Data Monetization Pathways and Complex Dynamic Game Equilibrium Analysis in the Energy Industry
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| Format: | Preprint |
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2024
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| _version_ | 1866910367633899520 |
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| author | Wang, Zongxian Song, Jie |
| author_facet | Wang, Zongxian Song, Jie |
| contents | As the most critical production factor in the era of the digital economy, data will have a significant impact on social production and development. Energy enterprises possess data that is interconnected with multiple industries, characterized by diverse needs, sensitivity, and long-term nature. The path to monetizing energy enterprises' data is challenging yet crucial. This paper explores the game-theoretic aspects of the data monetization process in energy enterprises by considering the relationships between enterprises and trading platforms. We construct a class of game decision models and study their equilibrium strategies. Our analysis shows that enterprises and platforms can adjust respective benefits by regulating the wholesale price of data and the intensity of data value mining to form a benign equilibrium state. Furthermore, by integrating nonlinear dynamical theory, we discuss the dynamic characteristics present in multi-period repeated game processes. We find that decision-makers should keep the adjustment parameters and initial states within reasonable ranges in multi-period dynamic decision-making to avoid market failure. Finally, based on the theoretical and numerical analysis, we provide decision insights and recommendations for enterprise decision-making to facilitate data monetization through strategic interactions with trading platforms. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_08082 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Monetization Pathways and Complex Dynamic Game Equilibrium Analysis in the Energy Industry Wang, Zongxian Song, Jie Computer Science and Game Theory As the most critical production factor in the era of the digital economy, data will have a significant impact on social production and development. Energy enterprises possess data that is interconnected with multiple industries, characterized by diverse needs, sensitivity, and long-term nature. The path to monetizing energy enterprises' data is challenging yet crucial. This paper explores the game-theoretic aspects of the data monetization process in energy enterprises by considering the relationships between enterprises and trading platforms. We construct a class of game decision models and study their equilibrium strategies. Our analysis shows that enterprises and platforms can adjust respective benefits by regulating the wholesale price of data and the intensity of data value mining to form a benign equilibrium state. Furthermore, by integrating nonlinear dynamical theory, we discuss the dynamic characteristics present in multi-period repeated game processes. We find that decision-makers should keep the adjustment parameters and initial states within reasonable ranges in multi-period dynamic decision-making to avoid market failure. Finally, based on the theoretical and numerical analysis, we provide decision insights and recommendations for enterprise decision-making to facilitate data monetization through strategic interactions with trading platforms. |
| title | Data Monetization Pathways and Complex Dynamic Game Equilibrium Analysis in the Energy Industry |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2403.08082 |