# Simulation of Subject Coordination for a Smart Campus Based on Complex Network and Evolutionary Game Theory

Authors: Shaoyong Guo; Ting Pang
Journal: Journal of Function Spaces
 (2022)

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## Abstract

The construction of smart campuses can help realize the modernization of higher education. However, the subjects and mechanisms are easily ignored in the construction of smart campuses in colleges and universities. The purpose of this paper is to construct an evolutionary game model based on the complex network structure characteristics of smart campus subjects and the dynamic relationship of collaborative games. Taking the scale-free network as the carrier, the decision-making behavior among subjects is explained. Then, the importance of subject coordination and the effectiveness of the mechanisms of leadership organization, special funds, assessment rewards, and punishments are analyzed through the simulation platform, i.e., Simpy. The results show that (1) moderate subject collaboration helps to avoid the phenomenon ofgetting something for nothing, (2) appropriate leadership and organization mechanisms can promote the construction process, (3) special fund mechanisms can ensure sustainable development, and (4) assessment reward and punishment mechanisms can increase the popularity of achievements. The proposed method enriches relevant theories of smart campus research and provides a reference for decision-making in smart campus construction.


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## Body

## 1. Introduction

With the continuous intelligence of network information technology and the more diversified needs of teachers and students, universities have vigorously advocated for wisdom education, and information construction has entered the smart campus stage. The COVID-19 epidemic has made the development of smart campuses perform a “bend overtake,” and it is becoming more urgent for smart campuses to reach a high level with high quality. A smart campus refers to the deep integration of scattered information systems and educational resources on a university campus using cloud computing, big data, artificial intelligence, the Internet of Things, mobile interconnections, and other new generations of network information technology to construct a comprehensive, perceptive, efficient, and convenient environment for teaching, scientific research, management, clothing, and living [1–3].The construction of a smart campus is a difficult task involving a wide range of areas, with heavy workloads and a long duration [4]. After years of research and practice, the smart campus has made a preliminary development. However, current research ignores two important issues: the subjects and the mechanism [5, 6]. Most of the work of constructing smart campuses is undertaken by the network information department. For the sake of responding to current interests, IT (information technology) enterprises may not keep up with subsequent maintenance. In addition, the relevant subjects of universities are not fully involved, resulting in a weak construction force, difficult coordination of data resources, and poor user experience. Research on smart campuses has exposed the shortcomings of head-heavy and feet-light [7, 8]. At the beginning, construction focuses on the excavation of the technical level and neglects the importance of the construction mechanism, which enacts the construction and implementation of smart campuses without solid guarantees. In addition, many subjects are involved in the construction of smart campuses in universities, and they have a competitive and cooperative relationship, which has the structural characteristics of a complex network and the dynamic relationship of a collaborative game [9–11]. However, research from the perspective of a complex network evolutionary game is still lacking.To alleviate the above constraints, this paper studied the construction of smart campuses in high-level universities based on the theory of complex networks and evolutionary games. Taking the scale-free network as the carrier, the evolutionary game model of public goods was constructed to explain decision-making behavior among subjects. Then, the importance of subject coordination and the effectiveness of the mechanisms of leadership organization, special funds, assessment rewards, and punishments are analyzed through the simulation platform, i.e., Simpy.This work enriches the relevant theories of smart campus research and provides suggestions and references for the construction of smart campuses in universities. The contributions of this work are fourfold. (1) Moderate subject collaboration helps to prevent the phenomenon of getting something for nothing. (2) Appropriate leadership and organizational mechanisms can promote the construction process. (3) Special fund mechanisms can ensure sustainable development. (4) Assessment reward and punishment mechanisms can increase the popularity of achievements.The structure of this paper is as follows. In Section2, we summarize and analyze related works on the construction of smart campuses. Our review covers three main aspects: theoretical connotations, strategic proposals, and technical methods. In Section 3, methods utilizing the theory of complex networks and evolutionary games are proposed. In Section 4, we describe the process and results of the simulation. In Section 5, the importance of subjects’ coordination and the effectiveness of the mechanism guarantees are discussed in detail. Finally, in Section 6, brief conclusions are provided.


## 2. Related Works

At present, the construction of smart campuses represents the direction of information development in universities, and it is also a trending issue among experts in related fields at home and abroad. It encompasses the following three main aspects.Theoretical connotations: Atif et al. studied the establishment of a smart campus environment to provide a ubiquitous learning mode [12]. Wang analyzed the wisdom concept, main characteristics, and design of the smart campus [13]. In discussing the essential elements and characteristics of smart campuses, Li highlighted existing problems and gaps [14]. Nasro et al. proposed the management evaluation and positioning of the existing infrastructure of higher education institutions based on the concept of the smart campus [15].Strategic proposals: Kang et al. expounded on the solutions of smart campuses, such as campus network construction, hardware infrastructure construction, security system construction, intelligent multimedia teaching application system construction, multimedia conference system construction, and software platform construction [16]. Chen carried out research from the aspects of organizational structure, follow-up capital investment, team construction, system perfection, and incentive policy and put forward corresponding suggestions for safeguard mechanisms [17]. Gan et al. elaborated the construction of smart campuses in foreign universities and focused on Chinese universities from seven dimensions: top-level design, construction vision, construction mode, smart teaching environment, smart teaching resources, smart campus management, and smart campus services [18]. Yan et al. pointed out that in the process of carrying out theoretical research on smart campuses, universities should always establish people-oriented concepts [19].Technical methods: Mar et al. designed a smart campus system including intelligent education, parking, and classroom using information technology [20]. Ikrissi et al. focused on several vulnerabilities and vulnerable attacks affecting the data and information security of smart campuses [21]. Luo et al. designed an overall architecture model of smart campuses in a big data environment. From bottom to top, the model has an infrastructure layer, data layer, application support layer, business application layer, and terminal display layer [22]. Yu et al. built smart campus private networks based on 5G edge-cloud fusion, sinking the services concentrated in the remote cloud center from core to edge [23].


## 3. Method

### 3.1. Basic Analysis

In this study, it is assumed that the subjects involved in the construction of smart university campuses include enterprises, network information departments (NIDs), business departments, faculties, and affiliated units (AU). Among them, the task of the enterprise is to provide comprehensive design and technology development services. The network information department coordinates all aspects of relations and leads the construction process. The business department is in charge of different matters and mastering different kinds of big data resources. These subjects need to communicate and connect. This study considers the party committee office (PCO), organization department (OD), publicity department (PD1), labor union (LU), youth league committee (YLC), principal’s office (PO), development planning department (DPD), personnel department (PD2), finance department (FD), science and technology department (STD), audit department (AD), asset management department (AMD), infrastructure department (ID), security department (SD1), logistics department (LD), retirement department (RD), student department (SD2), academic affairs department (AAD), enrollment and employment department (EED), library, and archives. Although there are a large number of departments and affiliated units, in most cases, they use the resources only after the completion of the smart campus, and each of them is regarded as a subject. Consequently, a total of 25 subjects were identified.A smart university campus is a kind of public good. First, the subject plays a very important role in the process of building a smart campus. We cannot ignore any party. Only by sharing resources and collaborative construction can we complete the task. Second, the products after completion are shared by each subject. For example, enterprises make profits and continue to provide updates and maintenance for deep-seated technology; the network information departments continue to lead the upgrading and construction of smart campus and basic technical maintenance services in the later stage; the business departments should constantly provide updated data and needs; colleges, departments, and affiliated units can enjoy the resources of a smart campus to carry out teaching, learning, scientific research, and life more conveniently. However, in the process of building smart campuses in universities, enterprises tend to pay attention to interests and neglect quality, the network information department lacks the leading force, the business department lacks a sense of ownership, and the departments and affiliated units are dependent. Therefore, there is both competition and cooperation between subjects, and there is a dynamic cooperative game relationship, which is also an evolutionary game. The game of public goods represents a typical study of cooperative behavior between multiple individuals [24]. Each individual has only two strategic choices, i.e., cooperation or noncooperation (betrayal), and there will be a gain coefficient to increase the gains of each collaborator and betrayer by a certain multiple. Therefore, cooperation is an altruistic behavior, and the betrayer can also obtain gains, so selfish individuals will betray, resulting in the phenomenon of hitchhiking or getting something for nothing. However, all individuals will not benefit if they choose not to cooperate, so there will be individual cooperation. Therefore, the game of public goods can not only improve the enthusiasm of individual cooperation but also inhibit the emergence of hitchhiking.At the same time, the subject of building a smart campus in universities and the cooperative game relationship between them can be regarded as a typical complex network. Nodes represent the subject, edges represent the relationship, and weights represent the strength of the actual connection [25]. In the process of building a smart campus in universities, enterprises are only in contact with the network information department, asset management department (bidding), etc., and departments and affiliated units have contact or communication only with individual business departments and network information departments. The business department contains most of the subjects, but there are only individual connections between them. The network information department is connected with all other subjects due to the coordination of relations in all aspects, which is in line with the characteristics of most nodes and few nodes are connected, and few nodes are connected with many nodes of the scale-free network. A scale-free network is a network with a power-law distribution, as opposed to a random network. A large number of real-world networks are scale-free networks [23].To sum up the experience of the construction of smart university campuses in recent years, the network information department alone is advocating for this change, but its ability and power are limited. The follow-up services of enterprises cannot be guaranteed, and the sense of ownership of relevant departments of the school is not strong. Hence, the effect and quality of smart campus construction are not ideal, and the construction process is slow. Even if there is investment and some achievements, the popularity of smart campus construction is not high. The latter leads to a waste of resources and school accountability, and the campus cannot be upgraded and updated, so construction falls into a bad cycle. To break the cycle, on the one hand, we should ensure sufficient subjective initiative and strengthen active cooperation between subjects. On the other hand, we should establish a sound mechanism, especially the strong guarantee of leadership organizations, the continuous support of special funds, and scientific and reasonable assessment, reward, and punishment. Next, it will be verified by model and simulation.


### 3.2. Model Construction

Based on the analysis presented in Section3.1, this paper uses the game of public goods to establish an evolutionary game model and takes the scale-free network as the complex network carrier to study the importance of subject coordination in the construction of smart university campuses [26] and the effectiveness of leading organizations, special funds, assessment, rewards, and punishments. Based on realistic considerations, the following assumptions are put forward for the construction of a complex network evolution game model of smart university campuses:H1: heterogeneous complex network. It has different types of nodes, and the impact on each node is different after completion.H2: weighted complex networks. Collaborative strength is represented by the weight of nodes.H3: the number of nodes in the network does not change.H4: each node becomes a game opponent only with its neighbors that have direct contact with it, and its income is only affected by the game opponent.H5: the game strategy of each node is only cooperation and noncooperation.H6: the input cost of each node per round of the game is set to 1.H7: nodes exhibit bounded rationality, and there is a possibility of choosing game strategy mistakes.


#### 3.2.1. The Complex Network

LetG=N,E,W represent the main network of smart campus construction in universities, where N represents the number of nodes, E represents the number of edges, and W represents the weight. The weighted degree of node i is
(1)kiω=kiωij,where ki is the degree of node i, node j is the neighbor of node i, and ωij0<ωij<1 is the weight, which represents the cooperative strength between nodes i and j.Section3.1 shows that N is 25. The complex network built on the smart university campus is quantified as a relationship matrix of 25×25, where the connection between nodes is determined according to the direct relationship between subjects. The complex network diagram is obtained by using NetworkX [27], as shown in Figure 1.Figure 1
Complex network diagram of smart campus construction for universities.


#### 3.2.2. Evolutionary Game

LetA denote the adoption of a cooperative strategy, and let the number of nodes be nA; then, the proportion of the nodes is as follows.
(2)xA=nAN.LetB denote the adoption of a noncooperative strategy, and the number of nodes is nB; then, the proportion of nodes is as follows.
(3)xB=nBN,where xA+xB=1. The symmetric game gains of node i1,2,⋯,i and neighbor node j1,2,⋯,j are shown in Table 1.Table 1
The matrix of gains for the symmetric game.
NodeiNodejABAabBcdThe lettersa, b, c, and d represent the benefit obtained by the first individual in the strategy pair A,A, A,B, B,A, and B,B, respectively. In the evolutionary game, each individual plays with its neighbors like the previous two games, and the sum of the income obtained is called the total income of the individual.In the first case, the average gains of nodei selecting A at time step t are
(4)fAi=∑m=0kiCkimxAm1−xAki−mma+ki−mb1+γαt2+βtkiω,where γ indicates the guarantee strength of the leadership organization mechanism, α indicates the guarantee strength of the special fund mechanism, and β indicates the guarantee strength of the evaluation, reward, and punishment mechanism.In the second case, the average gains of nodei selecting B at time step t are
(5)fBi=∑m=0kiCkimxBm1−xBki−mmc+ki−md1+γαt2+βtkiω.We adopt the synchronous learning game strategy, that is, each node carries out strategy learning at each time step [28]. At the same time, the game strategy of pairing comparison is used to update the rules; that is, node i randomly selects a neighbor node j to compare the gains, and there is probability pij of updating the strategy of node j.
(6)pij=11+exp−fj−fi/ε,where ε is a noise term that characterizes the decision-making error and bounded rationality of the subject [29]. When ε⟶0, the subject tends to make irrational decisions and decides to update to the neighbor strategy. When ε⟶∞, the subject cannot make rational decisions and randomly learn neighbor decisions. The meaning of probability pij is that when the income of node i is less than that of node j, the decision of node j is accepted with a great probability. In contrast, the decision of node i is accepted with a small probability.


## 3.1. Basic Analysis

In this study, it is assumed that the subjects involved in the construction of smart university campuses include enterprises, network information departments (NIDs), business departments, faculties, and affiliated units (AU). Among them, the task of the enterprise is to provide comprehensive design and technology development services. The network information department coordinates all aspects of relations and leads the construction process. The business department is in charge of different matters and mastering different kinds of big data resources. These subjects need to communicate and connect. This study considers the party committee office (PCO), organization department (OD), publicity department (PD1), labor union (LU), youth league committee (YLC), principal’s office (PO), development planning department (DPD), personnel department (PD2), finance department (FD), science and technology department (STD), audit department (AD), asset management department (AMD), infrastructure department (ID), security department (SD1), logistics department (LD), retirement department (RD), student department (SD2), academic affairs department (AAD), enrollment and employment department (EED), library, and archives. Although there are a large number of departments and affiliated units, in most cases, they use the resources only after the completion of the smart campus, and each of them is regarded as a subject. Consequently, a total of 25 subjects were identified.A smart university campus is a kind of public good. First, the subject plays a very important role in the process of building a smart campus. We cannot ignore any party. Only by sharing resources and collaborative construction can we complete the task. Second, the products after completion are shared by each subject. For example, enterprises make profits and continue to provide updates and maintenance for deep-seated technology; the network information departments continue to lead the upgrading and construction of smart campus and basic technical maintenance services in the later stage; the business departments should constantly provide updated data and needs; colleges, departments, and affiliated units can enjoy the resources of a smart campus to carry out teaching, learning, scientific research, and life more conveniently. However, in the process of building smart campuses in universities, enterprises tend to pay attention to interests and neglect quality, the network information department lacks the leading force, the business department lacks a sense of ownership, and the departments and affiliated units are dependent. Therefore, there is both competition and cooperation between subjects, and there is a dynamic cooperative game relationship, which is also an evolutionary game. The game of public goods represents a typical study of cooperative behavior between multiple individuals [24]. Each individual has only two strategic choices, i.e., cooperation or noncooperation (betrayal), and there will be a gain coefficient to increase the gains of each collaborator and betrayer by a certain multiple. Therefore, cooperation is an altruistic behavior, and the betrayer can also obtain gains, so selfish individuals will betray, resulting in the phenomenon of hitchhiking or getting something for nothing. However, all individuals will not benefit if they choose not to cooperate, so there will be individual cooperation. Therefore, the game of public goods can not only improve the enthusiasm of individual cooperation but also inhibit the emergence of hitchhiking.At the same time, the subject of building a smart campus in universities and the cooperative game relationship between them can be regarded as a typical complex network. Nodes represent the subject, edges represent the relationship, and weights represent the strength of the actual connection [25]. In the process of building a smart campus in universities, enterprises are only in contact with the network information department, asset management department (bidding), etc., and departments and affiliated units have contact or communication only with individual business departments and network information departments. The business department contains most of the subjects, but there are only individual connections between them. The network information department is connected with all other subjects due to the coordination of relations in all aspects, which is in line with the characteristics of most nodes and few nodes are connected, and few nodes are connected with many nodes of the scale-free network. A scale-free network is a network with a power-law distribution, as opposed to a random network. A large number of real-world networks are scale-free networks [23].To sum up the experience of the construction of smart university campuses in recent years, the network information department alone is advocating for this change, but its ability and power are limited. The follow-up services of enterprises cannot be guaranteed, and the sense of ownership of relevant departments of the school is not strong. Hence, the effect and quality of smart campus construction are not ideal, and the construction process is slow. Even if there is investment and some achievements, the popularity of smart campus construction is not high. The latter leads to a waste of resources and school accountability, and the campus cannot be upgraded and updated, so construction falls into a bad cycle. To break the cycle, on the one hand, we should ensure sufficient subjective initiative and strengthen active cooperation between subjects. On the other hand, we should establish a sound mechanism, especially the strong guarantee of leadership organizations, the continuous support of special funds, and scientific and reasonable assessment, reward, and punishment. Next, it will be verified by model and simulation.


## 3.2. Model Construction

Based on the analysis presented in Section3.1, this paper uses the game of public goods to establish an evolutionary game model and takes the scale-free network as the complex network carrier to study the importance of subject coordination in the construction of smart university campuses [26] and the effectiveness of leading organizations, special funds, assessment, rewards, and punishments. Based on realistic considerations, the following assumptions are put forward for the construction of a complex network evolution game model of smart university campuses:H1: heterogeneous complex network. It has different types of nodes, and the impact on each node is different after completion.H2: weighted complex networks. Collaborative strength is represented by the weight of nodes.H3: the number of nodes in the network does not change.H4: each node becomes a game opponent only with its neighbors that have direct contact with it, and its income is only affected by the game opponent.H5: the game strategy of each node is only cooperation and noncooperation.H6: the input cost of each node per round of the game is set to 1.H7: nodes exhibit bounded rationality, and there is a possibility of choosing game strategy mistakes.


### 3.2.1. The Complex Network

LetG=N,E,W represent the main network of smart campus construction in universities, where N represents the number of nodes, E represents the number of edges, and W represents the weight. The weighted degree of node i is
(1)kiω=kiωij,where ki is the degree of node i, node j is the neighbor of node i, and ωij0<ωij<1 is the weight, which represents the cooperative strength between nodes i and j.Section3.1 shows that N is 25. The complex network built on the smart university campus is quantified as a relationship matrix of 25×25, where the connection between nodes is determined according to the direct relationship between subjects. The complex network diagram is obtained by using NetworkX [27], as shown in Figure 1.Figure 1
Complex network diagram of smart campus construction for universities.


### 3.2.2. Evolutionary Game

LetA denote the adoption of a cooperative strategy, and let the number of nodes be nA; then, the proportion of the nodes is as follows.
(2)xA=nAN.LetB denote the adoption of a noncooperative strategy, and the number of nodes is nB; then, the proportion of nodes is as follows.
(3)xB=nBN,where xA+xB=1. The symmetric game gains of node i1,2,⋯,i and neighbor node j1,2,⋯,j are shown in Table 1.Table 1
The matrix of gains for the symmetric game.
NodeiNodejABAabBcdThe lettersa, b, c, and d represent the benefit obtained by the first individual in the strategy pair A,A, A,B, B,A, and B,B, respectively. In the evolutionary game, each individual plays with its neighbors like the previous two games, and the sum of the income obtained is called the total income of the individual.In the first case, the average gains of nodei selecting A at time step t are
(4)fAi=∑m=0kiCkimxAm1−xAki−mma+ki−mb1+γαt2+βtkiω,where γ indicates the guarantee strength of the leadership organization mechanism, α indicates the guarantee strength of the special fund mechanism, and β indicates the guarantee strength of the evaluation, reward, and punishment mechanism.In the second case, the average gains of nodei selecting B at time step t are
(5)fBi=∑m=0kiCkimxBm1−xBki−mmc+ki−md1+γαt2+βtkiω.We adopt the synchronous learning game strategy, that is, each node carries out strategy learning at each time step [28]. At the same time, the game strategy of pairing comparison is used to update the rules; that is, node i randomly selects a neighbor node j to compare the gains, and there is probability pij of updating the strategy of node j.
(6)pij=11+exp−fj−fi/ε,where ε is a noise term that characterizes the decision-making error and bounded rationality of the subject [29]. When ε⟶0, the subject tends to make irrational decisions and decides to update to the neighbor strategy. When ε⟶∞, the subject cannot make rational decisions and randomly learn neighbor decisions. The meaning of probability pij is that when the income of node i is less than that of node j, the decision of node j is accepted with a great probability. In contrast, the decision of node i is accepted with a small probability.


## 3.2.1. The Complex Network

LetG=N,E,W represent the main network of smart campus construction in universities, where N represents the number of nodes, E represents the number of edges, and W represents the weight. The weighted degree of node i is
(1)kiω=kiωij,where ki is the degree of node i, node j is the neighbor of node i, and ωij0<ωij<1 is the weight, which represents the cooperative strength between nodes i and j.Section3.1 shows that N is 25. The complex network built on the smart university campus is quantified as a relationship matrix of 25×25, where the connection between nodes is determined according to the direct relationship between subjects. The complex network diagram is obtained by using NetworkX [27], as shown in Figure 1.Figure 1
Complex network diagram of smart campus construction for universities.


## 3.2.2. Evolutionary Game

LetA denote the adoption of a cooperative strategy, and let the number of nodes be nA; then, the proportion of the nodes is as follows.
(2)xA=nAN.LetB denote the adoption of a noncooperative strategy, and the number of nodes is nB; then, the proportion of nodes is as follows.
(3)xB=nBN,where xA+xB=1. The symmetric game gains of node i1,2,⋯,i and neighbor node j1,2,⋯,j are shown in Table 1.Table 1
The matrix of gains for the symmetric game.
NodeiNodejABAabBcdThe lettersa, b, c, and d represent the benefit obtained by the first individual in the strategy pair A,A, A,B, B,A, and B,B, respectively. In the evolutionary game, each individual plays with its neighbors like the previous two games, and the sum of the income obtained is called the total income of the individual.In the first case, the average gains of nodei selecting A at time step t are
(4)fAi=∑m=0kiCkimxAm1−xAki−mma+ki−mb1+γαt2+βtkiω,where γ indicates the guarantee strength of the leadership organization mechanism, α indicates the guarantee strength of the special fund mechanism, and β indicates the guarantee strength of the evaluation, reward, and punishment mechanism.In the second case, the average gains of nodei selecting B at time step t are
(5)fBi=∑m=0kiCkimxBm1−xBki−mmc+ki−md1+γαt2+βtkiω.We adopt the synchronous learning game strategy, that is, each node carries out strategy learning at each time step [28]. At the same time, the game strategy of pairing comparison is used to update the rules; that is, node i randomly selects a neighbor node j to compare the gains, and there is probability pij of updating the strategy of node j.
(6)pij=11+exp−fj−fi/ε,where ε is a noise term that characterizes the decision-making error and bounded rationality of the subject [29]. When ε⟶0, the subject tends to make irrational decisions and decides to update to the neighbor strategy. When ε⟶∞, the subject cannot make rational decisions and randomly learn neighbor decisions. The meaning of probability pij is that when the income of node i is less than that of node j, the decision of node j is accepted with a great probability. In contrast, the decision of node i is accepted with a small probability.


## 4. Simulation and Results

### 4.1. Simulation Flowchart and Parameter Settings

(1) After the start of simulation, first, build a scale-free network, set the initial parameters, and randomly assign strategies to each node. Then, each node plays a game with its neighbors to calculate the income of each node. Next, the strategy of each node was updated synchronously, and the game turned back. Finally, the simulation ends when the predetermined timeT is over. The process is shown in Figure 2Figure 2
Flowchart of simulation execution.(2) The Simpy [30] platform was used for simulation analysis. The subject is simulated by process, and the game is simulated by event. The parameter settings are shown in Table 2Table 2
Parameter settings.
abcdεT57730.550


### 4.2. Simulation Results

#### 4.2.1. The Importance of Subjects’ Coordination to the Construction of Smart University Campuses

We assumed that the initial number of partners is 10,γ=0.5, α=0.5, and β=0.5. We used different coordinative intensity indices ω, that is, ω=0.2, ω=0.4, ω=0.6, and ω=0.8, to observe the changing trend in the number of partners. The simulation results are shown in Figure 3.Figure 3
The impact of cooperation on the number of partners.As seen from the comparison in Figure3, when ω=0.2, the number of partners first increased and then decreased and finally was less than the initial number of partners. When ω=0.4, although the number of partners also decreased to a certain extent, it still exceeded the initial number of partners in the end. When ω=0.6, the number of partners increased steadily, and finally, it realized cooperation among all partners. When ω=0.8, although the number of partners also showed an upward trend, it finally stabilized at approximately 22.


#### 4.2.2. The Effectiveness of the Mechanism Guarantees the Construction of Smart University Campuses

We assumed that the initial number of partners is 10. Whenω=0.6, α=0.5, and β=0.5, we used different values of γ for the guarantee strength of the leadership organization mechanism. We observed the changing trend of the average gains of the network information department (which plays a key role in connecting the preceding and the following) for γ=0.3, γ=0.6, and γ=0.9,respectively, and the simulation results are shown in Figure 4(a). When ω=0.6, γ=0.9, and β=0.5, we used different values of α for the special fund mechanism guarantee strength. We observed the changing trend in the average gains of the network information department for α=0.3, α=0.6, and α=0.9, and the simulation results are shown in Figure 4(b). Whenω=0.6, γ=0.9, and α=0.9, we used different values of β for the guarantee strength of the assessment, reward, and punishment mechanism. We observed the change trend in the number of partners in β=0.2, β=0.4, β=0.6, and β=0.8, and the simulation results are shown in Figure 4(c).Figure 4
The impact of the mechanism on the construction of smart university campuses: (a) leadership organizations; (b) special funds; (c) assessment rewards and punishments.
(a)(b)(c)As seen from the comparison in Figure4(a), when the value ofγ is increasing, the average gains of the network information department are growing steadily, and the convergence speed is accelerating. As seen from the comparison in Figure 4(b), when α=0.3, the average income of the network information department first decreased and then increased; when α=0.6, the average gains increased steadily. When α=0.9, the average gains first increased and then decreased. As seen from the comparison in Figure 4(c), when β=0.2, the number of partners decreased. When β=0.4, the number of partners first increased and then decreased, finally exceeded the initial number of partners and stabilized at approximately 15. When β=0.6, the number of partners continued to rise and finally realized whole network cooperation. When β=0.8, the number of partners first increased, then decreased, then increased, and finally stabilized at 23.


## 4.1. Simulation Flowchart and Parameter Settings

(1) After the start of simulation, first, build a scale-free network, set the initial parameters, and randomly assign strategies to each node. Then, each node plays a game with its neighbors to calculate the income of each node. Next, the strategy of each node was updated synchronously, and the game turned back. Finally, the simulation ends when the predetermined timeT is over. The process is shown in Figure 2Figure 2
Flowchart of simulation execution.(2) The Simpy [30] platform was used for simulation analysis. The subject is simulated by process, and the game is simulated by event. The parameter settings are shown in Table 2Table 2
Parameter settings.
abcdεT57730.550


## 4.2. Simulation Results

### 4.2.1. The Importance of Subjects’ Coordination to the Construction of Smart University Campuses

We assumed that the initial number of partners is 10,γ=0.5, α=0.5, and β=0.5. We used different coordinative intensity indices ω, that is, ω=0.2, ω=0.4, ω=0.6, and ω=0.8, to observe the changing trend in the number of partners. The simulation results are shown in Figure 3.Figure 3
The impact of cooperation on the number of partners.As seen from the comparison in Figure3, when ω=0.2, the number of partners first increased and then decreased and finally was less than the initial number of partners. When ω=0.4, although the number of partners also decreased to a certain extent, it still exceeded the initial number of partners in the end. When ω=0.6, the number of partners increased steadily, and finally, it realized cooperation among all partners. When ω=0.8, although the number of partners also showed an upward trend, it finally stabilized at approximately 22.


### 4.2.2. The Effectiveness of the Mechanism Guarantees the Construction of Smart University Campuses

We assumed that the initial number of partners is 10. Whenω=0.6, α=0.5, and β=0.5, we used different values of γ for the guarantee strength of the leadership organization mechanism. We observed the changing trend of the average gains of the network information department (which plays a key role in connecting the preceding and the following) for γ=0.3, γ=0.6, and γ=0.9,respectively, and the simulation results are shown in Figure 4(a). When ω=0.6, γ=0.9, and β=0.5, we used different values of α for the special fund mechanism guarantee strength. We observed the changing trend in the average gains of the network information department for α=0.3, α=0.6, and α=0.9, and the simulation results are shown in Figure 4(b). Whenω=0.6, γ=0.9, and α=0.9, we used different values of β for the guarantee strength of the assessment, reward, and punishment mechanism. We observed the change trend in the number of partners in β=0.2, β=0.4, β=0.6, and β=0.8, and the simulation results are shown in Figure 4(c).Figure 4
The impact of the mechanism on the construction of smart university campuses: (a) leadership organizations; (b) special funds; (c) assessment rewards and punishments.
(a)(b)(c)As seen from the comparison in Figure4(a), when the value ofγ is increasing, the average gains of the network information department are growing steadily, and the convergence speed is accelerating. As seen from the comparison in Figure 4(b), when α=0.3, the average income of the network information department first decreased and then increased; when α=0.6, the average gains increased steadily. When α=0.9, the average gains first increased and then decreased. As seen from the comparison in Figure 4(c), when β=0.2, the number of partners decreased. When β=0.4, the number of partners first increased and then decreased, finally exceeded the initial number of partners and stabilized at approximately 15. When β=0.6, the number of partners continued to rise and finally realized whole network cooperation. When β=0.8, the number of partners first increased, then decreased, then increased, and finally stabilized at 23.


## 4.2.1. The Importance of Subjects’ Coordination to the Construction of Smart University Campuses

We assumed that the initial number of partners is 10,γ=0.5, α=0.5, and β=0.5. We used different coordinative intensity indices ω, that is, ω=0.2, ω=0.4, ω=0.6, and ω=0.8, to observe the changing trend in the number of partners. The simulation results are shown in Figure 3.Figure 3
The impact of cooperation on the number of partners.As seen from the comparison in Figure3, when ω=0.2, the number of partners first increased and then decreased and finally was less than the initial number of partners. When ω=0.4, although the number of partners also decreased to a certain extent, it still exceeded the initial number of partners in the end. When ω=0.6, the number of partners increased steadily, and finally, it realized cooperation among all partners. When ω=0.8, although the number of partners also showed an upward trend, it finally stabilized at approximately 22.


## 4.2.2. The Effectiveness of the Mechanism Guarantees the Construction of Smart University Campuses

We assumed that the initial number of partners is 10. Whenω=0.6, α=0.5, and β=0.5, we used different values of γ for the guarantee strength of the leadership organization mechanism. We observed the changing trend of the average gains of the network information department (which plays a key role in connecting the preceding and the following) for γ=0.3, γ=0.6, and γ=0.9,respectively, and the simulation results are shown in Figure 4(a). When ω=0.6, γ=0.9, and β=0.5, we used different values of α for the special fund mechanism guarantee strength. We observed the changing trend in the average gains of the network information department for α=0.3, α=0.6, and α=0.9, and the simulation results are shown in Figure 4(b). Whenω=0.6, γ=0.9, and α=0.9, we used different values of β for the guarantee strength of the assessment, reward, and punishment mechanism. We observed the change trend in the number of partners in β=0.2, β=0.4, β=0.6, and β=0.8, and the simulation results are shown in Figure 4(c).Figure 4
The impact of the mechanism on the construction of smart university campuses: (a) leadership organizations; (b) special funds; (c) assessment rewards and punishments.
(a)(b)(c)As seen from the comparison in Figure4(a), when the value ofγ is increasing, the average gains of the network information department are growing steadily, and the convergence speed is accelerating. As seen from the comparison in Figure 4(b), when α=0.3, the average income of the network information department first decreased and then increased; when α=0.6, the average gains increased steadily. When α=0.9, the average gains first increased and then decreased. As seen from the comparison in Figure 4(c), when β=0.2, the number of partners decreased. When β=0.4, the number of partners first increased and then decreased, finally exceeded the initial number of partners and stabilized at approximately 15. When β=0.6, the number of partners continued to rise and finally realized whole network cooperation. When β=0.8, the number of partners first increased, then decreased, then increased, and finally stabilized at 23.


## 5. Discussion

A certain collaborative intensity can promote cooperation between subjects. When everyone is actively involved in the construction of smart campuses, it will help to prevent the phenomenon of “getting something for nothing” to improve the satisfaction of all staff as much as possible, form a good interactive cycle, and promote the construction progress of smart campuses. However, when the intensity is too high, the frequent interaction between the subjects is prone to contradictions and disputes. At the same time as coordinating between subjects, trust and cooperation need to be established through multiple games to finally achieve win–win results.An appropriate leadership organization mechanism can play a key leading role in the whole construction process of smart campuses, strengthen supervision, and accelerate the construction process of smart university campuses. An appropriate special fund mechanism can clarify the source of funds and ensure the stability and sustainability of smart campus construction. However, when the investment is too large, it can easily lead to the decentralization of control power and the dissatisfaction of other departments at a later stage. An appropriate assessment, reward, and punishment mechanism can mobilize subjects’ enthusiasm to participate and willingness to use the achievements after construction and can encourage them to constantly put forward suggestions for improvement. However, too many strengths can easily lead to rebellious psychology and are not conducive to development.In general, the appropriate subject coordination and organization mechanism, fund mechanism, and reward and punishment mechanism all directly affect the income function of each node subject in the smart campus construction network, thus affecting its behavior choice for smart campus construction. Suggestions for promoting the voluntary cooperation of all subjects in the construction of the smart campus are as follows. First, we should cultivate and enhance the awareness of all subjects in the construction of smart campuses. A convenient smart campus is a public property resource and requires the participation of all parties in construction. Second, from the institutional level, it strengthens the leadership of the information construction leading group, promotes the implementation of the information target assessment system, and curbs the emergence of “free riding” behavior. In addition, we should comply with future development trends and the general requirements of teachers and students, rely on the extensive participation of teachers and students, and jointly strengthen the promotion of smart campuses.As a typical representative of universities in underdeveloped areas, Xinxiang Medical University is below average in terms of informatization level in Henan Province, and the development level of smart campuses is still relatively backward. However, with the renaming of universities and the increasingly urgent requirements of educational modernization, smart campuses are of great significance to the development of Xinxiang Medical University. At present, Xinxiang Medical University focuses on promoting informatization at the leadership level, starting with the construction of a smart campus and with the help of information technology, to realize thecurve overtaking of educational modernization. Therefore, to carry out the construction of smart campuses more efficiently and effectively, we should appropriately promote the coordination of the subjects of smart campus construction while taking comprehensive planning as the foothold, including information, capital, and technology, and strengthen cooperation. In addition, we strive to make use of an appropriate organizational mechanism, funding mechanism, and reward and punishment mechanism to coordinate the relationship between construction subjects and give better play to the functions of each subject.


## 6. Conclusion

Taking the construction of smart campuses in universities as the research object, this paper qualitatively analyzes the subjects of construction and their relationship and quantitatively constructs an evolutionary game model based on a complex network. Twenty-five subjects are selected as nodes. According to the relationship between them and taking the synergy intensity index as the weight, a weighted scale-free network is constructed. At the same time, a cooperative evolution game model is established. Through simulation, the impact of collaboration between subjects, leadership organization, special funds, assessment, reward, and punishment on the construction of a smart university campus is deeply investigated. The results show that (1) appropriate subject coordination is very important, which is helpful to prevent the phenomenon ofgetting something for nothing, to establish trust and cooperative relations through multiple games, and finally to achieve win–win results. (2) An appropriate leadership and organization mechanism can strengthen supervision and promote the construction process of smart university campuses. An appropriate special fund mechanism can clarify the source of funds and ensure sustainable development. Appropriate mechanisms of assessment, reward, and punishment can mobilize subjects’ enthusiasm to participate and improve the popularity of construction achievements. The analysis and results of this paper have guiding significance for universities in promoting the construction of a complete, comprehensive, and high-quality smart campus. However, this paper is a preliminary study based on the abstraction of some factors. The influence of the changes in network structure and strategy renewal on the overall decision-making behavior of smart campuses needs to be further studied.



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*Source: 4141475-2022-06-26.xml*