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We propose a novel family of node centralities in social networks, named family of position centralities , which explicitly takes into account the importance of the links to assess the centrality of the nodes that support them through the Position value (Meessen, 1988). Our proposal shares with the family of Myerson centralities (Gómez et al., 2003) that it is a game-theoretic family of measures that allows to consider the functionality of the network modelled by a symmetric cooperative game. We prove that, like the Myerson centrality measures, every Position centrality measure also satisfies essential properties expected of a centrality measure. We analyse in detail the main differences between the Myerson and the position families of centrality measures. Specifically, we study the differences regarding the connection structures that share dividends and the fairness and stability properties. Along this analysis we consider the case of hub-and-spoke clusters, a prevalent model for studying transportation networks. Finally, a characterisation of the Position Attachment centrality is given, which is the Position centrality obtained when the functionality of the network is modelled by the attachment game. Some comparisons are made with the Attachment centrality introduced by Skibski et al. (2019), which is the analogue member of the family of Myerson centralities. • It is a game-theoretic centrality based on the network's topology and functionality. • It measures the node's centrality relying explicitly on the importance of edges. • It is well supported by many desirable properties. • Three key aspects distinguish it from the Myerson centrality. • The Position Attachment centrality is axiomatically characterised. [ABSTRACT FROM AUTHOR]

New freight routes are being planned and emerging globally, but often rely on government subsidies to enhance their competitiveness during the initial promotional phase. This study proposes a multi-stage game framework to explore the influence mechanism of behavioral strategies among three stakeholders: shipper, carrier, and government. Based on the assessment of shippers' route choice preferences, we examine the dynamic evolution of carriers' and governments' behavioral strategies in the practical context of bounded rationality and dynamic learning. We theoretically derive the evolutionary paths and stable outcomes of dynamic systems across eight scenarios encompassing all the possible practical values to be taken, which provide broader and more valuable decision support for new route promotion. Finally, the feasibility and validity of the proposed model are verified by the actual case of the New International Land-Sea Trade Corridor. The findings indicate that government subsidies are not always effective and provide a precise subsidy range that can incentivize carriers to switch to the new route. Moreover, the study clarifies the impact of key factors such as subsidy, tax, route reliability, transportation time, and time value of cargo on the promotion of new routes through numerical simulation, and provides feasible management recommendations for government and route operations. • Investigate the interaction of behavioral strategies in the freight transport system. • Propose a multi-stage game framework for the promotion of new freight routes. • Reveal possible evolutionary paths and stabilization outcomes for the dynamic system. • Identify the impact of government policy and route features on evolutionary dynamics. [ABSTRACT FROM AUTHOR]

Parrondo's paradox refers to the paradoxical phenomenon of combining two losing strategies in a certain manner to obtain a winning outcome. It has been applied to uncover unexpected outcomes across various disciplines, particularly at different spatiotemporal scales within ecosystems. In this article, we provide a comprehensive review of recent developments in Parrondo's paradox within the interdisciplinary realm of the physics of life, focusing on its significant applications across biology and the broader life sciences. Specifically, we examine its relevance from genetic pathways and phenotypic regulation, to intercellular interaction within multicellular organisms, and finally to the competition between populations and species in ecosystems. This phenomenon, spanning multiple biological domains and scales, enhances our understanding of the unified characteristics of life and reveals that adaptability in a drastically changing environment, rather than the inherent excellence of a trait, underpins survival in the process of evolution. We conclude by summarizing our findings and discussing future research directions that hold promise for advancing the field. • Parrondo's paradox illustrates how combining two losing strategies can result in a winning outcome. • Its applications extend across various disciplines, particularly in understanding ecosystems at different scales. • The paradox is explored in multiple fields, highlighting adaptability as a key factor in evolutionary survival. • Future directions are outlined, emphasizing the potential to enhance our understanding of the natural world and society. [ABSTRACT FROM AUTHOR]

We study a setting where a receiver must design a questionnaire to recover a sequence of symbols known to a strategic sender, whose utility may not be incentive compatible. We allow the receiver the possibility of selecting the alternatives presented in the questionnaire, and thereby linking decisions across the components of the sequence. We show that, despite the strategic sender and the noise in the channel, the receiver can recover exponentially many sequences, but also that exponentially many sequences are unrecoverable even by the best strategy. We define the growth rate of the number of recovered sequences as the information extraction capacity. A generalization of the Shannon capacity, it characterizes the optimal amount of communication resources required while communicating with a strategic sender. We derive bounds leading to an exact evaluation of the information extraction capacity in many cases. Our results form the building blocks of a novel, non-cooperative regime of communication involving a strategic sender. • An uninformed receiver aims to recover information from an informed strategic sender • No assumption of incentive compatibility or external incentives • Information-theoretic analysis shows a fundamental limit on recovered information • Real-world setting of screening of travellers using questionnaires can be studied • Novel theory combining Shannon's information theory and Myerson's mechanism design [ABSTRACT FROM AUTHOR]

In recent years, growing awareness of potential threats posed by near-Earth objects (NEOs) has led to increased interest in developing and implementing planetary defense measures. International cooperation and public support are crucial to ensuring the long-term success of planetary defense initiatives. However, a number of economic, political, legal, and social issues stand in the way of effective large-scale cooperation in pursuing planetary defense. This article examines the major challenges to international cooperation in planetary defense. The article begins by considering the major types of collective action problems facing international collaboration toward planetary defense. The article then examines the way in which adjacent space-related aspects of international affairs influence the arena of planetary defense. The article then analyzes the legal aspects of these cooperation problems, focusing on sources of perceived uncertainty that interfere with effective development of norms for cooperation. The article then proposes various possible strategies for avoiding cooperation problems and increasing collective buy-in. Noteworthy examples include the use of treaties as pre-commitment devices (which parties agree to undertake in order to limit their future options and commit themselves to pre-established lines of conduct) and amendments to existing legal frameworks to decrease perceived uncertainty and promote objective standards of cooperation. The article concludes by reflecting on the importance of promoting a widespread cultural perspective that emphasizes the pursuit of collective planetary welfare over competing national interests. • Numerous collective action problems can disrupt planetary defense initiatives. • Game theory principles help shed light on planetary defense challenges. • Planetary defense is sensitive to space-related international affairs. • Many strategies are needed to aligning national interests with global interests. • Legal reform and norm-building can help promote planetary defense collaboration. [ABSTRACT FROM AUTHOR]

This article addresses the challenges of load curve variability in integrated energy systems (IES) and emphasizes carbon neutralization in response to global environmental concerns. It introduces a game-theoretic approach to understand the collaborative dynamics within an integrated energy-hydrogen system (HGESS). The study develops a collaborative game model for the IES-HGESS partnership, focusing on carbon neutralization considerations. Using game theory, it investigates the determinants shaping this partnership and extends to create a hybrid model for day-ahead and real-time markets. To foster enduring cooperation, the study introduces a cooperation profit coefficient and devises an income distribution strategy for HGESS-IES interactions. A load management program is incorporated to reduce costs and enhance profitability. Addressing complexity and constraints, the article introduces an advanced multi-objective particle swarm optimization method. Tested on a sample system under various conditions, the results demonstrate that the alliance between IES and HGESS can significantly enhance net profits, achieving mutual benefit and a win-win outcome while advancing sustainability in energy management. • "IES load curve challenges and carbon neutralization". • "Game theory in IES-HGESS dynamics and cooperative models". • "Cooperation profit coefficient for enduring IES-HGESS collaboration". • "Load management program to reduce costs and enhance profitability". • "Multi-objective particle swarm optimization for IES-HGESS alliance profitability". [ABSTRACT FROM AUTHOR]

A collective choice (or opinion) supported by a majority of individuals is challenged recurrently by a new one in a society. We consider a long-run evolution of collective choice under majority rules by stochastic evolutionary game theory. The Condorcet winner is uniquely a long-run equilibrium for all (super-)majority rules. When the Condorcet winner does not exist, the long-run equilibria under all majority rules belong to the top cycle set. In a multidimensional choice problem where the top cycle set tends to become the whole policy space, a long-run equilibrium belongs to the min–max policy set if the core is non-empty. We show that stochastic evolutionary game theory can mitigate the indeterminacy problem in social choice. [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]

• Developed mandatory lane change models to various intelligent connected vehicles environments employing game theory. • Model demonstrated exceptional predictive accuracy for both lane changes and waiting events. • Predicted lane change positions varied significantly based on different intelligent connected vehicles environments. • Discrepancies in lane change duration were notably influenced by diverse game strategies. In the environment of intelligent connected vehicles, drivers are capable of making wiser and safer decisions. However, the interaction between drivers and vehicle systems has undergone changes in the intelligent connected vehicles environment, leading to a decrease in the applicability of existing microscopic driving models, such as the mandatory lane change model, which requires reevaluation or improvement. Therefore, to investigate the influence of different intelligent connected vehicles environments on the microscopic mandatory lane-changing model, this study developed three interaction systems to characterize different intelligent connected vehicles environments: the baseline, warning group, and guidance group. The Baseline provides basic information, the warning group adds icons of preceding vehicles and real-time headway information, while the guidance group further includes speed and voice guidance functions. The baseline describes the traditional environment, while the other two groups describe the intelligent connected vehicles environment. Using a self-developed intelligent connected vehicle testing platform, we conducted driving simulation experiments with 43 participants at the interchange merging area of a highway. This study, grounded in game theory, establishes function models for participants, strategies, and payoff functions in the mandatory lane-changing process. Utilizing data from driving simulation experiments, the parameters of the dual-layered planning model are calibrated. Evaluation of the constructed model is conducted through confusion matrices and lane-changing spatiotemporal characteristic indicators. The results demonstrate satisfactory predictive performance of the baseline group model, warning group model, and guidance group model across different intelligent connected vehicles environments. Specifically, compared to existing literature, the baseline group model exhibits improvements of 7 % and 2 % respectively in overall lane-changing detection accuracy by drivers. The warning group model shows improvements of 2.9 % and 1.7 %, while the guidance group model exhibits improvements of 5.1 % and 4.3 %. Additionally, the baseline group model reduces the mean absolute error in predicting different game strategies by 16.7 % and 5.6 % respectively compared to existing literature. Concerning lane-changing position prediction, the warning and guidance group models demonstrate minimal errors, whereas the baseline group model exhibits good consistency in predicting lane-changing duration. Furthermore, both the warning and guidance group models show some delay in predicting lane-changing duration. While intelligent connected vehicles environments significantly influence the prediction of lane-changing positions, they do not significantly affect the prediction of lane-changing duration. However, game strategies significantly impact the prediction of lane-changing duration but do not significantly affect the prediction of lane-changing positions. The study findings offer valuable insights into micro lane-changing behaviors of drivers in intelligent connected vehicles environments, bearing crucial significance for the in-depth investigation of real-time control and guidance strategies for vehicles in merge areas of highways under intelligent connected vehicles conditions. [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]

Multiclass imbalanced Classification problems are some of the most challenging tasks in machine learning, that are usually related to sensitive real-world applications. A Game-Theoretic Equilibria Classifier (GTEC) that enhances the One-Versus-Rest (OvR) approach for imbalanced data sets is proposed. GTEC innovatively applies a discrete-time differential game strategy to train OvR classifiers on strategically selected data sub-samples guided by the game-theoretic framework. The proposed method is tested on a set of synthetically generated datasets. GTEC's behavior on the Cardiotocography dataset illustrates the approach on a real benchmark. Numerical examples are used to illustrate the efficacy of the approach. [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]

The present research investigates the advancement of sustainable transportation by promoting electric vehicles, expanding public transit, and implementing tax increases on fossil fuels and automobiles, via crucial strategies. The present study has examined a market with a fossil fuel vehicle (FFV) manufacturer, an electric vehicle manufacturer, and the government. The manufacturers' pricing strategies are geared towards optimizing profits, whereas the government's objectives for transportation are centered on attaining sustainability across economic, environmental, and social dimensions. The government has considered two strategies, one taxation-based and the other non-taxation-based, to achieve its objectives. In the taxation-based strategy, fixed taxes on fossil-fuel vehicles and fossil fuels are considered, while in the non-taxation-based strategy, subsidies for purchasing electric vehicles and the number of buses purchased for public transportation are predefined. Based on this, eight scenarios with different values for the predefined factors of each strategy have been defined to attain their respective objectives. Game theory has been utilized to determine an optimal solution based on Norwegian data, owing to the interconnected decision-making among stakeholders. By analyzing the findings, managers can enhance their decision-making capabilities. The results suggest that appropriately adjusting subsidies, taxes, and bus numbers within specific thresholds is crucial for mitigating air pollution and achieving other objectives. The assertion that electric cars are a definitive solution may not be accurate, as it depends on the source of the generated electricity. While excessive subsidies in non-renewable energy countries can lead to increased pollution, even in Norway, where most electricity is renewable, rising demand for electric vehicles contributes to air pollution due to increased production. In countries where electricity generation is not primarily from renewable resources, the growth of electric vehicles further exacerbates pollution. Simultaneous increases in fuel taxes and tariffs on fossil fuel vehicles negatively impact the demand for fossil fuel vehicles and government revenues. Therefore, it is recommended that an increase in fuel taxes be implemented to optimize both government revenues and social welfare. This approach safeguards public revenues, promotes societal well-being, and mitigates air pollution. • Examining the impact of promoting Evs and public transportation on sustainable transportation development. • Pricing of electric and fossil fuel vehicles using game theory and based on a case of Norway. • Studying the impact of tax, subsidies, and public transportation expansion on sustainable transportation goals. • Investigating vehicle selection based on government policies (allowing EV access to dedicated bus lanes). • Examining the Rebound effect of excessive promotion of electric vehicles or buses. [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]

[Display omitted] • Erosion-based prioritization using a comprehensive approach is vital. • The Shazand sub-Watersheds, Iran, were prioritized using the game theory. • The Shazand sub-watersheds were prioritized differently in various scenarios. • The game theory-based Condorcet approach holistically and successfully ranked sub-watersheds. The soil erosion of the watershed is different in temporal and spatial scales due to complex climatic and environmental conditions and their dynamic state over time. Accordingly, it is necessary to prioritize sub-watersheds based on their dynamic conditions to reduce the destructive effects of soil erosion. However, this issue has to be sufficiently reported on the scale of the watershed. Towards that, a game theory-based prioritization approach was planned concerning changes in vegetation cover and rainfall in the Shazand Watershed of the Markazi Province, Iran. To achieve the purpose of the study, the amount of soil erosion was first estimated in different scenarios resulting from the changes in rainfall erosivity and vegetation cover using the RUSLE model. Next, pairwise comparisons between 24 sub-watersheds in all scenarios were made based on the sub-watersheds' ranks in soil erosion in each scenario. Considering all scenarios, the priority of the sub-watersheds was assessed using the Condorcet approach. The results showed that the mean soil erosion in the watershed ranged from 5.4 and 36 t ha−1 y−1 while applying various scenarios. It is worth mentioning that the prioritization results obtained from the Condorcet algorithm and the current soil erosion rate are highly similar. On the other hand, sub-watersheds 2 and 9 were selected as the first and last priority, providing an appropriate roadmap for managers and relevant departments to adopt appropriate soil erosion inhibition measures. [ABSTRACT FROM AUTHOR]

With the acceleration of energy storage construction, investors are paying attention to how the costs of storage projects can be recovered. Market rules and incentive policies affect the dissemination of energy storage costs. This article simulates the interactive behavior of multiple agents based on evolutionary game theory, taking into account market factors such as prices, capacity, and incentive policies. It views the dissemination of storage costs as an interactive decision-making problem involving multiple agents' adoption of technology, and analyzes how market factors influence the outcomes of cost dissemination. [ABSTRACT FROM AUTHOR]

Port digital transformation requires substantial investments. However, the benefits after transformation are difficult to accurately predict due to incomplete information available to ports. Balancing digital investments with benefits poses a key challenge for ports. We propose a game model based on revenue sharing between the port and the technology company to determine the digital level in equilibrium under incomplete information. Our study reveals that the incomplete information influences digital level of ports. When information is incomplete and ambiguous, the digital level of ports may be lower. However, significant ambiguous does not necessarily decrease the digital level due to risk pooling and agent cost effects. Additionally, revenue sharing reduces the incentive for technology company to withhold private information, and specific sharing ratios are identified. The study also finds that under incomplete information, digital level of port initially increases and then decreases with increasing optimism or the ratio of port's retained revenue. By considering these factors, our study provides guidance for ports in optimal investment strategies for digital transformation. • When information is significant ambiguous, the digital level of ports may not necessarily decrease due to risk pooling and agent cost effects. • Under incomplete information, digital level of ports initially increases and then decreases with increasing optimism or the ratio of port's retained revenue. • The revenue sharing ratio should also be carefully determined, with the port retention no less than 40%. [ABSTRACT FROM AUTHOR]

The closed-loop supply chain (CLSC) consisting of a manufacturer, a retailer, and two competing third-party recyclers with altruistic preference is focused by us. We separately consider three altruistic preference models of differential game and Bellman theory that contrast and analyse dynamic decision-making such as the wholesale price of electric vehicle batteries (batteries) and the design innovation investment on the quality level decisions of the manufacturer, the unit selling price decision of the retailer, the recycling rate decisions of two third-party recyclers and the quality level of batteries. The numerical simulation further explores the rule of the time evolution of CLSC performance under four models. From our research, we conclude that: the altruistic preference has an optimistic impact in normal circumstances; if both the manufacturer and the retailer have altruistic preferences, the benefit of the value function of CLSC revealed only when the competition is at a lower level; the competition between two third-party recyclers is disadvantageous to the value function of CLSC, consumers and recycling activity. [ABSTRACT FROM AUTHOR]

Co-opetition plays an important role in decision-making of the supply chain. This paper investigates an electric vehicle battery (EV battery) supply chain containing one manufacturer and two competing retailers. We construct three retailer co-opetition models and characterize equilibrium results using differential game theory and Bellman's optimality principle. By comparing and analyzing the wholesale price, retail prices, technology investment and quality level of batteries, we explore the influence of retailer co-opetition on supply chain dynamic decisions. The results show that: (1) the enhancement of the technology investment will lead to the improvement of each decision; (2) the cooperation between one retailer and the manufacturer benefits the supply chain extremely; (3) the cooperation between two retailers is detrimental to the consumer utility. [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]

In this study, a linear quadratic integral differential game approach is applied to regulate and track the Euler angles for a quadrotor experimental platform using two players. One produces commands for each channel of the quadrotor and another generates the worst disturbance based on the mini-maximization of a quadratic criterion with integral action. For this purpose, first, the attitude dynamics of the platform are modeled and its parameters are identified based on the Nonlinear Least Squares Trust-Region Reflective method. The performance of the proposed controller is evaluated for regulation and tracking problems. The ability of the controller is also examined in the disturbance rejection. Moreover, the influence of uncertainty modeling is studied on the obtained results. Then, the performance of the proposed controller is compared with the classic Proportional Integral Derivative, Linear Quadratic Regulator, and Linear Quadratic Integral Regulator. The results demonstrate the effectiveness of the Game Theory on the Linear Quadratic Regulator approach when the input disturbance occurs. [Display omitted] • Implementation of a linear quadratic controller based on differential game theory. • Introducing an action integral to the controller to eliminate steady-state errors. • Proof of optimality of the proposed controller. • Evaluation of the controller's performance on a quadrotor experimental setup. • Comparison of the controller's performance with other robust control methods. [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]

Mobility as a service (MaaS) is an emerging mobility service that has received high expectations from the transportation sector in recent years. MaaS integrates multiple transportation services into a single platform, enhancing the accessibility of various transportation resources. However, the complex integration involved in MaaS leads to uncertainty in stakeholder interaction and system implementation. For the successful deployment and development of MaaS, this study aims to identify stakeholder enablers and barriers as well as to solve the uncertainty in MaaS implementation. We construct a tripartite evolutionary game model among government, transportation service providers (TSPs) and travelers. By synthesizing the evolutionary stability of each participant through simulation, we explored the impact of factors on stakeholder strategy choices and further analyzed the stability of equilibrium points in the system. The results show that: 1) Government subsidies and penalties, TSPs' costs and revenues, travelers' utility and expenses all impact the strategy choices of stakeholders. 2) The costs of government policy-making and TSPs will influence the evolution of MaaS system into two different development modes: free-market model and policy-supported model. 3) Travelers' utilities play a crucial role in promoting the stability of travelers' adoption of MaaS. This study provides new insights into identifying stakeholder demands. The findings contribute to formulating specific policy recommendations and incentive measurements to help in the strengthen and forward movement of MaaS development in the future. • Employed tripartite evolutionary game theory to explore MaaS stakeholder interactions. • Identified the key factors influencing stakeholders' decisions. • Integrated analysis of model outcomes with business models for comprehensive evaluation. • Simulation analysis was conducted to verify the validity of the results. [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]

In the process of port integration, the competition between ports in overlapping hinterlands has intensified. Ports adopt differentiated strategies to compete, such as incentive strategy and price-cutting strategy. To analyze the optimal competitive strategies for asymmetric ports in the same region, this article develops a game theory model for port competition between superior and inferior ports. Different from the traditional price competition in ports, this article innovatively explores the market mechanism of incentive strategies based on the actual operation of ports. It also considers the economic status and reputation advantages of ports. Results show that if superior ports follow inferior ports to adopt the price-cutting strategy, it will lead to the prisoner's dilemma. And incentive strategies are the dominant strategies for superior ports, which has two effects: service quality effect and price distortion effect. Incentive strategies are conducive to incentivizing shipping companies and improving port profits. • The superior port follows the inferior port in price-cutting to form a prisoner's dilemma. • The incentive competition strategy has the service quality effect and price distortion effect. • The incentive competition strategy benefits both ports and shipping companies. [ABSTRACT FROM AUTHOR]

This study aims to analyze competition between two railway ticket reservation services from users' perspective. The existence of many services makes them compete to acquire users, with KAI Access and Traveloka being the most popular. To determine appropriate business strategies for fair competition, a game theory analysis was carried out. For preparing the model, their business strategies were identified. A survey was conducted to collect 156 users' preferences. The survey results then used to calculate each strategies' value by using game theory model. The game value obtained was 15.33, which produced two appropriate strategies for KAI Access and Traveloka. [ABSTRACT FROM AUTHOR]

The pandemic affected private tutorings' decrease in the number of students, which led to a financial crisis for the private tutoring. This research helps analyze and find optimal marketing strategies and their position in competition between private tutorings to increase registrants and income of private tutorings. Game theory is used to analyze marketing strategies using user preference data on the decision to select private tutorings. This study finds the optimal strategy and competitive position for private tutoring X is promotion, and private tutoring Y is a learning module. Recommendations are given to improve the marketing strategy. [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]

In rail-water combined transport, maintaining a fair and equitable distribution of income is crucial for sustaining the cooperative relationship between railway and waterway transportation, which forms the bedrock for efficient combined railway and waterway operations. To achieve this, a cooperative game model is proposed that encompasses both railway and waterway transportation. The model considers the structural relationship between transport routes, dividing their operational cooperation into two modes: intermodal transportation and shared transportation. Two allocation methods are introduced to address this: one based on the contribution of the transport network and the other utilizing the modified Shapley value method. An in-depth analysis of the revenue allocation scheme is provided. The results of the calculations demonstrate that the cooperative revenue allocation model is effective in fairly and reasonably distributing the cooperative revenue, thereby ensuring the ongoing collaboration between railroads and waterways. [ABSTRACT FROM AUTHOR]

Differentiable ARchiTecture Search (DARTS) is one of the most trending Neural Architecture Search (NAS) methods. It drastically reduces search cost by resorting to weight-sharing. However, this approach also dramatically reduces the search space, thus excluding potential promising architectures. In this article, we propose D-DARTS, a solution that addresses this problem by nesting neural networks at the cell level instead of using weight-sharing to produce more diversified and specialized architectures. Moreover, we introduce a novel algorithm that can derive deeper architectures from a few trained cells, increasing performance and saving computation time. In addition, we introduce DARTOpti, an alternative search space in which we optimize existing handcrafted architectures instead of searching from scratch. Our solution reaches competitive performance on image classification tasks. • A distributed paradigm for Differentiable NAS with individualized cells. • A novel cell-specific loss inspired by Game Theory and Shapley Values. • An algorithm to deepen discovered architectures. • A procedure for optimizing handcrafted CNN architectures. [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]

The Taxman game has proven to be hard to solve optimally, so efforts have been made to find heuristic strategies that do well in practice. We present results on the NP-hardness of a variant of the game via an equivalence to a particular kind of graph matching problem. Furthermore this equivalence is used to derive a winning strategy for all n along with efficiently computable lower and upper bounds on the optimal achievable score. [ABSTRACT FROM AUTHOR]

• Game theory effectively balances different weighting methods. • PG recycling technologies excel in resource and environmental criteria. • PG recycling technologies have lower performance in economy and society criteria. • Utility theory proves robust in evaluating solid waste recycling technologies. • The utility performance of PG recycling technology is sensitive to carbon emissions. The Chinese government is implementing policies, such as the "Guidance on comprehensive utilization of bulk solid waste for the 14th Five-Year Plan period", to stimulate phosphogypsum (PG) reduction and recycling. Thus, the comprehensive evaluation of PG recycling technologies for sustainable development is crucial. This study proposes a novel multi-criteria decision analysis (MCDA) method that considers the criteria of resources, environment, economy, and society and risk attitudes of decision-makers and integrates game theory (GT) and utility theory for criteria weighting and ranking to assess industrial-scale PG recycling technologies in China. The results demonstrate that GT provides more reasonable criteria weights than individual weighting methods. PG-based lightweight plaster is the top performer in the resource and environmental dimensions owing to its exceptional resource and energy efficiency. PG utilized for dry-mix mortar and organic fertilizer production exhibited the best utility performance of 0.74 and 0.73, respectively. Measures, such as subsidies and product publicity, should be implemented to promote these technologies. However, technologies with poor performance, such as PG used for the co-production of sulfuric acid and fertilizer or cement, may require optimization or substitution for the sustainable recycling of PG. The proposed MCDA method is robust and can serve as a reliable decision-making tool for other waste-recycling technologies. However, caution must be exercised when determining risk attitude using the MCDA method as it may vary with the number of technologies and affect the final rankings. [ABSTRACT FROM AUTHOR]

• Neuroscience needs analytical frameworks for understanding across levels of biology. • Information asymmetric signaling game theory can apply to neuroscientific problems. • Game theory uses local utilities, but global utility for information maximization. • Neurons do not know input information types, so can be deceived about those types. • Game theory equilibria are signaling conventions for information processing modes. We consider the possibility of applying game theory to analysis and modeling of neurobiological systems. Specifically, the basic properties and features of information asymmetric signaling games are considered and discussed as having potential to explain diverse neurobiological phenomena; we focus on neuronal action potential discharge that can represent cognitive variables in memory and purposeful behavior. We begin by arguing that there is a pressing need for conceptual frameworks that can permit analysis and integration of information and explanations across many scales of biological function including gene regulation, molecular and biochemical signaling, cellular and metabolic function, neuronal population, and systems level organization to generate plausible hypotheses across these scales. Developing such integrative frameworks is crucial if we are to understand cognitive functions like learning, memory, and perception. The present work focuses on systems neuroscience organized around the connected brain regions of the entorhinal cortex and hippocampus. These areas are intensely studied in rodent subjects as model neuronal systems that undergo activity-dependent synaptic plasticity to form neuronal circuits and represent memories and spatial knowledge used for purposeful navigation. Examples of cognition-related spatial information in the observed neuronal discharge of hippocampal place cell populations and medial entorhinal head-direction cell populations are used to illustrate possible challenges to information maximization concepts. It may be natural to explain these observations using the ideas and features of information asymmetric signaling games. [ABSTRACT FROM AUTHOR]

Reliable prediction of postoperative dislocation after reverse total shoulder arthroplasty (RSA) would inform patient counseling as well as surgical and postoperative decision making. Understanding interactions between multiple risk factors is important to identify those patients most at risk of this rare but costly complication. To better understand these interactions, a game theory–based approach was undertaken to develop machine learning models capable of predicting dislocation-related 90-day readmission following RSA. A retrospective review of the Nationwide Readmissions Database was performed to identify patients who underwent RSA between 2016 and 2018 with a subsequent readmission for prosthetic dislocation. Of the 74,697 index procedures included in the data set, 740 (1%) experienced a dislocation resulting in hospital readmission within 90 days. Five machine learning algorithms were evaluated for their ability to predict dislocation leading to hospital readmission within 90 days of RSA. Shapley additive explanation (SHAP) values were calculated for the top-performing models to quantify the importance of features and understand variable interaction effects, with hierarchical clustering used to identify cohorts of patients with similar risk factor combinations. Of the 5 models evaluated, the extreme gradient boosting algorithm was the most reliable in predicting dislocation (C statistic = 0.71, F 2 score = 0.07, recall = 0.84, Brier score = 0.21). SHAP value analysis revealed multifactorial explanations for dislocation risk, with presence of a preoperative humerus fracture; disposition involving discharge or transfer to a skilled nursing facility, intermediate care facility, or other nonroutine facility; and Medicaid as the expected primary payer resulting in strong, positive, and unidirectional effects on increasing dislocation risk. In contrast, factors such as comorbidity burden, index procedure complexity and duration, age, sex, and presence or absence of preoperative glenohumeral osteoarthritis displayed bidirectional influences on risk, indicating potential protective effects for these variables and opportunities for risk mitigation. Hierarchical clustering using SHAP values identified patients with similar risk factor combinations. Machine learning can reliably predict patients at risk for postoperative dislocation resulting in hospital readmission within 90 days of RSA. Although individual risk for dislocation varies significantly based on unique combinations of patient characteristics, SHAP analysis revealed a particularly at-risk cohort consisting of young, male patients with high comorbidity burdens who are indicated for RSA after a humerus fracture. These patients may require additional modifications in postoperative activity, physical therapy, and counseling on risk-reducing measures to prevent early dislocation after RSA. [ABSTRACT FROM AUTHOR]

There are rising concerns that reinforcement algorithms might learn tacit collusion in oligopolistic pricing, and moreover that the resulting 'black box' strategies would be difficult to regulate. Here, I exploit a strong connection between evolutionary game theory and reinforcement learning to show when the latter's rest points are Bayes–Nash equilibria, but also to derive a system of Pigouvian taxes guaranteed to implement an (unknown) socially optimal outcome of an oligopoly pricing game. Finally, I illustrate reinforcement learning of equilibrium play via simulation, which provides evidence of the capacity of reinforcement algorithms to collude in a very simple setting, but the introduction of the optimal tax scheme induces a competitive outcome. [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]

Determining areas that are experiencing severe soil erosion and prioritizing these areas for management and conservation planning is a critical contemporary need. The purpose of this research is to use two different approaches - the experimental model of the global soil loss equation (RUSLE) and three game theory algorithms (Fallback, Borda and Condorcet) - to prioritize areas with high erosion in Talar watershed, Iran. Five factors were employed to operationalize the RUSLE model – rainfall erosivity (R), soil erodibility (K), slope length (LS), cover (C), and support practice factor (P). The average of each factor in each sub-watershed provided inputs for the game-theory algorithms. The results indicated that the average soil loss in the entire watershed is 29 t ha−1 yr−1. The RUSLE model revealed that sub-watersheds 9, 11, 12, and 13 have the highest erosion rates in the study area. The three game-theory algorithms agreed that sub-watersheds 1, 2, 3, 4 and 5 possessed the highest erosion potential in the region. Anthropogenic activities like deforestation, farming, and tillage relative to certain slope directions and the development of rainfed agriculture are the main reasons for high soil loss rates in the study area. The conclusions drawn in this study can be helpful for prioritizing areas prone to critical levels of erosion and can help to determine appropriate erosion prevention and loss mitigation methods in other watersheds. [ABSTRACT FROM AUTHOR]

A game theory approach is used to study a market-based pollution trading strategy among different point sources. Point sources discharge effluents to the same watershed and participate as rational entities with particular objectives, interacting with each other in a hierarchical organization. Each point source must implement technologies to sell credits to other sources or buy credits to compensate for non-compliance with environmental regulations. The objective function of the formulations proposed considers the minimization of total annual costs for each point source. The participants in the pollutant credit trading are related in a Nash Game by a mixed complementarity problem (MCP) and in a Stackelberg Game by a bilevel optimization problem (BLP). Both systems are solved using the disjunctive constraint method. Different scenarios of interaction among the participants are proposed in order to observe the behavior of each decision strategy. These models should generate more reliable results with respect to non-competitive approaches as the interaction of the participants is considered. [Display omitted] • Optimal pollution trading decisions are found through mathematical programming. • A game theory approach incorporates competition/cooperation among point sources. • Nash equilibrium and a Stackelberg game are used as mathematical scenarios. • Individual rather than overall objectives are considered in the formulation. • The opportunity of forming alliances is studied. [ABSTRACT FROM AUTHOR]

In 1980 Richard H. Day co-founded and long edited the Journal of Economic Behavior and Organization (JEBO). It development reflected the intellectual journey that Day himself followed in his career from an early interest in decision-making by farmers using programming methods through studying adaptation in organizations facing bounded rationality and nonlinear dynamics to a broad evolutionary approach to long-run patterns of economic growth and development. A not widely recognized outcome of this was that JEBO would become a leading outlet for evolutionary economics, including all of its various strands, including biological, organizational, NeoSchumpeterian, Alchian efficiency, Simonian bounded rationality, evolutionary game theory, and complexity evolution. [ABSTRACT FROM AUTHOR]

• Without environmental regulation, Triple-B always relocates polluting industries. • Environmental regulation reduces the probability of relocation from 75 to 50%. • Higher customs duties reduce willingness of investors to take on investment risk. • Energy transition reduces the perceived level of punishment for polluters. • The current formula for applying carbon taxes must consider energy transition. Developed and developing countries have made remarkable efforts to address climate action. However, some developing countries have hung on to their comparative advantage in the polluting production of goods. By applying game theory, specifically a dynamic game with incomplete information, this study examines how the transfer of polluting industries can be discouraged by increasing associated costs through carbon taxes and custom duties on polluting imports. 24 developed countries and 18 developing countries were under analysis. The main findings suggest that energy transition could play a vital role in preventing this transfer. To attract clean FDI and boost energy transition, a stricter regulatory framework through levying carbon taxes and customs duties on polluting imports is recommended as an unstable regulatory framework may deter Foreign Direct Investment but also other desirable types of investment. Policymakers should reformulate how carbon taxes are applied to maintain their deterrent effect of reducing carbon emissions. [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]

We study the graph energy from a cooperative game viewpoint. We introduce the graph energy game and show several of its game-theoretic properties. In particular, we see that it is a superadditive game and that the vector of vertex energies belongs to the core of the game. These properties imply new bounds for the energy of graphs. We also consider a version based on p -Schatten norms. [ABSTRACT FROM AUTHOR]