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This paper builds on recent insights from network theory and on the rich dataset made available by the Panama Papers in order to investigate the micro-economic dynamics of tax-evasion. We model offshore financial entities documented in the Panama Papers as links between jurisdictions in the global network of tax evasion. A quantitative analysis shows that the resulting network, far from being a random collection of bilateral links, has key features of complex networks such as a core-periphery structure and a fat-tail degree distribution. We argue that these structural features imply that policy must adopt a systemic perspective to mitigate tax evasion. We offer three sets of insights from this perspective. First, we identify through centrality measures tax havens that ought to be priority policy targets. Second, we show that efficient tax treaties must contain exchange information clauses and link tax-havens to non-haven jurisdictions. Third, we show that the optimal deterrence strategies for a social-planner facing a strategic tax-evader in a Stackelberg competition can be characterized using the notion of Bonacich centrality. [ABSTRACT FROM AUTHOR]

Using the Stackelberg game theory, this paper evaluates the effectiveness of four subsidy schemes on the product eco-design and recycling rate in a manufacturing–recycling system that comprises a government, a manufacturer and a recycler. The principal results are: 1) For the cost-based subsidies, a fixed-cost subsidy to recyclers always yields higher recycling rate and social welfare. For the performance-based subsidies, the effects are closely related to market demand and the fixed cost coefficients of eco-design and recycling. Governments should select subsidy schemes according to the specific goals and market conditions. 2) Four subsidy schemes are the effective forces to promote product eco-design and recycling. Higher subsidy intensity brings better supply chain profits and better environment performance, but it does not always bring better social welfare. 3) Eco-design can increase waste recycling rates effectively, and recycling rates are more sensitive to eco-design when the government subsidizes the fixed costs of recyclers. [Display omitted] • Two cost-based subsidies and two performance-based subsidies are considered. • Government subsidy is useful to promote product eco-design and recycling. • For cost-based subsidy, government can prioritize recycler fixed cost subsidy. • For performance-based subsidy, government can adjust scheme with varied conditions. [ABSTRACT FROM AUTHOR]

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]

Abstract In this paper, we generalize classical von Neumann symmetrization of two-person zero-sum games to general linear games. We use this symmetrization to show that for a given general linear game there exists a symmetric linear game whose solution yields a solution to the underlying linear game. We define symmetric linear games of type gRPS (generalized Rock–Paper–Scissors) and prove that a symmetric linear game has a pure strategy equilibrium if and only if it is not a gRPS game. From this we deduce that a completely mixed symmetric linear game is gRPS. [ABSTRACT FROM AUTHOR]

• Evolutionary game theory is used to study the dynamic behaviors in WEEE recycling. • Fund Relief Policy can motivate the producers' green innovation behavior. • Fund Relief Policy can to some extent alleviate the government financial pressure. In an effort to regulate waste electrical and electronic equipment (WEEE) recycling, the Chinese government has issued China-WEEE regulations and a fund policy. In accordance with the 2012 fund policy, the government collects funds from producers and subsidizes recycling enterprises. However, this policy has encountered difficulties. The 2016 White Paper on WEEE Recycling, Treatment and Reuse states that while the funds collected from producers were 2.61 billion Yuan, the subsidies provided to recycling enterprises were 4.714 billion Yuan, which is a gap of 2.104 billion Yuan. In addition, this policy did not incentivize producers to invest in WEEE recycling. Our paper aims to answering the following questions: (1) What is the impact of the current WEEE processing fund policy? (2) How can producers' green innovation in WEEE recycling be promoted? We use evolutionary game theory to investigate the strategies used by producers and recyclers and explore their evolutionary behaviors under two policies: the 2012 fund policy (the current policy) and the Fund Relief Policy (a newly designed policy). The results indicate that the Fund Relief Policy can motivate producers' green innovation behavior, improve the profitability of recyclers, and alleviate the financial pressure on government to a certain extent. [ABSTRACT FROM AUTHOR]

• As WEEE are "valuable goods", an ERP funding leads to a dual dismantling system. • A game theory model was established and verified by Chinese WEEE funding system. • The effect of funding depends on the profits of informal dismantling enterprises. • WEEE recovery enterprises would raise WEEE prices to benefit from funding. This paper analyzes the characteristics of waste electrical and electronic equipment (WEEE) market in China and the findings are as follows: WEEE are still seen as valuable goods, and a recovery and recycling system is thus in place naturally in China like in many other developing countries. A game theory model is therefore built here to find out how effective funding is for this recovery and recycling system. Analysis of this model reveals: (1) Some dismantling enterprises (formal dismantling enterprises) are in this funding system, while others (informal ones) are not, thus forming a dual WEEE dismantling system, after the funding system was launched; (2) To ensure effectiveness of the funding system, the funding provided for formal enterprises for each piece of WEEE they handle, after deducting the incremental cost incurred for environmental compliance, must exceed the difference between the unit selling prices of the dismantled devices at formal and informal dismantling enterprises. This difference is RMB 70.2, RMB 67.9, RMB 22.2, RMB 79.4 and RMB 68.3 respectively for televisions, refrigerators, washing machines, air conditioners and computers; (3) When the funding system was launched, WEEE recovery enterprises would raise WEEE prices to seek profit and this would channel part of the funding into the recovery segment of the market. [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]

Advancements in uncertain reasoning systems within healthcare are crucial for navigating the complexities of patient data, requiring innovative methodologies that integrate AI interpretation capabilities and robust handling of inherent ambiguity. Healthcare systems face the challenge of handling uncertainty inherent in patient data, necessitating sophisticated decision-making tools like Uncertain Reasoning Systems (URS) for effective ambiguity navigation. Recognizing the complexity of healthcare scenarios, advancements in AI interpretation within URS are crucial beyond traditional methods. Conventional techniques like statistical approaches and rule-based systems often prove inadequate due to their rigid frameworks and limited ability to manage inherent ambiguity. This paper proposes an innovative methodology that integrates Min-Max normalization and robust missing data handling techniques with Hybrid Fuzzy Rule-Based Systems and Neural Networks, supplemented by Game Theory for model refinement. Through the integration of Game Theory, it can dynamically adjust its strategies to healthcare data uncertainties, thereby enhancing its resilience and efficacy. Implemented using Python tools, the proposed system achieves an exceptional 99.4 % accuracy, surpassing baseline methods such as FNN (88.1 %) and Naïve Bayes (90 %), highlighting its superior performance in healthcare decision-making. These findings represent significant strides in AI interpretation and decision-making within Uncertain Reasoning Systems, underscoring the practical relevance of the proposed approach. [ABSTRACT FROM AUTHOR]

Environmental crises and increasing pressure from governments are responsible for the appearance of various green approaches to be considered in supply chain management. One of these approaches is sustainable supply chains, which have been implemented in different aspects of supply chains. Most third-party logistics (3 PLs) companies now provide their services utilizing the sustainable supply chain approach. In this study, we consider a 3PL in a supply chain with two goals: decreasing carbon emission and delivery time using a game theoretic approach, which is considered for the first time. We suppose that consumers are aware about the green transportation. Also, it is assumed that two manufacturers deliver their products to the same retailer using a 3 PL. The manufacturers use the 3 PL mainly because it is a way to provide green transportation and increase customer satisfaction by ensuring that products are delivered in a timely fashion to the retailer. This paper examines product pricing, the greenness determination for the first manufacturer's product, and the variables for the 3 PL, including the amount of carbon dioxide emissions and delivery time. Three scenarios are considered in this research: a centralized game and two decentralized games. The results show that it is essential to establish competition between the supply chain members to obtain greater profitability and an acceptable level of sustainability indicators. In the decentralized Stackelberg game between the manufacturers, they earn the highest profit. In the centralized game, the product greenness is the lowest, and in the decentralized game when the 3 PL is the leader, the product greenness is the highest. • The production of one green and one non-green product by two competitor producers. • The producers send products to retailer by using a 3 PL. • Using game theory to investigate the effects of 3 PL in the sustainable SC. • Analyzing the problem with three scenarios: two decentralized and one centralized. [ABSTRACT FROM AUTHOR]

The project portfolio selection process aims to maximize synergy among chosen projects while delivering significant value to the parent organization. Due to the complexity of this decision, several approaches to project portfolio selection have been developed. In this paper, we adopt a cooperative and static game with complete information and implement the Shapley Value to determine the optimal mix of projects in a case study of a Colombian project portfolio. The application of the Shapley Value demonstrates its equitable nature, as it fairly distributes benefits considering the number of coalitions and the agreements made by each project within these coalitions. [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]

Peer-to-peer (P2P) electricity trading has become the next generation of energy management strategies that economically benefit prosumers by trading electricity as goods and services. The P2P electricity market is expected to support the grid to minimize reserve requirements, lower investment and operational costs, reduce peak demand, and improve reliability. Nonetheless, the deployment of P2P electricity trading in the electricity networks is challenging in terms of modelling the transactions in both the physical and virtual layers of the network. The physical layer is a physical network that enables the transfer of electricity from the sellers to the buyers once financial settlements between both parties are completed over the virtual layer platform. The virtual layer facilitates a secured connection for participants to decide on their electricity trading parameters. This paper systematically reviews the current developments in the P2P electricity trading literature. Importantly, the systematic literature review brings to light six essential components in P2P electricity trading processes, namely: P2P electricity trading platforms, P2P market schemes, P2P electricity trading market clearance algorithms, policy supporting P2P electricity trading, P2P electricity trading networks and P2P ICT infrastructure, which are discussed in detail in the paper. Accordingly, for each component, state-of-the-art technologies, notable findings, and best practice insights are comprehensively reviewed, based on which directions for future research are presented. The P2P electricity market is a rapidly emerging research domain with significant opportunities and prospects globally in the industry and academia. [ABSTRACT FROM AUTHOR]

Minimal balanced collections are a generalization of partitions of a finite set of n elements and have important applications in cooperative game theory and discrete mathematics. However, their number is not known beyond n = 4. In this paper we investigate the problem of generating minimal balanced collections and implement the Peleg algorithm, permitting to generate all minimal balanced collections till n = 7. Secondly, we provide practical algorithms to check many properties of coalitions and games, based on minimal balanced collections, in a way which is faster than linear programming-based methods. In particular, we construct an algorithm to check if the core of a cooperative game is a stable set in the sense of von Neumann and Morgenstern. The algorithm implements a theorem according to which the core is a stable set if and only if a certain nested balancedness condition is valid. The second level of this condition requires generalizing the notion of balanced collection to balanced sets. [ABSTRACT FROM AUTHOR]

With electrification and economic development, global carbon emissions are rising every year. Among them, the power industry accounted for the highest proportion of carbon emissions. To combat climate change, environmental pollution and economic losses caused by greenhouse effect, governments implement low-carbon policies in the electricity market. Considering the impact of generators' carbon emissions on consumers behaviors and environmental benefit, this paper compares two main emission reduction policies — carbon tax and carbon allowance in a market with two asymmetric generators and a group of heterogeneous eco-conscious consumers. Despite the widespread belief that environmental advocacy hurts economic performance and social welfare, we find that both policies improve social welfare due to price increases or production restrictions. And when neither generator has a significant cost advantage, the social welfare under carbon allowance is higher than that under carbon tax, and the total carbon emissions are lower. This is because the carbon tax indirectly regulates generators' output and total welfare by affecting prices. While carbon allowance can directly regulate generators' output through capacity limits, and further improve the total social welfare. Through numerical analysis, the optimal emission reduction strategies of the government under different conditions are determined. We also extend the main model and show that the key results are still valid. [ABSTRACT FROM AUTHOR]

This paper presents a cooperative game theory approach to design an optimal video steganography framework. An ideal and perfect video steganography system should provide enough space to embed the secret data without degrading the visual quality. However, in a steganographic system, the payload capacity and visual quality are always inversely proportional to each other. This paper proposes a novel game-theoretic approach using strategy adaption to address this issue. This approach utilizes the Iterative Elimination of Strictly Dominant Strategies (IESDS) method to acquire the optimal solution. The obtained optimal solution comes up with the best trade-off between capacity and visual quality. Also, experimental results infer that the optimal solution outperforms the contemporary methods by attaining significant outcomes. [ABSTRACT FROM AUTHOR]

A Cognitive Radio based Wireless Network (CRWN) is an emerging trend in wireless networks. Cognitive Radio (CR) has opened up many new opportunities to utilize the frequency spectrum to its fullest utilization. Many researchers and engineers are coming up with new protocols for CRWN. In this paper, existing MAC protocols for CRWN are studied and found that all the protocols are designed for optimized node performance but not the network performance. Moreover, individual node's optimized performance may harm network's performance. So, there is a gap of performance to be filled at an individual node and network-level. In this paper, the frame work of existing MAC protocol is modified to achieve the global optimized performance of CRWN under saturated and unsaturated traffic conditions. Also, majority of the existing work proposed their solutions assuming full information-based game theoretical models, whereas in this paper more realistic approach based on incomplete information is presented. A rigorous simulation study is carried out to compute various parameters such as channel capacity, throughput and delay to understand the limitations of the non-game theoretic approach. Later, a game theoretical framework to MAC protocol is applied for CRWN to improve the network performance in terms of these parameters. It is shown that the application of game theory to the MAC layer avoids collision and reduces the delay by 54% and energy consumption leading to enhancement of the overall throughput of a network by 57% respectively. [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]

Emissions trading has been playing a very important role in addressing pollution of estuarine and coastal waters. This paper aims to explain mechanism of the multi-agent collaborative management of coastal pollution from land-based sources from the perspective of emissions trading and identify its influencing factors. Given the current participation of polluting enterprises in emissions trading, this paper constructs a tripartite evolutionary game model consisting of local governments, polluting enterprises, and the public. Drawing on this model and the ABM (agent-based modeling) for simulation and using artificial neural networks for error corrections, the numerical simulation of the data of Dongying, a pilot city of emissions trading is conducted, to examine the influence of different factors on the evolutionarily stable strategy. The results show that: ➀ increasing each agent's initial willingness of participating in collaborative management can facilitate the evolution of their behavioral strategies towards collaborative management. Specifically, compared with the public, polluter's stronger willingness for emission reduction and the government's more proactive willingness for regulation can better promote collaborative management in land-based pollution; ➁ the increase in the reward standard for emissions cutters has a positive impact on enterprises and the public's proactive participation in collaborative management, yet a negative impact on local governments; ➂ higher reward from the provincial government and the increase in the emissions trading price have a positive impact on the enthusiasm of local governments, enterprises and the public in collaborative management; ➃ the increase in the unit cost of emission reduction will undermine polluting enterprises' enthusiasm in emission reduction. Policy suggestions are thus proposed, such as increasing the subsidies for emission reduction, promoting and perfecting the emission trading market, cutting the emission reduction cost of enterprises and the supervision cost of local governments, and increasing the public's concern on the governance of marine pollution from land-based sources. The conclusion will provide a theoretical underpinning for and guide related practice of future preservation of coastal ecologies and management of coastal pollution from land-based sources. [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]

This paper studies the empirical properties and identification for a broad class of adaptive learning models in games (e.g., experience-weighted attraction (EWA), payoff assessment, and impulse-matching models). I first show that adding a constant c ≠ 0 to the utility function could affect players' choices during the learning periods. This property is different from standard theories, such as expected utility theory and discrete choice models, where adding c ≠ 0 preserves the same choice. This result has two further implications. First, the constant c added to the utility function could have a structural interpretation, and this paper discusses how to interpret it. Second, the estimation of learning models usually normalizes the utility of one action profile to be zero (i.e., c = 0); for instance, the utility of zero dollars is zero, as commonly imposed in experimental economics. This normalization is innocuous in most applications of discrete choice models but could be misspecified in the context of learning. To deal with the above issue, this paper proposes to specify a player's utility as an unknown function of the action profile, without imposing the above normalization. Equivalently, c is specified as an unknown parameter to be estimated. Under weak conditions, I obtain the joint identification of both utility and learning parameters. Finally, I illustrate the importance of c in the estimation of learning models by conducting a Monte Carlo experiment and revisiting the experimental study by Feri et al. (2010). [ABSTRACT FROM AUTHOR]

The recent years have witnessed the explosive application of sequencing technologies to study tumor–microenvironment interactions and their role in shaping intratumoral heterogeneity, neoplastic progression and tumor resistance to anticancer drugs. Statistical modeling is an essential tool to decipher the function of cellular interactions from massive amounts of transcriptomic data. However, most available approaches can only capture the existence of cell interconnections, failing to reveal how cells communicate with each other in (bi)directional, signed, and weighted manners. Widely used ligand–receptor signaling analysis can discern pairwise or dyadic cell–cell interactions, but it has little power to characterize the rock–paper–scissors cycle of interdependence among a large number of interacting cells. Here, we introduce an emerging statistical physics theory, derived from the interdisciplinary cross-pollination of ecosystem theory, allometric scaling law, evolutionary game theory, predator–prey theory, and graph theory. This new theory, coined quasi-dynamic game-graph theory (qdGGT), is formulated as generalized Lotka–Volterra predator–prey equations, allowing cell–cell crosstalk networks across any level of organizational space to be inferred from any type of genomic data with any dimension. qdGGT can visualize and interrogate how genes reciprocally telegraph signals among cells from different biogeographical locations and how this process orchestrates tumor processes. We demonstrate the application of qdGGT to identify genes that drive intercellular cooperation or competition and chart mechanistic cell–cell interaction networks that mediate the tumor–microenvironment crosstalk. [ABSTRACT FROM AUTHOR]

• A binary function is devised to measure the conflict between any pieces of evidence. • A new method is proposed for conflict evidence fusion. • The method is based on composite discount factor and game theory. • The method achieves the optimal coordination between multiple conflict measurements. • The method is superior to the relevant methods with better effectiveness. Dempster–Shafer (D–S) evidence theory is widely used in various fields of information fusion. However, it is still an open issue that the D–S evidence theory may produce the counter–intuitive results in fusing high–conflict evidences. Aim at this problem, a novel conflict evidence fusion method based on the composite discount factor and the game theory is proposed in this paper. Firstly, an improved Shafer's conflict measurement formula based on the Jaccard similarity coefficient is devised, and combined with the Jousselme distance into a novel binary function to measure the global conflict between evidences as the evidence falsity. Then, the local conflict between evidences and the information volume of evidences are measured by using the Jousselme distance and belief entropy to indicate the credibility and uncertainty of evidences. Next, based on the game theory, the falsity, credibility and uncertainty are weighted and combined into the composite discount factors to correct each body of evidence (BOE). Ultimately, all corrected evidences are fused by Dempster's combination rule to obtain the final result. Two numerical examples are given to verify that the proposed method is effective and feasible, which outperforms the previous methods in handling the conflict evidences. [ABSTRACT FROM AUTHOR]

This paper provides a classification of all monotonic bipartite simple games. The problem we deal with is very versatile since simple games are inequivalent monotonic Boolean functions, functions that are used in many fields such as game theory, neural networks, artificial intelligence, reliability or multiple-criteria decision-making. The obtained classification can be implemented in an algorithm able to enumerate bipartite simple games. These numbers provide some light on enumerations of several subclasses of bipartite simple games, for which we find formulas. Complete simple games, a subclass of all simple games for which the desirability relation is a complete preordering, were already classified by means of two parameters: a vector and a matrix fulfilling some conditions. Complete simple games are inequivalent monotonic regular Boolean functions. In this paper, we deduce a procedure for bipartite non-complete games, which allows enumerating the number of bipartite simple games. Several formulas are obtained, in particular polynomial expressions for the number of bicameral meet games and the number of bicameral join games, two types of voting systems widely used in practice. [ABSTRACT FROM AUTHOR]

Device to Device communication involves communiqué between two devices without routing the packet through the cellular network. Ideally, the process involves reutilizations of the ideal resources of the cellular network for communication purpose. This ensures better management of radio spectrum and reduces communication delay. However, resource allocation has also been a major challenge because it contributes to various problems like interference, throughput issues and many other. A lot of work has been carried out in the recent past to address the above issue. The current paper reviews the work done in the area of resource allocation in the context of device to device communication. Among various mathematical tools available it was bring into being that game theory has been extensively used to address the problem. The main contribution of the paper, it provides the insight into the evolvement that has been made in the area of resource allocation and highlight various open issues that needs to be addressed. [ABSTRACT FROM AUTHOR]

The paper continues the investigations of the sensitivity of the equilibriums for the Cournot competition with uncertain market capacity. There are two types of agents - stubborn and flexible, and two types of interactions - with and without communications. The main idea was based on a calculation of the sensitivity of the equilibriums to control. The equilibriums depend on beliefs of agents about market capacity and their beliefs about the beliefs of other agents. We have calculated the sensitivity of the equilibriums separately for each combination of types of agents. This paper focuses on a mixed case - when there are at least four clusters - each cluster for every combination of types of agents and their interactions. [ABSTRACT FROM AUTHOR]

As a basis and guarantee for the effective enforcement of emission control area regulations, penalty policies play an important role in preventing violations. To more clearly understand the effectiveness of penalty policies, this paper proposes two assessment indicators and assesses Environmental Protection Agency's (EPA's) penalty policy based on the behaviors of governments and ships. By establishing an evolutionary game model that considers the competitive relationships between ships, we obtain evolutionary stable strategies. Furthermore, we quantitatively assess the effectiveness of EPA's penalty policy for different routes and ship types, and discuss the impacts of accurate penalty fine calculations. Finally, recommendations on penalty policy implications are proposed. The results of this paper show that EPA's penalty policy is less effective overall and that its effectiveness differs for different routes and ship types. Fortunately, the effectiveness of EPA's penalty policy can be improved by accurately calculating penalty fines, and using Automatic Identification System data to pre-identify violations and determine penalty fines is a potential way to improve EPA's penalty policy. • Establish two indicators to evaluate the effectiveness of penalty policies. • Utilize game theory to describe the strategic interactions between governments and ships. • Assess EPA's penalty policy for different routes and ship types based on proposed indicators. • Propose using AIS data to pre-identify violations and determine penalty fines. [ABSTRACT FROM AUTHOR]

• We examine a supply chain game in which the manufacturer's production process is imperfect. • Imperfect quality items can be distributed to a secondary market. Primary and secondary demands are considered simultaneously. • Optimal price and quality decisions are determined and compared with the case of an unrestricted secondary market. • A numerical example and sensitivity analysis emphasize the relevance of the model and give managerial insights. Imperfect production processes are frequently investigated in supply chain literature. Many recent articles assume that items that do not meet the quality requirements can be sold on a secondary market at a reduced price. Due to simplification, these papers ignore the demand of the secondary customers and assume instead that all imperfect quality items are salable. In this paper, we show that this ignorance can lead to unrealistic results as an unrestricted secondary market may reduce the manufacturer's incentive for an error-free production process. By introducing a price- and quality-dependent demand for both the primary and secondary markets, we obtain the following main insights: (i) a restricted secondary market leads to more realistic results compared with the unrestricted case; the secondary demand is only ignorable under certain conditions; (ii) quality decisions are mainly influenced by the market parameters of both market segments as well as quality cost parameters; (iii) in some cases, it is advantageous not to serve the secondary market and to concentrate the sales process on primary customers. [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]

The Dial-A-Ride Problem (DARP) designs pick-up and delivery routes for a set of customers. It arises in door-to-door transport services tailored to elderly and impaired people. It minimizes operational costs while accommodating as many drivers' and customers' constraints as possible; e.g., constraints on transit time and time windows on pick up and drop off. This paper develops a two-layer and a three-layer artificial neural network (ANN) to predict DARP parameters. The ANN is trained with real data provided by a transit agency from the Canadian city/region of Vancouver. Experimental results show that a three-layer ANN with rectified linear activation functions in conjunction with a stochastic gradient descent optimizer provides the most accurate output forecasts. Utilizing the predictions as parameter estimates for a deterministic mean DARP with multiple time windows (DARPMTW), this paper models the problem as a Company-Drivers-Customers' theoretic game that minimizes the total route duration and maximizes the minimal satisfaction of the drivers and the customers. It shows that non-dominated Pareto feasible solutions satisfy the Nash equilibrium conditions of the game-theoretic model. It then creates a multi-objective hybrid adaptive large neighborhood search (DD-HALNS) to estimate the Pareto front. The multi-objective DD-HALNS algorithm confines its search to feasible solutions that satisfy the Nash equilibrium conditions and employs local search operators based on their success rates. The operators are controlled by a learning mechanism that utilizes previous successful moves, cost savings, and utility maximization. Four hybridization operators are applied, namely Simulated Annealing, Tabu Lists, Genetic Crossover, and Restarts. The results of experiments conducted on real-world DARPMTW instances demonstrate the ability of the multi-objective DD-HALNS to enhance the best-known routing solutions, as well as its implementability. [ABSTRACT FROM AUTHOR]

Due to leachate contamination in groundwater and also soil, global concerns about improper waste management are today raising. On the other hand, with the emergence of the circular economy, increasing the recycling rate, reducing the landfilling rate, and producing by-products such as energy and compost from waste have become particularly important. In this regard in this paper, a chain consisting of a collector, a municipality, a compost plant, and a power plant is considered in a municipality-led Stackelberg game under government intervention. Indeed, the government supports citizens, the compost plant, and the power plant with subsidies to control the prices economically and sets penalties for greenhouse gas (GHG) emissions and leachate infiltration. The collector collects waste through two competitive channels: one from the city's trash bins and the other through buying recyclable waste from citizens, whereas each channel has its collection function. The municipality makes environmental efforts such as increasing people's awareness and investing in technology level to separate and treat leachate. The municipality also receives a fee from the collector for allowing the collection of recyclable waste. On the other hand, the compost and power plants produce compost and energy from waste and price their product based on the level of environmental efforts such as leachate treatment and GHG control, respectively. A real example has been finally implemented for the Tehran Waste Management Organization and the sensitivity analysis is presented. The results have shown that the source separation rate 72.36 % and the recycling rate 69.62 % of the ordinary waste channel are the highest and the landfilling rate 4.09 % is the lowest in the best government policy, which is environmental efforts compared to other policies; Moreover, the profit of the members is the maximum in this policy. [Display omitted] • Investigating the waste SC by game theory approach under government intervention. • Considering a collector, a municipality, a compost plant, and a power plant. • Proposing collection functions depend on level of environmental effort and price. • Subsidizing the purchase of recyclable waste from the citizens by government. • Pricing of by-product of waste supply chain (electricity and compost). [ABSTRACT FROM AUTHOR]

In this paper, novel multi-objective microgrids (MGs) formation method-based darts game theory optimization algorithm is proposed. The main objectives are to minimize the total losses, amount of load-shedding, and total restoration cost while ensuring a variety of topological and electrical constraints. The network graph theory represents the formed MG, and the non-linear equations of power flow and loss calculations are adopted in the MG formation model. In the proposed model, the DRERs include diesel generators, fuel cells, micro-turbines, and renewables wind and PV units with energy storage systems. The demand response program includes curtailable loads to improve load profile reduction during peaks. To assess the system's resiliency under extremely inevitable incidents, resiliency metrics are applied. To validate the effectiveness of the proposed approach, the modified IEEE 33-bus test system is utilized. Single-fault and multiple faults disaster case studies are performed with and without considering the emergency demand response program (EDRP). The results show the effect of EDRP in minimizing system losses, load-shedding, restoration cost, and improving the resiliency metrics after natural disasters. Finally, the analytic hierarchy process (AHP) model indicates the most efficient operation scenario while achieving the system objectives considering the problem alternatives. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models are becoming increasingly complex. In fact, a sophisticated model (XGBoost boosting or deep learning) generally leads to more accurate predictions than a simple model (linear regression or decision tree). There is therefore a trade-off between the performance of a model and its interpretability: what a model gains in performance, it loses in interpretability (and vice versa), where interpretability is the ability for a human to understand the reasons for a model's decision. However, explaining the predictions made by machine learning models aims at computing and interpreting the importance of features. To achieve this, game theory has recently gained attention for better understanding the similarity between group members. In this paper, we use SHAP (SHapley Additive exPlanations), which is a method based on cooperative game theory, to analyze and evaluate the properties of each group. More importantly, we rely k-means PCA and Light gbm classifier to improve the data preparation before grouping the features into multiple clusters. The simulation results prove the importance of shapley value in creating an accurate and meaningful representation of each cluster. [ABSTRACT FROM AUTHOR]

In this paper, a game-theoretical autonomous decision-making approach for efficient deployment of unmanned aerial vehicles (UAVs) in a multi-level and multi-dimensional assisted network is analyzed. The UAVs have directional antennas that work as wireless stations, which provide the best coverage for multiple ground mobile/fixed users. In general, UAVs work in a cooperative manner for achieving the suitable deployment with the optimal coverage values for the candidate region. In this paper, a game theory concept is used and the payoff function for each UAV is defined based on the coverage probability value, which depends on the altitude and the characteristic of antennas in the UAVs. We introduce a mathematical formulation for evaluating the payoff values based on a set of actions for each UAV, and the Nash equilibrium for this kind of game. This approach works in an intelligent way based on the interactions between the UAVs and their neighbors in a connected network and it might work even in harsh environments. In order to minimize interference, the UAVs' altitudes are adjusted based on the antennas and other deployment requirements (i.e. search and surveillance purposes) by using the minimum number of UAVs to cover the candidate geographical region. The simulation results show that the proposed approach achieves the maximum coverage value, converges fast with the environmental changes based on the power levels, and robust for failure scenarios. Finally, we compare our approach against one of the traditional approaches called Collaborative Visual Area Coverage Approach (CVACA) based on uniform coverage quality. The simulation results show that the game approach outperforms the traditional approach in term of the coverage value and the computational time. [ABSTRACT FROM AUTHOR]

• Formulateduopoly competition between two heterogeneous ride-sourcing platforms. • Considerasset-lightandasset-heavy strategiesin the ride-sourcing market. • Develop generalized waiting time cost function with increasing returns to scale. • Deriveoptimal pricing strategies with adoption of EVs under competition equilibrium. • Propose an optimization modelfor the determination of optimal subsidy strategies. • Suggest government coordinate subsidies on the EVs purchase and charging stations. This paper formulates duopoly competition between two non-cooperative heterogeneous ride-sourcing platforms considering the adoption of electric vehicles (EV) and government subsidies on EVs. One ride-sourcing platform adopts an EV asset-heavy strategy by undertaking EV depreciation costs, while the other ride-sourcing platform adopts an asset-light strategy by hiring drivers who own vehicles. The first-order conditions of each platform's pricingunder competitive equilibrium are derived based on a game-theoretical model that involves the stakeholders (i.e., riders, drivers, and ride-sourcing platforms). Based on the modeling framework ofduopoly competition between two heterogeneous ride-sourcing platforms, this paper proposes an optimization model with social welfare maximization for the determination of governmentsubsidy strategies. We conduct numerical illustrations to demonstrate how governmental subsidies on EV purchase and charging stations impact the endogenous variables in equilibrium (e.g., price for riders, wage for drivers, and market share for each platform) under different formulations of riders' waiting time cost function, which are increasing returns to scale. Also, a specialmodel with a fixed commission ratio is discussed. The results provide suggestions for decision-makers on how to allocate subsidy within the budget constraint. [ABSTRACT FROM AUTHOR]

Social dilemmas lead to natural conflict between cooperation and self interests among individuals in large populations. The emergence of cooperation and its maintenance is the key for the understanding of fundamental concepts about the evolution of species. In order to comprehend the mechanisms involved in this framework, here we study the Optional Public Good Games with focus on the effects of diffusive aspects in the emergent patterns of cyclic dominance between the strategies. Differently from other works, we showed that rock–paper–scissors (RPS) patterns occur by introducing a simple kind of random mobility in a lattice sparsely occupied. Such pattern has been revealed to be very important in the conservation of the species in ecological and social environments. The goal of this paper is to show that we do not need more elaborated schemes for construction of the neighbourhood in the game to observe RPS patterns as suggested in the literature. As an interesting additional result, in this contribution we also propose an alternative method to quantify the RPS density in a quantitative context of the game theory which becomes possible to perform a finite size scaling study. Such approach can be very interesting to be applied in other games generically. [ABSTRACT FROM AUTHOR]

• The interaction between semi-autonomous vehicle and human-driven vehicle is formulated as a misinformation game. • The dual iterative adaptive dynamic programming framework is adopted to model the driver's social behavior. • An optimal driver assistance controller is proposed based on the established surrounding vehicle's social behavior model. • The effectiveness of the proposed controller is verified by numerical simulation and driver-in-the-loop experiment. Driver assistance control in the cut-in scenarios is challenging, since the controller needs to ensure driving safety and avoid unnecessary intervention, while considering the interaction with surrounding vehicles. This paper proposes an optimal driver assistance controller considering the social behaviors of the surrounding vehicles to assist the drivers in the cut-in scenarios. To model the social behavior of the surrounding vehicle, we first formulate the interaction between the semi-autonomous vehicle and the surrounding vehicle as a misinformation game, which is achieved by assuming the surrounding vehicle is interacting with a hypothetical vehicle under the framework of non-cooperative game. Then, adaptive dynamic programming theory is utilized to find the Nash equilibrium, which is represented by deep neural networks and solved iteratively. Based on the established social behavior model and the nonlinear driver-vehicle dynamic model, an affine input nonlinear system model is obtained for the design of driver assistance controller, and the optimal assistance control strategy is also derived under the structure of adaptive dynamic programming. Several numerical simulations and driver-in-the-loop simulator experiments are conducted for validation. Results show that the proposed strategy can assist the driver in the cut-in scenario while addressing different social interactions with the surrounding vehicles. Importantly, by taking into account the surrounding vehicle's social behavior through our established social behavior model, our proposed strategy significantly outperforms the no-interaction strategy both in terms of driving safety and intervention degree, validating its effectiveness and superiority. [ABSTRACT FROM AUTHOR]