Data Mining Reveals the Crucial Factors That Determine When People Make Blunders | Hacker News Reader

Data Mining Reveals the Crucial Factors That Determine When People Make Blunders

technologyreview.com

19 pointsadamnemecek7 comments

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

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.

> 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)

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.

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.

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

Title is clickbait-y.

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