Beha: I started thinking about this book in the early years of Obama’s first term, more or less in the same period when the book itself is set. Most traditional pundits thought the 2008 election would be a nail-biter, but a few data-driven outsider types (Nate Silver most prominent among them) predicted a near-landslide for Obama, which is what happened.
If Obama himself appeared to represent something entirely new — not just because of his race, but because he was the first post-Boomer president, seemingly untouched by the Boomer-era culture wars that Bill Clinton and George W. Bush in different ways represented; because he seemed pragmatic, technocratic, non-ideological; because he had not “waited his turn” and seemed less beholden to the traditional political power structures — these “data journalists” were the media equivalent of this newness. They quickly established themselves in the mainstream, despite predictable grumbling from the old guard. I found this generational tension interesting, and it was one of the elements that led me to create a character (very loosely) based on Silver. I was also interested in the limits of the kind of quantitative thinking that this new guard represented.
Here it’s worth mentioning in fairness to Silver — whom I don’t know at all — that he is generally very thoughtful about the way he uses data, and that he actually talks quite a bit about the limits of quantification. But there are many people in the “quant” camp who do not share this humility, and more extreme characters are naturally more interesting for a novelist.
So I would say that I borrowed some broad facts from Silver’s biography — Waxworth is from the Midwest; he went from baseball modeling to political modeling; he rose to fame after correctly predicting the outcome of the 2008 election — but that I borrowed Waxworth’s mindset from some of Silver’s less thoughtful brethren (whom I won’t name here).
Cillizza: A novel at least partly about electoral predictions, polls and modeling — and their limits. How much was this book influenced by 2016? And what does it say about 2020 — whether intentionally or not?
Beha: As I said, I started thinking about the book shortly after the data journalists rose to fame in 2008. I began actually writing it soon after the 2012 election, another win for the quant crowd. I was most of the way through it by 2016, when all of the prognosticators fell flat on their faces. All of a sudden, the world of the book seemed very far away, and the novel became almost a work of historical fiction. I tried not to let the post-2016 viewpoint seep into my Obama-era setting, but the fact of Trump’s election certainly changed some things.
We talk a lot about all the ways in which Trump represents something completely new and unprecedented, but he also represents a throwback to the pre-Obama era. He is of the same generation as Clinton and Bush, and he has stoked the culture war flames that were a signature feature of those earlier presidencies. We are all acutely aware of how naïve the “post-racial” dream of Obama’s election really was, but one could say the same about the dream of a post-ideological — technocratic, data-driven, pragmatic — America that Obama’s election also seemed to promise.
Trump destroyed whatever was left of that dream, and so it’s sadly appropriate that his victory also destroyed the credibility of many data-driven journalists who rose to prominence during the Obama years. After 2016, the book became, in part, about a moment when a particular dream of a rationally ordered society seemed within reach and about why that moment was bound to disappoint. I’m not sure what any of this has to tell us about 2020, except that even if Trump loses it won’t do away with the psychological undercurrents — particularly, our strange desire for chaos and disorder — that helped make Trump possible.
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Cillizza: The book feels like a running argument between what can be empirically known (in politics, baseball, life) and what, well, can’t. And which matters more. Where do you think media coverage of this election falls on that continuum?
Beha: On the most fundamental level, the future by definition can’t be empirically known, because it doesn’t yet exist. (These days we are more aware of this than ever: if you’d asked a thousand pundits and futurists in August 2019 what August 2020 would look like, not a single one would have said we’d be coming off a double-digit drop in GDP and that we’d all be wearing masks.) In that sense, the results of an election that hasn’t happened yet is by definition unknowable. It’s natural for us to want to know the results now, since the outcome is important to us. And it’s natural for the media to cover certain events by putting them in relation to this unknowable future, particularly now that the election is actually quite soon. Something like Biden’s VP pick can only really be understood in terms of how it relates to his election chances — how it relates to those chances is what the pick is “about.”
But it is not only when the election is a few months away that the media puts things in this context. I remember reading something in early 2017, soon after Trump’s inauguration, about how the polling on some decision of his affected the Republicans’ midterm chances. There seemed to me only two possible answers to that question — either “it doesn’t” or “we can’t possibly know.” In any case, the impulse to pose the question in the first place struck me as pathological. All these outlets had just completely whiffed on 2016, and yet they could not break themselves of the habit of talking in pseudo-empirical terms about completely unknowable things.
Cillizza: You’ve created a Twitter look-alike in the book: Teeser. Why — and what role (positive, negative, neutral, something else) does social media (and Twitter in particular) play in both the book and our modern politics? [Beha himself is not on Twitter].
Beha: There are various ways in which the world of the book is just slightly askew from the real world. For example, the major New York newspaper where one character works is the Herald, rather than the Times. These things allow me to place fictional characters within otherwise non-fictional contexts. The creation of Teeser serves a similar role. Twitter was not quite ubiquitous in 2009, and I did not want to be held to the standards of documentary truth for what is, after all, a novel.
As far as your second question, I’m on the side of those who think that social media’s influence on politics, journalism, culture, society, and just about everything else has been almost completely pernicious. There are some exceptions, but the net accounting has to be negative. Donald Trump is paradigmatic public figure of the social-media era. I think that about sums the situation up.
Cillizza: Finish this sentence: “If Sam Waxworth was handicapping the 2020 election, he would give Biden a _______% chance of winning.” Now, explain.
Beha: Oh, I don’t know, let’s say 73.2.
It’s worth noting here that even this way of putting it — not “I predict that Joe Biden will win,” but “I calculate that Joe Biden has a 73.2% chance of winning ” — has been bequeathed to us by the data journalists, who have taught us that predictions have to be probabilistic rather than deterministic.
In some ways, this is an obvious improvement over the alternative, since it acknowledges the fact that we can’t really know today what will happen four months from how. But it also introduces the false sense of precision that comes from numbers. If I say, “Joe Biden is going to win” or even “Joe Biden is probably going to win,” it’s obvious that I’m just making a more or less educated guess. If I say, “there’s a 73.2% chance that Biden will win” this suddenly seems much more empirical, but at the end of the day, it’s still just my best guess. And the nice thing about probabilistic predictions, from the pundits’ standpoint, is that you’re never wrong — either outcome is given some chance.
One of the things that the data journalists promised to add to the punditry mix was some sense of accountability. That sort of went out the window after 2016.
