Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Artificial Intelligence for Sustainable Complex Socio-Technical-Economic-Ecosystems.

Version 1 : Received: 18 August 2021 / Approved: 23 August 2021 / Online: 23 August 2021 (13:23:42 CEST)

How to cite: Martinez-Garcia, A.N. Artificial Intelligence for Sustainable Complex Socio-Technical-Economic-Ecosystems.. Preprints 2021, 2021080441. Martinez-Garcia, A.N. Artificial Intelligence for Sustainable Complex Socio-Technical-Economic-Ecosystems.. Preprints 2021, 2021080441.


The strong couplings among ecological, economic, social and technological processes explains the complexification of human-made systems, and phenomena such as globalization, climate change, the increased urbanization and inequality of human societies, the power of information, and the COVID-19 syndemics. Among complexification’s essential features are non-decomposability, asynchronous behavior, components with many degrees of freedom, increased likelihood of catastrophic events, irreversibility, nonlinear phase spaces with immense combinatorial sizes, and the impossibility of long-term, detailed prediction. Sustainability for complex systems implies enough efficiency to explore and exploit their dynamic phase spaces and enough flexibility to coevolve with their environments. This in turn means solving intractable nonlinear semi-structured dynamic multi-objective optimization problems, with conflicting, incommensurable, non-cooperative objectives and purposes, under dynamic uncertainty, restricted access to materials, energy and information, and a given time horizon, aiming at enhancing the co-evolutionary power of the Biosphere and its human subsystems. Giving the high-stakes, the need for effective, efficient, diverse solutions, their local-global, present-future effects, and their unforeseen short, medium and long-term impacts, achieving sustainable complex systems implies the need for Sustainability-designed Universal Intelligent Agents, harnessing the strong functional coupling between human, artificial and nonhuman biological intelligence in a no-zero-sum game to achieve sustainability.


Artificial & Biological Intelligence; Complex Coevolutionary Systems Engineering; Sustainability; Multi-Objective Optimization; Sustainable Universal Intelligent Agents


Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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