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Faced with the pressure brought by major changes in the shipping industry such as liner alliances and larger ships, more and more ports are inclined to choose cooperation. This paper first explains the reasons for the lack of spontaneous cooperation motives between ports through game theory and then points out that in order to achieve stable cooperation between ports, it may be necessary to use external forces to establish a cooperation mechanism that enables both parties to achieve a win-win situation. Further, the article explores the evolution path of port cooperation and the stability of cooperation through evolutionary game theory and finds that the incremental benefits of port cooperation, the cost of cooperation, the additional revenue of free-riding, the compensation for unilateral cooperation, and the distribution coefficient of incremental benefits are the key factors affecting the stability of port cooperation. Then, with the help of numerical simulation experiments, the influencing factors affecting the evolution path and stability of port cooperation are discussed. Finally, in order to promote port cooperation and improve the stability of cooperation, this paper puts forward corresponding policy implications and management insights for policymakers based on relevant influencing factors. • The article explains the reasons for the lack of spontaneous cooperation motives between ports through classical game theory. • to promote the cooperation among ports, the article analyzes the related determinants affecting the evolutionary paths port cooperation. • The article calculates the equilibrium points of the evolutionary game model, determines the corresponding equilibrium strategies. • Numerical simulation analysis was carried out on the changes of relevant parameters reflecting corresponding influencing factors. [ABSTRACT FROM AUTHOR]

Hydrogen stations are an important component of urban clean energy development. In the event of an accident, they can have a serious impact on the safety and stability of the urban system. This paper identifies and analyzes the main influencing factors of hydrogen station safety resilience based on an improved HFACS model. It establishes a safety resilience evaluation index system from four dimensions: spatial resilience, engineering resilience, management resilience, and social resilience. Subjective index weights are determined through fuzzy analytic hierarchy process, objective index weights are determined through entropy method, and combined index weights are determined based on game theory. A system dynamics model of hydrogen station safety resilience is constructed, and model verification and comparative analysis are conducted. The influence of different dimensions on system safety resilience is analyzed, and quantitative evaluation and prediction research on hydrogen station safety resilience is carried out. Targeted safety resilience improvement suggestions are also proposed. The research results can provide a reference for the safety management of hydrogen stations. • Constructing a resilience evaluation index system using the modified HFACS model. • Modeling the safety resilience evaluation of hydrogen stations from four dimensions. • Simulation and prediction of safety resilience levels for hydrogen stations. [ABSTRACT FROM AUTHOR]

Public decision-making contains several decisions taken by multiple actors intended to lead to collective gains. The impact of these decisions varies over time. This paper presents a novel approach to explain the durability, i.e., impact, of decisions in relation to strategies performed by actors involved in infrastructure development projects. The concept of durability is operationalised in terms of actors fulfilling joint aims and creating joint focus. We aim to explain why some decisions have a durable (lasting and joint focus) impact and why other decisions have a non-durable (temporary and no joint focus) impact. Actor-strategies performed in the decision-making process are used to explain the different types of decision impact. After analysing several Dutch Railway projects, we discovered that durable decisions appear when (i) obstructive non-deciding and non-cooperative strategies are dealt with, (ii) responsibilities are clearly divided, (iii) the scope alternates between internal and external focus and coupling of issues happens, and (iv) the design of the decision-making process is made explicit and information asymmetry is managed. • Actor-strategies explain why some decisions have more impact than others. • Durable decisions fulfill actors' short- and long-term aims and create joint focus. • Alternate between internal and external focus creates durable impact. • Make the design of decision-making process explicit to reach durable outcomes. • A novel multi-actor and dynamic perspective on decision impact. [ABSTRACT FROM AUTHOR]

The classical Shapley value for cooperative games determines a payoff vector considering that the formation of the grand coalition is made by incorporating players one by one. Later, this method was generalized for games with restricted cooperation by several known mathematical structures: partitions, graphs, convex geometries, antimatroids, matroids or simplicial complexes. In this paper we consider games over simplicial complex with an extra information about the relationships of the agents, a filtration of the complex. Filtrations are very known simplicial structures in the social and neuronal networks. We propose a Shapley value for these situations and an axiomatization. [ABSTRACT FROM AUTHOR]

At present, two subsidy policies, which are based on electric vehicle (EV) consumers and EV charging station firms, have been widely implemented in different areas in China to promote EV adoption. However, the effectiveness of subsidy policies is significantly influenced by the indirect network and herd effects, depicting the positive impacts of the numbers of energy infrastructure firms and the sales of vehicles on consumers' utilities, respectively. Taking the aforementioned effects into consideration, this study examines the optimal subsidy policies by developing a Stackelberg game model. The game model analyzes the interaction and decisions of multiple stakeholders (i.e., consumers, energy infrastructure firms, EV and gasoline vehicle manufacturers, and the government). Interestingly, we find that a stronger herd effect will promote EV sales when the unit production cost of EVs is low; however, it will hinder EV sales when the unit production cost is high. Besides, we compare the effectiveness of the two subsidy policies for promoting EV adoption, reducing environmental impacts, and increasing the social welfare. Findings show that the effectiveness of subsidy policies highly depends on the extent of the indirect network and herd effects. Specifically, compared with EV consumers-based subsidy policy, EV charging station firms-based subsidy policy will be more effective in only two conditions: a weak indirect network effect and a strong herd effect. Otherwise, the EV consumers-based subsidy policy will be more effective. The findings of the paper are helpful in enhancing the effectiveness of subsidy policy in China. • Government subsidy cannot always promote electric vehicle adoption. • The indirect network and herd effects affect the effectiveness of subsidy policy. • A stronger herd effect will not always promote electric vehicle sales. • Either consumers or charging station investors can be the best subsidy object. [ABSTRACT FROM AUTHOR]

Motivated by the practical needs of improving the rescue efficiency of emergency events and reducing the damage, the problem of emergency material supplier selection arises. This paper aims to propose a novel group decision making framework, this framework can aggregate experts' opinions from different backgrounds to evaluate emergency material suppliers. Firstly, an evaluation indicator system considering the supplier's emergency supply capacity is established, to make up for the insufficient applicability of the traditional supplier evaluation indicator system in the field of emergency material supplier evaluation. Then, the game theory integrated weight (GTIW) method is used to gather the initial weights obtained by Maximum Consensus Minimum Hesitation Model (MCMHM) and expert background weights. Moreover, Exponential TODIM (ExpTODIM) method based on Hesitant fuzzy linguistic set (HFLs) named HF-ExpTODIM is introduced to solve the problem of emergency material supplier selection, TODIM is an acronym in Portuguese for Interactive and Multi-criteria Decision Making. Again, a case study of China on emergency medical mask supplier selection is presented, and the corresponding results are discussed. Finally, the practicality of the proposed framework in a real-world context is discussed, including the necessary preparation, operational priorities and caveats. This paper can provide a reference for group decision making of material supplier selection in emergencies. • A framework for selecting emergency material suppliers has been proposed. • Expert consensus and expert background differences are comprehensively considered. • Supplier's emergency supply capacity is added to the evaluation indicator system. • HF-ExpTODIM is proposed by combining HFLs and ExpTODIM. • The effectiveness of the proposed method is verified by a Chinese case study. [ABSTRACT FROM AUTHOR]

This paper is concerned with a conflict that N -pursuers versus M -evaders. It is a multi-pursuer multi-evader game extended from classical differential game theory to simultaneously address target selection and multi-player pursuit-evasion. Every pursuer attempts to intercept the evader it has chosen as its target while every evader does the opposite. This is modeled as a multi-player nonzero-sum differential game under a weighted directed graph. Moreover, a novel and more practical framework, the Unique-Choice (UC) games framework, is provided. Hamilton–Jacobi–Isaacs (HJI) is used for solving the game to derive the optimal distributed control strategy for each player based on graph theory. An important but has not been extensively studied issue, target selection, is studied. Different from the former research, this paper proposes a more reasonable criterion for target selection which makes the cost of pursuers minimized. The pursuers are proven to intercept evaders successfully with the proposed optimal pursuit strategy. Several illustrative examples under two different cases are presented, and the results show that the cost of pursuers with the proposed target selection algorithm is the minimum of all results. [ABSTRACT FROM AUTHOR]

We present the scientific results of the call for papers in SEPS titled 'Decision-Making Models for Common Goods in Environmental, Health, and Logistics Contexts.' As guest editors in Socio-economic Planning Sciences (SEPS), we describe the domain of our academic initiative and present the collected papers. Interestingly, our collection includes papers employing various methodologies such as multi-criteria decision making, game theory, operations research, microeconomics, computational models and insurance mathematics. We received more than fifty submissions from Asia, Europe and America. However, only thirteen papers were accepted for publication in SEPS. This demonstrates the extensive investigation of the problem of common goods and the ongoing academic debate surrounding it. [ABSTRACT FROM AUTHOR]

Accordingly to Duchet (1987), the first paper of convexity on general graphs, in english, is the 1981 paper "Convexity in graphs". One of its authors, Frank Harary, introduced in 1984 the first graph convexity games, focused on the geodesic convexity, which were investigated in a sequence of five papers that ended in 2003. In this paper, we continue this research line, extend these games to other graph convexities, and obtain winning strategies and complexity results. Among them, we obtain winning strategies for general convex geometries and winning strategies for trees from the Sprague-Grundy theory on impartial games. We also obtain the first PSPACE-hardness results on convexity games, by proving that the normal play and the misère play of the impartial hull game on the geodesic convexity is PSPACE-complete even in graphs with diameter two. [ABSTRACT FROM AUTHOR]

• The source-load uncertainties within the multi-energy microgrid are comprehensively considered, and a data-driven box-based uncertainty set is formulated. In the uncertainty analysis of EVs, an unsupervised clustering method is introduced in the construction of the data-driven uncertainty set containing EVs. • A three-stage min–max robust optimization model for MEM is developed to handle multiple uncertainties. Meanwhile, this paper adopts Stackelberg game theory to portray the interaction between multi-energy microgrid system operators and EV users. • Finally, this paper employs the column and constraint generation (C&CG) algorithm in solving the robust optimization model and constructs a numerical simulation experiment based on a real integrated energy park model. To improve the flexibility and robustness of energy systems, we propose a three-stage robust optimization (RO) model for a multi-energy microgrid (MEM). First, the source-load uncertainties within the multi-energy microgrid are comprehensively considered, and a data-driven box-based uncertainty set is formulated. In the uncertainty analysis of electric vehicles, an unsupervised clustering method is introduced in the construction of the data-driven uncertainty set containing EVs. Secondly, a three-stage min–max robust optimization model for MEM is developed to handle multiple uncertainties. Meanwhile, this paper adopts Stackelberg game theory to portray the interaction between multi-energy microgrid system operators and EV users. Finally, this paper employs the column and constraint generation (C&CG) algorithm in solving the robust optimization model and constructs a numerical simulation experiment based on a real integrated energy park model. The simulation results show that the uncertainty set constructed based on historical data can eliminate some low probability cases in describing EV uncertainty. Compared with deterministic optimization, the robust optimization approach can effectively resist multiple uncertainties without significantly increasing the operating cost of the MEM. Meanwhile, the C&CG algorithm can ensure fast convergence of the process when the model solves complex multilevel models. [ABSTRACT FROM AUTHOR]

With the increasing demand of users for distributed energy storage (ES) resources and the emerging development of peer to peer (P2P) transaction technology, shared energy storage (SES) has great potential to contribute into new business models of demand-side ES. In order to compromise essential elements like safety, stability and efficiency of P2P trading, as well as to improve the utilization rate of demand-side ES, this paper devotes to construct a P2P transaction framework based on a partially decentralized topology and proposes a two-stage trading optimization strategy of SES in a P2P market, considering the equilibrium state of supply and demand flow. In the first stage, this paper simultaneously balances the interests of buyers and sellers and brings the carbon trading mechanism into the transaction process. The interaction of interests of bilateral parties with consideration of carbon trading mechanism has been investigated, and a SES capacity sharing model is, then, established based on the bargaining game theory. In the second stage, a unique pricing mechanism for SES leasing fee is designed based on a multi-strategy evolutionary game model, considering bounded rational decision-making for SES operators and communities. Finally, numerical simulation verified the feasibility and superiority of the proposed P2P trading strategy of SES. [ABSTRACT FROM AUTHOR]

Food systems are dominated by multiple risks inflicted by their surrounding environments that pose pressures on the continuous and efficient supply of food products. Market dynamics represented by competitive and cooperative behaviors of the system's main players is one of the uncertainties that influence the availability and affordability of food products. Efficient modeling of these risks can assist decision-makers in simulating and predicting future actions that mitigate the impact that market dynamics have on the provision of food products. This paper suggests an Agent-based model-based framework that simulates the behavior and tracks the economic and environmental performances of the tomato supply system in the State of Qatar. It considers three different scenarios representing the current situation of the market in addition to a competitive and a cooperative trade network. Interactions between trade partners are modeled using two game theoretic approaches counting Cournot competition and Cartel collusion. Based on the research findings, a competitive global market can bring economic benefits to Qatar as an importing nation, but this approach also entails substantial water consumption and results in significant environmental emissions. Conversely, collaboration among exporting countries demonstrates a potential 20% reduction in water usage and a 15% decrease in environmental emissions compared to the competitive scenario, offering enhanced environmental performance albeit at higher economic costs. This study provides a prescriptive analysis that enables policymakers to quantify the trade-offs between economic efficiency and environmental sustainability. These insights are crucial for guiding strategic planning for food supply security and environmental conservation. By understanding the implications of market structure changes, policymakers can navigate potential challenges more effectively and develop targeted policy interventions that enhance the resilience of the food supply system against market volatility and environmental impacts. This analysis supports proactive policy-shaping that aligns with their desired outcomes, ensuring a balanced approach to managing economic benefits and environmental considerations. [ABSTRACT FROM AUTHOR]

The uncertainty of renewable energy necessitates reliable demand response (DR) resources for power system auxiliary regulation. Meanwhile, the widespread deployment of energy-consuming 5G base stations (gNBs) drives internet service providers (ISPs) to seek energy expenses reduction. This paper intelligently addresses these two complementary needs by integrating gNBs into the power system dynamic economic dispatch (DED). To achieve this, a combined DED model that incorporates both the power system and 5G communication network is developed, where numerous distributed gNBs and their backup energy storage systems (BESSs) are integrated as a virtual power plant (VPP) to offer power support and obtain economic incentives. Instead of traditional optimization methods, the combined DED model is solved by a cooperative game-based solution method in a benefit-balanced manner, facilitating the power system and 5G network to achieve a win-win DED solution. This method comprises a two-stage game model and a solution algorithm based on improved random drift particle swarm optimization (RDPSO). The improved RDPSO constructs an uncertainty set in each iteration, facilitating its solution of stochastic optimization involving power system uncertainties. Lastly, a VPP management function is developed within the 5G core network (5GC) to efficiently evaluate the VPP's dispatchable capacity and formulate optimal control policies that can minimize control costs. Simulations based on the IEEE 118-bus system and real device data demonstrate that the proposed method can simultaneously reduce the power system's operating costs and increase the ISP's economic revenue. • Addressing complementary needs by integrating gNBs into power system DED. • Game-based solution for DED balancing supply and demand sides benefits. • Improved RDPSO algorithm for power system DED considering uncertainties. • VPP function efficiently evaluating dispatchable capacity and formulating optimal control policy. • Economic and technical win-win results in simulation based on real data. [ABSTRACT FROM AUTHOR]

The concept of nimbers —a.k.a. Grundy-values or nim-values —is fundamental to combinatorial game theory. Beyond the winnability, nimbers provide a complete characterization of strategic interactions among impartial games in disjunctive sums. In this paper, we consider nimber-preserving reductions among impartial games, which enhance the winnability-preserving reductions in traditional computational characterizations of combinatorial games. We prove that Generalized Geography is complete for the natural class, I P , of polynomially-short impartial rulesets, under polynomial-time nimber-preserving reductions. We refer to this notion of completeness as Sprague-Grundy-completeness. In contrast, we also show that not every PSPACE-complete ruleset in I P is Sprague-Grundy-complete for I P. By viewing every impartial game as an encoding of its nimber—a succinct game secret richer than its winnability alone—our technical result establishes the following striking cryptography-inspired homomorphic theorem: Despite the PSPACE-completeness of nimber computation for I P , there exists a polynomial-time algorithm to construct, for any pair of games G 1 , G 2 in I P , a Generalized Geography game G satisfying: nimber (G) = nimber (G 1) ⊕ nimber (G 2). [ABSTRACT FROM AUTHOR]

This paper investigates the optimal fault-tolerant formation control problem of networked marine surface vehicles (NMSVs) in aggregative games, where each MSV suffers from external disturbances, actuator faults and failures. The hierarchical Nash equilibrium seeking algorithm is proposed to solve this control problem, including the decentralized decision layer and the local control layer. In the decentralized decision layer, the Nash equilibrium of the aggregative game for each MSV can be estimated in a decentralized manner, by constructing the cost function based on both its individual decision and the aggregative impacts of the decision of all the other MSVs. In the control layer, the fault-tolerant local controller is designed to stabilize the positions of the networked MSVs to the desired formation based on the estimated Nash equilibrium generated from the decision layer under the influences of actuator faults and failures. Then, based on the game theory and Lyapunov argument, the sufficient conditions on the selection of the algorithm parameters are presented for guaranteeing the convergence of the seeking algorithm. Finally, the practical simulation example is provided to demonstrate the effectiveness of the presented theoretical results. • The NMSVs system with actuator fault input is explored since it is more applicable to the real-world scenario. • The considered dynamics of MSV is more complex and in line with practical application scenarios. A hierarchical Nash equilibrium seeking algorithm based on estimator is proposed, which divides the Nash equilibrium seeking problem for complex systems into the decentralized decision layer and the local control layer. • The goal of minimum cost function while achieving the desired formation is achieved. [ABSTRACT FROM AUTHOR]

In this paper, we present four alternative solution methods to Shapley iteration for the solution of stochastic matrix games. We first combine the extended matrix norm method for stochastic matrix games with Shapley iteration and then state and prove the weak and strong hybrid versions of Shapley iterations. Then, we present the semi-extended matrix norm and iterative semi-extended matrix norm methods, which are analytic-solution-free methods, for finding the approximate solution of stochastic matrix games without determining the strategy sets. We illustrate comparisons between the Shapley iteration, weak and strong hybrid Shapley iterations, semi-extended matrix norm method, and iterative semi-extended matrix norm method with several examples. The results reveal that the strong and weak hybrid Shapley iterations improve the Shapley iteration and decrease the number of iterations, and the strong hybrid Shapley iteration outperforms all the other proposed methods. Finally, we compare these methods and present their performance analyses for large-scale stochastic matrix games as well. • Matrix norm-based methods are introduced for the solution of the stochastic matrix games. • Weak and strong hybrid Shapley iterations are presented. • The semi-EMN and iterative semi-EMN methods are introduced as analytic-solution-free methods for stochastic matrix games. • Illustrative and comprehensive analyses are given with performance comparisons of the proposed methods. • Performance analyses of the proposed methods for large-scale stochastic matrix games are conferred. [ABSTRACT FROM AUTHOR]

In this paper we use the repeated-games framework to design and analyze a master-worker (MW) mechanism, where a master repeatedly outsources computational tasks to workers in exchange for payment. Previous work on MW models assumes that all workers are perfectly rational and aim to maximize their expected utility. Perfect rationality is a strong behavioral assumption because it requires that all workers follow the prescribed equilibrium. Such a model may be unrealistic in a practical setting, where some agents are not perfectly rational and may deviate from the equilibrium for short-term gains. Since the correctness of these MW protocols relies on workers following the equilibrium strategies, they are not robust against such deviations. We augment the game-theoretical MW model with the presence of such bounded-rational players or deviators. In particular, we show how to design a repeated-games based MW Verifiable Crowd Computing mechanism that incentivizes the rational workers (or followers) to effectively punish such deviators through the use of terminal payments. We show that the master can use terminal payments to obtain correct answers to computational problems even in this more general model. We supplement our theoretical results with simulations that show that our mechanism outperforms related approaches. [ABSTRACT FROM AUTHOR]

Soft ideal spaces play a significant role in mathematics, particularly in the field of infinite games. They allow mathematicians to establish connections between different areas of mathematics, such as set theory, topology, and game theory. Moreover, soft ideal spaces provide a powerful framework for tackling various problems in analysis, algebra, and geometry. These spaces contribute to the development of mathematical theories, provide a framework for studying, analyzing various properties and operations in infinite game theory, and find applications in various fields including computer science and set theory. Also, these spaces help in characterizing various properties of infinite games, such as determinacy, strategies, and winning conditions. We can gain insights into the behavior of infinite games and develop strategies to optimize outcomes by studying these spaces. This paper aims to use the concept of soft ideal topological spaces to introduce infinitely long games. The study of soft ideal topological space by defining soft ideal separation axioms (τ i , i = 0 , 1 , 2 , 3 , 4) is extended followed by some related theorems. Several soft games are explained using soft separation axioms with flowcharts such as ŞĢ τ 0 , χ , ŞĢ τ 1 , χ , ŞĢ τ 2 , χ , ŞĢ τ 3 , χ and ŞĢ τ 4 , χ. Some results explain several conditions to make any player have a winning strategy. Using many figures and propositions to study the relationships between these types of games, with some examples of soft sets. [ABSTRACT FROM AUTHOR]

Soft ideal spaces play a significant role in mathematics, particularly in the field of infinite games. They allow mathematicians to establish connections between different areas of mathematics, such as set theory, topology, and game theory. Moreover, soft ideal spaces provide a powerful framework for tackling various problems in analysis, algebra, and geometry. These spaces contribute to the development of mathematical theories, provide a framework for studying, analyzing various properties and operations in infinite game theory, and find applications in various fields including computer science and set theory. Also, these spaces help in characterizing various properties of infinite games, such as determinacy, strategies, and winning conditions. We can gain insights into the behavior of infinite games and develop strategies to optimize outcomes by studying these spaces. This paper aims to use the concept of soft ideal topological spaces to introduce infinitely long games. The study of soft ideal topological space by defining soft ideal separation axioms (τ i , i = 0 , 1 , 2 , 3 , 4) is extended followed by some related theorems. Several soft games are explained using soft separation axioms with flowcharts such as ŞĢ τ 0 , χ , ŞĢ τ 1 , χ , ŞĢ τ 2 , χ , ŞĢ τ 3 , χ and ŞĢ τ 4 , χ. Some results explain several conditions to make any player have a winning strategy. Using many figures and propositions to study the relationships between these types of games, with some examples of soft sets. [ABSTRACT FROM AUTHOR]

Most metaheuristic methods employ a strategy designed to be generical and fixed. As a result, these methods often lack the capability to adaptively modify their performance in response to different scenarios or challenges encountered during the search process. This paper presents a new metaheuristic algorithm designed to dynamically adjust its search strategy throughout the optimization process for increased efficiency. This algorithm is based on Evolutionary Strategies (ES) due to their notable self-adaptive features. To further enhance its efficiency, we have incorporated elements from Evolutionary Game Theory (EGT). This integration ensures a more comprehensive strategy adaptation process, taking into account not only the information from the specific agent but also insights from other population members. Additionally, our approach alters the conventional EGT mechanism by including not just pairwise evaluations but also data from the top-performing individuals in the population, based on their outcomes. This broader adaptation strategy allows for a faster convergence to the most effective dominant strategy. To demonstrate the effectiveness of our method, we compared it against several established metaheuristic algorithms using 28 diverse test functions. Our findings reveal that this approach produces competitive results, delivering higher-quality solutions and faster convergence rates. [ABSTRACT FROM AUTHOR]

With the aid of partial supervised information, semi-supervised subspace clustering methods aim to obtain affinity matrices directly derived from raw data, and then those affinity matrices are utilized to get assignment matrices. These affinity matrices are susceptible to disturbances such as noise and outliers, which could significantly impact their quality. To mitigate this, it becomes essential to dynamically update the affinity matrix using the clustering result, reducing the high dependency on raw data. This paper presents a Game Model for Semi-supervised Subspace Clustering (GMSSC). The first submodule of GMSSC utilizes a Graph Convolutional Network to learn a mapping from raw data to the assignment matrix with the assistance of the affinity matrix. The second submodule constructs a semi-supervised self-expressive model to learn a discriminative affinity matrix. By integrating these submodules into a game model, GMSSC achieves a Nash Equilibrium during adversarial training, resulting in stable and robust affinity and assignment matrices. Additionally, we propose an iterative algorithm based on data-driven and model-driven approaches to solve the model. Experimental results on four open real-world datasets demonstrate that the proposed method not only achieves elegant results but also outperforms state-of-the-art semi-supervised subspace clustering algorithms. • Two submodules aimed at learning the assignment matrix and the affinity matrix. • A game model for learning the assignment matrix and affinity matrix. • An iterative algorithm combining model-driven and data-driven approaches. [ABSTRACT FROM AUTHOR]

This paper considers a marketplace coexisting a brand mobile application supplier and an ordinary one providing customers with substitutable software service. The two suppliers can either promote the software individually (SP strategy) or resort to the downstream distributor (MP strategy) to achieve higher market demand. Taking the downstream distributor's revenue sharing behavior and the demand expansion effect of MP strategy into consideration, we build a two-stage Stackelberg game model to explore the suppliers' equilibrium promotion and pricing decisions. Through analysis, we obtain the following significant conclusions and managerial implications. Firstly, both suppliers would price highest in MM model. Secondly, the brand supplier prices higher in MS model than that in SM model while the ordinary supplier prices higher in MS model than that in SM model only when the brand difference between two suppliers is sufficiently high. Thirdly, when the demand expansion effect of MP strategy and the brand difference between two suppliers are both relatively low, SS may become the equilibrium no matter how high the revenue retaining ratio is, which inspires the suppliers in application supply chain to think outside the box. That is, it is not always beneficial to choose MP strategy when the revenue retaining ratio is sufficiently high. Noticeably, the above conclusions remain robust in extended models. Finally, while SM would never be the unique equilibrium in simultaneous game, it can be the equilibrium in sequential game. [ABSTRACT FROM AUTHOR]

• We examine the retailer's returns policy and in-store service strategy in a supply chain. • In-store retail service helps manage customer returns. • Manufacturer is better off with the retailer's optimal returns policy and in-store service. • Manufacturer's optimal contract strategy is discussed. • An extension on the retailer's optimal service level is examined. Returns policies and in-store retail service are widely used strategies for managing customer returns. In this paper, we consider a supply chain with a manufacturer and a retailer, in which the retailer should decide its returns policy strategy by choosing between a no-refund policy (NR) or a money-back guarantee policy (MBG), as well as deciding whether to provide in-store service. We identify the retailer's optimal returns policy and in-store service strategies. We find that while the net salvage value of a returned product is a key factor influencing the retailer's decision on its optimal returns policy, the retailer's in-store service strategy is dependent on its chosen returns strategy. We show that offering an MBG policy can expand the market coverage of the supply chain, while providing in-store service does not yield the same effect. The retailer's optimal returns policy and in-store service benefit the manufacturer. However, there are cases where providing in-store service is not optimal for the retailer, but the manufacturer can benefit from it. In such circumstances, the manufacturer can use a contract to incentivize the retailer to provide in-store service. We also discuss extensions of the model to examine the impact of a non-zero residual value of the unsatisfactory product under an NR policy on the retailer's decisions, and where the retailer endogenously determines its service level through numerical exploration. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel neurodynamic approach with predefined-time stability that offers a solution to address mixed variational inequality problems. Our approach introduces an adjustable time parameter, thereby enhancing flexibility and applicability compared to conventional fixed-time stability methods. By satisfying certain conditions, the proposed approach is capable of converging to a unique solution within a predefined-time, which sets it apart from fixed-time stability and finite-time stability approaches. Furthermore, our approach can be extended to address a wide range of mathematical optimization problems, including variational inequalities, nonlinear complementarity problems, sparse signal recovery problems, and nash equilibria seeking problems in noncooperative games. We provide numerical simulations to validate the theoretical derivation and showcase the effectiveness and feasibility of our proposed method. [ABSTRACT FROM AUTHOR]

Users' energy use behavior has great impact on the economic and efficient operation of integrated energy systems (IES). This paper proposes energy usage strategies based on integrated demand response (IDR) by developing a Stackelberg game based simulation model, in which the user-side flexible resources (FRs) and comfort cost are considered. In the paper, a framework of community integrated energy system (CIES) and an IDR model considering the consumer psychology were established first, in which the community users were viewed as a user aggregator (UA). A Stackelberg game simulation model was then developed for studying the energy interactions between community integrated energy service provider (CIESP) as the leader and UA as the follower based on the proposed IDR model. The benefit gain and energy use behavior of the UA were analyzed, by using the model developed, in six scenarios. The study found the strategy developed with the consideration of FRs and consumer psychology could make the UA's cost of purchasing energy reduced by 17.14 %. Meanwhile, the consumer surplus could increase by 11.58 %. The results also showed that by introducing user-side gas boilers and distributed energy storage more benefit gain for users and improved load regulation capabilities for CIES could be achieved. • A Stackelberg game based simulation model is developed for a CIES. • An IDR model considering the consumer psychology is established. • The optimal energy use strategy is obtained considering the user-side flexible resources and comfort cost. • This strategy can reduce the cost of energy use and increase the consumer surplus. [ABSTRACT FROM AUTHOR]

In the context of global warming and energy scarcity, regional integrated energy system (RIES) can effectively improve energy utilization efficiency and reduce carbon emissions. However, this leads to the increased complexity of the market transactions. To this end, based on the mixed game strategy, a multi-agent low-carbon optimal dispatch model of RIES is proposed in this paper. Firstly, to fully consider the low-carbon objective of the system, a reward and punishment ladder carbon trading mechanism and a ladder integrated demand response (IDR) model are developed. Then, the decision models of the energy management operator (EMO), multiple energy hub agents (EHAs) and the user load aggregator (ULA) are constructed respectively with the goal of economy, and carbon emission flows are introduced as constraints. The interactive trading process of various stakeholders is simulated by a mixed game with master-slave game nested bidding strategy. Finally, the economics and carbon emissions of the system in different scenarios are analyzed by examples, and the effectiveness of the proposed model is verified. Numerical simulation results show that the proposed method not only has good economic and low-carbon environmental protection benefits, but also ensures the interests of various stakeholders. • A multi-agent energy trading mechanism is proposed to realize low-carbon operation. • A ladder integrated demand respond model considering carbon compensation is developed. • A multi-agent optimization strategy based on mixed game is proposed. [ABSTRACT FROM AUTHOR]

Carbon tax and emission trading compound policy (CT&ET), carbon tax policy (CTP), and emission trading policy (ETP) support countries in achieving "dual carbon" goals. Carbon verification is essential to these policies' efficacy. Policy changes will affect system equilibrium and present unclear risks to policy implementation. This paper compared the impact of three carbon emission reduction policies on the behavior of carbon verification subjects under various conditions, three policies' benefits and drawbacks at different stages, and proposed a dynamic penalty mechanism to restrain subject infractions. Finally, the simulations revealed three policies of system evolution path and parameter sensitivity in various scenarios. CT&ET best guarantees carbon verification quality has a high-risk tolerance, and restricts infractions in most scenes. The excessively conservative carbon quota and severe collusion penalty make CTP optimal for limiting ECE false reports. ETP reduces collusion best when carbon quotas are appropriately allocated. Adjusting taxes and carbon prices, increasing verification levels, and reducing false reporting will be easier under CTP and ETP. Dynamic penalties are particularly effective in limiting infractions. Static penalties stabilize compliance behavior effectively. This study proposed a policy parameter framework and optimization mechanism to improve the carbon verification mechanism and accelerate the "dual carbon" goal. • We innovatively introduce important factors such as verification level, collusion punishment risk, and profit distribution. Introducing carbon verification into the study of three carbon reduction policies expands the field of carbon verification and the study of carbon emission reduction policies. • We have innovatively revealed the advantages and disadvantages of carbon reduction policies under different conditions. Taking into account cost and operability, we have designed a strategy parameter system and dynamic penalty mechanism to prevent violations by entities. We can derive an ideal system evolution trajectory and stability. • The findings provide theoretical guidance for conducting CT&ET, CTP, and ETP in countries to ensure the quality of carbon verification, improve the efficacy of carbon emission reduction policies, and meet "dual carbon" objectives on time. [ABSTRACT FROM AUTHOR]

With the rapid development of offshore wind power, the construction and operation of multiple adjacent wind farms (WFs) in the form of cluster has become a trend. However, the method for single WF planning can not address the WF cluster layout optimization in the complex relationship of competition or cooperation. This paper proposes a novel method to design clustered offshore WFs. The objectives to be optimized include the maximization of WF generated power and the minimization of wind turbine (WT) turbulence intensity. To solve this problem, a mathematical model based on the potential game theory is established. In this planning model, each WF in the cluster is assumed to be an individual stakeholder, the decision variables and the constraints are mapped to be the strategies and the strategy sets of the stakeholders, respectively, and the objectives are set as their payoffs. The non-dominated sorting genetic algorithm II (NSGA-II) is synthesized with the dependency structure matrix genetic algorithm II (DSMGA-II) to tackle the multi-objective optimization problem (MOOP) and the fuzzy-membership function (FMF) method is used to select the final solution from the Pareto optimal set. The best-strategy-response iterative algorithm is put forward to search for the Nash equilibrium (NE) of the game. Case studies on three clustered offshore WFs in Qidong, China, are carried out to validate the effectiveness of the proposed approach. Also, the simulation results indicate that the synthesized algorithm outperforms the NSGA-II in solving this optimization problem. • The layouts of clustered WFs are optimized using the potential game theory. • The MOOP is solved by the DSMGA-II and the FMF is used to select the solution. • The alliance of WFs has a better result compared with the WFs are non-cooperative. • The DSMGA-II outperforms the NSGA-II in searching the optimum. [ABSTRACT FROM AUTHOR]

The development of dry ports has reshaped the international freight shipping market, introducing both competition pressure (i.e., the common hinterland) and cooperation opportunities (sea-rail intermodal) for seaports. This is more evident in China with the development of the China Railway Express. Unlike dry ports, seaports are more vulnerable to climate change-related disasters, calling for investment in adaptation measures. This paper examines how co-opetition with the dry port affects seaport adaptation investment using a two-stage game-theoretical economic model. Results indicate that cooperation leads to more seaport adaptation investment, while competition results in reduced investment. When compared to the social optimal level of adaptation investment, the seaport tends to under-invest (over-invest) when the cooperation (competition) market size is larger. We also analyze how government subsidies in the seaport's adaptation help achieve optimal social adaptation and how the Knightian uncertainty of the disaster occurrence affects seaport adaptation investments. The relevant policy implications are also discussed. [ABSTRACT FROM AUTHOR]

Tunnel structure are often subjected to continuous deformation caused by the creep effect of soft rock, leading to posing significant long-term operational risks. This paper focuses on ensuring the safe operation of a typical soft rock tunnel in the complex and dangerous mountainous area of western China. On the basis of studying the structural mechanic characteristics of the tunnel under the influence of soft rock creep, the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model based on Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to determine subjective and objective weights, and game theory (GT) to obtain comprehensive weights was established. Additionally, the differential evolution-grey wolf optimization (DE-GWO) improved extreme learning machine (ELM) model was also constructed. By comparing the safety evaluation results of the two models on typical monitoring sections of soft rock tunnel structure, it is demonstrated that the proposed knowledge-based decision-making and data-driven models effectively and accurately evaluated the long-term safety of typical monitoring sections. Ulteriorly, to address the issue of structure safety evaluation caused by the inability to comprehensively monitor the entire tunnel (missing monitoring data), the DE-GWO improved ELM model was further adopted, and the structure safety status of the entire tunnel was precisely deduced. [ABSTRACT FROM AUTHOR]

Designing a system to improve the performance of the supply chain by linking pricing and advertising will bring significant benefits to the supply chain components. Due to the expansion of customers' activities in social networks and society's greater interest in obtaining information from this space, companies have changed their marketing methods. Organizations have turned to influencer marketing for advertising to take advantage of social networks' potential. Despite the recent growth of influencer marketing, the issue of optimal identification and appropriate spending based on the characteristics of influencers to convey a company message or advertisement has not been carefully studied in the academic literature, nor has it been addressed in practice. Therefore, in this paper, a hybrid framework of machine learning and optimization is developed to help organizations (i) conduct successful and optimal marketing campaigns by selecting the best influencers and (ii) examine the effectiveness of advertising on the pricing of products in the supply chain. The framework designed in this study is validated using data extracted from the Instagram platform. The results demonstrate that businesses should adopt novel approaches to get the maximum benefit from advertisements on social media, which depends on a more accurate selection of influential users. Contrary to previous works, our results demonstrate that choosing influencers based on their influence network will not necessarily bring the expected efficiency for businesses. [ABSTRACT FROM AUTHOR]

Water quality assessment is a crucial step in water resource management and pollution prevention, and is essential for achieving sustainable development. Current methods for evaluating water quality often fall short of meeting the needs of water resource management due to the lacks of accuracy and applicability. In this paper, an enhanced water quality evaluation system was established by Principal Component Analysis / Factor Analysis (PCA/FA), Analytic Hierarchy Process / Entropy Weight Method / Game Theory (AHP/EWM/GT) and Variable Fuzzy Set Theory (VFST). The accuracy of this method was verified by evaluating the water quality of a landfill in a karst area and comparing with the conventional evaluation models. The results indicated that the highest influence on groundwater quality in the study area came from human factor, dissolved solid factor, biological factor, and nutritional factor. The quality of surface water and groundwater in the study area was primarily Grade Ⅱ, accounting for 62.5% and 75%, respectively. The predominant groundwater type was HCO 3 -Ca·Mg, which was strongly affected by karstification. The NH 3 -N and TP in the surface water had the highest variability, and the variation coefficients of K+, Na+, Ca2+, Cl−, NH 3 -N, NO 3 −, TDS, and TH in the groundwater were greater than 1. The surface water quality gradually improved between 2018 and 2019. Overall, the groundwater quality in the southern and central regions was inferior to that in the northern and surrounding areas. There could be a hydraulic link between the surface water and landfill in the research location, while karstification and leachate primarily impacted the groundwater. The water quality evaluation method established in this study can make a significant contribution to water resource management, which has important practical significance for environmental protection and indirect social value to the public. [Display omitted] • PCA/FA is used to reduce the dimension and determine the evaluation index. • The comprehensive weight is obtained by GT coupling AHP and EWM. • An improved VFST water quality evaluation model is established and verified. • The hydrochemical features and water-quality temporospatial variation are analyzed. [ABSTRACT FROM AUTHOR]

With the increasing number of integrated energy system (IES) construction, a multi-agent system group composed of multiple IESs would be formed in a certain region. Moreover, complex uncertainties gradually existed in the IES which affect the system optimal situation and optimization results. In order to take full use of each agent's advantages and improve the performance of the whole region under uncertainty, this paper focuses on the multiple energy types' interaction of multi-agent IESs within the region to realize the stable operation, economic and environmental improvement of each agent and the whole region. To this aim, a multi-objective multiple energy types' multi-agent interactive optimization model considering energy demand and renewable energy output uncertainties was built. The model was based on Nash bargaining cooperative game theory and took economy, flexibility and carbon emission as objectives. Moreover, energy interaction scheduling and its corresponding price can be optimized through the model. The results of non-cooperative operation mode without interaction and cooperative operation mode with interaction were compared to analyze the superiority of interaction from the cost, carbon emission and flexibility aspects. The overall regional carbon emission and the economic cost has been reduced by 13.84% and 9.94%, for each IES, the carbon emission and economic cost could reduce by up to 41.5% and 44.5%. Moreover, the flexibility of the IES was correspondingly increased by nearly 47%. These results denoted that after multi-agent interaction, not only the overall region's energy, economic and environmental performance are improved, but also that of each agent are significantly improved. In addition, different energy types among multiple agents could be effectively used and regional joint energy storage is realized, improving the region flexibility level and resilience. • A multi-objective integrated energy systems interaction model is established. • The interaction model considers multiple energy types and uncertainties. • The model can improve economy and flexibility of each system agent and the region. [ABSTRACT FROM AUTHOR]

The potential for profit-seeking suppliers in emergency supply chains (ESCs) to default and how to penalize defaulting suppliers has not been adequately discussed. In this paper, we construct an ESC consisting of a supplier and a government, in which the supplier pre-stocks supplies for the government for a pre-disaster contract period, and the government orders supplies from the supplier after the disaster based on actual demand. Our research question is how to choose an appropriate penalty measure (PM) in the face of default by suppliers who stock different types of supplies. The results show that the higher storage cost for supplies not be ordered after the contract period can reduce suppliers' incentives to pre-stock supplies and even lead to supplier default, and that a reduction in the quantity ordered by the government from suppliers can reduce the likelihood of supplier default. The government should take merciful PMs to penalize the defaulting supplier who pre-stocks supplies with high marketability or high market price, and strict PMs to penalize the defaulting supplier who pre-stocks supplies with low marketability or low market price. In addition, although it is difficult to accurately measure the losses incurred by default, we can still penalize defaulting suppliers based on the default quantity. Finally, we also find that constructing compromise PMs allows the government to find a balance between strictness and mercy. By adjusting the degree of compromise, the government can choose the penalty strength appropriate to the situation. • Even in humanitarian logistics, suppliers still have thoughts of default. • Government initiatives to lower supplier expectations can reduce default risks. • Strict or merciful punishment is related to the supplies type. • When default loss is imprecise, penalties can be based on default quantity. [ABSTRACT FROM AUTHOR]

• We analyse the optimal financing mode of an e-tailer in a three-tier supply chain. • We compare third party financing (LF) with platform financing (PF). • We determine optimal product prices and interest rates under each financing mode. • All supply chain players are better off in LF (PF) at low (high) operational risk. • We propose a decision support matrix based on referral fee and operational risk. In addition to providing logistics services, third-party logistics (3PL) firms (e.g., S.F. Express) have recently started extending financial services to capital-constrained e-tailers (referred to as 3PL financing (LF)). Another novel financing mode available for e-tailers is platform financing (PF), in which online platforms such as Amazon and Alibaba provide loans to e-tailers on their platforms. Both LF and PF facilitate trade in a capital-constrained supply chain. However, in the presence of an e-tailer's operational risk, the lenders are exposed to credit risk, which can negatively impact the whole supply chain. Moreover, when both financing modes are available, the e-tailer must develop an optimal borrowing strategy. Multiple factors, such as bankruptcy cost, referral fee, and shipping fee, can significantly influence players' financing decisions. Therefore, this paper examines supply chain members' optimal operational and financing decisions under LF and PF. We study a three-tier supply chain in which an e-tailer, exposed to operational risk, procures a product from a supplier via the 3PL firm and resells it to end customers through the online platform. The e-tailer obtains the working capital either through LF or PF. We find that at low (high) levels of operational risk, the e-tailer chooses LF (PF). Surprisingly, the 3PL firm and the platform do not always benefit from the LF and PF. We recommend a decision support framework for the e-tailer's optimal financing strategy for different product categories based on its operational risk. We show that for products with a low (moderate and high) referral fee, LF (PF) achieves a win-win-win outcome under low (high) operational risk. Further analysis shows that the e-tailer's initial working capital, logistics, and product costs can alter the players' financing decisions. [ABSTRACT FROM AUTHOR]

Quality control of sensing data poses a fundamental challenge in Spatial Crowdsourcing (SC) applications, and various incentive mechanisms have been proposed to effectively motivate workers. However, real-world SC systems involve multiple stakeholders, namely workers, platforms, and requesters, who exhibit bounded rationality. Moreover, prior research has often neglected the practical aspect of platforms needing to verify sensing data against ground truth information, which incurs a cost. Therefore, platforms face the constraint of verification cost and must judiciously select the verification rate. In this paper, a S ustainable Q uality C ontrol S ystem (SQCS) is proposed, which employs a three-party evolutionary game to model the dynamic interaction among workers, platforms, and requesters in practical SC scenarios. Based on this dynamic model: (1) In theory, we derive the minimum verification rate required for effective worker monitoring and the maximum verification cost that platforms can sustainably bear via the analysis of evolutionary stable strategies. These findings serve as guidelines for achieving sustainable quality control within the SQCS framework; (2) In practice, we apply the SQCS framework to the SC scenario of urban air pollution monitoring. The optimal quality control strategy that maximizes overall social benefits is analyzed via numerical methods. Extensive experimental validation is conducted to demonstrate the effectiveness of the proposed theory and practical approach. These findings provide valuable insights for the sustainable development of SC systems. [ABSTRACT FROM AUTHOR]

Today, due to the widespread use of smartphones, numerous applications, and their advantages in a number of daily tasks, subscribers desire a high-speed connection more than ever before. There may be some subscribers who choose to subscribe to more than one telecommunications service provider to satisfy their needs. Operators in such a competitive environment try to take advantage of this diversity of consumer needs and preferences in order to provide different services and pricing options. In this paper, a duopoly market consisting of two mobile operators is examined from a game theory perspective. By offering and pricing various bundled call and data services, as complementary services, operators try to maximize their profits. Subscribers choose the bundle with the highest surplus that best suits their telecom needs. Subscribers from various market segments may churn if they discover that other operators offer service plans with higher surpluses. The problem is formulated as a Generalized Nash Equilibrium Problem (GNEP) where operators price service plans simultaneously while they are coupled through shared constraints. After reformulating the mathematical model, a heuristic algorithm is proposed to find an equilibrium solution. In addition to extensive numerical experiments, a thorough sensitivity analysis is conducted. Finally, this study provides some managerial insights that may assist telecom operators in making optimal decisions regarding the pricing and design of their bundles in a competitive environment. • A GNEP model of competition between two telecom operators in a duopoly market. • Consideration of complementary service bundles and competitive pricing. • Considering customer churn in the GNEP model of competition among telecom operators. • Developing a heuristic algorithm for the proposed GNEP with mixed-binary variables. • Numerical experiments confirmed the performance of the model and algorithm. [ABSTRACT FROM AUTHOR]

Amidst high penetration of renewable energy, virtual power plant (VPP) technology emerges as a viable solution to bolster power system controllability. This paper integrates a novel flexible load, 5G base stations (gNBs) with their backup energy storage systems (BESSs), into a VPP for power system real-time economic dispatch (RTED). Leveraging BESSs dispatchable capacity, the VPP offers power support and gains economic incentives, where the dispatchable capacity is estimated based on distribution network reliability and gNB availability requirements. Then, an efficient and refined VPP management strategy is proposed, where numerous gNBs are aggregated into limited number of virtual generators (VGs). The VPP dispatchable capacity can be efficiently and accurately evaluated via VGs states. Each VG is assigned a control cost function, enabling optimal control decisions resembling power system RTED, to minimize overall VPP control costs. Finally, a power support and incentive determination mechanism is presented based on the Leontief union bargaining model, ensuring reciprocal transactions between VPP and power system. To solve the Leontief model and address information privacy issues between the two parties, a distributed iterative algorithm is developed. Simulations based on actual data and IEEE 118-bus system illustrate that the proposed approach can smoothen intra-day load profiles, achieving mutual economic benefits for the power system and VPP without compromising the security of 5G network operations. • The VPP technology is innovatively applied to manage gNB clusters. • An efficient and refined VPP management strategy with high expandability is proposed. • VPP control cost is optimized in a way similar to power system economic dispatch. • A Leontief game-based pricing mechanism is designed to reach win-win settlements. • A distributed algorithm is proposed to solve game model under data privacy issues. [ABSTRACT FROM AUTHOR]

The purpose of this article is to study existence conditions and generic stability of solution sets to the fuzzy generalized multiobjective games in infinite dimensional spaces. First, we revisit a new class of the fuzzy generalized multiobjective games. Second, we introduce the concept of the extended C i -quasi-concavity and establish existence conditions for fuzzy generalized multiobjective games using Browder type fixed point theorem in the noncompact cases. Third, we establish some sufficient conditions for generic solution stability conditions to such n -player multiobjective games under uncertainty. Finally, as a real-world application, we consider the special case of economic equilibrium models. Existence conditions and generic stability of solution sets for these real models are also obtained. The results obtained in this paper are new and different from the main results given by some authors in the literature. [ABSTRACT FROM AUTHOR]

• A composite index named GLCDES is developed to dynamically assess the level of Green Low-carbon Circular Development. • The combined-assignment VIKOR model based on game theory is developed to thoroughly estimate the construction level of GLCDES. • Southwest China's Green Low-carbon Circular Development Economic System construction level is examined from 2012 to 2021, showing how the long-side principle governs each region's value. • Selecting and strengthening the long side in combination with its reality is an effective way to improve the construction level of GLCDES. Emission peak and carbon neutrality are critical components of China's overall ecological civilization, and the establishment of a sound Green Low-carbon Circular Development Economic System (GLCDES) is a fundamental countermeasure for solving China's resource, environmental, and ecological problems. This paper used the new development concept as entry points to establish a comprehensive evaluation index system encompassing six subsystems: development dynamics, production and living systems, green benefits, low-carbon benefits, circularity benefits and socio-economic benefits. The combined-assignment VIKOR model, based on game theory, was used to thoroughly estimate the construction level of GLCDES by combining panel data from four southwestern provinces, including Chongqing, Sichuan, Guizhou and Yunnan, from 2012 to 2021. The study discovered that: the construction level of GLCDES in all four southwestern provinces increased over the past ten years, but the increase was not significant, the four development effectiveness indices, including green, low-carbon, circularity and socio-economic, were significantly improved, the development dynamics index was not significantly enhanced, and the index of production and living systems declined in fluctuation from year to year. The construction level of GLCDES between regions was very uneven and can be divided into two echelons, with the gap between Chongqing and Guizhou being particularly obvious. Regions can be categorized into three major categories based on the number of dominant subsystems. According to the above results, the total value of the dynamic comprehensive evaluation of each region is governed by the principle of the long side, which is an effective way to improve the construction level of GLCDES by selecting and strengthening the long side in combination with its reality. [ABSTRACT FROM AUTHOR]