Data Monetization Pathways and Complex Dynamic Game Equilibrium Analysis in the Energy Industry
Zongxian Wang, Jie Song

TL;DR
This paper models the strategic interactions between energy enterprises and trading platforms in data monetization using game theory, analyzing equilibrium strategies and dynamic behaviors to guide decision-making.
Contribution
It introduces a novel game-theoretic framework for understanding data monetization in energy industries, incorporating nonlinear dynamic analysis for multi-period decision processes.
Findings
Adjusting data wholesale prices and mining intensity can lead to benign market equilibria.
Dynamic parameters and initial states significantly influence long-term market stability.
Strategic regulation can prevent market failure in data trading ecosystems.
Abstract
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…
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Taxonomy
TopicsComplex Systems and Time Series Analysis
