Data Monetization Pathways and Complex Dynamic Game Equilibrium Analysis in the Energy Industry

TL;DR

The paper tackles data monetization in the energy sector by modeling the strategic interaction between an energy enterprise and a data trading platform through static and dynamic game frameworks. It derives explicit static equilibrium expressions for prices and data value, showing that concave profits require $\eta < \frac{\theta^2}{2 b}$ and providing closed‑form forms: $p_1 = r + \frac{1}{2} a \left( \frac{1}{b} + \frac{3 \eta}{3 b \eta - 2 \theta^2} \right)$, $p_2 = r + \frac{3 a \eta}{3 b \eta - 2 \theta^2}$, and $s = \frac{3 a \theta}{3 b \eta - 2 \theta^2}$. It then extends to a multi‑period dynamic game with bounded rationality, identifying eight equilibria and revealing rich dynamical behavior including local/global bifurcations, period‑doubling, and chaos, analyzed via the Jacobian and Jury stability criteria. To manage complexity, the authors propose a chaos‑control approach using a hybrid controller with parameter $\kappa$, showing stability for small $\kappa$ (e.g., $\kappa < 0.406$) and demonstrating how parameter tuning and governance can stabilize data markets. Overall, the work provides quantitative insights and policy guidance for designing stable, efficient data marketplaces between energy firms and trading platforms, while acknowledging limitations and outlining directions for incorporating customers and government policy.

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 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.

Figures (9)

• Figure 1: Model framework and decision sequence.
• Figure 2: The relationship between $\pi_1$ and $\pi_2$.
• Figure 3: Stable region in $(\alpha_1, \alpha_2, \alpha_3)$ space.
• Figure 4: Bifurcation and largest Lyapunov exponent diagrams of system \ref{['eq05']}.
• Figure 5: Bifurcation diagram and largest Lyapunov exponent diagram on the parameter plane of $(\alpha_1, \alpha_2)$.

Theorems & Definitions (8)

• Proposition 1
• proof
• Proposition 2
• proof
• Proposition 3
• proof
• Proposition 4
• proof