Dynamic Switching Networks: A Dynamic, Non-local, and Time-independent Approach to Emergence

TL;DR

This paper introduces a novel dynamic, non-local, and time-independent network-based method for modeling emergence, capable of generating complex patterns with various symmetries, addressing limitations of previous approaches.

Contribution

It presents a new approach that uses switching decisions in a network to model emergence, offering a more flexible and complex framework than existing methods.

Findings

Successfully generates patterns with different symmetries

Addresses limitations of previous emergence models

Demonstrates the flexibility of the network-based approach

Abstract

The concept of emergence is a powerful concept to explain very complex behaviour by simple underling rules. Existing approaches of producing emergent collective behaviour have many limitations making them unable to account for the complexity we see in the real world. In this paper we propose a new dynamic, non-local, and time independent approach that uses a network like structure to implement the laws or the rules, where the mathematical equations representing the rules are converted to a series of switching decisions carried out by the network on the particles moving in the network. The proposed approach is used to generate patterns with different types of symmetry.