
Data Mining Reveals the Crucial Factors That Determine When People Make Blunders - adamnemecek
https://www.technologyreview.com/s/601774/data-mining-reveals-the-crucial-factors-that-determine-when-people-make-blunders/
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Noseshine
TL;DR quote:

> _The bottom line is that the difficulty of the decision is the most
> important factor in determining whether a player makes a mistake. In other
> words, examining the complexity of the board position is a much better
> predictor of whether a player is likely to blunder than his or her skill
> level or the amount of time left in the game._

That first sentence almost comically funny, especially in the context of the
click-bait headline that does a great job at not revealing any useful
information.

All based on playing chess, so go ahead if you like and use one experiment
using one game to extrapolate to the entire world...

Link to the study, to skip that article:
[http://arxiv.org/abs/1606.04956](http://arxiv.org/abs/1606.04956)

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selectron
As a strong chess player (around 2000 blitz on chess.com) I find it very
surprising that time was not the most important factor. I know from experience
when you get to under 1 second per move remaining the probability of
blundering goes way up.

In regards to the skill-anomalous positions, I'd have to look at the positions
to be sure but my guess is that these are end game positions where the most
reliable path to victory is not the quickest path to victory. In a winning
position an experienced chess player will try to simplify and win reliably
over winning quickly. The model might consider this a blunder because it gives
up some material advantage, but it doesn't actually change the end result of
the game. My guess is that given these positions where a lower skilled player
allegedly does better, in reality the better players win a higher percentage
of the time.

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beefield
> Economists might well ask what all this means for buying decisions

I would not hold my breath on this one. Most economists and mainstream
economic models seem to be still struggling to grasp the concept that people
_can_ make mistakes (i.e. not maximize their utility)[1], so an idea that you
could analyze why and when people make mistakes must be completely
mindboggling to them.

[1] If anyone has pointers, I am interested in even remotely mainstream micro
models that do not reduce to utility maximization (with a possible
unsystematic error term)

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cowpig
The study only looks at positions in which there is a defined set of moves
that can be shown to be winning (endgames), and treats all non-winning moves
as "blunders".

Then it considers the "difficulty" of the position, as the proportion of moves
that are "blunders".

So, assuming you're looking at any other than a perfect player, this seems
like it will inherently be true in any game.

~~~
AstralStorm
Additionally, it neglected the fact that grand masters plain old memorise a
lot of good positions and transformations. This is why some are more likely to
blunder in new ones. Say, a weak move can throw a typical plan off, while
there is a great counter, this is not a typically tried path, so the player
might pick a vastly suboptimal move - they are reduced from memorisation to
actual positional and combinatorial analysis.

This often leads to untried, complex positions. There are a few that look like
wtf moves until you go way deeper in analysis.

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danvayn
It's only a matter of time until wage slave becomes a more literal term..

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sreejithr
Title is clickbait-y.

