# Basis to develop a platform for multiple-scale complex systems modeling and visualization: MoNet

## TL;DR

This paper introduces MoNet, a digital platform for multi-scale modeling and visualization of complex systems, emphasizing its design principles, graphical capabilities, and application benefits for understanding emergent phenomena.

## Contribution

The paper details the development criteria and features of MoNet, a novel platform enabling multi-scale visualization and modeling of complex systems, enhancing pattern recognition and understanding.

## Key findings

- MoNet effectively visualizes systems at multiple scales.
- The platform highlights emergent phenomena through graphical representations.
- Applications demonstrate improved pattern recognition in complex systems.

## Abstract

This work presents some characteristics of MoNet, a digital platform for the modeling and visualization of complex systems. Emphasis is on the ideas that allowed the successful progressive development of this modeling platform, which goes along with the implementation of applications to the modeling of several studied systems. The platform can represent different aspects of systems modeled at different observation scales. This tool offers advantages in the sense of favoring the perception of the phenomenon of the emergence of information, associated with changes of scale. This paper also includes some criteria used for the construction of this modeling platform. The power of current computers has made practical representing graphic resources such as shapes, line thickness, overlaying-text tags, colors, and transparencies, in the graphical modeling of systems. By visualizing diagrams conveniently designed to highlight contrasts, these modeling platforms allow the recognition of patterns that drive our understanding of systems and their structure. Graphs reflecting the benefits of the tool regarding the visualization of systems at different scales of observation are presented to illustrate the application of the platform.

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Source: https://tomesphere.com/paper/1701.04064