5 ms·

Do you mean to say that 100% random RPSAIs have a lower winrate vs humans than RPSAIs that learn and exploit human patterns? Surely a 100% random RPSAI doesn't

Do you mean to say that 100% random RPSAIs have a lower winrate vs humans than RPSAIs that learn and exploit human patterns? Surely a 100% random RPSAI doesn't have a poor win rate against any other RPSAI?

T-hawk 10y ago
A 100% random RPS AI doesn't have a poor 1-on-1 win rate against any other RPS AI, but it absolutely can have a poor rate of winning tournaments, if "poor" is defined broadly enough. For a tournament that pays cash to the top 10%, most human players would consider anything in the bottom 90% to be poor, which would include a 50% win rate from a random AI. This happens because some entrants aren't 100% random, and the worse of them can be exploited by the better of them. What happens is that the results involving any random AI essentially degenerate into noise, while the tournament is really contested between the nonrandom entrants and will be won by the one of them with the best strategy. Put another way: to win or place highly in a tournament, you don't just want expected win-rate, you want variance. If there is no difference in reward between a 50% win-rate versus a 10% win-rate (both are far out of the money), but there is a big difference between a 50% win-rate and a 90% win-rate (the latter wins the tournament), you will seek the 90% at the cost of potentially ending up at 10%.
- deleted 10y ago
  [deleted]
- _dps 10y ago
  To add something, and to clarify (though I don't think you've made any mistake here, but many people misunderstand this subject): The 33%-each Nash Equilibrium is the mixed strategy Nash Equilibrium of the micro game (i.e. a single round of RPS, averaged over all possible randomizations). This is in no way the Nash strategy of the tournament game, which is "win the tournament given a pool of unknown participants and a set of rules for whom you face when". You have to add additional assumptions (e.g. that everyone else is going to play the uniform random strategy) in order for uniform random to be the Nash strategy for the tournament game. If the pool includes players who deviate from the single-round nash equilibrium strategy, there is opportunity to exploit them (and in doing so, open yourself to possible exploitation). This is why pure random play can often perform very poorly at the tournament game.
  - BigJono 10y ago
    > You have to add additional assumptions (e.g. that everyone else is going to play the uniform random strategy) in order for uniform random to be the Nash strategy for the tournament game. Isn't that literally what a Nash Equilibrium is though? It's my understanding that if there is players playing exploitably in the game then it cannot (by definition) be a Nash Equilibrium, so the Nash strategy may no longer be the optimal or maximally exploitative one.
    - xapata 10y ago
      Correct. Nash equilibrium exists only if everyone takes the equilibrium-seeking strategy.
- xapata 10y ago
  Similarly, poker tournaments are only profitable for a professional because so many weak players will register -- the "dead money" so to speak.
- thefalcon 10y ago
  Thanks, I understand now what you're saying.

T-hawk 10y ago

A 100% random RPS AI doesn't have a poor 1-on-1 win rate against any other RPS AI, but it absolutely can have a poor rate of winning tournaments, if "poor" is defined broadly enough. For a tournament that pays cash to the top 10%, most human players would consider anything in the bottom 90% to be poor, which would include a 50% win rate from a random AI. This happens because some entrants aren't 100% random, and the worse of them can be exploited by the better of them. What happens is that the results involving any random AI essentially degenerate into noise, while the tournament is really contested between the nonrandom entrants and will be won by the one of them with the best strategy. Put another way: to win or place highly in a tournament, you don't just want expected win-rate, you want variance. If there is no difference in reward between a 50% win-rate versus a 10% win-rate (both are far out of the money), but there is a big difference between a 50% win-rate and a 90% win-rate (the latter wins the tournament), you will seek the 90% at the cost of potentially ending up at 10%.

deleted 10y ago
[deleted]
_dps 10y ago
To add something, and to clarify (though I don't think you've made any mistake here, but many people misunderstand this subject): The 33%-each Nash Equilibrium is the mixed strategy Nash Equilibrium of the micro game (i.e. a single round of RPS, averaged over all possible randomizations). This is in no way the Nash strategy of the tournament game, which is "win the tournament given a pool of unknown participants and a set of rules for whom you face when". You have to add additional assumptions (e.g. that everyone else is going to play the uniform random strategy) in order for uniform random to be the Nash strategy for the tournament game. If the pool includes players who deviate from the single-round nash equilibrium strategy, there is opportunity to exploit them (and in doing so, open yourself to possible exploitation). This is why pure random play can often perform very poorly at the tournament game.
- BigJono 10y ago
  > You have to add additional assumptions (e.g. that everyone else is going to play the uniform random strategy) in order for uniform random to be the Nash strategy for the tournament game. Isn't that literally what a Nash Equilibrium is though? It's my understanding that if there is players playing exploitably in the game then it cannot (by definition) be a Nash Equilibrium, so the Nash strategy may no longer be the optimal or maximally exploitative one.
  - xapata 10y ago
    Correct. Nash equilibrium exists only if everyone takes the equilibrium-seeking strategy.
xapata 10y ago
Similarly, poker tournaments are only profitable for a professional because so many weak players will register -- the "dead money" so to speak.
thefalcon 10y ago
Thanks, I understand now what you're saying.