How computers have changed chess

Prior to computers, limited depth of analysis meant chess strategies were “calibrated to model”. Now they’re calibrated to actual results and that results in better strategies (unconstrained by aesthetics)

With the chess Candidates tournament starting in Moscow today (to decide World Champion Magnus Carlsen’s next challenger), I’ve been watching a few chess videos of late, and participating in discussions on why Anand has been finding it hard to play of late.

One thing that people have widely agreed is that computers have changed the way chess is played, and the “new generation” (Carlsen, Hikaru Nakamura, Fabiano Caruana, Anish Giri, etc.) have learnt the game in a completely different way from the old-timers, which dictates the way they play.

For example, these new guys play the kind of positions that earlier generations wouldn’t dream of playing. Given a position and a bunch of moves that seem similarly strong, the moves the new generation picks is different from what an older player would pick. And computer analysis is credited with this.

The basic advantage with computer analysis is that positions can now be evaluated easily to a much larger “depth” (number of moves from current position) compared to earlier manual analysis. In the manual analysis, you could evaluate the position for a few moves after which you would reach a position that you would judge manually. Judging different possible continuations this way, you would evaluate a position and figure what was a good continuation.

The problem with limited depth search was that after a certain depth, you simply had to use your judgment on what was a good position, and this judgment (the “boundary condition” that went into your model) would have a profound effect on how you evaluated different moves. Over time, all you cared about was the aesthetics of the chessboard, and not really how you could translate the position to victory (or a draw).

In other words, in the days before computers, chess players were building their strategies by calibrating them to a model rather than by calibrating them to actual results on the board. And this resulted in a bias towards “pretty strategies” and those that gave advantages that were obvious.

With computers, however, there is no such constraint on the depth of ply. You can analyse the position to far greater depth and get really close to the result in the course of your analysis. And so you don’t really care about the aesthetics of the positions you reach, as long as you know how they can translate to the result you want.

So the “new generation”, which has always been trained using computers, see the game differently. People of Anand’s generation (there’s also Veselin Topalov and Levon Aronian in the ongoing Candidates tournament) learnt the game with classic aesthetics and optimise their play to get there. Carlsen’s generation has no such biases and they play to what is the¬†actual advantage irrespective of aesthetics.

And that’s how the battle is building up! This should be an interesting tournament!