#Principal-Agent models in Python
This repository serves as a hub for principal-agent codes in python. For now, I have two theoretical codes online that use SymPy (a Python alternative to Mathematica) and a web application that replicates the Axelrod tournament (famous for the idea of cooperation in iterated prisoner's dilemmas).
The first two codes in this repository using the symbolic SymPy language as an alternative to Mathematica. The two models are based on the Robert Gibbons course on principal-agent models. Some plotting functionality is added to obtain the graphs in the slides as well.
Source: Robert Gibbons: Organizational Economics and Corporate Strategy
A replication of the famous tournament where tit-for-tat was the big winner. A classic in every game theory, but often the performance of tit-for-tat is assumed. This app allows to get some better graphical grasp of why it performs that well, what strategies it performs well with and how much better it actually performs than other strategies.
You can now visit the application here!
Source: Axelrod, R. (1984). The evolution of cooperation. New York: Basic Books
Preferably, I would want to turn this into a (small) sort of tutorial file for principal-agent models. I want to extend this with code on empirical principal-agent models and add a d3 interactive graph to the Axelrod tournament application.