Can Complex Collective Behaviour Be Generated Through Randomness, Memory and a Pinch of Luck?

TL;DR

This paper presents a simple model of collective behavior driven by randomness, memory, and luck, demonstrating population dynamics and migration patterns using minimal computational resources.

Contribution

It introduces a novel population growth and migration model based on reinforcement learning principles, implemented on low-tier hardware without advanced AI tools.

Findings

The model accurately predicts population development around food sources.

Population dynamics fit logistic functions in different scenarios.

The approach demonstrates complex behavior emergence from simple rules.

Abstract

Machine Learning techniques have been used to teach computer programs how to play games as complicated as Chess and Go. These were achieved using powerful tools such as Neural Networks and Parallel Computing on Supercomputers. In this paper, we define a model of populational growth and evolution based on the idea of Reinforcement Learning, but using only the 3 sources stated in the title processed on a low-tier laptop. The model correctly predicts the development of a population around food sources and their migration in search of a new one when the known ones become saturated. Additionally, we compared our model to a pure random one and the population number was fitted to a logistic function for two interesting evolutions of the system.