Complex adaptive systems science in the era of global sustainability crisis

Abstract

A significant number and range of challenges besetting sustainability can be traced to the actions and interactions of multiple autonomous agents (people mostly) and the entities they create (e.g., institutions, policies, social network) in the corresponding social-environmental systems (SES). To address these challenges, we need to understand decisions made and actions taken by agents, the outcomes of their actions, including the feedbacks on the corresponding agents and environment. The science of complex adaptive systems—complex adaptive systems (CAS) science—has a significant potential to handle such challenges. We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science, the generic features of CAS, and the key advances and challenges in modeling CAS. Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’ behaviors, detect SES structures, and formulate SES mechanisms.

Keywords

Social-environmental systems / Complex adaptive systems / Sustainability science / Agent-based models / Artificial intelligence / Data science

CRediT authorship contribution statement

Li An: Methodology, Investigation, Funding acquisition, Data curation, Conceptualization. B.L. Turner: Writing – review & editing, Conceptualization. Jianguo Liu: Writing – review & editing, Conceptualization. Volker Grimm: Writing – review & editing, Conceptualization. Qi Zhang: Writing – review & editing, Data curation. Zhangyang Wang: Conceptualization. Ruihong Huang: Data curation.

Declaration of competing interests

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Acknowledgements

The National Science Foundation funded this research under the Dynamics of Coupled Natural and Human Systems program (Grants No. DEB-1212183 and BCS-1826839). This research also received financial and research support from San Diego State University and Auburn University.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2024.09.011.

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