Econometric Sense

Episode 104 of the TalkPython podcast discussed game theory.
[フレーム]
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.

Below are links to a couple of gists with R and Python code for some very basic data manipulation and statistics. I have been using R and SAS for almost a decade, but the R code originates to some very basic scripts that I used when I was a beginning programmer. The python script is just a translation from R to python. This does not represent the best way to solve these problems, but provides enough code for a beginner to get a feel for coding in one of these environments. This is 'starter' code in the crudest sense and intended to allow one to begin learning R or python with as little intimidation with the simplest syntax as possible. However, once started, one can google other sources or enroll in courses to expand their programming skillset.

Basic Data Manipulation in R

Basic Data Manipulation in Python

Basic Statistics in R

Basic Statistics in Python

For more advanced applications in R posted to this blog see all posts with the tag R Code.