# Replicator Equation: Applications Revisited

## TL;DR

This paper revisits the replicator equation, a fundamental model in evolutionary game theory, highlighting its diverse applications across biology, social sciences, machine learning, and computer vision.

## Contribution

It provides an overview of the replicator equation's applications beyond biology, emphasizing its relevance in modern computational fields.

## Key findings

- Replicator equation models stable Nash Equilibria and ESS.
- It has broad applications in machine learning and computer vision.
- The paper discusses the equation's expanded role in social and computational sciences.

## Abstract

The replicator equation is a simple model of evolution that leads to stable form of Nash Equilibrium, Evolutionary Stable Strategy (ESS). It has been studied in connection with Evolutionary Game Theory and was originally developed for symmetric games. Beyond its first emphasis in biological use, evolutionary game theory has been expanded well beyond in social studies for behavioral analysis, in machine learning, computer vision and others. Its several applications in the fields of machine learning and computer vision has drawn my attention which is the reason to write this extended abstract