Machina Economicus: A New Paradigm for Prosumers in the Energy Internet of Smart Cities
<|reference_start|>Machina Economicus: A New Paradigm for Prosumers in the Energy Internet of Smart Cities: Energy Internet (EI) is emerging as new share economy platform for flexible local energy supplies in smart cities. Empowered by the Internet-of-Things (IoT) and Artificial Intelligence (AI), EI aims to unlock peer-to-peer energy trading and sharing among prosumers, who can adeptly switch roles between providers and consumers in localized energy markets with rooftop photovoltaic panels, vehicle-to-everything technologies, packetized energy management, etc. The integration of prosumers in EI, however, will encounter many challenges in modelling, analyzing, and designing an efficient, economic, and social-optimal platform for energy sharing, calling for advanced AI/IoT-based solutions to resource optimization, information exchange, and interaction protocols in the context of the share economy. In this study, we aim to introduce a recently emerged paradigm, Machina Economicus, to investigate the economic rationality in modelling, analysis, and optimization of AI/IoT-based EI prosumer behaviors. The new paradigm, built upon the theory of machine learning and mechanism design, will offer new angles to investigate the selfishness of AI through a game-theoretic perspective, revealing potential competition and collaborations resulting from the self-adaptive learning and decision-making capacity. This study will focus on how the introduction of AI will reshape prosumer behaviors on the EI, and how this paradigm will reveal new research questions and directions when AI meets the share economy. With an extensive case analysis in the literature, we will also shed light on potential solutions for advancements of AI in future smart cities.<|reference_end|>
@article{hou2024machina,
    title={Machina Economicus: A New Paradigm for Prosumers in the Energy Internet
  of Smart Cities},
    author={Luyang Hou and Jun Yan and Yuankai Wu and Chun Wang and Tie Qiu},
    journal={arXiv preprint arXiv:2403.14660},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14660},
    primaryClass={cs.CY cs.AI}
}