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Methods to rank documents in large-scale citation data are increasingly assessed in terms of their ability to identify small sets of expert-selected papers. Here, we propose an algorithm for the accurate identification of milestone papers from citation networks. The algorithm combines an influence propagation process with game theory concepts. It outperforms state-of-the-art metrics in the identification of milestone papers in aggregate citation network data, while potentially mitigating the ranking's temporal bias compared with metrics that have similar milestone identification performance. The proposed method sheds light on the interplay between ranking accuracy and temporal bias. • We transform citation networks into influence propagation networks by reversing the citation relationships. • We propose a novel diffusion-based metric for ranking papers, combining influence propagation and game theory. • Experiments on APS and DBLP datasets demonstrate the effectiveness of our metric in identifying milestone papers. [ABSTRACT FROM AUTHOR]

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]

Abstract: Two players of Rock-Paper-Scissors are modeled as adaptive agents which use a reinforcement learning algorithm and exhibit chaotic behavior in terms of trajectories of probability in mixed strategies space. This paper demonstrates that an external super-agent can exploit the behavior of the other players to predict favorable moments to play against one of the other players the symbol suggested by a sub-optimal strategy. This third agent does not affect the learning process of the other two players, whose only goal is to beat each other. The choice of the best moment to play is based on a threshold associated with the Local Lyapunov Exponent or the Entropy, each computed by using the time series of symbols played by one of the other players. A method for automatically adapting such a threshold is presented and evaluated. The results show that these techniques can be used effectively by a super-agent in a game involving adaptive agents that exhibit collective chaotic behavior. [Copyright &y& Elsevier]

Abstract: We investigate a population of individuals who play the Rock–Paper–Scissors (RPS) game. The players choose strategies not only by optimizing their payoffs, but also taking into account the popularity of the strategies. For the standard RPS game, we find an asymptotically stable polymorphism with coexistence of all strategies. For the general RPS game we find the limit cycles. Their stability depends exclusively on two model parameters: the sum of the entries of the RPS payoff matrix, and a sensitivity parameter which characterizes the personality of the players. Apart from the supercritical Hopf bifurcation, we found the subcritical bifurcation numerically for some intervals of the parameters of the model. [Copyright &y& Elsevier]

Abstract: We study the formation of multi-armed spirals and multi-pairs antispirals in spatial rock–paper–scissors games with mobile individuals. We discover a set of seed distributions of species, which is able to produce multi-armed spirals and multi-pairs antispirals with a finite number of arms and pairs based on stochastic processes. The joint spiral waves are also predicted by a theoretical model based on partial differential equations associated with specific initial conditions. The spatial entropy of patterns is introduced to differentiate the multi-armed spirals and multi-pairs antispirals. For the given mobility, the spatial entropy of multi-armed spirals is higher than that of single armed spirals. The stability of the waves is explored with respect to individual mobility. Particularly, we find that both two armed spirals and one pair antispirals transform to the single armed spirals. Furthermore, multi-armed spirals and multi-pairs antispirals are relatively stable for intermediate mobility. The joint spirals with lower numbers of arms and pairs are relatively more stable than those with higher numbers of arms and pairs. In addition, comparing to large amount of previous work, we employ the no flux boundary conditions which enables quantitative studies of pattern formation and stability in the system of stochastic interactions in the absence of excitable media. [Copyright &y& Elsevier]

Abstract: Randomization among successive choices is important in adaptive decision-making, particularly for strategic interactions in which the optimal strategy is a mixed strategy. Patients with schizophrenia have been reported to have deficits in random sequential behaviors arising from impaired executive function. However, whether schizophrenic patients exhibit distinct behaviors for response randomization in one- and two-person games requiring different behavioral strategies is not known. The aim of this study was to examine the response randomization of 48 schizophrenic patients and 50 healthy subjects in one- and two-person Rock–Paper–Scissors games. Here we found that the schizophrenic patients exhibited non-random biases distinct from those of the healthy subjects (i.e., stereotypic switching in the one-person game and the tendency to choose the best response against the opponent's previous choice in the two-person game). The entropy of the choice sequences was prominently decreased in the schizophrenic patients for both games, thereby indicating an overall disturbance in the behavioral randomization in adaptive decision-making. These results suggest that the impairment of response randomization in schizophrenic patients manifests differently in interactive and non-interactive situations, which may be useful for the diagnosis and quantification of the severity of the disease. [Copyright &y& Elsevier]

Abstract: The popularity of augmented reality (AR) applications on mobile devices is increasing, but there is as yet little research on their use in real-settings. We review data from two pioneering field trials where MapLens, a magic lens that augments paper-based city maps, was used in small-group collaborative tasks. The first study compared MapLens to a digital version akin to Google Maps, the second looked at using one shared mobile device vs. using multiple devices. The studies find place-making and use of artefacts to communicate and establish common ground as predominant modes of interaction in AR-mediated collaboration with users working on tasks together despite not needing to. [Copyright &y& Elsevier]

Abstract: Game theory provides a solution to the problem of finding a set of optimal decision-making strategies in a group. However, people seldom play such optimal strategies and adjust their strategies based on their experience. Accordingly, many theories postulate a set of variables related to the probabilities of choosing various strategies and describe how such variables are dynamically updated. In reinforcement learning, these value functions are updated based on the outcome of the player''s choice, whereas belief learning allows the value functions of all available choices to be updated according to the choices of other players. We investigated the nature of learning process in monkeys playing a competitive game with ternary choices, using a rock–paper–scissors game. During the baseline condition in which the computer selected its targets randomly, each animal displayed biases towards some targets. When the computer exploited the pattern of animal''s choice sequence but not its reward history, the animal''s choice was still systematically biased by the previous choice of the computer. This bias was reduced when the computer exploited both the choice and reward histories of the animal. Compared to simple models of reinforcement learning or belief learning, these adaptive processes were better described by a model that incorporated the features of both models. These results suggest that stochastic decision-making strategies in primates during social interactions might be adjusted according to both actual and hypothetical payoffs. [Copyright &y& Elsevier]

Abstract: We study a novel five-species system on two-dimensional lattices when each species have two superior and two inferior partners. Here we simplify the huge parameter space of predation probability to only two parameters. Both of Monte Carlo simulation and Mean Field Theory reveal that two of strategies may die out when the ratio of the two parameters is close to the golden point 0.618, and the remaining three strategies are provided a cyclic dominance system. [Copyright &y& Elsevier]

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]

Abstract: The game ‘rock–paper–scissors’ model, with the consideration of the effect of the psychology of conformity, is investigated. The interaction between each two agents is global, but the strategy of the conformity is local for individuals. In the statistical opinion, the probability of the appearance of each strategy is uniform. The dynamical analysis of this model indicates that the equilibrium state may lose its stability at a threshold and is replaced by a globally oscillating state. The global oscillation is induced by the local conformity, which is originated from the synchronization of individual strategies. [Copyright &y& Elsevier]

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]

This paper presents an innovative approach to optimize traffic networks in a supplier-customer system based on specific strategies of game theory. The traffic network is represented as a routing configuration in which the arcs represent various possible roads between supplier and customer entities, each route having an associated transport cost for the product unit. The game structure is defined by a matrix of transport costs between the players, supplier and customer, with their corresponding strategies. Based on these costs, the game matrix is built to which the existing quantities in warehouses are added, respectively the quantities requested by the final customers. By solving the game associated to the transportation traffic network, the different strategies are proposed for choosing the optimal solution. The simulation results confirm the effectiveness of the proposed approach and recommend some performant procedures for solving the game, such as Hurwicz, Laplace and Savage, close to optimal solution. This research opens new directions for management optimization in other domains such as comunication, electrical and administrativ networks. [ABSTRACT FROM AUTHOR]

Abstract: Cyclic competition game models, particularly the “rock–paper–scissors” model, play important roles in exploring the problem of multi-species coexistence in spatially ecological systems. We propose an extended “rock–paper–scissors” game to model cyclic interactions among five species, and find that two of the five can coexistent when biodiversity disappears, which is different from the “rock–paper–scissors” game. As the number of fingers is five, we named the new model the “fingers” game, where the thumb, forefinger, middle finger, ring finger, and little finger cyclically dominate their subsequent species and are dominated by their former species. We investigate the “fingers” model in two ways: direct simulations and nonlinear partial differential equations. An important finding is that the number of species in a cyclic competition game has an influence on the emergence of biodiversity. To be specific, the “rock–paper–scissors” model is in favor of maintaining biodiversity in comparison with the “fingers” model when the variables (population size, reproduction rate, selection rate, and migration rate) are the same. It is also shown that the mobility and reproduction rate can promote or jeopardize biodiversity. [Copyright &y& Elsevier]

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]

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]

The design of electricity markets may be facilitated by simulating actors' behaviors. Recent studies model human decision-makers within markets as agents which learn strategies that maximize expected profits. This work investigates the problem of 'non-stationarity' in the context of market simulations, a problem with the learning-algorithms used by such studies which results in agents behaving irrationally, thus limiting the studies' applicability to real-world strategic behavior. Isolating the source of the problem for a day-ahead electricity market, this paper proposes methods which meliorate this problem in simple test-cases, and proves requirements under which 'centralized-training, decentralized-execution' value-learning methods will converge to correct behavior in general. Subsequently, this paper proposes a framework for 'adversarial market design' that includes the market-designer as an agent. This allows the optimization of market-designs subject to possibly strategic behavior of participating firms — in turn enabling the automated selection of the optimal market from any set of markets. • Reinforcement learning can be used to simulate firm behavior in electricity markets. • Reinforcement-learning algorithms may fail to converge in cases with multiple agents. • The value function must account for other firms' actions to guarantee convergence. • 'Adversarial market design' uses reinforcement learning to create new market designs. [ABSTRACT FROM AUTHOR]

Modular construction and operation significantly impact building lifecycle management, with various stakeholders playing key roles. Digital twin technology emerges as a cutting-edge tool for information exchange, management, and forecasting in the construction sector, offering comprehensive services for diverse stakeholders. However, research primarily explores potential applications and practical solutions, with limited focus on application strategies considering all stakeholders' interests. This gap hinders the transition of digital twin technology from theoretical to practical use. Addressing this, the paper employs game theory to develop models that analyze key stakeholder decisions during the modular construction and operational phases. It creatively introduces mathematical models for complex conditions in the operation phase and conducts numerical simulations. Findings show that the diffusion rate of digital twin technology significantly affects decision-making by main stakeholders in the construction industries in two stages. The diffusion rate in other industries influences the decision of facility management contractors from different backgrounds, yet its implementation ultimately depends on their status and profitability during the operation process. These diffusion rates are influenced by various factors like government subsidies, development costs, and revenues. To enhance the application rate of digital twin technology, the paper suggests initiating pilot projects, creating project pools, expanding industry-university-research collaborations, and implementing subsidies and incentive programs. • We utilized game theory to analyze the decisions during the modular construction and operation phases • Mathematical models were developed under complex conditions to facilitate numerical simulations of modular construction and operation • We found that the diffusion rate of digital twin technology could impact owners' decisions • Measures were proposed to enhance the application rate of the digital twin technology [ABSTRACT FROM AUTHOR]

The rapid development of intelligent manufacturing is inseparable from collaboration among smart factories (SFs). In the real unmanned intelligent collaborative production process, each SF exists as an independent individual peer. However, due to the selfishness of SFs and intelligent manufacturing equipment (IME), they do not actively participate in data sharing, which brings serious obstacles to the collaborative development of SF. In order to solve the above problems, this paper combines blockchain technology and game theory technology to propose a high-efficiency data sharing incentive method based on the Stackelberg game. Specifically, We first take the SF as the leader and the IME as the follower, and we define the data sharing incentive problem among SFs as the master–slave game problem. Then, the lower game problem and the upper game problem are solved, respectively, and finally the Nash equilibrium is derived. Then, a high-efficiency data sharing incentive method based on the Stackelberg game is designed and implemented. Finally, we conducted simulation experiments on the method proposed in this paper. Through theoretical and simulation experiments, it has been proven that the proposed method effectively reduces the impact of the selfishness of SFs and IME and improves the efficiency of data sharing among SFs. [ABSTRACT FROM AUTHOR]

This paper is concerned with the multi-player stochastic differential pursuit-evasion game problem based on directed graphs. To solve the challenges posed by limited communication and ubiquitous noise, a novel Riccati equation is proposed based on the linear–quadratic exponential cost function under both complete and partial observations. The optimal strategies for pursuers and evaders are obtained based on the direct method of completing the square and Radon–Nikodym derivative, without the need to solve the complex Hamilton–Jacobi–Isaacs equation. The strategy presented in this paper is distributed and can be implemented without requiring any global information, under the constraints of a directed topology. Additionally, the proposed partial algorithm introduces an information filter to estimate partial observations. We also demonstrate that the proposed strategy constitutes a Nash equilibrium. Numerical simulations confirm the effectiveness of our strategy in both complete and partial observation scenarios. [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]

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]

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]

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]

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]

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]

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]

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]