A research tool for the Iterated Prisoner's Dilemma
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Updated
Jun 3, 2024 - Python
A research tool for the Iterated Prisoner's Dilemma
Behavioral economic games with interface to showcase at events
This is a test for the Iterative Prisoner's Dillema (IPD) with both preprogrammed algorithms and a neural network.
A prisoner's dilemma agent based model simulation for investigating effects of differing strategies on emergent behaviours and spatial patterns with configurable environments.
Welcome to the BlockBusters A* Search Game Challenge! Test your skills against an A* search algorithm in both control mode and competitive mode sessions. Compete to see who can earn the most points, choosing between easy, normal, and hard difficulty levels.
Code and materials for the paper S. Phelps and Y. I. Russell, Investigating Emergent Goal-Like Behaviour in Large Language Models Using Experimental Economics, working paper, arXiv:2305.07970, May 2023
A practical look at the Axelrod tournament.
A simulation of the infamous Iterated Prisoner's Dilemma by Robert Axelrod
Simulator of the iterated prisoner's dilemma.
A Julia package for the quantum prisoner's dilemma model.
Iterative Prisoner Dilemma - Tournament of 20+ classic strategies and an ML player built with DQN
Iterative Prisoner Dilemma - Tournament of 20+ classic strategies and an ML player built with DQN in Javascript
An implementation of iterative prisoner dilemma with reinforcement learning via q_table (NOT DQN) in Python with around 30 strategies
The prisoner's dilemma in python
Agent-based model of multi-level group selection
Консольное приложение, позволяющее симулировать повторяющуюся "Дилемму заключённого” для троих заключённых
A minimalist multi-agent implementation of the social dilemma problem with governance kernels
This program simulates and quantifies outcomes of parameterized prisoner’s dilemma simulation in various MAS networks. This is the third lab in the series of 3 lab projects designed to introduce Multi-Agent Systems (MAS) as a base for Machine Learning.
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