Essay
Version 1
This version is not peer-reviewed
Perspectives: Two Approaches in Computational Social Sciences
Version 1
: Received: 19 October 2024 / Approved: 21 October 2024 / Online: 22 October 2024 (13:04:56 CEST)
How to cite: Tang, S. Perspectives: Two Approaches in Computational Social Sciences. Preprints 2024, 2024101654. https://doi.org/10.20944/preprints202410.1654.v1 Tang, S. Perspectives: Two Approaches in Computational Social Sciences. Preprints 2024, 2024101654. https://doi.org/10.20944/preprints202410.1654.v1
Abstract
Big data-driven machine learning and artificial intelligence (ML/AI) is not all computational social sciences (CSS) have. Agent-based modeling (ABM) or multi-agent system (MAS) is another fundamentally different but equally useful approach in CSS. In fact, the two approaches start from very different orientations and their differences have deeper root in ontology. ML/AI aims to imitate and then surpass human capacities, from sensing to perceiving, reasoning, calculating, and acting. In contrast, ABM seeks to simulate how social outcomes emerge from the complex interactions of agents’ actions within a specific environment. Yet, precisely because these two technologies are different, they can be complementary to each other. There is a bright future for integrating ABM with ML/AI for tackling real world challenges.
Keywords
computational social sciences; machine learning; artificial intelligence; agent-based modeling
Subject
Social Sciences, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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