The Black Swan of American Politics

19/11/2016 — 1 Comment
Donald Trump

Image source: AP/LM Otero

A Trump presidency in the wings who’d have thought! And what a total shock it was to all those pollsters, commentators and apparatchiks who are now trying to explain why they got it so wrong. All of which is a textbook example of what students of risk theory call a Black Swan event.

Now, you might ask, ‘What’s a Black Swan event?” Well in short they’re a metaphor coined by Nasim Taleb to explain why we are perpetually being surprised by ‘out of left field’ events. From the GFC to Fukushima and of course the latest American election such events, according to Talib, go through three stages:

  • First it’s a complete and shocking surprise (tick)
  • Second it has a major effect (we’ll give this a provisional tick), and
  • Finally after the event there’s a whole lot of rationalising of how (with hindsight) it could have been predicted (yep, tick, tick, tick).

Returning to the American election we’re definitely into the third stage with a whole lot of the aforementioned professional pundits explaining how, they could have predicted such a win. Very amusing all around. I’m sure the surviving Thanksgiving turkey’s will also be just as busy explaining away their failures in prediction likewise.

What’s almost as funny, is watching all the politicians who’d written off Trump as a huckster and a fraud, “barking mad” comes to mind, having to scramble back from their previous positions given that in a little over two months he’ll be the leader of the free world. There’s also a deal of desperate wishful thinking, read denial, going on… Ah schadenfreude how sweet you are.

🙂

One response to The Black Swan of American Politics

  1. 

    To many, the results were a black swan event; so much so that, as the results became apparent, many objects not designed for flight reportedly became projectiles in the loser’s quarters, pharmaceutical adjustments had to be made, and her aides had to prevent her from facing the public until the next day.

    What many saw as a surprise resulted from (a) polls that were, as they always are, biased in such a way as to encourage one side and discourage the other — intentionally self-fulfilling prophecies — (b) a segment of the electorate hiding their intentions out of fear, introducing aleatory uncertainty (As we see from the news, the side that lost is demonstrating that the fear was justified.), and (c) wishful thinking.

    The polls or indexes with the best track records, however, correctly forecasted the outcome. Factor (a) happens because over 90% of the media favor the incumbent party. The heavy partisanship of the reporting reflects that. Journalism is, after all, a profession that attracts people who want to “change the world.” Due to factor (b), the margins of error in the less accurate polls were always wider than the losing side realized.

    To me, the lessons are that, (a) when studying a situation in order to reduce epistemic uncertainty, we should pay as much attention to the methods and the agents as we pay to the results; and (b) we should not commit everything to a particular outcome when a different outcome lies within the limits of the aleatory uncertainty.

    (I hope I used the words correctly.)

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