GNU Backgammon

 Interpreting JSD's

```If you do a GNU rollout which terminates once a certain JSD is reached
then what is the likely 'actual' JSD when the rollout stops.

I say this as the JSD seems to be a bit of a 'random walk' thats
ultimately headed to zero (if the outcomes are identical) or infinty if
not. But if I stop the first time I get to say 3 jsds, am I not really
getting the full 3 jsds worth of 'confidence'.

I guess its going to depend somehow on the number of trials / volatility
of the position as well somehow?
```

 Maik Stiebler  writes: ```A typical example of a rollout setting is: stop when plays are x JSDs apart, but do at least 300 trials and not more than 10000. I simulated this by drawing rollout results from a uniform distribution (which is probably not a very good model). Instead of JSDs I used (current deviation of the sample mean from the known population mean) /(standard error as estimated from the rollout). With x=2.5 and settings as above, the rollout hit the stopping criteria before the 10000th trial (i.e., it gave a 'wrong' result) in 15% of the cases, with x=2.7 it happened 10% of the time. Then I changed the settings to at least 500 trials, and for x=2.5 the number of wrong results decreased to 10%. Then I allowed the rollouts to be extended to 30000 trials and, of course, it got worse again: The probability of a wrong result was more than 40% for x=2.0, but less than 6% for x=3.0. I'd conclude from that that a threshold of 3 JSDs is usually very good in practice and that 2 JSDs may be a bit too low really to get good statistical significance. I cannot see how that would depend on the volatility of the position. It does depend on the number of trials in the sense that the lower the minimum number of trials and the larger the maximum number, the worse the confidence gets. If you decide before the rollout that you will do exactly N trials, then x=2.0 will give you fine confidence as long as N is sufficiently large (say larger than 100). ```

 Adrian Wright  writes: ```Thanks, that's helpful. What I meant by the volatility of the position was that some situations, such as a last roll position or a relatively low contact position will converge very rapidly, whereas others my be inherently much more complex with many possible bi-furcations, say a prime vs prime or back game. I would have thought the more complex positions would require more trials to get an equivalent confidence. If I've understood you correctly then if I'm doing 2ply-2ply rollouts and stopping when over 3 jsd with minimum 648 trials and maximum 3888 that should produce a high degree of confidence. ```

 Maik Stiebler  writes: ```The simpler the position, the better variance reduction will work, i.e. you get lower standard errors with fewer trials. That is very helpful and will tend to reduce the number of trials needed to reach the 3 JSDs difference threshold. It does not affect the relationship between # of JSDs and confidence levels, however. The settings you give as an example would indeed give a very high confidence level in my model. ```

 Robert-Jan Veldhuizen  writes: ```I think there's not much need for setting the JSD to stop a move at, to values like 3 or higher even, when you'd settle for >1.65 JSD otherwise. As long as you keep the minimum trials value high enough, like 216 or higher when the moves are close or SE's are high. Part of the reason is that GNUBG keeps measuring the JSD diff even after a certain rollout candidate has stopped. Should the JSD drop below your set value later, GNUBG will re-start the interrupted rollout of the trial(s) and get them up to the current number of trials, then proceed as usual. If you're planning on doing 1296 or maybe 2592 trials maximum, my idea is that setting it at minimum 324 trials with 2.2 JSD is enough, if you'd settle for 1.65 JSD after the maximum number of trials. Very hard to really test or model this though... ```

### GNU Backgammon

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Importing PartyGammon matches  (rew+, July 2006)
Improving your game using GnuBG  (D.U.G.+, Nov 2002)
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Interpreting JSD's  (Adrian Wright+, Feb 2005)
JSD's and confidence intervals  (Daniel Murphy+, Jan 2005)
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Luck rate  (Kees van den Doel+, May 2002)
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