Tis the season for good trolling. Trollololol lol lol lol lol.

Once you place the constraint at "reasonable thinking time" (e.g. practical for usual tournament/casual play with human opponent), it does not really matter that much. It is a significant factor, but not drastically so; you may gain or lose two or three ranks but you will remain in the same ballpark. Either the algorithm is too generic and scales extremely slowly, or it is highly tuned and it gains the baseline strength very quickly, but gets caught in bad biases in hairy situations and will scale even poorer in these.

Need to take this apart (several issues)

1) The performance of some of these algorithms may be extremely non-linear with respect to time (time being interpreted as a combination of machine power and real time). Below enough time performanace might be terrible but above some critical amount improvement with reasonable additional time modest and above some point improvement very slow as time increases exponentially.

2) You can't go by how the algorithms of several years ago behaved so the rest of what you say is outdated. The dominant algorithm now used by all the strongest programs does not behave the way you have described. Currently performance is limited purely by time and isn't "biased" in the way you think. Given enough time these algorithms would discover the best next move. For these programs "tuning" is adjusting behavior so as to get the best performance within the constraint of actual time given the allowed computer power. How that is done might or might not introduce "bias" (it doesn't have to -- need not be deterministic*)

3) Objectives differ. Are we after the strongest possible program (given the time/machine power constraint) or the strongest one that can pass or come close to passing the Turing test within that constraint? (not obviously identifiable as a non human player -- if presented with a set of games some of which between two humans and some between a human and this program you could not easily/certainly separate into the two subsets)

* And here don't try to go into a discussion of "pseudo-random" vs "random" since could use an approximate true random (for example, the time used by the opponent to make the previous move)

Tis the season for good trolling. Trollololol lol lol lol lol.

EDIT: I'm not the OP, but was responding to his attempt at trolling.

Haha, I didn't realize this was a response to another thread, I just assumed that the subject line was good rickroll material. My bad!

Actually I agree with pasky here. Both of us have experience in Computer Go using MCTS (the dominant algo you speak of). Yes, MCTS programs do gain more strength from more time, but within "reasonable" time limits that humans might use, the difference is not so big. MCTS seems to gain about 1 stone with every doubling of the thinking time. This corresponds to "It is a significant factor, but not drastically so; you may gain or lose two or three ranks but you will remain in the same ballpark."

I think you are considering too small a portion of the curve.

Consider the shape of the curve performance vs time (number of algorithm steps) over a large range. What I am saying is that below some number of steps (too little time) won't be other than random moves. In this region the curve is very steep, great improvement when more time is allowed. And at the other end gogin to take a lot more than doubling to increase one level. So yes, somewhere in between you would observe what you say you do (doubling time per level improvement). But I think:

a) That's over a relatively small number of playing levels. Keep in mind that even an exponent of 2 grows quickly.
b) The strongest programs are currently above this point pon the curve. In other words, the implementations are fast enough that they are able to play at acceptable speed (from the human point of view) at a level where for them to go up another level would take much more than doubling the time.

You want a practical example? How about MFOG 12.21? It is supposed to be at 1 dan on a "standard" 2 core machine that a program buyer might be expected to have but the bot on KGS is playing at 2 dan on a machine about 6 times more powerful than "standard" (equivalent to six times the time).

I think you are considering too small a portion of the curve.

Consider the shape of the curve performance vs time (number of algorithm steps) over a large range. What I am saying is that below some number of steps (too little time) won't be other than random moves. In this region the curve is very steep, great improvement when more time is allowed. And at the other end gogin to take a lot more than doubling to increase one level. So yes, somewhere in between you would observe what you say you do (doubling time per level improvement).

You want a practical example? How about MFOG 12.21? It is supposed to be at 1 dan on a "standard" 2 core machine that a program buyer might be expected to have but the bot on KGS is playing at 2 dan on a machine about 6 times more powerful than "standard" (equivalent to six times the time).

The trend I describe actually continues for a quite some time. Have a look at this study: http://cgos.boardspace.net/study/index.html This extended up to 8388608 playouts for Mogo and many more for Fatman. I have not seen or done any tests myself to support this trend on 19x19, but I see no reason this trend should not hold.

This is very interesting. As the playouts is presumably the primary factor, a bigger board should have the curve arrive later due to the additional time to complete the payouts presumably? If so, how much longer does a 19x19 playout take than a 9x9 playout, having averaged out including overhead time?

A very quick test with my program shows that playouts on a 9x9 are about 4.68 time faster than a 19x19, and considering 361/81=4.46, the speed of playouts seems to be directly proportional to intersections on the board.