Toward parallel intelligence: an interdisciplinary solution for complex systems

TL;DR

This paper discusses the development and application of the parallel systems method, rooted in interdisciplinary approaches, to address the challenges of modeling, analyzing, and controlling complex real-world systems through iterative data and knowledge cycles.

Contribution

It introduces the parallel systems method based on ACP, highlighting its interdisciplinary foundation and its role in advancing solutions for complex systems over two decades.

Findings

Developed the ACP-based parallel systems method

Demonstrated versatile applications across fields

Projected future challenges and opportunities

Abstract

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling, analysis, management, and control. To meet these demands, the parallel systems method rooted in Artificial systems, Computational experiments, and Parallel execution (ACP) approach has been developed. The method cultivates a cycle, termed parallel intelligence, which iteratively creates data, acquires knowledge, and refines the actual system. Over the past two decades, the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines, offering versatile interdisciplinary solutions for complex systems across diverse fields. This review explores the origins and fundamental concepts of the parallel systems method, showcasing its accomplishments as a diverse array of parallel technologies and applications, while also prognosticating potential challenges. We posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.