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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.
A prisoner's dilemma agent based model simulation for investigating effects of differing strategies on emergent behaviours and spatial patterns with configurable environments.
This Python program simulates the Prisoner's Dilemma game, allowing players to choose between different prisoner strategies (Cooperator, Defector, Revenger). The game runs for a specified number of rounds, with a brief delay between each round, and includes an option to view descriptions of each strategy before starting.
This is a model plugin for Evoplex. It implements the spatial prisoner's dilemma game proposed by Nowak, M. A., & May, R. M. (1992). Evolutionary games and spatial chaos. Nature, 359(6398), 826.