Evolutionary Game Analysis of Data Resale Governance in Data Trading
Abstract
:1. Introduction
2. Model Design
2.1. Model Description
2.2. Model Assumptions
2.3. Payment Matrix
3. Model Analysis
3.1. Analysis of Replication Dynamics
3.2. Stable Equilibrium Analysis
4. Numerical Simulation
4.1. Influence of Data Price and Volume Trading on the Evolutionary Game System
4.2. Influence of the Behavioral Parameters of the Data Trading Platforms on the Evolutionary Game System
4.3. Influence of Data Supplier Behavior Parameters on the Evolutionary Game System
4.4. Influence of Data Demanders Behavior Parameters on the Evolutionary Game System
5. Discussion
6. Implications
7. Conclusions
- (1)
- When certain conditions are met, E2 (0, 0, 1), E4 (1, 0, 0), E5 (1, 1, 0), E6 (1, 0, 1), E7 (0, 1, 1), and E8 (1, 1, 1) have progressive stability, where E5 (1, 1, 0) is a stable equilibrium strategy that can satisfy the effective governance of data trading platforms and can lead to data trading platforms choosing to govern, data suppliers choosing to innovate positively, and data demanders choosing not to resale.
- (2)
- The higher the price and amount of data trading, the lower the probability that the data trading platforms will choose to govern, and the greater the probability that the data suppliers will choose a positive innovative strategy, while the probability that the data demanders will choose a resale strategy increases. The higher the price and the number of data transactions, the more difficult it is to govern data transactions.
- (3)
- The higher the cost of governance on data trading platforms, the lower the incentive to govern, the lower the incentive for data suppliers to innovate, and the higher the incentive for data demanders to resell. The greater the competitive pressure on data trading platforms not to govern, the greater the incentive to govern, the greater the incentive for data suppliers to innovate and the lower the incentive for data demanders to resale.
- (4)
- The data innovation incentives of data trading platforms can significantly promote the motivation of data suppliers to innovate data positively; when the incentives are too high, data trading platforms will weaken their level of governance or even move towards non-governance; in addition, the greater the cost of innovative data for data suppliers, the lower their motivation to innovate data; the greater the level of data innovation for data suppliers, the higher their motivation to choose innovative data strategies.
- (5)
- The higher the payoff factor for data demanders to resell data, the greater their incentive to choose a resale strategy; and the greater the incentive for data suppliers to innovate data positively, the weaker the incentive for data trading platforms to govern it. The greater the data resale penalty on the data trading platforms, the weaker the incentive for data demanders to resell data; the greater the probability of data resale being reported, the less incentive for data demanders to resale data.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Description |
---|---|
Cg | Cost of governance of data trading platforms |
D | Competitive pressure of data trading platforms if they choose not to govern |
A | Innovative support for data suppliers from data trading platforms |
W | Penalties for resale by data trading platforms to those who demand data |
L | Reputational damage to data trading platforms caused by resale behavior |
θ | Coefficient of revenue from each transaction |
Ci | Data processing costs when data suppliers choose innovative strategies |
P | Price of data general trading |
Q | Quantity of data general trading |
β | Degree of data innovation |
m | Coefficient of gain from reselling data |
α | Probability of being reported for supplying low-quality data |
x | Probability of data trading platforms choosing to govern |
y | Probability of data suppliers choosing to innovate positively |
z | Probability of data demanders choosing to resale |
Data Trading Platforms Choose to Govern (x) | Data Trading Platforms Choose not to Govern (1 − x) | |||
---|---|---|---|---|
Data Suppliers Choose to Innovate Positively (y) | Data Suppliers Choose to Innovate Negatively (1 − y) | Data Suppliers Choose to Innovate Positively (y) | Data Suppliers Choose to Innovate Negatively (1 − y) | |
Data demanders choose to resell (z) | θ P Q β − A − Cg − L P Q β − Ci + A + W − P Q β + m P Q β − W | θ P Q − Cg − L P Q + α W − P Q + m P Q − α W | θ P Q β − L − D P Q β − Ci − P Q β + m P Q β | θ P Q − L − D P Q − P Q + m P Q |
Data demanders choose not to resell (1 − z) | θ P Q β − A − Cg P Q β − Ci + A − P Q β | θ P Q − Cg P Q − P Q | θ P Q β − D P Q β − Ci − P Q β | θ P Q − D P Q − P Q |
Equilibrium Point | Eigenvalue | Results |
---|---|---|
(0, 0, 0) | λ1 = −Cg + D; λ2 = −Ci − P (Q − Q β); λ3 = m P Q | When −Cg + D > 0 and Ci (−1 + P Q β) > 0, it is an unstable point, otherwise a saddle point |
(0, 0, 1) | λ1 = −Cg + D + W α; λ2 = −Ci - P (Q − Q β); λ3 = m P Q | When −Cg + D + W α < 0 and −Ci − P (Q − Q β) < 0, it is a stable point, otherwise a saddle point or unstable point |
(0, 1, 0) | λ1 = −A − Cg + D; λ2 = Ci +P (Q − Q β); λ3 = m P Q β | When −A − Cg + D > 0 and Ci + P (Q − Q β) > 0, it is an unstable point, otherwise a saddle point |
(1, 0, 0) | λ1 = Cg − D; λ2 = A − Ci − P (Q − Q β); λ3 = m P Q − W α | When Cg − D < 0, A − Ci − P (Q − Q β) < 0 and m P Q − W α < 0, it is a stable point, otherwise a saddle point or unstable point |
(1, 1, 0) | λ1 = A + Cg − D; λ2 = −A + Ci +P (Q − Q β); λ3 = −W + m P Q β | When A + Cg − D < 0, −A + Ci + P (Q − Q β) < 0 and −W + m P Q β < 0, it is a stable point, otherwise a saddle point or unstable point |
(1, 0, 1) | λ1 = Cg – D − W α; λ2 = A − Ci + W − P (Q − Q β); λ3 = m P Q + W α | When Cg − D − W α <0, A − Ci + W − P (Q − Q β) < 0 and m P Q + W α < 0, it is a stable point, otherwise a saddle point or unstable point |
(0, 1, 1) | λ1 = −A − Cg + D; λ2 = Ci + P (Q − Q β); λ3 = m P Q β | When −A − Cg + D < 0 and Ci + P (Q − Q β) < 0, it is a stable point, otherwise a saddle point or unstable point |
(1, 1, 1) | λ1 = A + Cg − D; λ2 = −A + Ci − W + P (Q − Q β); λ3 = W m P Q β | When A + Cg − D < 0, −A + Ci − W + P (Q − Q β) < 0 and W m P Q β < 0, it is a stable point, otherwise a saddle point or unstable point |
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Sun, Y.; Zhang, Y.; Li, J.; Zhang, S. Evolutionary Game Analysis of Data Resale Governance in Data Trading. Systems 2023, 11, 363. https://doi.org/10.3390/systems11070363
Sun Y, Zhang Y, Li J, Zhang S. Evolutionary Game Analysis of Data Resale Governance in Data Trading. Systems. 2023; 11(7):363. https://doi.org/10.3390/systems11070363
Chicago/Turabian StyleSun, Yong, Yafeng Zhang, Jinxiao Li, and Sihui Zhang. 2023. "Evolutionary Game Analysis of Data Resale Governance in Data Trading" Systems 11, no. 7: 363. https://doi.org/10.3390/systems11070363