What would the pdf look like for 'N' chess computers with the same rating

In summary: And yes, I am asking about the distribution of ratings as the number of games approaches infinity. In summary, the conversation discusses the potential shape of the probability density function of the ratings of 'N' advanced computers as they play an infinite number of games with each other. There is some uncertainty about the specifics of the scenario, such as whether the computers are identical and if they are reset before each game or if they learn from experience. The central limit theorem is mentioned as a possible trend for the distribution of ratings as 'N' gets very large, with some debating whether it would be normal or uniform. The conversation concludes with some questions about defining the random variable and the implications of a uniform distribution on the ratings vector.
  • #1
iVenky
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12
If we have 'N' advanced computers (where N-> infinity) each with exactly the same rating to begin with and make them play with each other for an infinite number of games, what would the shape of the probability density function of the ratings eventually look like?
 
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  • #2
Are they reset to the same factory setting before each game?
 
  • #3
I find this an interesting question partially because it is rather vaguely worded. For instance are the computers identical? If not how can their ratings be exactly the same? If not how does one quantify this? Would the result depend upon the tournament rules?
I guess that in the proposed infinite limit the ratings would end up the same and equal..
 
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  • #4
Keith_McClary said:
Are they reset to the same factory setting before each game?
You mean the ratings?
 
  • #5
hutchphd said:
I find this an interesting question partially because it is rather vaguely worded. For instance are the computers identical? If not how can their ratings be exactly the same? If not how does one quantify this? Would the result depend upon the tournament rules?
I guess that in the proposed infinite limit the ratings would end up the same and equal..
Yes, it was a shower thought. The computers are exactly identical. So you mean it would be a uniform distribution?
 
  • #6
iVenky said:
You mean the ratings?
I mean the computers. Are they reset before each game? Or perhaps they are AIs that learn from experience?
 
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  • #7
Keith_McClary said:
I mean the computers. Are they reset before each game? Or perhaps they are AIs that learn from experience?

Ah , I see, that's an interesting question that I didn't think of. Let's consider both.

1) If computers don't update their neural settings after every game
2) If computers learn after each game and update their settings.
 
  • #8
Given identical digital computers with identical software and barring any hardware glitches. They should by definition act in the same way.

The only variation would be when each started to process a task. If they used their clock to initialize any random seed generators then they could wildly vary in how they compute some probabilistic spread due solely to the random seed generator and anything that depended on the generator as well.
 
  • #9
By the central limit theorem, normal.
 
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  • #10
Deepmind, to my knowledge, has not published data on the millions of training games played by AlphaZero - that would be an interesting real life example. Do not need separate computers, can do these games within the same software.

But this would not be the same as the identical version of Stockfish playing itself, where I would guess the outcomes are set, as the program is not 'learning' as Deepmind does
 
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  • #11
pbuk said:
By the central limit theorem, normal.

That's what I am not sure of, if it would be normal or uniform.
 
  • #12
I think by the central limit theorem the distribution of ratings would trend (as N gets very large) to an ever sharper spike at the initial exactly similar rating.
I realize I don't really know how the rating is assigned so this may be incorrect (but the central limit trend is true for some reasonable measure).
So perhaps this is not so interesting after all!
 
  • #13
iVenky said:
That's what I am not sure of, if it would be normal or uniform.
Uniform within what range? How would you account for the discontinuities at the bounds of that range?
 
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  • #14
As ELO measures wins in zero sum games, ignoring draws and white vs black , the distribution of wins for identically skilled chess players would look like fair coin flips, so what is the distribution of ELOs for an infinite number of coin flippers?
 
  • #15
BWV said:
As ELO measures wins in zero sum games, ignoring draws and white vs black , the distribution of wins for identically skilled chess players would look like fair coin flips, so what is the distribution of ELOs for an infinite number of coin flippers?

I think elo rewards the lower rated person more points for winning than it takes away when they lose, so it's a random walk that drifts back towards the starting point.
 
  • #16
iVenky said:
So you mean it would be a uniform distribution?
What do mean by "it"? Define the random variable ( random vector?) that you are asking about.

Are you asking about a vector representing the ratings of each machine as a sequence of round robin tournaments proceeds? A uniform distribution on such vectors would imply that a vector of ratings that were "highly unequal" would have the same probability as a vector of ratings that were all equal.
 
  • #17
Stephen Tashi said:
What do mean by "it"? Define the random variable ( random vector?) that you are asking about.

Are you asking about a vector representing the ratings of each machine as a sequence of round robin tournaments proceeds? A uniform distribution on such vectors would imply that a vector of ratings that were "highly unequal" would have the same probability as a vector of ratings that were all equal.

I mean the ratings of the computers
 

1. What is a PDF in relation to chess computers?

A PDF (probability density function) is a mathematical function that describes the likelihood of a specific outcome occurring in a random event. In the context of chess computers, the PDF represents the probability of a certain rating being achieved by a group of chess computers.

2. How is the PDF calculated for a group of chess computers with the same rating?

The PDF for a group of chess computers with the same rating is calculated using a statistical model that takes into account the individual ratings of each computer, as well as the distribution of ratings within the group. This model uses the Elo rating system, which is commonly used in chess to measure the relative skill levels of players.

3. Can the PDF be used to predict the performance of a group of chess computers with the same rating?

Yes, the PDF can be used to make predictions about the performance of a group of chess computers with the same rating. However, it should be noted that the PDF is based on statistical probabilities and cannot account for unexpected factors that may affect the performance of the computers.

4. How does the PDF change as the number of chess computers in the group increases?

The PDF will become more narrow and peaked as the number of chess computers in the group increases. This is because with a larger sample size, the distribution of ratings within the group becomes more consistent and predictable.

5. Are there any limitations to using the PDF to analyze a group of chess computers with the same rating?

Yes, there are limitations to using the PDF to analyze a group of chess computers with the same rating. The PDF is based on assumptions and may not accurately reflect the true performance of the computers. Additionally, it does not take into account external factors such as the quality of opponents or the specific strategies used by each computer.

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