Dynamical Systems and Markov Chains

TL;DR

This paper explores how Markov chains, modeled through stochastic matrices, can be used to understand system evolution and improve decision-making based solely on current state information.

Contribution

It demonstrates the application of Markov chains to decision-making processes using a specific stochastic matrix example.

Findings

Markov chains effectively model system dynamics.

Current state information suffices for decision improvements.

Potential for faster, better decisions using Markov models.

Abstract

This project is going to work with one example of stochastic matrix to understand how Markov chains evolve and how to use them to make faster and better decisions only looking to the present state of the system.