 3: heads up poker.
Speaker 2: It's hard to make a hand in poker. You're never
Speaker 2: supposed to do that. And so now and people are torn. Right.
Speaker 1: There are people who say, well, they didn't hurt anyone
Speaker 1: because they were heads up, right, so it's not like
Speaker 1: they took chips from a third player. Now, I'm writing
Speaker 1: a book right now about cheating in games, and so
Speaker 1: this is something that kind of I've thought about a lot.
Speaker 1: And on the one hand, I am sympathetic a little
Speaker 1: bit to that argument because a lot of the worst
Speaker 1: cheaters and a lot of the big cheating things in games,
Speaker 1: it hurts other people, right, it hurts other players. You're
Speaker 1: taking away equity from other players. If we assume that
Speaker 1: they were playing completely above board up until this point,
Speaker 1: then no one else was hurt. However, However, there is
Speaker 1: another consideration here. Right. So one of the things that
Speaker 1: I am a huge proponent on, and we've talked about
Speaker 1: this on the show, is sportsmen.
Speaker 2: Right.
Speaker 1: There are two concepts, gamesmanship and sportsmanship. Gamesmanship is you
Speaker 1: know I'm going to win, and you know, I don't
Speaker 1: care how I get there, right, Like, I will take
Speaker 1: every single edge I can possibly take because I want
Speaker 1: to get there. Sportsmanship is doing it in kind of
Speaker 1: a the spirit of the game, right, the spirit of
Speaker 1: the sport, in a to use an old school term,
Speaker 1: in a gentlemanly fashion, right, that you actually respect the
Speaker 1: game you're playing. When you're looking at a televised competition
Speaker 1: that's representing the game and people are watching it and
Speaker 1: expecting something to instead of competition, instead of an actual match,
Speaker 1: to give people, you know, chip dumbing or whatever it is,
Speaker 1: that goes against the spirit of the game, and that
Speaker 1: is something.
Speaker 2: Also it also.
Speaker 3: Lates the rules, Like the first rule of poker is
Speaker 3: that one player to a hand. It's an individual game,
Speaker 3: not a team game. Right, So that fundamental precept of
Speaker 3: poker is violated.
Speaker 1: It does, and you know, I was thinking about it
Speaker 1: in kind of broader games. Like imagine if you were
Speaker 1: watching a tennis match, right, that's also a heads up match,
Speaker 1: a player one against the other, and it became clear
Speaker 1: that someone was throwing the match, and that's happened, right.
Speaker 1: Tennis is actually one of the sports where cheating is.
Speaker 1: There's a lot of it going on, and if you
Speaker 1: watch that, you feel cheated as a spectator and as
Speaker 1: a lover of the game, you kind of feel like
Speaker 1: something was taken away from you. So I can understand.
Speaker 1: And by the way, I really like Jesse, like the
Speaker 1: player who ended up winning. He's sweet and sweet, wonderful guy.
Speaker 1: But you have to be careful, right, if you like someone,
Speaker 1: it doesn't mean that what they did know in the rest.
Speaker 3: It's just kind of a theme that will tie this
Speaker 3: into kind of larger themes at this conference. So I
Speaker 3: not just straddle back and forth being poker and like
Speaker 3: the real world, but like I am involved in like
Speaker 3: lots of small, tight knit communities, right, I'm not an
Speaker 3: effective out your wist, But I know a lot of
Speaker 3: people who describe themselves that way, right, I know, like
Speaker 3: the sports analytics nerds, I know people who work in
Speaker 3: politics and politics analytics nerds right, and like over and
Speaker 3: over again, people are very short sighted about the weird
Speaker 3: by normal person standards, norms and their small communities and
Speaker 3: don't think about what happens when this kind of translates
Speaker 3: to the outside world that hey, actually there's such a
Speaker 3: thing as like the state of Nevada has gaming regulations.
Speaker 3: If there's no regularity, are actually obligated to report that?
Speaker 3: Or the fact that, okay, why are we having this
Speaker 3: world series poker? It's bigger every year, despite the tariffs
Speaker 3: and despite various headwinds, writing their setting record fields again
Speaker 3: this year.
Speaker 2: Well where do the players come from? Right?
Speaker 3: They come from like watching poker on TV or on
Speaker 3: video and saying that would be extremely fun if one
Speaker 3: day I were to make a final table and I've
Speaker 3: done it, you know, I have one time finished second
Speaker 3: place an event like this, right, you know, it's a
Speaker 3: thrill of a lifetime. But like to undermine that and
Speaker 3: to do it in this way that like, I don't know,
Speaker 3: I think it a narrowness of like perspective.
Speaker 1: It does, it does, and I do think that. You know,
Speaker 1: when you're even talking about game theory, there's short term
Speaker 1: and there's a long term, and you need to be,
Speaker 1: you know, no matter what, you need to have the
Speaker 1: long term in mind and the long term health of
Speaker 1: the game and of how it's perceived. And on that note, Nate,
Speaker 1: do you want to switch gears and go a little
Speaker 1: bit into the game theory of a different arena of politics.
Speaker 1: So we are both New Yorkers and we just had
Speaker 1: a Democratic primary that is national news. That is rare
Speaker 1: for a primary, even in New York, but we had
Speaker 1: a very unexpected winner of the primary and zar on
Speaker 1: mom Donnie showing a lot of shifts in the Democratic Party,
Speaker 1: the Democratic Party base, and the potential the game theory
Speaker 1: for the Democratic Party going forward. This is one kind
Speaker 1: of potential blueprint should they choose to accept it.
Speaker 3: So one amazing thing is that younger voters actually turned
Speaker 3: out at a higher rate than older voters in a selection,
Speaker 3: which basically never happens in elections anywhere. Right, So he
Speaker 3: spoke very much the vernacular of vertical video and kind
Speaker 3: of how young people think about in general. He's a
Speaker 3: you know, charismatic guy that's kind of a New York
Speaker 3: archetype of this kind of multi ethnic hustler type, but
Speaker 3: friendly guy and obviously a smart ambitious guy. Right, It's
Speaker 3: like a type we recognize, I think have some appreciation
Speaker 3: for in New York. He's a problem member the Democratic
Speaker 3: Socialist of America. During the campaign emphasized affordability, cost of living,
Speaker 3: had tangible proposals on like free buses, rent freezes for
Speaker 3: certain types of buildings, right, city owned grocery stores, thirty
Speaker 3: dollars minimum wage. Right, So tangible focused on that, not
Speaker 3: culture worse stuff. And he winds up beating former governor
Speaker 3: and Dupomo, who in a terrible campaign but was very complacent.
Speaker 3: And I think this is a kind of a generational moment.
Speaker 1: I think it is, and I think it's very interesting
Speaker 1: to see that Momdani was actually able to get the
Speaker 1: voters who turned to Trump during the presidential election, that
Speaker 1: demographic to actually come out and vote for the Democrats.
Speaker 1: And there are you know, videos from him campaigning where
Speaker 1: he actually manages to talk to kind of that exact
Speaker 1: demographic that you know, voted for not just a different
Speaker 1: type of Democrat, but for Republicans in the presidential election.
Speaker 3: Yeah, I mean it's a little bit because you know,
Speaker 3: so we had comparatively high turnout about a million, probably
Speaker 3: one point oh five million, right.
Speaker 1: Yeah, this was our high start.
Speaker 2: But there are eight.
Speaker 3: Million people in New York City of whom three point
Speaker 3: three to three point four million are registered Democrats, right,
Speaker 3: so you're still only getting twenty five or thirty percent
Speaker 3: of the electorate.
Speaker 2: With that said, very broad based.
Speaker 3: Right, He won precincts even in Staten Island, which is
Speaker 3: kind of cuomo home turf. Not all of them, but
Speaker 3: some of them.
Speaker 2: Right.
Speaker 3: You know, Cuomo wins the Central Parkies, but you go
Speaker 3: even a couple of blocks into the Upper east Side,
Speaker 3: one of the more conserve neighborhoods, he started some mom
Speaker 3: don Aid precincts right there. So, you know, a very
Speaker 3: impressive performance. And of course we will be accused as
Speaker 3: the New Yorkers are being provincial. But New York is
Speaker 3: so big and so diverse, right that not in the
Speaker 3: same proportions, But we have like lots of Conservatives in
Speaker 3: New York are out number by liberals. We have lots
Speaker 3: of conservatives, right, We have every imaginable ethnic group and
Speaker 3: slice of racial demographic right. There are lots of working
Speaker 3: class people in New York. He mostly did well among
Speaker 3: the kind of upper middle class, you know, kind of
Speaker 3: white people. Basically, but there are those two, right, and
Speaker 3: did well enough? This is important, did well enough among
Speaker 3: the other groups, right, because sometimes can they say, Okay, well,
Speaker 3: we're not going to win any you know Democrats, Right,
Speaker 3: We're not going to win any working class white men,
Speaker 3: so just write them off, treat them as a zero,
Speaker 3: when actually, like getting thirty percent of working class non
Speaker 3: college men as opposed to twenty percent makes a big deal. Right,
Speaker 3: Trump in this past election got something like eighteen to
Speaker 3: twenty percent of the black vote, which is a significant minority.
Speaker 2: Right.
Speaker 3: Mitt Romney got four percent against Barack Obama in twenty twelve. Right,
Speaker 3: So going from four percent to twenty percent the population,
Speaker 3: you can do that math, right, that's quite a big swing.
Speaker 1: Actually, yeah it is, mate. Do you actually think that
Speaker 1: the election was at all affected by the fact that
Speaker 1: this was the second primary where ranked choice voting was
Speaker 1: actually in use, because that was something that people really were,
Speaker 1: you know, emphasizing during the.
Speaker 3: Candidate You guys know what rank choice voting or run off?
Speaker 3: Okay everyone.
Speaker 1: Festival, and we covered it for our listeners on the
Speaker 1: pod last week. So I mean, okay, so ranked choice
Speaker 1: voting means you have five choices, you rank your choices.
Speaker 1: If your first choice candidate is not one of the
Speaker 1: top two candidates, then your choice gets moved to your
Speaker 1: next ranked candidate, and so on down the line.
Speaker 3: Yeah, so Zoran seemed to understand this and Cuomo did not,
Speaker 3: yes one thing. He formed alliances with other candidates, right,
Speaker 3: especially Brad Lander, the third place candidate. Now, from a
Speaker 3: game theory standpoint, I don't think this is necessarily rational
Speaker 3: for Brad Lander, because like, if you're in third place
Speaker 3: and the other guy's in second place or maybe a
Speaker 3: first place, right, you don't necessarily want to help him out, right,
Speaker 3: But I think Brad Lander hates Andrew Cuomo's guts, has
Speaker 3: a lot of people do in New York and so
Speaker 3: and so there was that surround a very positive campaign
Speaker 3: and a substantive campaign, which is a change from how
Speaker 3: campaigns are often run.
Speaker 2: And I do think, like not uniquely the left in
Speaker 2: the United States. I mean, look at Trump also.
Speaker 3: In NAGA, right, but like there often is a lot
Speaker 3: of negativity, a lot of anxiety, right, and that wasn't
Speaker 3: what he was doing, right. He was a smart, substantive guy,
Speaker 3: you know, who literally walked the entire length of Manhattan,
Speaker 3: like the last day before the campaign wearing a suit,
Speaker 3: and so like did every podcast in the world.
Speaker 2: I think I've done. I've done like six podcasts in
Speaker 2: the day before.
Speaker 3: I think Zorn probably beat that record, but he very
Speaker 3: much earned it, and Cuomo was pure negative. We do
Speaker 3: have a general election still where Cuomo is running again, right,
Speaker 3: I don't know if that.
Speaker 1: And so is Sarah Adams maybe and Eric.
Speaker 3: Adams and come a mirror defective from your practic party
Speaker 3: friendly with Trump. Right, you also have the proper Republican nominee,
Speaker 3: Curtis Sliwa, who is a guy who founded The Guardian
Speaker 3: Angels and owns like fifty six cats or something like that.
Speaker 1: So yeah, yeah, So I'm also interested, you know, from
Speaker 1: from the point of view of like the game theory
Speaker 1: of the Democratic Party. Right, we have this election which
Speaker 1: seems like an anomaly but might not be, and a
Speaker 1: lot of the Democratic establishment don't want to embrace Zora
Speaker 1: Mom Donnie right there. They think that he is, you know,
Speaker 1: too fringe, right, that he is too radical, that some
Speaker 1: of his ideas are are too kind of too out there,
Speaker 1: and that they want to go safer, They want to
Speaker 1: go more middle of the road, which is what they
Speaker 1: were saying during the presidential election that they lost.
Speaker 3: Yeah, it's kind of it's kind of hard to be
Speaker 3: like an exciting centrist is part of the problem.
Speaker 2: You know, I don't know if Mayor Pete is exciting.
Speaker 3: I guess he's a well spoken person, for example. But like,
Speaker 3: sometimes more the energy comes from the left. Again, like
Speaker 3: to me, and look, I'm a University of Chicago economics major.
Speaker 2: I'm unapologetically kind of a neoliberal.
Speaker 3: Right, it does seem to me like, look, the left
Speaker 3: has not had the economic left has not had a
Speaker 3: lot of power in the United States recently, right, And
Speaker 3: so I've become more sympathetics because I'm like, well, we
Speaker 3: haven't made those mistakes. They will make mistakes, and it's
Speaker 3: very hard to govern a state like New York. It's
Speaker 3: particularly hard to govern New York City because New York
Speaker 3: State controls the taxation policy, a lot of the budget
Speaker 3: and things like that. They're always rivalries between Albany and
Speaker 3: and city Hall, right that without being different.
Speaker 1: Yeah, absolutely, And I also wonder, you know, if mom
Speaker 1: Donnie does get elected. Right, this is a big if.
Speaker 1: Still right, we're we're i mean twenty five percent, but
Speaker 1: seventy five percent, as we know, is not one hundred percent,
Speaker 1: as we very well know. So he but let's assume
Speaker 1: he gets elected. It's going to also be a very
Speaker 1: interesting shift where kind of candidates like Mom Dannie have been,
Speaker 1: you know, the underdog right, and it's been kind of
Speaker 1: the the underdog story of someone who is kind of
Speaker 1: young and kind of more radical and has those ideas
Speaker 1: and wins despite the odds. Now, what happens when you
Speaker 1: become the establishment right like that that underdog story shifts
Speaker 1: your incentives. We were talking just now in poker about incentives.
Speaker 1: Incentives matter in everything. Your incentives shift, right, the things
Speaker 1: that you were able to promise suddenly you have to
Speaker 1: make coalitions with other you know, with lobbyists, with other
Speaker 1: ruling factions, with you know, you need to face the
Speaker 1: realities of power. And so it's much easier to kind
Speaker 1: of be the idealist before you get elected. And once
Speaker 1: you're once you the establishment, that changes and you change.
Speaker 1: And so I'm going to be very very curious to
Speaker 1: see what happens and how that story kind of ends
Speaker 1: up shifting, and then what happens to the supporters kind
Speaker 1: of what all the downstream effects are going.
Speaker 2: To be Yeah, you're registered in Nevada. Now I am, Yeah,
Speaker 2: I'm in New York.
Speaker 3: This is the rarer time when I have a general
Speaker 3: election vote in New York that could plausibly matter. And
Speaker 3: you know, people like me who might be kind of
Speaker 3: on vaguely the center of the culturally center left and
Speaker 3: economically centered something right, A lot of people are are
Speaker 3: concerned about him, but like, to me, it's a bit
Speaker 3: like if you're picking a founder for a startup, right,
Speaker 3: you're kind of going more off like the rod intelligence
Speaker 3: and talent and work ethic, maybe more than like the
Speaker 3: particular program or platform, right, or at least it's kind
Speaker 3: of like what I'm telling myself potentially, And the fact
Speaker 3: that he's a lot of dexterity in talking about policy, right,
Speaker 3: He went on, like the Odds odd Lots podcast, which
Speaker 3: is Joe Wisenthal and Tracy Alloway, which is kind of
Speaker 3: in the same family as Risky Business, right, and like
Speaker 3: talking a lot of wankery about policy and things like that, Right,
Speaker 3: And like I like politicians who who are elected officials
Speaker 3: and are responsive to.
Speaker 2: Some degree to fulblic opinion.
Speaker 3: I think it's how democracy should should function, right, and
Speaker 3: he can say here are my priors. I'll stay for
Speaker 3: the working class. I don't like capitalism all that much. Right,
Speaker 3: but he's not proposing anything too radical and like and
Speaker 3: it's not angry. You know, Bernie Sanders understood that.
Speaker 2: Right.
Speaker 3: The one moment when the Sanders campaign got a little
Speaker 3: bit angry was toward toward twenty twenty when they won
Speaker 3: the first couple of primaries and they were instead of
Speaker 3: bending the knee to the Timparty Party. Remember going to
Speaker 3: a rally in Nevada, Bernie wound up winning basically three
Speaker 3: states in a row. Iowa was disputed between him and
Speaker 3: their pete, So we won't get into that, right, but
Speaker 3: two and a half out of three, right, and he
Speaker 3: gave species that were like, yeah, well screwed you Democrats establishment,
Speaker 3: right now, you have to kiss my ring. And there
Speaker 3: they still had leverage, right, they still had leverage. They
Speaker 3: had the leverage to have Jim Clyburn and Amy club
Speaker 3: Ashar Mayor Repete everybody else rally behind.
Speaker 2: Joe Biden successfully for one term.
Speaker 3: You know, whether that was a good decision of a
Speaker 3: long run or not, we'll be I think debated, but like,
Speaker 3: but yeah, you don't want to like rub it in
Speaker 3: someone's face when they still have chips on the table,
Speaker 3: so to speak, and so and so, you know, it
Speaker 3: would behooves around to understand there's still a losible. We're
Speaker 3: gona talk about what these probabilities mean. Twenty five percent
Speaker 3: is a real possibility of losing, right, and so he
Speaker 3: would be mindful to to keep up the strategy that
Speaker 3: he had in the in the in the.
Speaker 1: Primary, and uh mindful to h if he gets selected
Speaker 1: to try to uh keep up the same strategy if
Speaker 1: you want.
Speaker 3: It's a really it's a really hard it's hard. It's
Speaker 3: a really hard job.
Speaker 1: It is it's an incredibly hard job.
Speaker 3: You can probably do better than narrow dams, right, But
Speaker 3: it's a it's a that's.
Speaker 1: A it's it is. It's it is an incredibly hard job.
Speaker 1: And I think we do need to remember that. Nate,
Speaker 1: Do you have any other kind of final thoughts on
Speaker 1: the election or should we talk probabilities more generally?
Speaker 3: No, again, I kind of said at the top, I
Speaker 3: do think that like the fact that Cuomo called in
Speaker 3: all the cavalry right, he called him to Clinton to
Speaker 3: endorse him. He called him like a Bloomberg called him
Speaker 3: Jim Clyburn. Every black group, Jewish group, Italian group, got
Speaker 3: all the ethnic groups in New York, right, lots and
Speaker 3: lots and lots of unions, right, And that's how you
Speaker 3: used to win in machine cities, machine states, that's how
Speaker 3: you win elections.
Speaker 2: And like and like Cuomo, Cuomo.
Speaker 3: Kind of actually got the numbers that ordinarily would win
Speaker 3: a New York mayoral race, right since two thousand and one,
Speaker 3: he's the second highest total of votes. But like Mom, Donnie,
Speaker 3: reached into new parts of the electorate, not totally new.
Speaker 3: These are people who vote in presidential primaries and things
Speaker 3: like that. But this is the case where it turnout
Speaker 3: did matter quite a bit in a race where maybe
Speaker 3: a third of Democrats turn out if you're lucky, maybe
Speaker 3: twenty five percent. Then and getting people excited, being I mean,
Speaker 3: being interesting. So many politicians are either psychopaths or boring, right,
Speaker 3: like ninety four of one hundred US Saturday either psychopaths,
Speaker 3: boring or both.
Speaker 1: Yeah, yeah, no, And a lot a lot can be
Speaker 1: said for being nice and having energy and actually being
Speaker 1: excited about the job you're going to do, even though
Speaker 1: it's going to be a really really hard job, and
Speaker 1: I think that's tough, right to show up every day
Speaker 1: with that kind of energy. I mean, how many of
Speaker 1: you can walk the entire length of Manhattan and tape
Speaker 1: however many podcasts and I get.
Speaker 2: Not in a suit though I'm a sweater, not in
Speaker 2: a suit.
Speaker 1: I can do it in high heels.
Speaker 4: Now, I kid, you're.
Speaker 2: Gonna hear a quick break and then we'll be back.
Speaker 1: We want to play a little game with you about probabilities.
Speaker 1: We'll play it with each other first, but then we're
Speaker 1: going to involve you. It's a game called how likely
Speaker 1: is it? So how likely are certain probabilities? So let's
Speaker 1: give you just a little bit of context behind this.
Speaker 1: So back in nineteen fifty one March of nineteen fifty one,
Speaker 1: there was a CIA analyst whose name was Sherman Kent,
Speaker 1: and he filed a report on the probability of an
Speaker 1: invasion by of Yugoslavia in the year nineteen fifty one.
Speaker 1: We all know how that turned out. So how probable
Speaker 1: is it that the USSR is going to invade? And
Speaker 1: he concluded in his report that it was a quote
Speaker 1: serious possibility. So what did he mean by serious possibility?
Speaker 1: In retrospect. When he explained this, he thought it meant well,
Speaker 1: first of all, just think in your heads what you
Speaker 1: think it means, Nate, what do you think it mean?
Speaker 1: Just you don't have to answer, Okay, So he thought
Speaker 1: it meant sixty five percent or higher. So, right, more
Speaker 1: than sixty five percent. Unfortunately, the person to whom he
Speaker 1: conveyed his report and his findings, who was a chairman
Speaker 1: of the policy Planning initiative, they had understood him to
Speaker 1: mean that it was a lot lower than that. So
Speaker 1: in their minds, serious possibility was like, oh, twenty percent
Speaker 1: or maybe like a range from twenty to seventy eighty percent,
Speaker 1: certainly not sixty five percent or higher. So there was
Speaker 1: a huge miscommunication which resulted in massive policy missteps. Right,
Speaker 1: just because how serious a serious possibility was was not Well.
Speaker 3: People are there were number match totobic right, you know
Speaker 3: at former thirty eight, no silver bulletin. Right, we have
Speaker 3: an election model where we give you know, a probability
Speaker 3: is fundamentally defined, is a number between zero percent and
Speaker 3: one hundred percent.
Speaker 2: Right, And a lot of times.
Speaker 3: People use weaseled They're like, well, Nicklint, you had Hillary
Speaker 3: Clinton with a seventy one.
Speaker 2: Percent chance of winning the election. So you were wrong.
Speaker 3: We have the semantics of that, right, But like, but
Speaker 3: you know, but you put a number there and people
Speaker 3: think it's like, you know, understand that that represents it
Speaker 3: bakes in the uncertainty in the model, the uncertainty in
Speaker 3: the real world.
Speaker 1: Right.
Speaker 3: If I were to say, I think, by the way,
Speaker 3: serious possibility to me implies like twenty percent. Right, if
Speaker 3: I said that there's still a serious possibility Zaron could
Speaker 3: lose a general election, right, because you're not saying probability,
Speaker 3: You're saying, like, take this seriously, twenty.
Speaker 1: Twenty se Yeah, Well, when you put it in that context, right,
Speaker 1: a serious possibility that Zar loses the election twenty five percent? Right, Now,
Speaker 1: that sentence makes sense, which is why So when I
Speaker 1: was a grad student and studying decision making, one of
Speaker 1: the things that we talked about was how do you
Speaker 1: communicate risk? Right? How do you communicate probabilities to people?
Speaker 1: And it's really really hard, and it's hard in all
Speaker 1: sorts of context. Right, how do you communicate probabilities when
Speaker 1: it comes to things like global warming, right, or or
Speaker 1: elections or something like that, And people just do not
Speaker 1: understand probabilistic thinking. As we've talked about on the show
Speaker 1: many many times, our brains just suck at probabilities and
Speaker 1: at what they mean. Just by the way, why poker
Speaker 1: is so good because it actually forces you to learn
Speaker 1: what probability feels like viscerally, which is how the brain
Speaker 1: learns the best. You know, I, for instance, you know
Speaker 1: no bad beat stories on the podcast or in real life,
Speaker 1: but I know how it feels to lose on day
Speaker 1: four of the main event of the World Series as
Speaker 1: a ninety eight point six percent favorite in a multi
Speaker 1: millionship pot. It happens, right, It feels like it shouldn't
Speaker 1: because ninety eight point six percent feels like it should
Speaker 1: be one hundred percent. But it's not. Probabilities matter, and
Speaker 1: we're so so bad at them. And yet something that
Speaker 1: you just said, Nate, which is absolutely crucial, is that
Speaker 1: we're number phobic. When you tell people, you know what,
Speaker 1: the best way to communicate probability is, state the probability.
Speaker 1: State the damn thing. Put a number on it. Actually
Speaker 1: say you know it's I think the probability is between
Speaker 1: ten percent and fifteen percent, and I am seventy five
Speaker 1: percent sure In this estimate.
Speaker 3: Rights, be explicit about the language that you're using it.
Speaker 3: I believe the IPCC the Global Climate reports. Right, they
Speaker 3: will use language like very likely a lot. But in
Speaker 3: the appendix you look it up and they say very
Speaker 3: likely means whatever it is ninety five percent or higher?
Speaker 2: Right, nay?
Speaker 1: How many people reading the report then go to the appendix.
Speaker 3: To see see s report. My whole family once gave
Speaker 3: each other the Iraq Study Group report for like Christmas
Speaker 3: or something. It's that kind of that kind of family.
Speaker 3: But no, but and then you know, look house this year,
Speaker 3: but no, look uh, because the apsode is people use
Speaker 3: this as weasel words. Right, They're intentionally ambiguous to say
Speaker 3: later on, no, I was right, I was wrong, right,
Speaker 3: which is lame.
Speaker 2: Right.
Speaker 3: People don't get any better unless they feel the pain of,
Speaker 3: like of putting something which is in the public record
Speaker 3: and is accountable to some degree.
Speaker 2: Obviously, you need like.
Speaker 3: A large sample of predictions to be able to evaluate,
Speaker 3: you know, do do of your seventy percent probabilities? Do
Speaker 3: seven out of ten come true in the long run? Right,
Speaker 3: But you want to play a little bit of the
Speaker 3: game here, how likely is it?
Speaker 1: Yeah, let's absolutely do it? So the way that this
Speaker 1: game is going to work at first is I am
Speaker 1: going to tell you a phrase, Nate, and then you
Speaker 1: will tell me what percent probability you think that that
Speaker 1: phrase represents, and then I'll think of my answer as well,
Speaker 1: and if you guys want to play along, feel free
Speaker 1: to think of yours. All right, So if I tell
Speaker 1: you almost certainly, what are you thinking?
Speaker 2: Ninety five percent plus?
Speaker 1: Okay, so almost certainly ninety five percent plus. I was
Speaker 1: actually going to go a little bit lower and say
Speaker 1: ninety percent plus, just knowing that in this world basically
Speaker 1: nothing is certain. And if we're at above ninety percent probability,
Speaker 1: like holy shit. You know, as a gambler or as
Speaker 1: an investor, if I told you that you had an
Speaker 1: investment that had a ninety percent probability of succeeding, how
Speaker 1: many of you would take that investment? And I hope,
Speaker 1: I hope most of you. But and if I told
Speaker 1: you that you could take a gamble with a ninety
Speaker 1: percent probability of success.
Speaker 2: Yeah, people are there gambling, right.
Speaker 3: I mean, you'll learn in poke if you can get
Speaker 3: like a fifty four percent edge, yeah, it's pretty good.
Speaker 1: It's amazing. But I'm not saying that that means almost certain,
Speaker 1: but to me, like above ninety percent, given how much
Speaker 1: uncertainty and ambiguity there are in the world, so those
Speaker 1: are two different things, right. Uncertainty is we're not quite
Speaker 1: sure what the probabilities are. Ambiguity is we're not even
Speaker 1: quite sure kind of what the factors are. We're not
Speaker 1: sure if our model is one hundred percent correct and
Speaker 1: is accounting for everything. So given all of that, I
Speaker 1: would actually say, like above ninety percent would be almost certainly.
Speaker 1: Do you I don't know if that makes you rethink
Speaker 1: your answer or not.
Speaker 3: You know, I watched en f NBA playoff games where
Speaker 3: teams blue seventeen point leads and things like that in
Speaker 3: the fourth quarter, So like, I'm feeling I want the
Speaker 3: security of ninety five percent.
Speaker 1: All right, you want the security of.
Speaker 3: After playing poker for a few weeks. Yeah, We're nothing
Speaker 3: is certain that I'm like, I want my almost certainly
Speaker 3: s at ninety five Right.
Speaker 1: That makes that makes sense? So what would you would
Speaker 1: you actually like what would you say for like a
Speaker 1: ninety plus or an eight? Is there something that you
Speaker 1: is there a phrase that you would think more accurately.
Speaker 2: Represents that very likely highly likely.
Speaker 1: Huh interesting because I would think that very likely would
Speaker 1: be below ninety percent. See, this is this is how,
Speaker 1: this is why this is ambiguous.
Speaker 2: All right?
Speaker 1: So I am going to give you another phrase. If
Speaker 1: I said that something was unlikely to happen.
Speaker 3: Yeah, I'd say unlikely or for that matter of the counterpart.
Speaker 3: Likely is one of the worst and most ambiguous terms
Speaker 3: to use in that context, right, because literally it can
Speaker 3: mean depending on the context. Anythink from like zero point
Speaker 3: zero zero zero percent one percent zero zero zero one
Speaker 3: to like forty nine percent.
Speaker 2: Right, it's very ambiguous.
Speaker 3: I would tend to think unlikely means twenty five or
Speaker 3: thirty percent, somewhere in the middle of the below fifty
Speaker 3: percent range.
Speaker 2: What about you, Yeah, I was.
Speaker 1: Going to be at around twenty five percent or below. Right,
Speaker 1: Any of that is unlikely, and you know, we can
Speaker 1: start we can start kind of playing around with it
Speaker 1: a little bit with adjectives. But I totally agree with
Speaker 1: you that likely. What about likely? By the way, would
Speaker 1: would you say likely is above above what percentage is likely?
Speaker 1: Is it above fifty?
Speaker 3: If feel like you're a little asymmetric like likely, I
Speaker 3: think you have to be sixty or so.
Speaker 2: Right, Yeah, it is.
Speaker 3: Asymmetric, possible or probable. These ones are pretty bad, right,
Speaker 3: You want to do one more?
Speaker 1: Yeah, let me let's do it all right? So, how
Speaker 1: about if I actually add an adjective to unlikely and
Speaker 1: say highly unlikely. Now, what are we talking now?
Speaker 3: Ten percent or below? I think it's kind of the
Speaker 3: counterpart to I don't know why. I think it's in
Speaker 3: percent of blowing. There was like ninety five percent.
Speaker 1: I was about to say, why they asymmetry, so you said,
Speaker 1: you know, the just highly likely was ninety five I
Speaker 1: have still.
Speaker 3: A large language model teasing out implicit contexts from the
Speaker 3: way these phrases are used in the real world.
Speaker 2: I don't know what checchiput would say.
Speaker 1: So highly unlikely, I would say it is interesting because
Speaker 1: I might actually be lower than you. I might say,
Speaker 1: like below if it's highly unlikely, like below five percent,
Speaker 1: Like it's just you're telling me that it's almost certainly
Speaker 1: not going to happen to me. Right, that like highly
Speaker 1: unlikely and almost certainly kind of go hand in hand.
Speaker 1: But let's if you actually kind of go out in
Speaker 1: context a little bit with that. Think about weather reports,
Speaker 1: which is something where we experience kind of that's one
Speaker 1: of these things where we do get probabilities, where we
Speaker 1: do get percentages, and we experience those on a daily basis. Right, you,
Speaker 1: what what is your threshold, Nate for bringing an umbrella?
Speaker 5: Like?
Speaker 1: What's your an umbrella?
Speaker 2: I find umbrella?
Speaker 1: Okay, that was about this is true?
Speaker 2: Why is umbrella technology not improved more?
Speaker 1: Nate and I have walked together in New York City
Speaker 1: rainstorms where I'm like, do you want to share my umbrella?
Speaker 1: He's like, I got my baseball cap. I'm good. But
Speaker 1: but you know, for people who are not like you
Speaker 1: and who actually care about rain, like, what's your threshold
Speaker 1: for bringing an umbrella? Like for me, if it's like
Speaker 1: fifty percent chance of rain, I'm probably not going to
Speaker 1: bring it. You know, if it's like above sixty, I'll
Speaker 1: probably probably bring an umbrella. You know, it just it
Speaker 1: really just depends on what I'm doing. And so you
Speaker 1: know that's but you think about those days when it
Speaker 1: was ninety percent chance of rain and it didn't rain.
Speaker 2: Okay's great, let's get the audience.
Speaker 1: Let's do it all right, one last one. We're going
Speaker 1: to do it with you guys, and I'm gonna So
Speaker 1: I'm going to ask you to raise your hands.
Speaker 3: So if I say something is possible, that's a secret password, right, yeah, possible,
Speaker 3: and you have to take one of the following ranges
Speaker 3: zero twenty five percent, twenty about to fifty fifteen, seventy
Speaker 3: five pcent five one hundred. Right, if I have something
Speaker 3: is possible, raise your hand. If you interpret that as
Speaker 3: zero to twenty five percent, okay, we have about a
Speaker 3: quarter about z you're just twenty percent the hands up
Speaker 3: in the audience, right, yeah, quite a few hands. I
Speaker 3: will say twenty five to fifty percent a little bit more,
Speaker 3: kind of solid.
Speaker 1: It's kind of similar, but probably a little more. Okay,
Speaker 1: how about fifty to seventy five and.
Speaker 3: There are a handful of hands hands possible, that could
Speaker 3: be And how about seventy five percent or more one day?
Speaker 3: So the way to average is like thirty six or
Speaker 3: something percent. We're like doing we're doing science here, yeah,
Speaker 3: doing it on the fly. Yeah, but people think it
Speaker 3: means anything from five percent to eighty percent, right if
Speaker 3: you ask people to write down individual guesses so like,
Speaker 3: so not very useful to have that, to have.
Speaker 1: That for you. No, No, it's really not because what
Speaker 1: you know, what do you even mean probbable likely? Like
Speaker 1: it is? You know, it's such a hedgeword. And what
Speaker 1: we're trying to do is actually force you to be
Speaker 1: more precise in your probabilistic judgments, because precision matters, right,
Speaker 1: whether it's the precision of the USSR invading Yugoslavia or
Speaker 1: a gamble that you're taking out the poker table right out.
Speaker 3: When it comes to like politics and things like that,
Speaker 3: political questions, like, people are also just often interpreting stuff
Speaker 3: in bad faith, right, Like I think that I think
Speaker 3: that when you say there's a third chance of rain
Speaker 3: and doesn't rain, people don't get as mad as when
Speaker 3: there's a third chance of Donald Trump winning and he wins,
Speaker 3: or things like that for example. But like, but you know,
Speaker 3: politics makes people's brains misfire in various ways absolutely well.
Speaker 1: As soon as there's personal motivation there, right, as soon
Speaker 1: as your identity is tied up in something like politics,
Speaker 1: all of a sudden, your ability to parse these things.
Speaker 1: You start thinking in absolutes, which is how are our
Speaker 1: brains are programmed to think? Right? Ninety percent basically one
Speaker 1: hundred seventy is basically one hundred sixty five basically one
Speaker 1: hundred thirty basically zero.
Speaker 2: Right. We just let's at least.
Speaker 3: Get sixty five, right, is certainly either fifty fifty zero,
Speaker 3: one hundred. Let's at least get those middle zones right
Speaker 3: as a stepping stone to some more PI.
Speaker 1: Yeah, and I think I think that the bottom line
Speaker 1: is that it's very difficult to communicate probabilities in plain language,
Speaker 1: and you do need to get people accustomed to having
Speaker 1: numbers associated with them, and one of the best ways
Speaker 1: of doing that is to try to figure out, Okay,
Speaker 1: what experience can I tie that to in their life
Speaker 1: that makes them understand what this actually means?
Speaker 2: Right?
Speaker 1: That percent is not zero?
Speaker 2: I'm not way serious.
Speaker 1: Yeah, no, teacher, kids, poker. I've actually talked about this
Speaker 1: many times, and I actually think that that is one
Speaker 1: of the best ways that you can combat this.
Speaker 2: And we'll be right back after this break.
Speaker 1: Do you want to open it up to questions?
Speaker 3: Yeah, I say it's extraordinarily likely that we're going to
Speaker 3: open it up to Q and A at this point.
Speaker 2: So there are mic runners. We can also point and shop,
Speaker 2: but yeah.
Speaker 1: Okay, yeah, there are mic runners in the audience. So
Speaker 1: raise your hand if you have a question. Yep, there's
Speaker 1: one coming to you. Thanks.
Speaker 6: I studied statistics in graduate school and ended up in communications,
Speaker 6: and I think one of the things that confounds this
Speaker 6: problem is if somebody says to me, for example, climate
Speaker 6: change isn't caused by us, I would say, not believing
Speaker 6: myself that that's possible.
Speaker 4: That's my opinion.
Speaker 6: I know other people disagree, but in order to have
Speaker 6: a conversation, I validate their opinion and move on. And
Speaker 6: that's standard operating procedure when you're trying to actually change
Speaker 6: minds of what you're saying is we're inaccurate and communicating.
Speaker 4: The real probabilities.
Speaker 6: But part of what's going on is that most of
Speaker 6: the conversation is about trying to find some middle ground.
Speaker 1: So how do you separate those two uses?
Speaker 3: Yeah, it just and this kind of gets into the
Speaker 3: conversation about Zoran earlier, right where he's going on podcasts
Speaker 3: where the audience might not be favably inclined to him.
Speaker 3: And I've done this too, Like marketing my book, I
Speaker 3: talked to conserative audiences, liberal audiences, everything in between. Right,
Speaker 3: And you're basically a politician, right, You're like you're trying
Speaker 3: to code switch back and forth. You're trying to be
Speaker 3: empathetic to your audience, You're trying to find points of
Speaker 3: agreement in that disagreement. Maybe more politicians should take that lesson.
Speaker 3: I mean, look, I generally think that when scientific communicators
Speaker 3: try to like dumb things down too much, it's like
Speaker 3: quite the right term. There's empathy, and there's there's empathy,
Speaker 3: there's dumbing down, and there's like kind of like skipping
Speaker 3: over the rough patches of the argument in a way.
Speaker 3: They create like false certainty times, right, Like I generally
Speaker 3: believe that like treat the audience as being reasonably smart
Speaker 3: and sophisticated, right, I mean as poker players too, you know,
Speaker 3: I think we are naturally suspicious, right, And I think
Speaker 3: people can like people can tell I think when a
Speaker 3: politician is being inauthentic or when they're not being kind
Speaker 3: of communicated with fully forthrightly. Right, Like I thought this
Speaker 3: was an issue at the beginning of the COVID pandemic.
Speaker 3: We're changing advice about like masks and things like that, Right.
Speaker 3: I think whenever people kind of like say, oh, the
Speaker 3: science has settled on this question, well, on some questions,
Speaker 3: it's settled on some questions that isn't right, and it's
Speaker 3: like often used as a cudgel where there is genuine
Speaker 3: uncertainty and so so yeah, I mean it's it's look,
Speaker 3: we wrestle with this every day journalists too. But like,
Speaker 3: I'm air more on the side of like, you know,
Speaker 3: you can be empathetic in how you present something, and obviously,
Speaker 3: if you're in a kind of question like not everything
Speaker 3: you say in response, you're not going to correct any way.
Speaker 3: Oh literally, you're wrong, you're wrong, but let me tell
Speaker 3: you something else. Right, So I'm yeah, empathetic, but when
Speaker 3: it official sign of a communication, I think should aim
Speaker 3: more in the side of precision.
Speaker 1: I think that I think that we are also not
Speaker 1: tolerant of ambiguity. Right that it's much easier to win elections.
Speaker 1: It's much easier to win over a crowd if you
Speaker 1: speak in absolutes, if you speak in certainties, and the
Speaker 1: person who says, you know, I'm really not sure about this,
Speaker 1: you know, they're here are the pros, here are the
Speaker 1: cons And who gives the more nuanced response is not
Speaker 1: the person who ends up getting ahead. And so there's
Speaker 1: a fundamental mismatch between the need to communicate ambiguity and
Speaker 1: uncertainty and the fact that you know some things we
Speaker 1: just don't know, and that there are these kind of
Speaker 1: confidence intervals there, and the need to tell a story
Speaker 1: and tell a compelling story. And this is something that
Speaker 1: you know, Nate, You and I have to struggle with
Speaker 1: us storytellers too. I mean, we're communicators, we're journalists. We
Speaker 1: make choices all the time when we write an article,
Speaker 1: when we write a book, how do we tell the story?
Speaker 1: What story do we tell? And you can't constantly. I
Speaker 1: mean a book will be absolutely unreadable if every single
Speaker 1: paragraph is a caveat where you know, well, you know,
Speaker 1: you can also interpret it this way and this way
Speaker 1: and I'm not quite sure about this, right, you can't.
Speaker 1: You can't work that way, and so you make choices
Speaker 1: all the time.
Speaker 3: Of course, sometimes ambiguity is strategic, right, Like let's say
Speaker 3: Marie and I are on a double date with our
Speaker 3: respective spouses and then a third couple that we don't know,
Speaker 3: that a friend kind of set us with for a
Speaker 3: friend's day, and they're super fucking annoying and we hate
Speaker 3: their guts. Right, you know, if I say something like Marie,
Speaker 3: what time did you say your flight was tomorrow morning?
Speaker 2: Right?
Speaker 3: Yeah, that's ambiguous. What it really means is that let's
Speaker 3: gett's great.
Speaker 1: And Nate and I have known each other for long
Speaker 1: enough that I would say, you know, it's at seven am,
Speaker 1: we really really have to go. Yeah, so there are
Speaker 1: strategic uses of it. Yeah, there are more questions back there.
Speaker 4: I am a longtime listener, first time caller.
Speaker 1: Wellco to the show.
Speaker 2: Yeah, yes.
Speaker 4: My question is how much do we do we think
Speaker 4: Zauron's win is the inter syncracies in New York City
Speaker 4: and him being a great candidate and Cuomo sucking, and
Speaker 4: how much is the political climate has changed in some
Speaker 4: way since the last election?
Speaker 1: That is, I mean, that is such a good question,
Speaker 1: because the Cuomo sucking part of it is actually huge.
Speaker 1: That sounds like that sounds like a silly statement, but
Speaker 1: I actually think that that is an idiosyncrasy of this
Speaker 1: election from a psychological standpoint, because never underestimate the power
Speaker 1: of people to band together and coalitions to actually stick
Speaker 1: together when there's one enemy, right, when there's a clear
Speaker 1: someone who no one wants to see, When all of
Speaker 1: a sudden, people who would never vote together, who would
Speaker 1: never kind of partners partner up. And this is from
Speaker 1: a psychological stamp.
Speaker 3: What you see that he both ran a bad campaign
Speaker 3: and kind of, to us an informal term, kind of
Speaker 3: like cock blocked all the other candidates in the field,
Speaker 3: right where all the money from hedge funders and people
Speaker 3: like that went to Cuomo. And you saw a quotes
Speaker 3: when people were like big Cemo donors like I didn't
Speaker 3: really care about that much, but it seemed like that's
Speaker 3: what everyone was backing. I'm not going to back the
Speaker 3: socialist right and so therefore and so yeah, you know,
Speaker 3: if you'd had a fully fledged campaign between Zoran and
Speaker 3: Brad Lander or something, or Adrian Adams, it might have
Speaker 3: been closer.
Speaker 2: But he's a good can.
Speaker 3: I mean, look a lot of times political analysts like
Speaker 3: me are left saying, yeah, the wins and losses are
Speaker 3: what matter, but for analyzing trends and close counts, right
Speaker 3: you know. So you know, look, if he had lost
Speaker 3: seven point instead of won by seven points, I'd still
Speaker 3: say that was pretty impressive.
Speaker 5: Right.
Speaker 2: It might be a sign of change here.
Speaker 3: It's a cleaner story, I think, But usually surprising outcomes
Speaker 3: are overdetermined, right, you know, it has to do with
Speaker 3: the changing of the guard generationally. It has to do
Speaker 3: with like he kind of figured out the right formula
Speaker 3: for more left wing ideas. It's not wilk culture stuff, right,
Speaker 3: it's affordability, it's working class people, things like that.
Speaker 2: Right.
Speaker 3: He's a very well presenting guy, right, who's very New York.
Speaker 3: I think in some ways his mom was a filmmaker,
Speaker 3: and his visuals are all quite stunning and beautiful and cuomo.
Speaker 2: So it's like four things coming together.
Speaker 3: But he also won by enough margin where maybe only
Speaker 3: three of those things or two, and to have it
Speaker 3: to come together, we still have one probably.
Speaker 1: Yeah. And when things are over determined, by the way,
Speaker 1: we can't answer your question right, When things are over determined,
Speaker 1: you can't quite know what the crucial factors were. So
Speaker 1: I think that the real answer to your question remains
Speaker 1: to be seen. But it's a really interesting thing to
Speaker 1: try to disambiguate and try to see kind of how
Speaker 1: did we end up here? And I think that this
Speaker 1: is an election that people will be looking back and
Speaker 1: doing some commentary on for many years.
Speaker 7: So I want to ask political polls, so I think
Speaker 7: you know something about it. So now I've heard the
Speaker 7: lecture on words. And so if I see that one
Speaker 7: candidate has a forty two percent chance of winning and
Speaker 7: another candidate has a forty six percent chance, should I
Speaker 7: interpret that to say they both have low probability? Or
Speaker 7: they have or I mean I guess, or is it
Speaker 7: so close? Really doesn't matter?
Speaker 3: Well, for one thing, people who work in polling or
Speaker 3: who report on pulling need to be clear about a
Speaker 3: probability versus a percent of the vote. Right, If it's
Speaker 3: Trump forty six percent in say Texas, Harris forty two
Speaker 3: percent undecided, third party is the remaining whatever eighteen percent?
Speaker 2: Right, like a four point lead. How often does that
Speaker 2: translate into a win?
Speaker 3: That's an empirical question, right, The answer might be something
Speaker 3: like seventy percent of the time.
Speaker 2: Russia, And in New York, what were the polls saying before?
Speaker 2: In New York?
Speaker 3: No, the polls underestimated, I mean, had they had shown
Speaker 3: the race tightening a lot. Some of the polls showed
Speaker 3: Zoran pulling ahead after ranked choice voting. Almost none Maybee,
Speaker 3: with one exception, had him winning the first round outright,
Speaker 3: and none of them had him winning by like that
Speaker 3: wide margin. Right, So this is what happens with polls
Speaker 3: is like in a New York race, they assume turnouts
Speaker 3: fairly low. They're surveying the people that like always turn
Speaker 3: out in New York MARYORL races, who consideringly unlikely voters
Speaker 3: are twenty percent of electorate.
Speaker 1: Right.
Speaker 3: Cuomo got his numbers among that twenty percent, but then
Speaker 3: Zoran turned out people that never vote in mayoral elections
Speaker 3: in e York. By the way, a lot of New
Speaker 3: York's are transplants from around the country, all around.
Speaker 2: The world, right, so we don't have that like tie
Speaker 2: to local politics. Right.
Speaker 3: I'm a person covers politics, listen to New York. I
Speaker 3: don't know that much about like New York politics, right,
Speaker 3: and it's very typical in some ways. But he he
Speaker 3: broke through and became its cliche, but became viral in
Speaker 3: a way that you know, I guess pollsters were too
Speaker 3: conservative in their in their methods and missing and missing
Speaker 3: these voters.
Speaker 1: We have time for one more question, hi.
Speaker 5: Things I was just curious. I know this is probably annoying,
Speaker 5: like too long in the future kind of question, but
Speaker 5: does Zorin's win at all change the probabilities that you
Speaker 5: think about candidates in twenty twenty eight for Democratic primary
Speaker 5: or I guess.
Speaker 1: The general election too.
Speaker 3: So with actually my former colleague at five thirty eight,
Speaker 3: Galen Druke, we did a Democratic twenty twenty eight primary draft,
Speaker 3: right or do it so it's like a draft, like
Speaker 3: a fancy football draft or something. Galen got the first
Speaker 3: pick and the first candidate on my list was AOC.
Speaker 3: I thought he'd take Josh Shapiro or someone and said
Speaker 3: he took AOC too, So it was mad about that.
Speaker 3: But like we both thought, and this is before Zoran,
Speaker 3: that she was let me put this carefully, from talking
Speaker 3: about probabilities, right to say the most likely candidate might
Speaker 3: mean a fifteen percent chance.
Speaker 2: Right, there's nobody in the field who is a clear
Speaker 2: front runner.
Speaker 3: But we both thought that she was more luckily than
Speaker 3: any other candidate to become the nominee in twenty twenty eight,
Speaker 3: before before Mom Donnay came along, right, And again it's
Speaker 3: probably because like look Democrats, of all these retreads, right,
Speaker 3: the Clinton family, the Cuomo family, the Biden family. I
Speaker 3: bet Jill will probably fantasize sometimes what if I become president? Right,
Speaker 3: like House of Cards or something. Right, I'm I think
Speaker 3: people are tired of all these families.
Speaker 2: Right.
Speaker 3: I remember seeing in twenty sixteen, I was in New
Speaker 3: Hampshire covering the race for five thirty eight in ABC News, Right,
Speaker 3: and like Hillary Clinton spoke to a group of like
Speaker 3: then at college, right, Franklin Pierce College or something like that.
Speaker 2: Right, you know, the average age is twenty or something.
Speaker 3: She's talking about like the Cold War, and like the
Speaker 3: students size are gleazing over they don't remember the Cold War, right,
Speaker 3: And now that was you know, was eight nine years
Speaker 3: ago now, right, And so like, look, I think I
Speaker 3: think it's a good thing to see. I mean, Zaraan
Speaker 3: was literally half of like Cuomo's age. Right, I think
Speaker 3: it's a good thing. Mcrat parties about present and was
Speaker 3: how can everyone's so freaking old? Right, I think it's
Speaker 3: a good thing. Is is to you know, have cannates
Speaker 3: that are good on social media, are substantive. I think
Speaker 3: she's a pretty canny and smart politician and shows some
Speaker 3: restraint at times.
Speaker 2: So yeah, it does change my views a little bit. Maria,
Speaker 2: do you have a take now?
Speaker 1: I mean, this is one of the things that I
Speaker 1: was talking about when I was talking about some of
Speaker 1: the long term consequences of this election, right, And I
Speaker 1: think a lot of it will depend on what ends
Speaker 1: up happening once Zorn takes power, right, if he's elected,
Speaker 1: how he actually handles himself in office, how popular he is.
Speaker 1: I think that that will actually have a large bearing
Speaker 1: on your question in terms of, you know, what does
Speaker 1: the Democratic Party end up taking from this, because they
Speaker 1: could take two very different lessons one AOC Two if
Speaker 1: Zorn tanks for some reason right and becomes incredibly unpopular
Speaker 1: and like the you know, we don't know, we can't
Speaker 1: we don't have a crystal ball. But if something like
Speaker 1: that were to happen, I think they'd be like, see,
Speaker 1: we told you so right, we need to go back
Speaker 1: to so I think you could see either thing working out,
Speaker 1: so you can a lot rest on his shoulders.
Speaker 2: I guess.
Speaker 1: Yeah. That was our first Risky Business Live and we're
Speaker 1: so happy you were here. That was our show from
Speaker 1: the Aspen Ideas Festival. By the way, our segment on
Speaker 1: probabilities was inspired by Adam Kucharski's newsletter Understanding the Unseen.
Speaker 1: We'll have a link to that post in the show notes.
Speaker 1: Thanks for listening. Let us know what you think of
Speaker 1: the show. Reach out to us at Risky Business at
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Speaker 1: Risky Business is hosted by me Maria Kanikova.
Speaker 2: And by me Nate Silver.
Speaker 3: The show is a co production of Pushkin Industries and iHeartMedia.
Speaker 3: This episode was produced by Isabelle Carter. Our associate producer
Speaker 3: is Sonia Gerwin. Sally Helm is our editor, and our
Speaker 3: executive producer is Jacob Boldstein. Mixing by Sarah Bruguier.
Speaker 1: Thanks so much for tuning in.