Dynamic Equilibria Prediction: Experience-Weighted Attraction (EWA), Python Implementation (1.0.0)
This project is based on a Jupyter Notebook that describes the stepwise implementation of the EWA model in bi-matrix ( 2×2 ) strategic-form games for the simulation of economic learning processes. The output is a dataset with the simulated values of Attractions, Experience, selected strategies, and payoffs gained for the desired number of rounds and periods. The notebook also includes exploratory data analysis over the simulated output based on equilibrium, strategy frequencies, and payoffs.
Release Notes
First version - model implementation and exploratory data analysis
Associated Publications
Dynamic Equilibria Prediction: Experience-Weighted Attraction (EWA), Python Implementation 1.0.0
Submitted by
Vinicius Ferraz
Published Dec 02, 2022
Last modified Dec 05, 2024
This project is based on a Jupyter Notebook that describes the stepwise implementation of the EWA model in bi-matrix ( 2×2 ) strategic-form games for the simulation of economic learning processes. The output is a dataset with the simulated values of Attractions, Experience, selected strategies, and payoffs gained for the desired number of rounds and periods. The notebook also includes exploratory data analysis over the simulated output based on equilibrium, strategy frequencies, and payoffs.
Release Notes
First version - model implementation and exploratory data analysis