GLPK

Repository to share my personal studies on the algorithmic game theory subject.

This is an implementation of a learning algorithm (Best Response Dynamics) to find Nash Equilibria for voting games, specifically for the Plurality and Borda voting rules.

Comparative system for multiagent algorithms with different learning strategies. The analysis is carries with the helps of a nash equilibria comparison, the replicator dynamic and a simple grand table with the average reward obtained.

A deep neural network to find Nash equilibria of normal-form stage games

Heads Up Push or Fold Nash Equilibrium Solver

Play Bor-Bor Zan strategically!

A python implementation of the Policy-Space Response Oracles (PSRO) algorithm

Nash equilibria finder, hacked for an university course on game theory.

The prisoner's dilemma is a standard example of a game analyzed in game theory that shows why two completely rational individuals might not cooperate, even if it appears that it is in their best interests to do so. Based on paper by Diego Queiroz and Jaime Sichman.

A Nash Equilibria solver for normal form (matrix) games (bi-matrix, zero-sum, and symmetric). Solves for pure and mixed strategies.

Supporting code for my project on simulation and modeling for choice of CEO from the board of directors in a company, using game theory and finding Nash Equilibriums using python

This is a python application which finds all pure Nash equilibriums and mixed Nash equilibriums for a given strategic game

Games theory course sample tasks and theory

AMPL code for computing a Proper Equilibrium of a 2 players, zero-sum game in normal form. Using implementation from Miltersen, P. B., & Sørensen, T. B. (2006). Computing proper equilibria of zero-sum games.

Trabajo final de Investigacion Operativa II - UCASAL

Desktop software for coordinating companies in a tree-like supply chain functioning according to the Cournot economic model

The repository contains data and scripts for the study "From Prediction Markets to Interpretable Collective Intelligence" by Alexey V. Osipov and Nikolay N. Osipov (arXiv:2204.13424 [cs.GT]).

Normal-form game simulations and norms