Episode 104 of the TalkPython podcast discussed game theory.
Here are a few slices:
"Our guests this week, Vince Knight, Marc Harper, and Owen Campbell are
here to discuss their Python project built to study and simulate one of
the central problems in game theory, "The Prisoner's Dilemma"
"Yeah, so one of the things is how people end up cooperating. If we're
all incentivized not to cooperate with each other yet we look around, we
see all these situations where people are cooperating, so can we devise
strategies that when we play this game repeatedly that coerce or
convince our partners that they're better off cooperating with us than
defecting against us......Okay, excellent. Give us a sense for some of the, you have some clever
names for the different strategies or players, right? Strategy and
player is kind of the same thing. You've got the basic ones. The
cooperator and the defector, but what else?Probably the most famous one is the tit for tat strategy. Because in
Axelrod's original tournament, one of the interesting results that came
out with his work was that this strategy was one of the most successful."
And then they get into incorporating machine learning:
"We've extended that method of taking a strategy based on some kind of
machine learning algorithm, training it against the other strategies and
then adding the fact of the tournaments to see about those. Right now,
those are amongst the best players in the library, in terms of
performance."
See my previous post for some concepts and examples from game theory that were discussed in this podcast. You can find more references from this podcast including papers, code etc. here.
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