### Jason Swanson

Shared publicly -This article is misguided. It is trying to draw a distinction between two kinds of uncertainty. On the one hand, there is uncertainty caused by mechanisms such as dice or spinners. On the other hand, there is uncertainty caused by the unobserved activity of neurons in the brain. The article would like to reserve the word "luck" for situations involving only the first type.

Okay, fine. So there is a semantic distinction between these two types of uncertainty. Perhaps there is even an important philosophical distinction. But when it comes to making decisions in the middle of a game, there is no distinction at all. To make decisions in the face of uncertainty, we must know how to measure it and how to think about it. This is what probability theory is for. From the point of view of probability theory, it doesn't matter if it's dice or neurons, it's all the same. In fact, the article concedes this point when it implicitly cites the law of large numbers:

"In brief encounters, variability [of human performance] will have a greater effect than it will in longer stretches of play, so a single event will be a poorer measure of the competitors’ skill. By contrast, when players or teams compete in a long series of games, as in a chess tournament or a basketball season, it makes sense to see skill as the primary factor in success."

Both types of uncertainty share the same fundamental status. A serious study of poker would force anyone to acknowledge this. A strategic analysis of a decision at the poker table is done with probability. It must use probabilities that are calculated from the cards. And it must also use probabilities that are estimated from reading the opponent. From a logical and mathematical point of view, both kinds of probabilities are treated in exactly the same manner.

A chess player could adopt a similar attitude. There is uncertainty is his opponents' choice of moves. There is uncertainty in his own performance, which varies according to the situation. In the face of this uncertainty, he could strive to find the move that maximizes his current probability of winning. This is different from trying to find the best move in the current position, as though one were solving a puzzle or exercise. For example, the grandmasters may say that a certain opening is unsound. But if you're not playing grandmasters, then maybe that doesn't matter. If the opening gives you a statistical edge against your current circle of opponents, then it's a good one.

By telling people there is no "luck" in chess, we are discouraging players from adopting this kind of attitude. We are instead promoting the myth that there is one "best" move in every position and your job is to find it. That kind of thinking is fine for puzzles and exercises. But in a game, there are three things to consider: the position, the opponent, and yourself. You have perfect information about only one of them.

Okay, fine. So there is a semantic distinction between these two types of uncertainty. Perhaps there is even an important philosophical distinction. But when it comes to making decisions in the middle of a game, there is no distinction at all. To make decisions in the face of uncertainty, we must know how to measure it and how to think about it. This is what probability theory is for. From the point of view of probability theory, it doesn't matter if it's dice or neurons, it's all the same. In fact, the article concedes this point when it implicitly cites the law of large numbers:

"In brief encounters, variability [of human performance] will have a greater effect than it will in longer stretches of play, so a single event will be a poorer measure of the competitors’ skill. By contrast, when players or teams compete in a long series of games, as in a chess tournament or a basketball season, it makes sense to see skill as the primary factor in success."

Both types of uncertainty share the same fundamental status. A serious study of poker would force anyone to acknowledge this. A strategic analysis of a decision at the poker table is done with probability. It must use probabilities that are calculated from the cards. And it must also use probabilities that are estimated from reading the opponent. From a logical and mathematical point of view, both kinds of probabilities are treated in exactly the same manner.

A chess player could adopt a similar attitude. There is uncertainty is his opponents' choice of moves. There is uncertainty in his own performance, which varies according to the situation. In the face of this uncertainty, he could strive to find the move that maximizes his current probability of winning. This is different from trying to find the best move in the current position, as though one were solving a puzzle or exercise. For example, the grandmasters may say that a certain opening is unsound. But if you're not playing grandmasters, then maybe that doesn't matter. If the opening gives you a statistical edge against your current circle of opponents, then it's a good one.

By telling people there is no "luck" in chess, we are discouraging players from adopting this kind of attitude. We are instead promoting the myth that there is one "best" move in every position and your job is to find it. That kind of thinking is fine for puzzles and exercises. But in a game, there are three things to consider: the position, the opponent, and yourself. You have perfect information about only one of them.

We often confuse the ups and downs of human performance in games with luck.

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