Research on Supply Chain Information Transparency Mechanism Based on Blockchain

In recent years, information technology has developed rapidly, and the emerging information technology represented by artificial intelligence, Internet of Things, cloud computing and big data has been widely studied and applied in people’s production as well as life, which has brought great changes to social life. Among them, the supply chain from the physical form to the operation mechanism has produced a great change, in the modern management system, the supply chain is based on the Internet technology, the transaction activities between the business subjects by the traditional business model into a network-based e-commerce transaction process, as well as the corresponding control and management of information flow, logistics, capital flow [1-4].

The main forms of emerging supply chains include supply chain e-commerce, intelligent supply chain, supply chain combined with Internet of Things, supply chain combined with big data, and supply chain combined with blockchain [5-8]. These emerging supply chain forms are still in the process of continuous development, and some supply chain manifestations are the mutual integration of a variety of emerging supply chains, which are embodied in the supply chain management of different fields and promote the improvement of supply chain management efficiency in different fields. Therefore, the current supply chain structure has changed a lot compared with the early supply chain structure, and the integration and coordination of the supply chain have been greatly improved [9-11].

The supply chain relies on computer network and takes e-commerce as its main business activities. In the modernized supply chain, in addition to the core enterprise, other supply chain business subjects in and out of the supply chain flexibility greatly improved, improve the transparency of information, sharing and supply chain of different business content business subjects optional, at the same time in the supply chain of each link of the business of the group decision-making also has a certain degree of application of the progress of research [12-14].

Compared with the traditional single chain structure of the supply chain, the modern supply network chain structure reflects the greater complexity, due to the sharing of network information and the virtual nature of the system structure, the supply chain structure also reflects the variability of the supply chain, the supply chain of each link of the business subject of the counterparty’s dynamic choice has become possible [15].

The development of information technology and network technology, supply chain informatization and network transactions promote the supply chain information transparency. Supply chain information transparency refers to a state in which each business subject or each business link in the supply chain can share the information of other business subjects or business links [16-17]. The application value and prospect of blockchain in the field of supply chain management have been generally recognized by academia and industry, and now it has also become an important field of blockchain application and research and development [18-20].

Nowadays, blockchain has been researched and applied in different industries such as finance, education, healthcare, Internet of Things, etc., and its application in the supply chain field is quite extensive [21]. Literature [22] describes to the use of blockchain for supply chain according to tracking to improve transparency. Blockchain acts as a decentralized database to provide security of information records [23]. Confidentiality of the supply chain is valued by market participants, and in the initial blockchain design, complete transparency and uncontrolled access created concerns for these participants [24]. However, this issue has improved significantly with improvements in blockchain. Literature [25] alone mentions that blockchain is safe and effective in the supply chain domain for visibility of their products. Literature [26] also cited to show that blockchain improves the source of supply chain, business process and its security.

Accurate supply chain information facilitates profitability and its information with misunderstanding leads to a crisis of trust between supply partners. Therefore, literature [27] investigated the blockchain technology augmentation for distribution information to be monitored so as to reduce the fault tolerance of transactions. The result of their study is evident that the application of blockchain improves supply chain information transparency. Literature [28] used blockchain in the food supply chain to ensure its transparency and traceability, guaranteeing a reliable source of food and facilitating tourism consumption and product dissemination. In addition, literature [29] used blockchain to address the sharing of product information in the supply chain to further resource integration and reorganization with transparent information, improving the learnability of the supply chain. Blockchain provides guarantees for the storage and access control of supply chain information, and literature [30] has realized the intelligent operation of maritime supply chain by constructing a supply chain system with the assistance of blockchain. In addition, literature [31] mentions that blockchain technology improves the efficiency of humanitarian relief supply chain and reduces the work of supply chain managers. Literature [32] also mentions that blockchain technology optimizes the supply chain transaction process and consensus co-management mechanism, reducing supply chain management time while improving quality. Similarly, literature [33] utilized blockchain to construct an information sharing mechanism to promote the increase of supply chain resilience and achieve efficient information replacement. There are many similar studies, but the research on supply chain information transparency mechanism in blockchain is still insufficient.

The article first focuses on relevant concepts of deductive games and provides the principles, characteristics, and applications of blockchain technology. It introduces the theory of replicating dynamic equations and the evolutionary stabilization strategy in evolutionary game theory. And then, based on analyzing and reviewing the information sharing strategy, the framework for the research model of this paper is constructed. Based on the existing theories, the corresponding assumptions are put forward to establish the game model of supply chain information sharing, to solve the game equilibrium point of information supply and demand sides, and the dynamic evolution process of information sharing strategy selection of game subjects in the supply chain. Then, numerical simulation and analysis experiments are carried out in combination with the evolutionary game model to analyze in detail the influence of changes in key factors on the choice of information sharing strategies of supply chain nodes under blockchain, and finally, corresponding information sharing mechanisms are proposed based on the experimental results.

2

Overview of Deductive Game Theory and Important Influences

2.1

Blockchain technology

2.1.1

Principles of Blockchain Technology

Blockchain is an underlying technology used to record transaction information. Blockchain technology is not a single technology, but rather a comprehensive technology system integrated by several core technologies such as consensus mechanism, cryptography, and distributed storage. It can be categorized into coalition chains, public chains, and private chains according to the nature of the participating objects [34]. Among them, the coalition chain applies to the virtual alliance constituted by members of many organizations, which needs to be operated and maintained by the members together, does not fully disclose the information, and the relevant important and sensitive information needs to be accessed by the members with authorization. Public chain means that the blockchain can be used and maintained by all people. Private chain applies to institutional organizations, the blockchain is managed by a small number of people within the organization, has the right to write to the blockchain, does not disclose the information to the public, and only grants the right to read to the internal members of the organization selectively.

2.1.2

Characteristics of blockchain technology

Blockchain technology is characterized by decentralization, common maintenance, tamper-proof, traceability, and intelligence. 1)

Decentralization

Blockchain is essentially a distributed database, so the data sent, verified, and stored on the blockchain are all based on a distributed system architecture, relying on algorithms and procedures to establish a trustworthy mechanism rather than a third-party institution. Decentralization is a typical characteristic of public chains, while coalition chains are usually partially decentralized, or have polycentric architecture.

2)

Common Maintenance

All participating nodes on a blockchain need to go to a common maintenance of the system in which they are located. Each node on the blockchain can perform maintenance on the blocks (data blocks), and the entire system depends on each node for its operation.

3)

Tamper-proof

Data recording and storage on the blockchain must be organized in a specific chronological order and encrypted using cryptographic principles, making it difficult to tamper with the data.

4)

Traceability

On the blockchain, each block is timestamped. The timestamp not only identifies each block’s unique identity, but it also enables them to be sequenced.

5)

Intelligence

Blockchain relies on the programmable attributes of smart contracts, allowing people to create and deploy relevant programs on the blockchain according to specific application scenarios, and achieve intelligent operation through smart contracts.

2.2

Evolutionary game theory

Evolutionary game theory is an applied theory that combines evolutionary theory and game theory. Evolutionary game theory and traditional game theory assume different game subjects. Evolutionary game theory is based on limited rationality for the research subject, while traditional game theory is based on complete rationality for the research subject. In the game process, the subject of limited rationality in order to obtain new knowledge and uninterrupted learning, and at the same time use the new knowledge to constantly adjust and improve their own strategies to seek better strategies [35]. Therefore, evolutionary game theory can be regarded as based on the limited rationalization of the subject’s thinking, combined with game theory and dynamic evolutionary processes, to find the final stable strategy. The fundamental concept of evolutionary game theory is to duplicate dynamic equations and evolutionary stable strategies.

2.2.1

Replicating the dynamic equations

Replication dynamics is the basic dynamics of evolutionary game theory, which can depict the behavior of individuals from “choosing strategy”, to “evolving”, to “choosing new strategy”, and then “Evolution”, and can accurately predict group behavior [36]. The basic principle is: in a group composed of limited rational individuals, the strategy with better-than-average results will be gradually adopted by more individuals, and thus the proportion of game parties adopting various strategies in the group will change. It is expressed by the differential equation as: 1 $F (x_{i}) = \frac{d x_{i}}{d t} = x_{i} [U (s_{i}, x) - U (x, x)], i = 1, 2, 3, \dots \dots, n$

Where S_i is the strategy chosen by the subject of the game i, x_i is the probability of the subject of the game choosing the strategy, $U (s_{i}, x)$ represents the expected return obtained by choosing the subject of the game i choosing the strategy S_i, and $U (x, x) = \sum_{i = 1}^{n} x_{i} U (s_{i}, x)$ represents the average return of all strategies of the subject of the game.

2.2.2

Evolutionary stabilization strategies

Evolutionary stabilization strategy is developed based on the theory of biological evolution. In the process of constantly repeating the game, non-fully rational individuals will change their own strategies to maximize their own interests through continuous learning, so that the system tends to the state of dynamic equilibrium, at this time, any individual will try their best to avoid changing their own strategies, because changing their own strategies has a negative impact on their own interests, and this state of the strategy is the evolutionary stable strategy.

By analyzing the replication dynamics equation, the evolutionary stability strategy of the game population can be determined. Make the replicated dynamic equation equal to 0, find all potential equilibrium solutions of the replicated dynamic equation, and then discuss the stability of their neighborhoods, only the “equilibrium state that is robust to small perturbations” can be called truly evolutionarily stable. All potential equilibrium solutions include pure-strategy equilibrium points E as well as mixed-strategy equilibrium points E*, in asymmetric evolutionary games the asymptotically stable equilibrium must be a strict Nash equilibrium, and the evolutionary stable strategy (ESS) of the evolutionary game must be a pure-strategy Nash equilibrium, so that the mixed-strategy equilibrium point E* must not be an ESS in asymmetric evolutionary games [37].

2.3

Significant influencing factors

In the supply chain, when exchanging information, whichever party has more and more accurate information will have stronger competitiveness. In the new organizational model, there are many factors that will impact the more transparent and secure sharing of information. This section adopts the interview method, domestic and international literature combing and other methods, and combines them with the characteristics of the development of information sharing in the new organizational model of the supply chain, and ultimately obtains the influencing factors that can promote the information sharing among project members in the new organizational model.

Information-sharing costs C_ij: The establishment of this new form of organization requires a certain amount of human resources, technology and financial support, and information-sharing costs are an investment that cannot be ignored in this process. Under the new form of organization, the introduction of high-quality human resources plays a pivotal role in the adjustment of organizational structure and the restructuring of enterprises. Efficient information sharing requires high-level technological support, which includes the acquisition and updating of information sharing systems. In this new form of organization, information providers have to invest more human and material resources to obtain more accurate and timely information. Therefore, the level of information sharing will be profoundly affected by the magnitude of the cost of information sharing under the new organizational approach.

Two-way cooperation affinity α: In the new organizational approach, cooperation affinity between related units also has an impact on information sharing between related units. The current level of trust between companies has a positive impact on information sharing. In the collaboration between related units, mutual benefits and a win-win situation are realized, which is conducive to the exchange of information between the two sides.

Subsidies and penalties incentives from relevant authorities θ: In the new organizational approach, relevant policies and penalties incentives will also affect information sharing among members in the supply chain, and the relevant authorities are now working to further strengthen subsidies in the supply chain by means of BIM and cloud platforms. The operator’s side also creates a valuable reward and penalty incentive system for information flow.

Risk Factor γ: In the new form of organization, information sharing can be both advantageous and disadvantageous to the project participants, who can lose their market advantage, reduce the net income from information sharing, and even experience unpredictable dangers when obtaining high returns. However, if there is no information sharing, then the asymmetry of information and non-safety factors may also cause project participants to make wrong decisions. In this case, how to evaluate the risk of sharing information will be directly related to the choice of partners in the new supply chain.

Information transfer coefficient Z_i: The information transfer coefficient of each node of the supply chain in the new organizational model also affects the degree of information sharing.

3

Construction of supply chain information sharing strategy model

3.1

Supply chain information sharing strategy model based on evolutionary game

3.1.1

Description of the information sharing strategy

The project’s implementation begins with the owner’s investment requirements, and the general contractor will communicate with the survey and design parties based on the owner’s needs. The designer completes the design scheme according to the survey data and relevant regulations and provides it to the construction team. After reviewing the design, the construction party will formulate the construction plan according to the design and operating conditions, coordinate the supply of materials, and organize the construction in a reasonable manner. After the construction begins, the supervisor enters the site and completes his supervisory duties. It can be seen that the supply chain uses project schedule information to facilitate cross-organizational collaboration between the main information enterprises.

The main body of supply chain information sharing to the general contractor as the core, which is responsible for the project’s progress, schedule, cost of the three major responsibilities, project implementation of the survey, design, construction, etc. are based on the general contractor’s progress plan. In order to rationally allocate resources, general contractors usually subcontract construction and design to specialized contractors. The vulnerability of supply chain cooperation makes general contractors and subcontractors maximize their own interests without considering the value-added issue of the supply chain. In order to maximize their own interests, general contractors may squeeze the interest space of lower-level subcontractors, and subcontractors may use unfair competition to ensure their own interests.

Subcontractors may use unfair competition means to ensure their own interests, which will have a great impact on the project. If you want to ensure the interests of both parties, you need to seek for the excess returns of the supply chain, and adequate information sharing will drive the value appreciation while promoting the cooperation of all parties in the supply chain. Supply chain information sharing behavior is a double-edged sword, part of the information provided by the main body of information is conducive to the nodes of the production and supply decision-making, but there is also the risk of core information leakage. Therefore, whether general contractors and subcontractors, as the main body of information, choose the “information sharing” strategy in the process of project implementation is not a simple choice, but a decision-making issue with obvious game characteristics.

3.1.2

Information Sharing Strategy Modeling

As the core node in the supply chain, the general contractor masters the progress information of the entire project and dominates the smooth operation of the supply chain. According to the definition of supply chain in the theoretical study of this paper, a secondary supply chain model is constructed with the general contractor and subcontractors cooperating with each other, and the two information subjects represent the two sides of the game. The set of strategies in the game process between the general contractor and the subcontractor can be expressed as {information sharing, information not sharing}. According to the principle of replicated dynamic equations, both of them will choose the strategy that is higher than the average return, so the strategy choice of the information subject will change with the change of the other party’s strategy. When the general contractor and the subcontractor go through a sufficient game, the system will eventually evolve into a stable state.

According to the evolutionary game theory, the following assumptions are made: 1)

The probability of the general contractor and subcontractor choosing information sharing is x and y respectively, and the payment matrix is shown in Table 1. The so-called payment matrix, which is essentially a representation of the benefits of both parties, consists of three major components: initial benefits, information sharing benefits, and information sharing costs.

2)

The spillover effect occurs when both the general contractor and the subcontractor engage in information sharing, i.e., the benefit of information sharing between the two parties is higher than the initial benefit, and the benefit of information sharing between the two parties is higher than the benefit of information sharing between one party.

3)

When only one party engages in information sharing, the gain of the information sharing party is lower than the initial gain due to the unilateral information exposure harming its own gain.

Table 1.

Payoff Matrix

Pay		Subcontractor
Pay		Sharing $(y)$	Nonsharing $(1 - y)$
General Contractor	Sharing $(x)$	E_a + λ_aθ_bI_b − C_a, E_b + λ_bθ_aI_a − C_b	E_a + A_a − C_aa, E_b + φ_bθ_aI_a
General Contractor	Nonsharing $(1 - x)$	E_a + φ_aθ_bI_b, E_b + A_b − C_bb	E_a, E_b

The related variables are described as follows: 1)

The initial benefits for the general contractor and the subcontractor without information sharing are E_a and E_b, respectively.

2)

The cost of information sharing for both the general contractor and the subcontractor is C_a and C_b respectively, and the cost of information sharing for one party is C_aa and C_bb respectively.

3)

The amount of information possessed by the general contractor and the subcontractor is I_a and I_b respectively, and the information sharing rate is θ_a and θ_b respectively; the general contractor and the subcontractor share information with a spillover effect, and the coefficients of the spillover are λ_a and λ_b respectively, so that the general contractor benefits from sharing information with the subcontractor and the subcontractor benefits from sharing information with the subcontractor are λ_aθ_bI_b and λ_bθ_aI_a respectively.

4)

The gain factors for general contractors and subcontractors are φ_a and φ_b, respectively, when only one party shares information. When only the general contractor shares information, although it cannot obtain benefits from the other party, because the contributed information helps the other party to optimize the decision and thus promote the supply chain to generate additional benefits, the general contractor obtains A_a benefits and the subcontractor obtains φ_bθ_aI_a benefits from the supply chain’s additional benefits. Correspondingly, subcontractor-only information sharing yields A_b and general contractor yields φ_aθ_bI_b.

5)

When both the general contractor and the subcontractor share information, the spillover effect makes the benefits of both parties higher than the initial benefits, i.e., λ_aθ_bI_b − C_a > 0, λ_bθ_aI_a − C_b > 0. And the benefits when both parties share information are higher than the benefits when one party shares information, i.e., λ_aθ_bI_b − C_a > φ_aθ_bI_b, λ_bθ_aI_a − C_b > φ_bθ_aI_a.

6)

When only one party engages in information sharing, the revenue of the firm that engages in information sharing is lower than the original revenue, i.e., when only the general contractor chooses to share information, i.e., A_a − C_aa < 0, and when only the subcontractor engages in information sharing, i.e., A_b − C_bb < 0.

3.1.3

Information Sharing Strategy Model Solution Analysis

1)

Information sharing strategy model solution

Based on the payment matrix in the previous section, the solution process for the general contractor and subcontractor as information subjects in the evolutionary game model is as follows:

The payoff when the general contractor carries out information sharing is: 2 $\begin{array}{rcl} U_{x} & = & y (E_{a} + λ_{a} θ_{b} I_{b} - C_{a}) + (1 - y) (E_{a} + A_{a} - C_{a a}) \\ = & E_{a} + y λ_{a} θ_{b} I_{b} + A_{a} - y A_{a} - y C_{a} + y C_{a a} - C_{a a} \end{array}$

The benefits when the general contractor does not engage in information sharing are: 3 $\begin{array}{rcl} U_{1 - x} & = & y (E_{a} + φ_{a} θ_{b} I_{b}) + (1 - y) E_{a} \\ = & E_{a} + y φ_{a} θ_{b} I_{b} \end{array}$

The average return to the general contractor is then: 4 $\begin{array}{rcl} \bar{U_{x}} & = & x U_{x} + (1 - x) U_{1 - x} \\ = & x (E_{a} + y λ_{a} θ_{b} I_{b} + A_{a} - y A_{a} - y C_{a} + y C_{a a} - C_{a a}) \\ + & (1 - x) (E_{a} + y φ_{a} θ_{b} I_{b}) \end{array}$

Similarly, the average return to subcontractors is: 5 $\begin{array}{rcl} \bar{U_{y}} & = & y U_{y} + (1 - y) U_{1 - y} \\ = & y (E_{b} + x λ_{b} θ_{a} I_{a} + A_{b} - x A_{b} - x C_{b} + x C_{b b} - C_{b b}) \\ + & (1 - y) (E_{b} + x φ_{b} θ_{a} I_{a}) \end{array}$

According to the principle of replication dynamics, it can be seen that both parties to the game can improve their own strategy choices through continuous imitation, comparison, and learning, which will eventually make each other converge to a certain stable strategy. Therefore, the replication dynamic equations for general contractors and subcontractors are solved as follows: 6 $\begin{array}{rcl} F (x) & = & \frac{d x}{d t} = x (U_{x} - \bar{U_{x}}) \\ = & x (1 - x) [y (λ_{a} θ_{b} I_{b} - φ_{a} θ_{b} I_{b} - A_{a} - C_{a} + C_{a a}) + A_{a} - C_{a a}] \end{array}$ 7 $\begin{array}{rcl} F (y) & = & \frac{d y}{d t} = y (U_{y} - \bar{U_{y}}) \\ = & y (1 - y) [x (λ_{b} θ_{a} I_{a} - φ_{b} θ_{a} I_{a} - A_{b} - C_{b} + C_{b b}) + A_{b} - C_{b b}] \end{array}$

The possible evolutionary stabilization points based on the principle of replicated dynamic equations are introduced as follows: O(0, 0), A(1, 0), B(0, 1), C(1, 1), $D (x^{*}, y^{*})$ . 8 $\begin{array}{l} x^{*} = \frac{C_{b b} - A_{b}}{λ_{b} θ_{a} I_{a} - φ_{b} θ_{a} I_{a} - A_{b} - C_{b} + C_{b b}} \\ y^{*} = \frac{C_{a a} - A_{a}}{λ_{a} θ_{b} I_{b} - φ_{a} θ_{b} I_{b} - A_{a} - C_{a} + C_{a a}} \end{array}$ 2)

Information sharing strategy model analysis

The neighborhood stability of the equilibrium point of the evolutionary game is analyzed based on the local stability of the Jacobi matrix, and the Jacobi matrix can be expressed as: 9 $J = (\begin{array}{l} \frac{\partial F (x)}{\partial x} & \frac{\partial F (x)}{\partial y} \\ \frac{\partial F (y)}{\partial x} & \frac{\partial F (y)}{\partial y} \end{array}) = (\begin{array}{l} a_{11} & a_{12} \\ a_{21} & a_{22} \end{array})$

Introduced from the replication dynamics equation: 10 $\frac{\partial F (x)}{\partial x} = (1 - 2 x) [y (λ_{a} θ_{b} I_{b} - φ_{a} θ_{b} I_{b} - A_{a} - C_{a} + C_{a a}) + A_{a} - C_{a a}]$ 11 $\frac{\partial F (x)}{\partial y} = x (1 - x) (λ_{a} θ_{b} I_{b} - φ_{a} θ_{b} I_{b} - A_{a} - C_{a} + C_{a a})$ 12 $\frac{\partial F (y)}{\partial y} = (1 - 2 y) [x (λ_{b} θ_{a} I_{a} - φ_{b} θ_{a} I_{a} - A_{b} - C_{b} + C_{b b}) + A_{b} - C_{b b}]$ 13 $\frac{\partial F (y)}{\partial x} = y (1 - y) (λ_{b} θ_{a} I_{a} - φ_{b} θ_{a} I_{a} - A_{b} - C_{b} + C_{b b})$

Then substituting it into the Jacobi matrix yields: 14 $\begin{array}{l} J = (\begin{array}{l} (1 - 2 x) [y (λ_{a} θ_{b} I_{b} - φ_{a} θ_{b} I_{b} - A_{a} - C_{a} + C_{a b}) + A_{a} - C_{a a}] \\ y (1 - y) (λ_{b} θ_{a} I_{a} - φ_{b} θ_{a} I_{a} - A_{b} - C_{b} + C_{b b}) \end{array} \\ \begin{array}{l} x (1 - x) (λ_{a} θ_{b} I_{b} - φ_{a} θ_{b} I_{b} - A_{a} - C_{a} + C_{a a}) \\ (1 - 2 y) [x (λ_{b} θ_{a} I_{a} - φ_{b} θ_{a} I_{a} - A_{b} - C_{b} + C_{b b}) + A_{b} - C_{b b}] \end{array}) \end{array}$

In addition, the evolutionary stabilization strategy (ESS) needs to satisfy the following inequality: 15 $\det J = | \begin{array}{l} a_{11} & a_{12} \\ a_{21} & a_{22} \end{array} | = a_{11} a_{22} - a_{12} a_{21} > 0$ 16 $t r J = a_{11} + a_{22} < 0$

According to the assumptions made in the previous section, the local stability analysis is shown in Table 2 when λ_aθ_bI_b − C_a > φ_aθ_bI_b, λ_bθ_aI_a − C_b > φ_bθ_aI_a, A_a − C_aa < 0, and A_b − C_bb < 0.

Table 2.

Stability analysis of equilibrium point

Equilibrium Point	det J	tr J	Stability
O(0, 0)	+	-	ESS
A(1, 0)	+	+	Instability
B(0, 1)	+	+	Instability
C(1, 1)	+	-	ESS
$D (x^{}, y^{})$	-	0	Saddle Point

The system evolution path is shown in Figure 1.

It can be seen that the evolutionary stable strategies (ESS) of the information subjects are point O(0,0) and point C(1,1), the stable strategy indicated by point O(0,0) is that neither the general contractor nor the subcontractor carries out the information sharing, and point C(1,1) indicates that both of them carry out the information sharing. The evolutionary game of supply chain information sharing is a long-term process, and which stable state is reached is limited by the starting state of the first game. If the first game point falls in the OADB region in the figure, the stable state of the game will inevitably converge to the point O(0,0), and the general contractor and subcontractor give up information sharing. If the first game point falls in the ADBC region in the figure, the stable state of the game will develop to point C(1,1), and both the general contractor and the subcontractor choose to share information.

Since the steady state of the general contractor and subcontractor’s strategies for information sharing may be either both sharing information or neither sharing information, this paper needs to design an incentive mechanism to promote the decision making of the general contractor and subcontractor towards both sharing information.

3.2

Analysis of dynamic evolution

According to Eq. It can be seen that there are four possible equilibria for both sides of the game, i.e. (0, 0), (1, 0), (0, 1), and (1, 1).

Express $(x^{*}, y^{*})$ where x^*, y^* ∈ [0, 1] by the following equation: 17 $x^{*} = (C_{B} + γ K_{B} - J_{B}) / (β Z_{B} K_{A} - Z_{B} K_{A} + θ K_{A} + α T)$ 18 $y^{*} = (C_{A} + γ K_{A} - J_{A}) / (β Z_{A} K_{B} - Z_{A} K_{B} + θ K_{B} + α T)$

There are several evolutionary scenarios: 1)

when C_A + γK_A − J_A > βZ_AK_B − Z_AK_B + θK_B + αT and C_B + γK_B − J_B > βZ_BK_A − Z_BK_A + θK_A + αT, at this time the benefits of both sides of the game are less than the cost of inputs, the strategy stability point at $(0, 0)$ Conclusion for the information (not shared, not shared), the dynamic evolution phase diagram (1) is shown in Figure 2:

2)

when C_A + γK_A − J_A > βZ_AK_B − Z_AK_B + θK_B + αT Fig. C_B + γK_B − J_B < βZ_BK_A − Z_BK_A + θK_A + αT, at this time the game party A benefits are less than the input cost, game party B benefits are greater than the input cost, the strategy stabilization point in the $(0, 1)$ Conclusion for the information (not shared, shared), the dynamic evolution phase diagram (2) is shown in Fig. 3:

3)

when C_A + γK_A − J_A < βZ_AK_B − Z_AK_B + θK_B + αT and C_B + γK_B − J_B > βZ_BK_A − Z_BK_A + θK_A + αT, at this time the game party A benefits are greater than the input cost, the game party B benefits are less than the input cost, the strategy stabilization point is at $(1, 0)$ Conclusion for the information (shared, not shared), the evolutionary phase diagram (3) is shown in Figure 4:

In the two cases shown in (2) and (3), according to the results of the game, it can be seen that the two sides of the game choose a single choice in whether or not to share the information strategy, i.e., one side shares and the other side does not share. In the beginning of the game, the game party chooses information sharing will get a larger benefit, along with the repetition of the game, the game party chooses information sharing will face the increase of risk and cost, which will have an impact on the choice of strategy of the two sides of the game. In this case, both sides of the game will finally choose to tend not to share, the final evolution of the stable strategy becomes $(0, 0)$ , the dynamic evolution phase diagram (4) shown in Figure 5: 4)

When C_A + γK_A − J_A < βZ_AK_B − Z_AK_B + θK_B + αT and C_B + γK_B − J_B < βZ_BK_A − Z_BK_A + θK_A + αT, at this time, the benefits of both sides of the game are greater than the cost of inputs, and there are two evolutionary stabilization points for both sides of the game, $(0, 0)$ and $(1, 1)$ , which are (shared, shared) and (unshared, unshared), respectively. The dynamic evolutionary phase diagram (5) is shown in Fig. 6:

On both sides of the folded line CDA, two outcome distributions: one above the line is the strategy stabilization point $(1, 1)$ i.e., the strategy choices of both sides of the game are (shared, shared). Below the line is the strategy stabilization point $(0, 0)$ , i.e., the strategy choices of both sides of the game are (not shared, not shared). Line CDA and line BDO intersect at point D. Point $D (x^{*}, y^{*})$ is the key point of the evolutionary game. The closer point D is to point O, the smaller the area S_ACOD of ACOD is, and the smaller the probability that both sides of the game choose $(0, 0)$ , i.e., choose (no sharing, no sharing), while the larger the area S_BCDA of BCDA is, the larger the probability that both sides of the game choose $(1, 1)$ . The change of the position of point $D (x^{*}, y^{*})$ depends on the relevant parameters of both sides of the game. Where the area of quadrilateral ACOD is: 19 $\begin{array}{rcl} S_{A C O O} & = & 1 - \frac{1}{2} (x^{*} + y^{*}) \\ = & 1 - \frac{1}{2} (\frac{M_{B} + g G_{B} - T_{B}}{β Z_{B} K_{A} - Z_{B} K_{A} + θ K_{A} + α T} \\ + & \frac{M_{A} + g G_{A} - T_{A}}{β Z_{A} K_{B} - Z_{A} K_{B} + θ K_{A} + α T}) \end{array}$

4

Simulation experiments and analysis

4.1

Numerical simulation of blockchain-based supply chain information sharing game

4.1.1

Numerical simulation design

In this section, the evolutionary game model is simulated and verified by MATLAB to specifically analyze the strategy choice of supply chains in introducing blockchain technology for information sharing. From the constructed replication dynamic equations, it can be seen that the parameters affecting the evolutionary game path of blockchain-based supply chain related subjects for information sharing are information integration coefficient, information complementarity coefficient, cost of blockchain technology application, risk coefficient of loss of market advantage, and incentive coefficient. Since there is no unified index to quantify the parameter variables and there are no mature cases of blockchain application in supply chain information sharing, we can only assign values to the relevant parameters in this paper by reviewing the relevant literature, and use the numerical simulation method to explore the sensitivity and the degree of influence of the change of each factor on the information sharing selection strategy of each node of the supply chain under the blockchain, based on which, we put forward a proposal on the introduction of blockchain Based on this, corresponding countermeasures are proposed for the introduction of blockchain technology to realize positive information sharing among nodes. Participating nodes in the supply chain will be more inclined to choose a positive information sharing strategy. The initial values of the simulation parameters are set as shown in Table 3, so that the values of other parameters remain unchanged, and the numerical sizes of the information integration coefficient, the information complementarity coefficient, the level of blockchain information sharing application, and the coefficient of the risk of loss of market advantage are changed respectively, to explore how the evolution process and the results change with the change of the values of the different parameters, and the simulation of the change of the information integration and information complementarity coefficients are shown in Figure 7 and Figure 8. The simulation of the change of blockchain technology application cost is shown in Figure 9. The simulation of the change of the market advantage loss risk coefficient is shown in Figure 10.

Table 3.

The initial value setting of the parameter

Parameter	Initial value	Parameter	Initial value
E_n	1	θ	0.7
$q_{n n^{'}}^{c}$	0.9	δ^c	1
$q_{n n^{'}}^{p}$	0.2	δ^p	0.8
α	0.5	η	5
ρ	2	μ	0.6

The initial proportion P of supply chain participant nodes choosing positive information sharing will change due to different incentive coefficients θ. From the analysis of the three different sharing strategies, it is clear that sharing strategy 1 will not make the supply chain participant nodes tend to choose the positive information sharing strategy, so the parameter assignments are made and numerical simulations are carried out to verify this under two scenarios of sharing strategy 2 and sharing strategy 3. The initial values of the fixed parameters for the two sharing strategies are shown in Table 4 and Table 5, respectively, and the values of the test parameters are shown in Table 6 and Table 7, respectively, and the simulation of the change in the incentive coefficients under strategy 2 and strategy 3 is shown in Figure 11 and Figure 12.

Table 4.

Strategy 4 parameter initialization

Parameter	Initial value	Parameter	Initial value
E_n	1	δ^c	1.4
$q_{n n^{'}}^{c}$	0.7	δ^p	1
$q_{n n^{'}}^{p}$	0.4	η	6
α	0.5	μ	0.6
ρ	2

Table 5.

Strategy 3 parameter initialization

Parameter	Initial value	Parameter	Initial value
E_n	1	δ^c	1.3
$q_{n n^{'}}^{c}$	0.8	δ^p	1
$q_{n n^{'}}^{p}$	0.2	η	4
α	0.5	μ	0.4
ρ	4

Table 6.

Strategy 2 test parameter numerical setting

Parameter	Test 1	Test 2	Test 3	Test 4	Test 5	Test 6
θ	1.3	1.3	1.3	0.9	0.9	0.9
P	0.4	0.6	0.8	0.4	0.6	0.8

Table 7.

Strategy 3 test parameter numerical setting

Parameter	Test 1	Test 2	Test 3	Test 4	Test 5	Test 6
θ	-1.5	-1.5	-1.5	-1	-1	-1
P	0.4	0.6	0.8	0.4	0.6	0.8

4.1.2

Analysis of results

As can be seen from Fig. 7, under the condition of other parameters being unchanged, the number of games between nodes in the supply chain group is increasing, the larger the value of En is, the faster the evolution curve tends to 1, and the larger the proportion of nodes in the supply chain group participating in positive information sharing under the blockchain, so it can be seen that improving the information integration coefficient is conducive to enhancing the willingness and enthusiasm of the supply chain nodes to participate in information sharing. Similarly, it can be seen from Figure 8 that, under the condition of other parameters remaining unchanged, the number of games between nodes in the supply chain group is increasing, the value of α becomes larger, the speed of the evolution process curve tending to 1 becomes faster, and the proportion of nodes in the supply chain group choosing positive information sharing becomes larger, which can be seen that, the higher the coefficient of information complementarity is more conducive to the enhancement of the enterprise as well as its cooperation with the departments, companies and other nodes’ willingness and enthusiasm for information sharing. Positivity.

As can be seen from Figure 9, under the condition that other parameters remain unchanged, with the increase of the number of games, the larger the value of δ_c, the slower the evolution curve tends to 1, and the smaller the proportion of nodes in the supply chain group choosing positive information sharing strategy, it can be seen that the high cost of blockchain information sharing application has weakened the willingness and enthusiasm of the enterprise, as well as the nodes of the departments and companies cooperating with it in information sharing. Similarly, as can be seen from Figure 10, with other parameters unchanged, as the number of games increases, the speed of the evolution curve converging to 1 slows down with the increase, and the proportion of nodes in the supply chain group choosing positive information sharing strategy is smaller, so it can be seen that the higher the risk coefficient of the loss of market advantage weakens the willingness and motivation of the nodes in the supply chain parties to share information.

As can be seen from Fig. 11 and Fig. 12, when the incentive coefficient satisfies the conditions of sharing strategy 2, no matter what the initial proportion of nodes in the supply chain group choosing positive information sharing is, the final information sharing evolution curve will converge to 1. That is to say, at this time, the blockchain network platform only needs to provide the nodes with reasonable incentive rewards, and nodes of the enterprise, the iron sector, the company, etc., will be inclined to introduce blockchain technology for positive information sharing, to a certain extent, to achieve the state of information synergy. According to the sharing strategy 3, it can be seen that the critical point ΔP of the proportion of nodes choosing positive information sharing strategy varies with the incentive coefficient θ. When θ = −0.5, ΔP ≈ 0.35, and when θ = −1, ΔP ≈ 0.58, the initial proportion of nodes in the supply chain group choosing positive information sharing strategy P, if greater than the corresponding critical point ΔP, the evolution process curve will eventually converge to 1, and less than the corresponding critical point ΔP, the evolution process curve will eventually converge to 0. At this time, the information sharing decision of participating nodes in the supply chain group is affected by both the proportion P of nodes that initially choose positive information sharing strategy and the incentive coefficient θ. Comparing the different initial node proportions in Fig. 12, it can be seen that the larger the initial node proportion P in the supply chain group that chooses positive information sharing strategy under the blockchain is, the faster the curve of the evolution process tends to 1. Therefore, if the initial proportion of participating nodes choosing positive information sharing strategy in the game process is larger than its critical point, the blockchain network platform can charge the incentive cost of information sharing within a certain range, so that, on the one hand, the blockchain network platform can obtain the corresponding benefits, and on the other hand, it improves the motivation of the participating nodes of the supply chain to share information under the blockchain as well as the benefit of information sharing.

It can be seen that the information integration coefficient and information complementarity coefficient are positively correlated with the supply chain nodes’ strategy of choosing to introduce blockchain for positive information sharing, i.e., the greater the information integration gain and information sharing gain, the more conducive to the realization of information synergies among the supply chain participating nodes. Blockchain technology application cost, market advantage loss risk coefficient is negatively correlated with supply chain nodes’ strategy of choosing to introduce blockchain for positive information sharing, i.e., the higher the cost of blockchain technology application, the more unfavorable it is to the formation of a new mode of supply chain information sharing as well as the development of information synergy. The higher the loss of market advantage, the less favorable it is for information sharing among supply chain nodes. Dynamic adjustment of incentive coefficients according to the information sharing strategy of supply chain nodes can incentivize more participating nodes to shift from negative to positive information sharing after the introduction of blockchain technology, and enable blockchain information sharing platforms and enterprises, as well as cooperating enterprises, departments, companies, and other parties to benefit from information sharing.

4.2

Numerical Simulation of Evolutionary Games

In this section, Matlab will be used to study the effect of variable changes on the evolutionary process and outcome of the game system in Case 1, with the initial values of the variables shown in Table 8. 1)

Simulation analysis considering information sharing cost C_ij

Changing the value of the information sharing amount q while keeping other variables constant for the set initial values, the dynamic evolution phase diagrams at C_1j = 15, C_2j = 10 and C_1j = 20, C_2j = 16, respectively, and at C_1j = 15, C_2j = 10 and C_1j = 20, C_2j = 16 are shown in Fig. 13. It can be obtained that, when other variables remain unchanged, increasing the value of C_ij, the initial point of convergence to the point (1,1) increases, then the evolution of the game between subject 1 and subject 2 is the greater the probability of “information sharing”, the greater the amount of effective information provided by the members of the supply chain, and then the greater the benefits it reaps. When the value of C_ij increases, x^* and y^* decrease, and the closer the saddle point $D (x^{*}, y^{*})$ is to the origin O(0, 0), the larger the area of ADBC region is. Therefore, increasing the amount of information sharing will enhance the cost of information sharing among supply chain members.

2)

Simulation analysis of two-way cooperation affinity α

The dynamic evolution phase diagrams at a₁ = 0.6, a₂ = 0.4 and a₁ = 1.1, a₂ = 0.7 are obtained by changing the value of information absorption capacity a_i while keeping other variables constant for the set initial values, and the dynamic evolution phase diagrams at a₁ = 0.6, a₂ = 0.4 and a₁ = 1.1, a₂ = 0.7 are shown in Fig. 14, respectively. According to the figure, when other variables are kept constant, the larger the value of a_i is, the more initial points converge to the point (1,1), and then the probability of “information sharing” between subject 1 and subject 2 is larger. As the value of a_i becomes larger and larger, the denominators of x^* and y^* will also increase, and the saddle point $D (x^{*}, y^{*})$ will move in the direction of the origin O(0, 0), and the ADBC region located in the upper right will gradually increase. The information absorption ability of each member of the supply chain is directly related to the benefits gained by the enterprise in the process of information sharing, however, due to the varying level of information quality of each member of the supply chain, which affects their trade-offs in the strategy of “information sharing”. In the current market environment of information explosion, each member of the supply chain is under the high pressure of information, in order to make full use of the information absorption function and improve the information acquisition efficiency of enterprises, it is necessary to strengthen the information technology transformation of each node enterprise in the supply chain and improve the information quality of enterprises.

3)

Simulation analysis of subsidies and penalties from the relevant authorities for incentive factor θ

The dynamic evolution phase diagrams at θ =1.1 and θ =1.9 are obtained by changing the value of the degree of synergy μ while ensuring that the other variables are the same as the set initial values, respectively. The dynamic evolution phase diagrams at θ =1.1 and θ =1.9 are shown in Fig. 15. It can be obtained that, when other variables are kept constant, the larger the value of θ is, the more initial points converge to point $(1, 1)$ , then the probability of “information sharing” between subject 1 and subject 2 is greater. As the value of θ becomes larger and larger, the denominators of x* and y* will also increase, and the saddle point $D (x *, y *)$ will move in the direction of the origin $O (0, 0)$ , and the region of ADBC, which is located in the upper right, will gradually increase. The establishment of incentive mechanism is a direct way to reduce the costs and risks invested by each node enterprise in the supply chain in information sharing activities, and the appropriate increase of incentives can promote the information exchange between each node enterprise in the supply chain. Similarly, a reasonable penalty mechanism is needed to maintain a stable cooperative relationship. To guarantee fairness in the supply chain, maintain mutual trust between member enterprises, and increase the willingness of each member to choose information sharing strategies, certain speculators must be punished accordingly.

4)

Simulation analysis of risk factor γ

The dynamic evolution phase diagrams at γ =2 and γ =2.2 are obtained by changing the value of the risk factor γ while ensuring that the other variables are constant at the set initial values, respectively. The dynamic evolution phase diagrams at γ =2 and γ =2.2 are shown in Fig. 16. It can be obtained that, when other variables remain unchanged, the larger the value of γ, the final convergence to the point (1,1) at the initial point increases, then subject 1 and subject 2 game evolution results in “information sharing” probability is greater. As the value of γ becomes larger and larger, the numerator of x^* and y^* will decrease, the saddle point $D (x^{*}, y^{*})$ will move in the direction of the origin O(0, 0), and the ADBC region located in the upper right will gradually increase.

5)

Simulation analysis of information transfer coefficient Z_i

The dynamic evolution phase diagrams at Z_n=1.5 and Z_n=1.9 are obtained by varying the value of the information transfer coefficient Z_i while ensuring that the other variables are constant at the set initial values, respectively. The dynamic evolution phase diagrams at n=1.5 and n=1.9 are shown in Fig. 17. It can be obtained that, when other variables remain unchanged, the larger the value of n is, the more initial points converge to the point (1, 1), and then the probability of “information sharing” is greater in the evolution of subject 1 and subject 2’s game. As the value of n becomes larger and larger, the denominators of x^* and y^* will also increase, and the saddle point $D (x^{*}, y^{*})$ will move in the direction of the origin O(0, 0), and the ADBC region located in the upper right will gradually increase.

Table 8.

Variable initial value

Variable	q₁	q₂	α₁	α₂	μ	m	n	θ	C₁	C₂
Initial Value	18	12	0.5	0.3	1.2	3	1.8	2	10	9

5

Blockchain-based supply chain information-sharing mechanisms

5.1

Blockchain-based approach to supply chain information operation

5.1.1

The Idea of Blockchain Application to Realize Supply Chain Information Sharing

Blockchain, as a distributed ledger technology, effectively achieves the openness and transparency of information. Since the blockchain system can effectively integrate the supply chain business subjects into one system and supervise each other, and share the benefits, the application of blockchain technology can promote the coordinated management of supply chain business subjects on the content of different businesses, realize the sharing of supply chain information by business subjects, and prevent supply chain related information from being from being maliciously changed and destroyed, and provide technical guarantee for the scientific nature of the selection and decision-making of supply chain business subjects.

5.1.2

Blockchain-based supply chain architecture for manufacturing companies

Blockchain uses P2P network broadcasting technology, and at the same time requires the network to have a certain amount of computing power and storage capacity, and the consensus mechanism requires that there are enough authentication nodes in the network, which are the hardware environment required for the application of blockchain technology. In the supply network chain with production enterprises as the core, each business subject is the enterprise group, business group or customer group in the corresponding network system, and the computer equipment of these subjects reaches a certain scale, which can provide high information computing and storage capacity through interconnection. The blockchain-based supply network chain of production enterprises relies on industry-specific networks or the Internet to build, and at the same time requires that only the business subjects of the supply network chain be authorized to join the blockchain system, so as to avoid the interference of users unrelated to this system on the operation of the system, and to reduce the risk of malicious users jointly falsifying supply chain information.

5.1.3

Blockchain-based hierarchical model of supply chain information flow

In the network environment, the proposal and final formation of the business content of the supply chain involves the operation of different hardware and software of the network system, and the blockchain operation involves the different levels of the computer network system, so the operation process of the supply chain based on the blockchain can be described by the hierarchical model of the supply chain’s information flow in its underlying network. The model represents the blockchain system operation process of demand-side→supply-side demand information transmission of a certain business content of the supply chain, and the blockchain system operation process of supply-side→demand-side supply information transmission corresponding to this business content, that is, the transmission and processing process of the demand information and the transmission and processing process of the supply information are similar, only that the sender and the receiver of the information are each other corresponding to the other party.

5.1.4

Blockchain-based methods for proving supply chain information

In the blockchain-based supply chain, all supply and demand data of the supply chain can be retrieved in the blockchain, which ensures the immutability of the information of the whole operation process of the supply chain, and helps to trace the quality of the products in the supply chain. Once a certain link in the supply chain is found to have a problem with the quality of the product, the information on the production and processing of the product can be retrieved through the blockchain in the reverse direction, so as to find out the root cause of the problem generated by the product and Once a product quality problem is found in a link of the supply chain, the information of the production and processing of the product can be retrieved in reverse through the blockchain, so that the root cause of the problem can be found and the responsibility can be traced.

5.1.5

Blockchain-based supply chain smart contracts

The blockchain-based supply chain smart contract is to replace manual calculation to realize the supply, demand and production decision-making of supply chain materials based on the existing supply or demand capacity of supply chain business entities, and provide complete decision-making solutions. Instead of manual calculation, a blockchain-based supply chain smart contract can complete the selection of supply sides in each business link and provide analysis results. In addition, the blockchain-based supply chain smart contract should enable traceability of supply chain information and automatic collection of information.

5.2

Block Recording Methodology for Information on Supply Chain Internal and External Data Sources

5.2.1

Structure and Analysis of Multi-source Data Composition within and outside the Supply Chain

Supply chain business decision-making activities include supply-side selection, business activity decision-making, and business activity tracing, etc. These business decision-making activities will generate internal data of the supply chain system, but the data on which supply chain business decision-making activities are based include not only internal supply chain data generated by the operation of the system, but also external data of the supply chain system introduced from outside the system. Internal supply chain business entities have the authority to use blockchain-based supply chain system data, while external supply chain businesses only have the authority to provide data for the blockchain system, but not the authority to access blockchain data.

5.2.2

Methods for recording blocks of data information from multiple sources

This paper proposes a method of block summary recording of external multi-source data in blockchain, i.e., when the demand side needs to know the external multi-source data information related to the supply side’s transaction in the transaction process, it will put forward a request for analyzing the information related to the supply side to the external multi-source data analysis center of the supply chain, and the certified subjects of the supply chain management system will transmit the information collection demand to the external multi-source data analysis center in accordance with the established rules. The multi-source data analysis center will determine the data set for data collection and processing according to the information collection requirements, and finally return the relevant result information to each certified subject of the supply chain, and the certified subject that obtains the bookkeeping right will broadcast the relevant result information of the supply side in the blockchain system after obtaining the result information of the supply side, and the other certified subjects of the blockchain system will authenticate the broadcasted information and carry out distributed bookkeeping.

5.3

Supply Chain System Internal and External Information Block Link Generation Methods

Based on this, in order to facilitate the management and retrieval of supply chain management related data, this paper proposes to construct the virtual link relationship between the basic data with mapping relationship with this block in the directory layer of each block of the blockchain, and at the same time, for the correlation relationship between the data with mapping relationship with different blocks, the corresponding virtual link relationship is also constructed between the directory layer of the corresponding block, because the data in the blockchain is connected by hash function pointers to each other, so it is possible to quickly find the related data with the blockchain through the hash function pointers and the directory layer and the virtual link between them. As the data in the blockchain is connected by hash function pointers, the relevant basic data mapped to the blockchain can be found quickly through the virtual links between the hash function pointers and the directory layer in the block. The structure formed by the corresponding virtual links is the link network of the basic data, so that even if the storage body of the categorized basic data has set the access rights to the data, it can still retrieve the structure and storage location of the basic data through the blockchain, and at the same time, because of the directory layer of the information is stored in the block, it ensures the immutability of the directory.

5.4

Blockchain-based supply chain information storage and access methods

In view of the confidentiality requirements of the transaction data of the supply chain management content and other relevant data of the transaction subject, this paper proposes that the supply chain business transaction certification entity certifies the authenticity, reliability and legitimacy of the transaction data and other relevant data of the transaction subject, and can store the basic data related to the business activities corresponding to the blockchain, but does not have the right to independently store the basic data of the supply chain business activities, and the basic data of the supply chain business activities will be stored in the corresponding storage body of the “front-end cloud platform” of the cloud environment after being generated. When the data capacity of the basic data of different classifications will be automatically transferred from the data storage body to the corresponding storage body of the “back-end cloud platform” according to the generation time series, and the basic data of different classifications transferred to the “back-end cloud platform” will maintain the data structure of the original data in the “back-end cloud platform”, or the original data structure will be removed and stored in other data structure forms, and the basic data of different classifications of the “front-end cloud platform” will be established and transferred to the “back-end cloud platform” The virtual association link relationship and data summary of the underlying data corresponding to different classifications. The basic data of the different classifications of “front-end cloud platform” and “back-end cloud platform” are stored in a distributed manner.

6

Conclusion

The blockchain-based supply chain information sharing mechanism is analyzed through evolutionary game-related theories and simulation experiments in this paper, and a corresponding coordination method is designed accordingly. Through research, this paper is able to obtain the following conclusions:

In the simulation experiment of incentive coefficient change under strategy 3 (supply chain participating nodes tend to choose positive information sharing strategy). When θ = -0.5 strategy, ΔP ≈ 0.35, when θ = -1, ΔP ≈ 0.58, the initial proportion P of nodes in the supply chain group choosing the positive information sharing strategy will eventually converge to 1 if it is greater than the corresponding critical point ΔP, and will eventually converge to 0 if it is smaller than the corresponding critical point ΔP. Overall, the coefficient of information integration, the coefficient of information complementarity, and the nodes in the supply chain node choosing the strategy of introducing blockchain for active information sharing is positively correlated. The cost of blockchain technology application and risk of market advantage loss are negatively correlated with the supply chain node’s strategy of introducing blockchain for positive information sharing. Therefore, the incentive coefficients should be dynamically adjusted according to the information sharing strategy of supply chain nodes, and enable blockchain information sharing platforms and enterprises as well as cooperative enterprises, departments, companies and other parties to benefit from information sharing.

This paper proposes a blockchain-based supply chain information sharing mechanism based on the results of simulation experiments, in which the supplier group, production enterprises, and seller group will act as independent authentication subjects and jointly form a “multi-certification center”. A hierarchical model of supply chain information flow based on blockchain has been constructed. It is proposed to store the basic data of blockchain in “front-end cloud platform” and “back-end cloud platform” in a hierarchical manner and implement access control. Through the construction of a blockchain-based supply chain information sharing mechanism, the dynamic ability of the supply chain is enhanced.

In summary, the research through this paper will promote the application of blockchain in the supply chain information transparency mechanism, and will also promote the development of blockchain application research in other fields, which has a certain degree of foresight and positive social value.

Idioma:: Inglés

Calendario de la edición:: 1 veces al año
Temas de la revista:: Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros

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Research on Supply Chain Information Transparency Mechanism Based on Blockchain

Jianqiang Peng

Jianfang Guo

Publicado en línea: 21 mar 2025

Recibido: 28 oct 2024

Aceptado: 20 feb 2025

DOI: https://doi.org/10.2478/amns-2025-0574

Palabras claveBlockchain, Evolutionary game theory, Dynamic evolution, Information transparency

© 2025 Jianqiang Peng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
Blockchain, Evolutionary game theory, Dynamic evolution, Information transparency