While we're killing time I can translate and summarize a podcast talk I gave in 2017 (in German) on how Donald Trump confounded the poll aggregators and won the election. Because clearly the message still hasn't gotten across, or we wouldn't still be waiting. 1/♾
The story started for me in March 2016 when a coworker predicted during lunch that Trump (still mostly a joke then) would win the election. I proposed that the only way for him to do it would be to stay on message and at the same time discourage Clinton voters from voting.
If you remember, a lot of pundits assumed that Trump was playing an outsider role to clear the field during the primaries and then would turn more "presidential" in order to appeal to the centrist establishment voters. As has been the masterplan for decades.
This masterplan has a name: median voter theorem, aka Harold Hotelling's spatial competition model, aka "ice cream vendors on the beach". In the ice cream version, you're supposed to ask yourself where two vendors would position themselves if each bather buys exactly one cone.
The mildly surprising answer is that under certain conditions both vendors, or candidates, should position themselves right next to each other, so that each ice cream vendor serves exactly one half of the beach dwellers.
Translated into politics, this means both candidates try to appeal to the median, middle-of-the-road voter as much as possible, and provide stability in the process. In healthy democracies, elections are boring.
But Hotelling's model depends on a number of assumptions: a roughly one-dimensional beach, two candidates, each beach dweller gets exactly one ice cream cone from the closest vendor, no matter how far that is. Once stripped off these assumptions, things turn interesting.
For one, turnout. You might still schlep from one end of the beach to the center for an ice cream, but picking between two interchangeable candidates neither of which will do much for you is hardly worth getting your butt off the couch on election day.
In the US, presidential election turnout usually hovers around 60%, so the winning candidate if non-votes are considered is usually "neither". This is a sizable group of voters to pick up for an entrepreneurial candidate.
So let's split the common left-right dichotomy in two to get: left disenfranchised, left establishment, right establishment, right disenfranchised. This is a pretty rough model, but it'll do for our purposes.
In a typical primary contest a challenger emerges that champions the outsider vote, but after an initial tussle the establishment candidate wins the day and moves on into the main contest. This was the general expectation that Trump upended for the Republicans in 2016.
There were many signals that the popular support for a Republican establishment candidate had eroded enough to make an outside campaign feasible, but clearly most pundits and pollsters didn't see the signals in 2016, and didn't adjust their models accordingly.
If we remember, Trump spent about 15 minutes in the general election trying to look more "presidential" and then went back to his support base, immeasurably helped by Clinton's "deplorables" comment. It was the "47% moochers" of 2016.
But since I'm talking election forecasting rather than politics, how did this affect forecasting? If you assume a Hotelling world with a broad support for centrist candidates and near-full turnout, the decision csn be expressed as "candidate A vs candidate B".
In a world where establishment support erodes and turnout barely crosses 60%, voters face two separate decisions: "candidate A or stay at home" vs "candidate B or stay at home". The old Hotelling choice loses predictive power.
Turnout becomes the all-encompassing factor and the strategy I described earlier: don't try to convince the opposing voters to defect, but rather convince them to stay at home, don't expand your support base, but convince them to vote, becomes superior.
But turnout is at best a secondary consideration in most forecasting models (except the LA Times which forecast a Trump win 2016 based on a stable panel survey), and this has not really changed in 2020.
But what has changed is that the Democratic party has inherited both the remnants of the Bush-era establishment right and has to bridge the gap to the Sanders-voting post-industrial disenfranchised left, a gap that is only to become bigger and will ultimately erode turnout.
With increasing tension between the camps, this remains a formidable challenge for the Democrats. For the pollsters and poll aggregators who did not understand turnout, it means they misinterpreted the role of a small but coherent support coalition and got things wrong. Again.
For anyone who wants to hear me discuss this in German and far more mathematically, the podcast for @modellansatz is here: math.kit.edu/ianm4/seite/ma…

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More from @oliverbeige

21 Aug
Very interesting thread about the relevance of Coase's Nature of the Firm for the platform age. I've written a few things about how working on a dynamic ridesharing* service in 2009 helped me connect Coase/Williamson to digital business models, so here's a few summary comments.
The current focus of the public debate is the often contentious relationship between drivers and platform, but in order to understand the structure of the industry, it's also important to look at the underlying relationship, namely that between driver and passenger.
A key problem we had to consider when conceptualizing our service was how to safeguard both drivers and passengers. Turns out when you look into the history of taxicab and limo services, it is fraught with what we like to call "contractual hazards"...
Read 10 tweets
24 Apr
Hospital routing, hospitals in crisis, empty hospitals, whole hospital systems facing collapse. A short thread.

There are currently lots of seemingly conflicting news items about hospitals like the ones above. The dissonance is a sign of an information cascade.
Finding the right balance between patient needs: give sick patients the best care available quickly, and hospital needs: have all resources in place to provide this care, is the ultimate determining factor in a successful response to Corona.

It's what I look at first.
The shorthand for this is "hospital routing": the many complex decisions, in the government and on the ground in the hospitals, about which patients to admit, and which patients to send into home quarantine.

No country will be able to handle this perfectly.
Read 11 tweets
23 Apr
A short thread on graph theory and network science. Both have long histories, but I'll focus on two people: Frank Harary, the "godfather of modern (American) graph theory", and Duncan Watts, the "reinventor" of network sociology, which morphed into network science.
The idea that all kinds of bilateral relationships can be simplified to some kind of network structure is quite old (Euler's Königsberg bridge problem is canonical), but for the longest time it was considered "toy mathematics" at best.
Most academics should probably not publish more than 3-5 pieces in their lives. One of the few exceptions was Frank Harary at the University of Michigan, who was on a mission to show the world that everything can be explained with dots and lines: graphs.
Read 19 tweets
12 Apr
Machine scheduling, operations research, production, and machine learning. An anecdote thread for @sidwindc.

Biographical background: from age 15 to about 21, I spent my summers working for the laboratory equipment manufacturer that also employed my dad (in "construction").
After that I moved to prepoduction at Siemens, also for healthcare equipment, mostly coding production and tooling processes in CNC. This is where I stayed until my master's thesis.

I might be somewhere in this picture.
For my master's thesis, and this is the topic of this thread, I moved to what is now Novartis in Basel into the scheduling group. This was both moving from a blue collar to a PhD level white collar environment and from the Bundesliga to the Champions League.
Read 18 tweets
10 Apr
This is basically it. "Capital stock" in a production context means a jumble of machinery that needs to be put into a certain shape in order to be productive. The discipline for that is called machine scheduling. That's combinatorial optimization. That's operations research.
In order to translate "capital" from the production domain ("capital stock") to the finance domain ("capital structure") you need a transformation from the physical object to a fair expression of the value of the physical object. The name of that transformation is accounting.
So there is basically nothing that needs to be resolved. This is undergrad knowledge for anyone who has ever taken business and economics or operations research and economics together. The only way this could create any confusion is if your knowledge is so compartmentalized...
Read 5 tweets
7 Apr
Let's talk about the incredible punch Tom Schelling's seemingly simple models carry in an extremely light package. One of my favorite of his models is the highway gawker model. It starts with a curiosity he likely picked up in the local news. Image
What we've got here, one is inclined to conclude, is failure to communicate. More precisely, the failure to negotiate over a shared resource called the buffer: the strip of empty roadway between two cars. Image
If we are negotiating over a shared resource with negative externalities and catastrophic consequences, textbook economics tells us we should simply build fences and establish property rights. But how do you fence in a constantly shifting piece of empty roadway? Image
Read 10 tweets

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