Data Element Sharing in Convergence Media Ecology Based on Evolutionary Game
Abstract
:1. Introduction
- (1)
- Content data
- ①
- Literal data. Literal data are one of the most basic elements in the convergence media, including news, reports, comments, analysis and other text content.ar
- ②
- Image data. Image data refer to non-text media, such as pictures and photos, which can be used to supplement and enrich the text content and improve the reading experience of readers.
- ③
- Audio data. Audio data refer to the media content presented in the form of sound, including music, radio programs, podcasts, etc., which can convey information and emotion through sound.
- ④
- Video data. Video data refer to the media content presented in the form of video, including news reports, movies, documentaries, etc., which can be conveyed through multiple dimensions, such as vision and sound.
- (2)
- Interactive data: These are the data describing the interaction between the content and users, including users’ comments, likes and sharing of the content. Interactive data can help parties within the ecosystem to understand users’ interests and feedback on content and improve the quality of content within the ecosystem.
- (3)
- Content copyright data: These include copyright ownership and authorization of original content, which can be recorded and stored safely using blockchain and other technologies so as to realize the protection and maintenance of content copyright data.
- (4)
- Value assessment data: These describe the value of content and user value, which can help parties within the ecosystem assess the value of content and the value of users. Value evaluation data can provide a basis for resource allocation and content recommendation within the ecology.
- (5)
- Advertising data: These include the display amount of advertising, clicks, conversion amount and other data, which can be safely recorded and stored so as to realize the credibility and transparency of advertising data [27].
- (6)
- User data: These describe the user information, including the user’s basic information, account information, behavioral data, etc. User data can help parties in the ecosystem understand user needs and behaviors and better-targeted services [28].
- (7)
- Authentication data: These describe the content authentication information and the user identity authentication information. Authentication data can help parties in the ecosystem to confirm the authenticity of the content and the identity of users and improve the credibility of information within the ecology [29].
- (8)
- Transaction data: These include payment, transfer, points exchange and other data between users, which can be safely recorded and stored so as to realize the security and traceability of transaction data.
- (9)
- Circulation data: These describe the circulation of digital assets, including digital asset transaction records, distribution records, etc. Circulation data can help parties within the ecosystem assess the value of the content and better manage digital assets.
- (1)
- Insufficient willingness to participate in sharing.
- (2)
- The circulation mechanism of a data element is incomplete.
- (3)
- The implementation of an incentive mechanism is not in place.
- (4)
- Data privacy and security issues. It must be carried out on the premise of complying with national laws and relevant ecological requirements. Blockchain and other technologies can ensure the immutability and security of data, but some sensitive data also need to be encrypted to protect data privacy.
- (5)
- Data quality issues. The quality of data is crucial to the normal operation of the media ecology, so data need to be monitored and reviewed to ensure its quality.
- (6)
- Data standard issues. Data standards may vary for different organizations and individuals, so uniform data standards need to be used to facilitate the flow of data elements.
- (7)
- Data equity issues. Data element sharing may cause disputes over data rights and interests, so it is necessary to clarify the ownership of data and the right to use them to avoid disputes.
2. Related Work
3. Data Element Sharing Model Based on Evolutionary Game
3.1. Node Classification
- (1)
- Producer and consumer model
- ①
- Clear role: Producers focus on producing high-quality data elements, while consumers focus on acquiring, screening, sharing and interacting data elements.
- ②
- Help to maintain data quality: Producers can better ensure data quality and reliability and reduce the risk of false information dissemination.
- ③
- Research iteration: The node function in the system is clear, which is more convenient to study the system sharing efficiency so as to promote the system update iteration.
- ①
- Limiting innovation and engagement: Too much explicit role division may limit the motivation of common users to participate in data element production, reducing content innovation and diversity.
- ②
- Information asymmetry: There may be information asymmetry between producers and consumers, resulting in some data elements that cannot be effectively spread and shared.
- (2)
- Undifferentiated node pattern
- ①
- Innovation and diversity: Encourage users to participate in the production of data elements and increase the innovation and diversity of content.
- ②
- Fluent information flow: Undifferentiated node mode helps to break down the information barriers and realize the wider sharing and dissemination of data elements.
- ①
- Data quality is difficult to control: the lack of clear producer and consumer roles may make it difficult to guarantee data quality and reliability.
- ②
- Resource allocation efficiency may be reduced: In the undifferentiated node model, participants may need to distract between production and consumption, potentially reducing the efficiency of resource allocation and utilization.
- ③
- Difficult to target research: In the undifferentiated node mode, the functional characteristics of nodes are weakened, and it is difficult to reflect the targeted characteristics of research.
- (1)
- Business organization node (B). Business organization nodes involved in the convergence media ecological data element sharing mainly as data producers for common users or other institutions to provide raw data and resources, such as articles, pictures, audio, video, statistics, etc., these nodes usually adjust strategy according to the incentive mechanism to share data element, in order to obtain relatively higher income.
- (2)
- Common user node (U). Common user nodes participate in data element sharing in the convergence media ecology mainly as consumers of data elements participate in the acquisition, use and quality feedback of data elements.
- (3)
- Regulator node (R). The participation of regulator nodes in data element sharing in the convergence media ecology is mainly responsible for the regulation of the quality of data element sharing. It urges business organization nodes to actively participate in the production and sharing of high-quality data elements and common user nodes to participate in data element consumption and quality feedback. The regulator nodes distribute rewards according to the behavior of the user nodes and punish the node violations.
3.2. Basic Assumption
- (1)
- Node-wise finite rationality. The business organization node, common user node and regulator node are all limited and rational subjects.
- (2)
- Policy space (S). All three parties of the evolutionary game are users in the convergence media ecology. The policy space of the business organization node = {positive data element sharing, negative data element sharing}, the probability of the business organization node to positively participate in data element sharing is x; the policy space of the common user node = {participate in data element sharing, not participate in data element sharing}, the probability of the common user node participating in data element sharing is y; the policy space of the regulator node = {regulate data element sharing, not regulate data element sharing}, and the probability of the regulator node participating in data element sharing regulation is z. In order to facilitate the research, this paper regards the business organization node, common user node and regulator node as a whole, respectively.
- (3)
- Participation cost (G). In the process of data element sharing, nodes need to pay a certain cost, which includes but is not limited to data, time cost of content production, labor cost, economic cost of data abuse, etc. In this model, the positive sharing cost of business organization nodes is denoted as , and negative sharing costs are denoted as ; the cost of common user nodes participating in sharing is denoted as , and the cost of not participating in sharing is 0; and the cost of regulation carried out by the regulator node is denoted as , and the cost of not regulating is 0.
- (4)
- Benefits of participation in sharing (). When the business organization node chooses to positively share data elements and the common user node chooses to participate in data element sharing, the node will receive sharing revenue. The business node is mainly generated by sharing its own data element with other nodes, mainly determined by the value of the shared data element and unit price; the common user node is mainly generated by the revenue generated by consumption after obtaining the data element, which depends on the value of shared data element, the value transformation ability of data element and the sharing revenue coefficient of data element. In this paper model, , represent the revenue of positively participating in sharing and the revenue of negatively participating in sharing of business organization nodes (), and represents the revenue of common user nodes participating in sharing. When the business organization node is positively sharing, and the common user node is participating in sharing, it will promote the harmonious development of ecology, and the two parties will have collaborative benefits .
- (5)
- Dynamic incentive benefits (I). In the media ecology, in order to encourage nodes to participate in data sharing, dynamic incentive rewards are given to participating nodes, and the size of incentive value is positively correlated with node credit C and node sharing participation P. In this model and are the dynamic incentive benefits of business organization nodes positively participating in data element sharing and common user nodes participating in data element sharing.
- (6)
- Regulatory benefits (). The basic revenue generated by the regulator node choosing to regulate the ecological node is R. The additional revenue coefficient obtained from the successful urging business organization nodes to positively participate in sharing is α(α∈[0, 1]), and the additional revenue coefficient obtained from the successful urging common user nodes to participate in sharing is β(β∈[0, 1]).
- (7)
- Punishment mechanism. When the business organization nodes choose negative data element sharing, they will be punished accordingly. When the regulator nodes choose not to regulate, there is a corresponding penalty .
3.3. Model Construction and Evolutionary Path Analysis
- (1)
- Expected revenue function and replication dynamic equation of business organization node.
- ①
- When , for any , we can obtain . It shows that all the strategies that meet the constraints of the business organization nodes are stable states, and the positive sharing probability x of the business organization nodes will not change with time.
- ②
- When , we can obtain and , which are two evolutionary stable strategies for the business organization nodes. At this point, we need to discuss the distribution of values:
- When , because , so . According to the monotonicity of the function L(y), we obtain , then we can obtain and , so x = 0 is an evolutionary stable point, which indicates that the increased revenue of the business organization node is less than the increased cost of the active sharing, and the negative sharing of the business organization node is the evolutionary stability strategy.
- When , because , so . According to the monotonicity of the function L(y), we obtain , and then we can obtain and , so x = 1 is an evolutionary stable point, indicating that the increased revenue of the business organization nodes is more than the increased cost of the active sharing, and the positive sharing of the business organization nodes is an evolutionary stability strategy.
- When , it was also necessary to discuss the size relationship between y and :
- (2)
- Expected revenue function and replication dynamic equation of common user node
- ①
- When , for any , we can obtain , and it indicates that all the policies meeting the constraints of the common user node are in a stable state, and the common user node chooses to participate in the sharing probability y will not change over time.
- ②
- When , or are the two evolutionary stability strategies of common user node, the distribution values of should be discussed:
- When , because , so . According to the monotonicity of the function L(x), we obtain , and then we can obtain and , so y = 0 is an evolutionary stable point, which indicates that the increased revenue of the common user node is less than the increased cost of the participation in sharing, and the not participation in sharing of the common user node is the evolutionary stability strategy.
- When , because , so x . According to the monotonicity of the function L(x), we can obtain and , so y = 1 is an evolutionary stable point, which indicates that the increased revenue of the common user node is more than the increased cost of the participation in sharing, and the participation in sharing of the common user node is the evolutionary stability strategy.
- When , it was also necessary to discuss the size relationship between x and :
- (3)
- Expected revenue function and replication dynamic equation of regulator node.
- ①
- When or , for any , we can obtain: , which indicates that all the policies meeting the constraints of the regulator node are in a stable state, and the regulator node chooses to participate in the sharing regulation probability z will not change over time.
- ②
- When and , or are the two evolutionary stability strategies of the regulator node, and the distribution values of should be discussed:
- When (), because , so we can obtain: (). According to the monotonicity of the function L(x, y), we obtain , and then we can obtain and , so z = 0 is an evolutionary stable point, which indicates that the increased revenue of the regulator node is less than the increased cost of the participation in regulation, and the not participation in regulation of the regulator node is the evolutionary stability strategy.
- When (), because , so we can obtain: (). According to the monotonicity of the function L(x, y), we obtain , and then we can obtain and , so z = 1 is an evolutionary stable point, which indicates that the increased revenue of the regulator node is more than the increased cost of the participation in regulation, and the participation in regulation of the regulator node is the evolutionary stability strategy.
- When (), it was also necessary to discuss the size relationship between x and (y and ):
4. Results and Discussion
4.1. Evolutionary Stable Strategy
4.2. Stable Strategy Simulation
- (1)
- Numerical simulation analysis of the equilibrium point E1(0,0,0)
- (2)
- Numerical simulation analysis of the equilibrium point E8(1,1,1)
- (3)
- Numerical simulation analysis of the equilibrium point E2(0,0,1)
- (4)
- Numerical simulation analysis of the equilibrium point E7(1,1,0).
4.3. Model Comparison
5. Conclusions
- (1)
- For business organization nodes, incentive revenue of positive participation in sharing , negative sharing punishment and negative sharing cost play a promoting role in positively participating in data element sharing; the cost of positively share restricts the positive participation in data element sharing; collaborative revenue H, the revenue of positively participating in sharing and the revenue of negative participation in sharing are not fixed on the influence of the business organization node, affected by the state of other parameters.
- (2)
- For common user nodes, the revenue from participating in the sharing and the system incentive revenue for a common user node play a promoting role in participating in data element sharing; the cost of participating in sharing restricts the participation of the common user node in data element sharing; collaborative revenue H is not fixed on the influence of the business organization node, affected by the state of other parameters.
- (3)
- For the regulator node, the punishment for not participating plays a promoting role in participating in data element sharing regulation; the cost of regulation restricts the participation in data element sharing regulation; the revenue of regulation R is not fixed on the influence of the regulator node, affected by the state of other parameters.
- (4)
- The probability of business organization nodes choosing to participate in positively sharing is complementary to the probability of common user nodes choosing to participate in sharing, which increases with the increase in the participation probability of the other party.
- (5)
- According to the model presented in this paper, the ecological nodes finally reach the corresponding evolutionary stability strategy under the corresponding state conditions, and the research results can provide solutions to solve the problem of nodes not participating in data element sharing or supervision faced by ecological development.
- (1)
- In promoting the sharing of data elements, it is necessary to formulate standardized behavior rules and regulatory implementation plans to prevent the implementation of behavioral rules so as to improve the confidence of nodes in sharing data elements in ecology and lay a foundation for promoting the sharing of data element in convergence media ecology.
- (2)
- The system needs reasonable planning; severe punishment may hinder nodes from joining ecological and participating in the data sharing attempt, and low punishment cannot play a guiding role in the ecosystem. High system incentives will destroy the ecological balance of the system, and low system incentives cannot stimulate the participation of the node.
- (3)
- The model evolution path analysis and macro-control of the state parameters of the corresponding nodes under specific state conditions should be made good use of so as to promote the ecological nodes to positively participate in the data element sharing.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Symbol | Parameter Description |
---|---|
The revenue of business organization nodes positively participate in data element sharing. | |
The revenue of business organization nodes negatively participate in data element sharing. | |
The cost of business organization nodes positively participate in data element sharing. | |
The cost of business organization nodes negatively participate in data element sharing. | |
The dynamic incentive revenue of business organization nodes positively participate in data element sharing. | |
The system punishment for business organization nodes negatively participate in data element sharing. | |
The revenue of common user nodes’ participation in data element sharing. | |
The cost of common user nodes’ participation in data element sharing. | |
The system dynamic incentive revenue of common user nodes participating in data element sharing. | |
The revenue of regulator nodes participating in data element sharing regulation. | |
The cost of regulator nodes participating in the regulation of data element sharing. | |
The additional revenue coefficient obtained from the successful urging business organization nodes to positively participate in sharing. | |
The additional revenue coefficient obtained from the successful urging of common user nodes to participate in sharing. | |
The system punishment of regulator nodes does not participate in data element sharing regulation. | |
H | The revenue received by the parties participating in the sharing. When the business organization node actively participates in sharing and the common user node participates in sharing. |
x | The probability of the business organization nodes to positively participate in data element sharing. |
y | The probability of the common user nodes to participate in data element sharing. |
z | The probability of regulator nodes participating in data element sharing regulation. |
The Revenue Function (S) | Regulator (R) | Business Organization (B) | ||
---|---|---|---|---|
Positively Share (x) | Negatively Share (1 − x) | |||
Common User (U) | Share (y) | Regulate (z) | B: U: R: | B: U: R: |
Do Not Regulate (1 − z) | B: U: R: | B: U: R: | ||
Do Not share (1 − y) | Regulate (z) | B U:0 R: | B U:0 R: | |
Do Not Regulate (1 − z) | B U:0 R: | B U:0 R: |
Equilibrium Points | Eigenvalues | Stability Conditions | ||
---|---|---|---|---|
E1(0,0,0) | ||||
E2(0,0,1) | ||||
E3(0,1,0) | ||||
E4(0,1,1) | ||||
E5(1,0,0) | ||||
E6(1,0,1) | ||||
E7(1,1,0) | ||||
E8(1,1,1) |
ID | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 15 | 10 | 25 | 7 | 10 | 5 | 10 | 25 | 5 | 5 | 20 | 0.3 | 0.5 | 5 | 5 |
2 | 15 | 10 | 25 | 7 | 10 | 5 | 10 | 20 | 15 | 5 | 20 | 0.3 | 0.5 | 25 | 10 |
3 | 15 | 10 | 25 | 7 | 10 | 5 | 10 | 30 | 10 | 5 | 20 | 0.3 | 0.5 | 25 | 5 |
4 | 15 | 10 | 25 | 7 | 10 | 5 | 10 | 25 | 20 | 5 | 20 | 0.3 | 0.5 | 10 | 5 |
Indicator | Indicator Advantage | Alipour-Vaezi, M [62] | Wang, E. K [63] | Xu, Z. Q. [64] | Wang, JG [65] | This Article |
---|---|---|---|---|---|---|
Industry | Adapt to industry characteristics | Media | Intenet | Medical | Retail | Media |
Stability strategy | Strategy guidance | FALSE | TRUE | TRUE | TRUE | TRUE |
Based on media | Adapt to the characteristics of the media industry | TRUE | FALSE | FALSE | FALSE | TRUE |
Regulator node | Adapt to media industry standards | FALSE | FALSE | TRUE | TRUE | TRUE |
Data element requirement | Adaptive data circulation | TRUE | FALSE | FALSE | FALSE | TRUE |
Degree of node participation | Promoting model activity | FALSE | TRUE | FALSE | FALSE | TRUE |
Node credit | Guarantee node credit | FALSE | TRUE | FALSE | FALSE | TRUE |
Incentive strategy | Encourage nodes to participate actively | FALSE | TRUE | FALSE | TRUE | TRUE |
Penalty strategy | Maintain the balance of ecological values | FALSE | TRUE | TRUE | TRUE | TRUE |
Artificial intelligence-based | Model intelligence | TRUE | FALSE | FALSE | FALSE | FALSE |
Network attack defense | Ensure network security | FALSE | TRUE | FALSE | FALSE | FALSE |
Model verification | The way of verification | Analysis and Discussion | Simulation Evaluation | Numerical Simulation | Numerical Simulation | Numerical Simulation |
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Share and Cite
Hu, H.; Wang, Y.; Song, G.; Fan, W.; Liu, C. Data Element Sharing in Convergence Media Ecology Based on Evolutionary Game. Appl. Sci. 2023, 13, 10089. https://doi.org/10.3390/app131810089
Hu H, Wang Y, Song G, Fan W, Liu C. Data Element Sharing in Convergence Media Ecology Based on Evolutionary Game. Applied Sciences. 2023; 13(18):10089. https://doi.org/10.3390/app131810089
Chicago/Turabian StyleHu, Hongbin, Yongbin Wang, Guohui Song, Weijian Fan, and Chenming Liu. 2023. "Data Element Sharing in Convergence Media Ecology Based on Evolutionary Game" Applied Sciences 13, no. 18: 10089. https://doi.org/10.3390/app131810089
APA StyleHu, H., Wang, Y., Song, G., Fan, W., & Liu, C. (2023). Data Element Sharing in Convergence Media Ecology Based on Evolutionary Game. Applied Sciences, 13(18), 10089. https://doi.org/10.3390/app131810089