Research output: Contribution to journal › Article › peer-review
Virtual Cities : From Digital Twins to Autonomous AI Societies. / Nechesov, Andrey; Dorokhov, Ivan; Ruponen, Janne.
In: IEEE Access, Vol. 13, 2025, p. 13866-13903.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Virtual Cities
T2 - From Digital Twins to Autonomous AI Societies
AU - Nechesov, Andrey
AU - Dorokhov, Ivan
AU - Ruponen, Janne
PY - 2025
Y1 - 2025
N2 - Virtual Cities (VCs) transcend simple digital replicas of real-world systems, emerging as complex socio-technical ecosystems where autonomous AI entities function as citizens. Agentic AI systems are on track to engage in cultural, economic, and political activities, effectively forming societal structure within VC. This paper proposes an integrated simulation framework that combines physical, structural, behavioral, cognitive, and data fidelity layers, allowing multi-scale simulation from microscopic interactions to macro-urban dynamics. A composite fidelity metric (F0) provides systematic approach to evaluate accuracy variations across applications in VCs. We also discuss autonomy of AI entities and classify them according to their capacity to modify goals - ranging from "tools"with fixed objectives to "entities"capable of redefining their very purpose. We also outline the requirements to define a coefficient to evaluate the degree of autonomy for AI beings. Our results demonstrate that such virtual environments can support the emergence of AI-driven societies, where governance mechanisms like Decentralized Autonomous Organizations (DAOs) and an Artificial Collective Consciousness (ACC) provide ethical and regulatory oversight. By blending horizon scanning with systems engineering method for defining novel AI governance models, this study reveals how VCs can catalyze breakthroughs in urban innovation while driving socially beneficial AI development - consequently opening a new frontier for exploring human-AI coexistence.
AB - Virtual Cities (VCs) transcend simple digital replicas of real-world systems, emerging as complex socio-technical ecosystems where autonomous AI entities function as citizens. Agentic AI systems are on track to engage in cultural, economic, and political activities, effectively forming societal structure within VC. This paper proposes an integrated simulation framework that combines physical, structural, behavioral, cognitive, and data fidelity layers, allowing multi-scale simulation from microscopic interactions to macro-urban dynamics. A composite fidelity metric (F0) provides systematic approach to evaluate accuracy variations across applications in VCs. We also discuss autonomy of AI entities and classify them according to their capacity to modify goals - ranging from "tools"with fixed objectives to "entities"capable of redefining their very purpose. We also outline the requirements to define a coefficient to evaluate the degree of autonomy for AI beings. Our results demonstrate that such virtual environments can support the emergence of AI-driven societies, where governance mechanisms like Decentralized Autonomous Organizations (DAOs) and an Artificial Collective Consciousness (ACC) provide ethical and regulatory oversight. By blending horizon scanning with systems engineering method for defining novel AI governance models, this study reveals how VCs can catalyze breakthroughs in urban innovation while driving socially beneficial AI development - consequently opening a new frontier for exploring human-AI coexistence.
KW - AI autonomy
KW - AI governance
KW - Virtual cities
KW - artificial collective consciousness
KW - blockchain
KW - digital twins
KW - predictive modeling
KW - urban metaverse
KW - virtual economies
KW - virtual twins
UR - https://www.mendeley.com/catalogue/34e50726-3a2c-3444-a6f4-0fa4807a5a01/
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85216527844&origin=inward&txGid=3bc2dd21bef8fc6f87e9120511685c3e
U2 - 10.1109/ACCESS.2025.3531222
DO - 10.1109/ACCESS.2025.3531222
M3 - Article
VL - 13
SP - 13866
EP - 13903
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -
ID: 64573073