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Reactive jammers select jamming strategies according to the users' responses; thus, conventional anti‐jamming methods such as frequency hopping are inadequate to defeat the jamming attack. In this article, the authors propose a novel uncoupled deception scheme to trap the reactive jammer into attacking a decoy channel in distributed networks. Specifically, the authors design a multi‐functional network utility for every user to mislead the jammer with a minimum energy consumption while achieving the highest network throughput. Based on the network utility, the anti‐jamming problem is formulated as an exact potential game such that the existence of Nash equilibrium can be guaranteed theoretically. The authors further propose a back‐to‐back coordination‐based learning algorithm to reach the optimal channel selection and power adaption in a non‐cooperative way. To alleviate the lack of mutual information exchange, the back‐to‐back coordination mechanism derives all users to deceive the jammer by inferring others' strategies based on a shared belief. Simulation results show that the proposed algorithm yields higher network throughput and efficiency‐cost ratio compared to the state‐of‐the‐art cooperative schemes. [ABSTRACT FROM AUTHOR]

We investigate a robust equilibrium investment-reinsurance problem for n ambiguity-averse competitive insurers, n ≥ 2 . Each insurer is allowed to purchase proportional reinsurance and invest in a risk-free asset and a risky asset. Each insurer aims to maximize the expected utility of a weighted relative terminal wealth with respect to the other competitors. In this article, the risky asset is assumed to follow a general and flexible model: the square root factor process. Following the game theory approach, we derive the closed solutions of the robust equilibrium investment-reinsurance strategies. Moreover, the verification theorem is provided in this article. Finally, we demonstrate some numerical analyses and give the economic explanations as well. [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]

We propose a 3D stochastic cellular automaton model, governed by evolutionary game theory, to simulate bone remodeling dynamics. The model includes four voxel states: Formation, Quiescence, Resorption, and Environment. We simulate the Resorption and Formation processes on separate time scales to explore the parameter space and derive a phase diagram that illustrates the sensitivity of these processes to parameter changes. Combining these results, we simulate a full bone remodeling cycle. Furthermore, we show the importance of modeling small neighborhoods for studying local bone microenvironment controls. This model can guide experimental design and, in combination with other models, it could assist to further explore external impacts on bone remodeling. Consequently, this model contributes to an improved understanding of complex dynamics in bone remodeling dynamics and exploring alterations due to disease or drug treatment. [ABSTRACT FROM AUTHOR]

Since the introduction of China's ‘Golden Agriculture Project’ in 1994, the government has been actively promoting agricultural informatization, digitalization, and intelligence. This paper utilises game theory to develop a model for the sharing of agricultural science and technology information resources between the government and enterprises in four scenarios. The study reveals that cost and benefit are critical variables influencing all actors, leading the government and enterprises to adopt strategies that maximise their own benefits. Key measures for the government include strengthening enterprise supervision, actively sharing agricultural science and technology information resources with enterprises, and promptly meeting their sharing needs. The strategy choices of enterprises are influenced by various factors. When both the government and enterprises act as suppliers and demanders, enterprises may exhibit free-riding behaviour. In situations where the government is the supplier and enterprises are the demanders, the optimal strategy for enterprises is to actively share and request the agricultural science and technology information resources provided by the government. This research provides valuable insights for formulating policies aimed at achieving sustainable sharing of agricultural science and technology information resources and further promoting agricultural modernization. [ABSTRACT FROM AUTHOR]

There has unavoidably been a problem for international grain cooperation in the Asia–Europe region as a result of the Russian–Ukrainian conflict. Analysis of the impact of Russian–Ukrainian conflict on the evolution of grain trade cooperation, identifying key influencing factors and formulating corresponding measures are of practical value for safeguarding grain security in the Asia–Europe region. In this paper, a grain trade network is constructed according to grain trade data from the Asian–European grain trade cooperation countries. A grain trade network game (GTNG) model of grain trade cooperation is established in order to investigate the elements that influence trade cooperation. The reciprocal preference is introduced. Through simulation experiments, the effects of cooperation costs, neighboring countries’ incentives, and punitive pressures, the degree of goodwill perception, the types of game, and the proportion of initial cooperators on trade cooperation are examined. This study may give useful policy recommendations for promoting Asia–Europe grain trade cooperation under the influence of the Russian–Ukrainian conflict. [ABSTRACT FROM AUTHOR]

In the context of an energy crisis, efficient energy management has become an unavoidable issue for sustainability, regardless of the domain under consideration. Smart grids are no exception; they aim to motivate energy optimization according to billing strategies and users’ comfort. In this paper, two optimization problems (OP) are proposed involving billing strategies and users’ flexibility. A single-centralized OP aims to minimize the total energy provided by a company, while a distributed OP targets minimizing individual user costs independently, involving users’ flexibilities, different billing strategies, and a variable number of users, with random appliances assigned during simulations. The resolution was carried out using the Non-dominated Sorting Algorithm II and Multi-Criteria Analysis, with a Game-based algorithm also utilized. Additionally, simulations were performed under three billing mechanisms. The findings show that costs decrease exponentially with user participation. Similarly, both individual user costs and total costs at the energy provider level were minimized as users’ flexibilities increased. The Peak-to-Average-Ratio is minimized and exhibits a bimodal behavior when observed as a random variable. Regarding the interplay of billing mechanisms, simulation results demonstrate that the smart billing mechanism proposed by the authors outperforms other billing models proposed in the literature for both consumers and utility companies. [ABSTRACT FROM AUTHOR]

This article provides an overview of utility functions and expected utility theory in economic decision-making. It discusses the history of expected utility theory, including the St. Petersburg paradox and the development of prospect theory as an alternative. The article also explores the concept of utility saturation and its impact on risk aversion. It introduces the use of Markov chains to analyze decision-making and concludes by acknowledging the limitations of models in understanding expected utility theory. [Extracted from the article]

The prefabricated building has been widely promoted in recent years as it can effectively alleviate the conflict between economic growth and environmental resources. However, the development of the prefabricated building has fallen short of anticipated goals under the influence of the dynamic circumstances and behavioral strategies of multiple stakeholders. Understanding the relevant stakeholders' behavioral strategies and collaborative evolution mechanisms is key to promoting prefabricated buildings' orderly and efficient development. Therefore, this study combines the evolutionary game theory with system dynamics, introduces Gaussian white noise stochastic disturbance terms to model the complex characteristics of multiagent behavior toward prefabricated buildings, and establishes evolutionary game models and stochastic evolutionary game models for local governments, contractors, and consumers. Subsequently, the influences of strategy choice behavior with or without central government supervision were analyzed to study the collaborative driving mechanism of prefabricated buildings under the multiple effects of the government and market. The findings of this research underscore the necessity for government and market collaboration in championing the sustainable evolution of prefabricated buildings. While central government supervision spurs the growth of these structures, its static reward–punishment approach offers only fleeting collaborative momentum and fails to ensure market steadiness. In contrast, the improved dynamic incentive and disincentive mechanism can effectively control fluctuations in the evolutionary process, which is critical in achieving stable development and collaborative governance toward prefabricated buildings. This study contributes to the body of knowledge by broadening the horizons of evolutionary game theory applications and providing a perspective for understanding the behavioral strategies driving the development of prefabricated buildings by both government and market forces. Therefore, a series of driving mechanisms is proposed, providing theoretical guidance and practical insights to prompt the long-term development of prefabricated buildings more effectively. [ABSTRACT FROM AUTHOR]

Blockchain, essentially a distributed and immutable database, has emerged as a potential solution to governance problems of accountability, transparency, and trust in the construction industry. In terms of construction quality management (CQM) applications, previous works mainly took a technical perspective, and little is known about the potential of blockchain-based governance for quality opportunism. Moreover, we have seen very few successful blockchain implementations in construction practices. Stakeholders meet the trade-off between potential benefits and risks in blockchain adoption decisions. Previous works mainly focused on barrier analysis, neglecting the dynamic interactions of stakeholders' strategic behaviors on adopting blockchains. Against this backdrop, this research aims to systematically analyze blockchain impacts on CQM from a governance perspective and then investigate the owner and the main contractors' strategic behaviors (e.g., adopting blockchains in CQM or not). The agency theory was used as the theoretical lens to examine how blockchain curbs contractors' quality opportunistic behaviors as a governance mechanism. An evolutionary game theory model was developed to simulate adoption decision interactions between the owner and the main contractor. Finally, policy suggestions were highlighted for policymakers. This research could be one of the first studies examining blockchain potential to CQM from an opportunism governance perspective. Such discussions could inspire more discussions on blockchain-based governance and broaden researchers' and practitioners' understandings of blockchain impacts. The proposed policy suggestions from the equilibrium analysis could facilitate blockchain diffusion in the construction industry. [ABSTRACT FROM AUTHOR]

Analytics services provided by marketplace platforms have become increasingly important for sellers seeking market insights. In this paper, we examine a scenario in which an analytics service plays a vital role in enhancing sellers' understanding of market size and improving their decision-making. Using a game-theoretic model, we analyze the pricing strategies of the platform and the adoption strategies of sellers for the analytics service. Our study identifies two distinct effects of analytics services: the competition effect and the accuracy effect. Specifically, the competition effect manifests in opposing ways across different market scenarios, with a competition-intensifying effect in lowdemand markets and a competition-weakening effect in high-demand markets. Consequently, sellers using an analytics service command lower prices in low-demand markets and higher prices in highdemand markets. More interestingly, our results reveal that offering an analytics service could potentially hurt the total market demand, subsequently impacting the platform's revenue from the marketplace service and potentially leaving the platform worse off. Additionally, driven by both the accuracy and competition effects, adopting an analytics service may adversely affect seller profitability and consumer surplus without necessarily improving overall welfare. Moreover, the transaction fee for the marketplace service plays a crucial role in the interplay between the analytics and marketplace services. Specifically, in low-demand (high-demand) markets, as the transaction fee increases, platforms should consider reducing (increasing) the subscription fee to encourage more (fewer) sellers to adopt the analytics service, thereby enhancing overall market demand and increasing revenue from the marketplace service. Our findings also suggest that platforms should refrain from offering analytics services in high-demand markets when the transaction fee is relatively high. Furthermore, policymakers (sellers) should be mindful of the potential negative consequences associated with the adoption of analytics services in high-demand (low-demand) markets. [ABSTRACT FROM AUTHOR]

In this paper we study Nash equilibria in auctions from the kinetic theory of active particles point of view. We propose a simple learning rule for agents to update their bidding strategies based on their previous successes and failures, in first-price auctions with two bidders. Then, we formally derive the corresponding kinetic equations which describe the evolution over time of the distribution of agents on the bidding strategies. We show that the stationary solution of the equation corresponds to the symmetric Nash equilibrium of the auction, and we prove the convergence to this stationary solution when time goes to infinity. We also introduce a more general learning rule that only depends on the income of agents, and we apply to both first- and second-price auctions. We show that agents learn the Nash equilibrium in first- and second-price auctions with these rules. We present agent-based simulations of the models, and we discuss several open problems. [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]

We consider the problem of approximating Nash equilibria of N functions f 1 , ... , f N of N variables. In particular, we show systems of the form u ̇ j (t) = − ∇ x j f j (u (t)) (j = 1 , ... , N) are well-posed and the large time limits of their solutions u (t) = (u 1 (t) , ... , u N (t)) are Nash equilibria for f 1 , ... , f N provided that these functions satisfy an appropriate monotonicity condition. To this end, we will invoke the theory of maximal monotone operators on a Hilbert space. We will also identify an application of these ideas in game theory and show how to approximate equilibria in some game theoretic problems in function spaces. [ABSTRACT FROM AUTHOR]

An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), epi-centred in Hubei Province of the People's Republic of China, quickly spread worldwide and caused COVID-19 pandemic. It infected hundreds of millions of people and caused millions of deaths. In this paper, we develop a compartmental ODE model of COVID-19 transmission. We consider a possibility of breakthrough infections after the vaccination and account for both symptomatic and asymptomatic infections and transmissions. We also incorporate game theory to study the optimal vaccination decisions from the individuals' perspective. We show that vaccination alone is unlikely to eliminate COVID-19. To achieve herd immunity, the individuals would have to receive a dose of a vaccine more frequently than once every 3 months. It is therefore crucial to adhere to various guidelines, such as quarantine, isolate and wear a mask if tested positive for COVID-19. [ABSTRACT FROM AUTHOR]

Aiming at safety regulation in the operation of major infrastructure projects (MIPs) to prevent potential risk loss and adverse social impacts, this research presents a novel model integrating evolutionary game and system dynamics (SD) for optimizing safety regulation strategies with different stakeholders involving the operating company (OC), government section (GS), and public under the bounded rationality, where the evolutionary game theory is applied to describe the interactions among stakeholders in the safety regulation of MIPs followed by simulating through adopting the SD to analyze the effects of different strategies on equilibrium solutions and the stability of game equilibrium. In view of the simulation results based on five scenarios, the dynamic penalty-incentive scenario not only effectively restrains the fluctuations of the strategy selection, but also provides an ideal evolutionary stable strategy, in which the OC could nearly choose to comply with the regulations, while the public could nearly choose to supervise the OC as their optimal strategy to prevent risks. All results indicate that the application of the evolutionary game with the SD model is an effective way to analyze the effects of different strategies and provide effective solutions to study complex multi-player game problems. Overall, this research contributes to developing an evolutionary game with the SD model for the safety regulation of MIPs, which can serve as a platform to identify reasonable regulatory strategies with great practical application. [ABSTRACT FROM AUTHOR]

Old industrial areas have numerous ecological safety problems, requiring green regeneration to mitigate related risks. This study considered the impact of public participation and willingness to adopt green regeneration of old industrial areas and developed an evolutionary game model for the local government and developers. Then, we analyzed the initial strategy and relevant variables of the game through MATLAB (version 2021a) simulation to inform the establishment of green regeneration of old industrial areas. The study results showed that public willingness to use regenerated industrial areas and green preferences could motivate the adoption of green regeneration among developers. The incremental cost of ecological restoration of old industrial areas considerably hinders developers from opting for green regeneration. When the incremental cost exceeds the government's financial subsidies and policy preferences, developers often opt for traditional renovation. Furthermore, punitive measures enforced by local government against developers were found to be more effective than incentives; when the cost of punishment exceeds the incremental cost of ecological restoration, developers often opt for green regeneration. Although the ecological safety concerns in old industrial areas can prompt local governments to opt for stringent regulations and developers for green regeneration at the beginning of the game model, evolutionary stability is not reached. It is necessary to study the green regeneration of old industrial areas as this issue has attained global consensus and is related to the sustainable development of the ecological environment and the improvement of the public quality of life. By modeling a multi-interest game scenario, this paper further clarified the key factors hindering the green regeneration of old industrial areas, which is helpful in solving the practical problems existing in the implementation of such projects and promoting the development of green regeneration in old industrial areas. At the same time, based on the problem of ecological security constraints in the old industrial areas, the research guided by public satisfaction has promoted the development of green regeneration projects in the old industrial areas, effectively improved the current situation of the old industrial areas and its surrounding ecological environment, accelerated the process of urban renewal, and injected new vitality into the construction of national ecological civilization. [ABSTRACT FROM AUTHOR]

Task scheduling is the core research content of cloud computing. It studies how to allocate tasks between computing nodes, so that tasks are evenly allocated or the execution cost of each task is minimized or the overall performance of the system is optimized. Different from the previous task slices that perform independent tasks sequentially in a model with processing time as the goal, we construct a mathematical model with the goal of optimizing response time, in which task slices are executed in parallel. Then, a solution method based on the improved adjustment entropy function is given and a new task scheduling algorithm is designed. Finally, we implement our proposed task scheduling algorithm and compare it with the game theory algorithm and the balanced scheduling algorithm. From the experimental results, by comparing the response time of the task with the size of the control system, the system load and the communication bandwidth, it is found that the task scheduling algorithm we proposed is superior to the game theory algorithm and the balanced scheduling algorithm in response time, and the algorithm we proposed is fair. [ABSTRACT FROM AUTHOR]

The economics of cloud computing result from amassing computation resources while being able to distribute workload in space and time. The backbone of this ability is virtualization, which abstracts the host hardware, sharing it through virtual machines. This means of interface is also a primary vehicle and target for attackers. The counter-measures to this threat consider the costs and benefits to the cloud's essential functions. Where the future development of the cloud is also considered, this competition between attackers and victims can be modeled in extended game theory. Yet, the attacker and victim costs and benefits, expressed as measures of expense and utility, necessary for game-theory methods are elusive. This paper establishes such a game as a predator–prey contest played out on a data-center environment. A set of contestant parameters are applied at the threshold of a viable model to the characteristic boundaries. Measurement of system health is extracted in relief with individual cost and benefit then contrasted to risk. An examination of metrics capable of validating extended interaction is found to demonstrate variation on three orders of magnitude. [ABSTRACT FROM AUTHOR]

This paper proposes a game theory-based on-ramp merging controller for connected automated vehicles (CAVs) in mixed traffic flow. First, a two-layer decision-making framework based on the Stackelberg game is designed to consider the fuel consumption and safety payoffs of mixed traffic flow under different driving behaviors. The upper layer of the framework determines the optimal merging decision (i.e. merging time and location) for on-ramp vehicles (RVs) based on the Stackelberg game. The lower layer optimizes the merging trajectory of CAVs to reduce energy consumption and safety risks during the ramp-merging process. Then, a driving behavior estimation algorithm is developed to describe the differences in mainline vehicles (MLVs) response to the merging behavior of RVs. Finally, the simulation experiments are adopted to verify the effectiveness and stability of the proposed framework. The results indicated that, the proposed framework promotes environmental protection, operational efficiency, and traffic flow stability in different traffic scenarios. [ABSTRACT FROM AUTHOR]

Facing the exponential demand for massive connectivity and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets. Particularly, the operators have to cope with the continuous prosperity of the Internet of things (IoT) along with the ever-increasing deployment of machine-type devices (MTDs). In this regard, due to its compelling benefits, non-orthogonal multiple access (NOMA) has sparked a significant interest as a sophisticated technology to address the above-mentioned challenges. In this paper, we consider a hybrid NOMA scenario, wherein the MTDs are divided into different groups, each of which is allocated an orthogonal resource block (RB) so that the members of each group share a given RB to simultaneously transmit their signals. Firstly, we model the densely deployed network using a mean field game (MFG) framework while taking into consideration the effect of the collective behavior of devices. Then, in order to reduce the complexity of the proposed technique, we apply the multi-armed bandit (MAB) framework to jointly address the resource allocation and the power control problem. Thereafter, we derive two distributed decision-making algorithms that enable the users to autonomously regulate their transmit power levels and self-organize into coalitions based on brief feedback received from the base station (BS). Simulation results are given to underline the equilibrium properties of the proposed resource allocation algorithms and to reveal the robustness of the proposed learning process. [ABSTRACT FROM AUTHOR]

In this paper, the H∞$$ {H}_{\infty } $$ optimal output tracking control problem for Markov jump systems is investigated, where the two cases with known or completely unknown transition probabilities are both considered. Based on game theory and H∞$$ {H}_{\infty } $$ performance, quadratic cost is considered, where a discount parameter is introduced into the quadratic cost in order to track unstable systems and eliminate the assumption that the noise energy is bounded. The game coupled algebraic Riccati equation and the corresponding controller are presented by dynamic programming. The stochastic stability of the tracking error system is further investigated. Moreover, iterative and reinforcement learning‐based algorithms are proposed for solving the H∞$$ {H}_{\infty } $$ optimal tracking controller with known or completely unknown transition probabilities, respectively. Finally, some numerical simulations on a DC motor are performed to validate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

This study uses Stackelberg game theory, considering different combinations of carbon emission reduction policies and that high-carbon-emission enterprises may face various carbon emission reduction regulations, to explore the production inventory problems in a multinational supply chain system. The purpose is to determine the manufacturer's optimal production, shipping, carbon reduction investment, and the retailer's replenishment under the equilibrium for different carbon emission policy combinations. To develop the production inventory models, this study first develops the total profit and carbon emission functions of the supply chain members, respectively, and then obtains the optimal solutions and total profits of the manufacturer and the retailer under different carbon emission policy combinations through the mathematical analysis method. Further, this study used several numerical examples to solve and compare the proposed models. The results of numerical analysis show that regardless of the increase in carbon price or carbon tax, the manufacturer and retailer will adjust their decisions to reduce carbon emissions. Specifically, an increase in the carbon price contributes to an increase in the total profit of manufacturers, while an increase in the carbon tax reduces the total profit of manufacturers. This study also explores a sensitivity analysis on the main parameters and has yielded meaningful management insights. For instance, in cases where low-carbonization strategies are required, the manufacturer or retailer can effectively reduce the carbon emissions resulting from production or purchasing activities, thereby significantly reducing overall carbon emissions. It is believed that the results of this study can provide enterprises/supply chains with reference to their respective production, transportation, carbon reduction investment, and inventory decisions under carbon emission policies, as well as information on partner selection and how to adjust decisions under environmental changes. [ABSTRACT FROM AUTHOR]

Evaluating and selecting appropriate contractors is critical to the success of specific construction projects in the building industry. Existing approaches for addressing this problem are unsatisfactory due to the ignorance of the multi-dimensional nature of the evaluation process and inappropriate consideration of existent risks. This study presents a DEA game cross-efficiency model with loss aversion for evaluating and selecting specific contractors. The competitiveness of the evaluation process is modeled using game theory with respect to the adoption of the cross-efficiency model. The attitude of the decision maker toward risks is tackled with the use of loss aversion, which is a phenomenon formalized in prospect theory. As a result, the proposed approach can adequately screen available contractors through prequalification and adequately consider the attitude of the decision maker toward risks, leading to effective decisions being made. An example is presented to demonstrate the applicability of the proposed model in evaluating and selecting appropriate contractors for specific construction projects. The results show that the proposed model is effective and efficient in producing a unique solution for contractor selection through appropriate modeling of the multi-dimensional contractor selection process and adequate consideration of the competition between the contractors and the attitude of the decision maker toward risks in practical situations. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to routing architecture based on the Session–Cookie protocol. The proposed architecture performs service discovery by integrating IoTs geographic clustering technique and polymorphism mechanism. Prioritizing requests is done in cookies and the load balancing on sessions. The efficiency of the proxy server's distributed cache and service discovery is improved. Smart devices, vehicles, and people inaugurate a p2p connection to receive the service, exchange data, and share files in applications such as parking systems, navigation, and vehicle insurance companies. According to the results, the average response time and the bandwidth consumption have decreased, and the cache hit rate has reached 86%. Based on the principles of game theory, the proposed architecture in the extensive game model with imperfect information in the conditions ((mtcp, mudp), (Y, Y); λ = 1, µ = 0)) has a weak sequential equilibrium and Nash equilibrium. [ABSTRACT FROM AUTHOR]

The 21st century is an era of rapid development of high-tech industry. Through continuous collection and sharing of data, IoT technology connected by things has penetrated into every aspect of human life. Modern organizations find that they increasingly rely on knowledge and information sharing and interconnection to enhance their innovation and development capabilities. Therefore, this paper studies the strategic decision-making of knowledge sharing among employees on the IoT platform drawing on evolutionary game theory, this paper constructs a three-party game model composed of "organization-knowledge sharers-knowledge seekers" from the perspective of stakeholders, and discusses the strategic choice of organization and employee behavior under the dynamic decision mechanism and when the game reaches equilibrium and stability. Moreover, this paper uses MATLAB 2016a to simulate the model. The results show that under the premise of higher organizational rewards, synergistic benefits, high-shared extra income, knowledge-sharing preferences, incentive preferences of the organization, the system is easier to reach the ideal state. With the smaller cost of sharing and incentive, organization and employees are more willing to be motivators and sharers, while the incentive degree of organization should be controlled within a reasonable range. This paper can provide specific theoretical and practical guidance for the practice of organizational knowledge management. [ABSTRACT FROM AUTHOR]

Retinal disorders pose a serious threat to world healthcare because they frequently result in visual loss or impairment. For retinal disorders to be diagnosed precisely, treated individually, and detected early, deep learning is a necessary subset of artificial intelligence. This paper provides a complete approach to improve the accuracy and reliability of retinal disease identification using images from OCT (Retinal Optical Coherence Tomography). The Hybrid Model GIGT, which combines Generative Adversarial Networks (GANs), Inception, and Game Theory, is a novel method for diagnosing retinal diseases using OCT pictures. This technique, which is carried out in Python, includes preprocessing images, feature extraction, GAN classification, and a game-theoretic examination. Resizing, grayscale conversion, noise reduction using Gaussian filters, contrast enhancement using Contrast Limiting Adaptive Histogram Equalization (CLAHE), and edge recognition via the Canny technique are all part of the picture preparation step. These procedures set up the OCT pictures for efficient analysis. The Inception model is used for feature extraction, which enables the extraction of discriminative characteristics from the previously processed pictures. GANs are used for classification, which improves accuracy and resilience by adding a strategic and dynamic aspect to the diagnostic process. Additionally, a game-theoretic analysis is utilized to evaluate the security and dependability of the model in the face of hostile attacks. Strategic analysis and deep learning work together to provide a potent diagnostic tool. This suggested model’s remarkable 98.2% accuracy rate shows how this method has the potential to improve the detection of retinal diseases, improve patient outcomes, and address the worldwide issue of visual impairment. [ABSTRACT FROM AUTHOR]

In the realm of evolutionary game theory, the majority of scenarios involve players with incomplete knowledge, specially regarding their opponents' actions and payoffs compounded by the ever‐shifting landscape of players' interactions. These dynamics present formidable challenges in both the analysis and optimization of game evolution. To address this, a novel model named the networked evolutionary game (NEG) is proposed based on incomplete information with switched topologies. This model captures situations where players possess limited insight into their opponents' benefits, yet make decisions based on their own payoffs while adapting to different networks and new players. To bridge the gap between incomplete and complete information games, R. Selten's transformation method is leveraged, a renowned approach that converts an incomplete information game into an interim agent game, thereby establishing the equivalence of pure Nash equilibria (NE) in both scenarios. Employing the semi‐tensor product (STP) of matrices, a powerful tool in logistic system, the evolution of the model is articulated through algebraic relationships. This enables to unravel the patterns of game evolution and identify the corresponding pure Nash equilibria. By introducing control players, strategically positioned within the game, optimized control is facilitated over the evolutionary trajectory, ultimately leading to convergence towards an optimal outcome. Finally, these concepts are illustrated with a practical example within the paper. [ABSTRACT FROM AUTHOR]

The article discusses the 2021 European Meeting on Game Theory (SING16), which is a significant conference dedicated to game theory. The conference received 68 submissions, out of which 28 were selected for publication in a special issue. The accepted papers cover various topics in game theory, including cooperative games, non-cooperative games, dynamic games, and applications of game theory in different fields. The article provides a brief description of the contributions included in the special issue, which range from the study of power indices in voting games to the analysis of supply chain dynamics and the effects of government intervention in green supply chains. Additionally, the article mentions contributions on networks and voting. [Extracted from the article]

In practice, many firms launch winner-take-all crowdsourcing contests on a self-built or an intermediary platform to harness the wisdom of open crowds. In this study, we analyze the optimal choice of crowdsourcing mode for a contest seeker. Using game theory and auction theory approaches, we model a game between the seeker and participating solvers and derive the equilibrium decisions and payoffs under each mode. The results first show that on the self-built platform, the seeker benefits from controlling the number of solvers and providing the combination of monetary and non-monetary rewards, which meets the different preferences of solvers. Second, a large pool of solvers on the intermediary platform is not always beneficial for the seeker, such that a free-entry contest is less likely to be optimal. Moreover, some high-ability solvers will exert more effort but obtain less expected surplus when facing increased competition. Finally, we present conditions under which one of the two modes is optimal for the firm. Our findings provide firms with an appropriate mode in hosting the crowdsourcing contest. [ABSTRACT FROM AUTHOR]

The present study examines a supply chain consisting of a manufacturer and a retailer. The manufacturer produces a product with a national brand (NB) and the retailer selling both the NB product and his own premium store brand (PSB) product. The manufacturer competes with the retailer through improving the quality by using innovation over time. It is assumed that both advertising and enhanced quality play positive roles in customers' loyalty over time for the NB product. We propose four scenarios, including: (1) Decentralized (D), (2) Centralized (C), (3) Coordination with a revenue-sharing contract (RSH), and (4) Coordination with a two-part tariff contract (TPT). A Stackelberg differential game model is developed, and parametric analyses and managerial insights are provided based on a numerical example. Our results show that: (1) Introducing a PSB product alongside selling the NB product is profitable for the retailer, (2) In Scenario D and RSH, the manufacturer tries to increase the quality gap with the PSB product through innovation, (3) Customers' loyalty leads to higher prices, levels of innovation, quality, and advertising for the NB product, (4) The TPT contract can lead to perfect coordination and yield higher profits for the manufacturer and the retailer. [ABSTRACT FROM AUTHOR]

We investigate the RFID adoption and chain structure (i.e., integrated or decentralized) decisions in two supply chains under two competition modes (i.e., Bertrand or Cournot competition). Each chain consists of a manufacturer and an exclusive retailer, who suffers from inventory misplacement problem. Two supply chains first simultaneously choose chain structure and then decide whether to invest in RFID technology. We develop an analytical model to derive the equilibrium outcomes, then further analyze the interactions between RFID adoption and chain structure under two competition modes. We find that in equilibrium, both chains prefer to choose integration under Cournot competition, while they might be better off from decentralization under Bertrand competition. Moreover, there may exist a prisoner's dilemma for the equilibrium strategies on RFID adoption and chain structure. Specifically, when both chains adopt RFID, the prisoner's dilemma occurs if the competition is fierce; when only one chain adopts RFID, the chain who forgoes RFID adoption is easier to trap into the prisoner's dilemma; when no chain adopts RFID, a high misplacement rate may aggravate the prisoner's dilemma if the competition is weak, and the supply chain members under Cournot competition may escape such dilemma if the competition is fierce. In addition, competition mode doesn't affect the optimal strategies for RFID adoption and chain structure. Further, in the case where the manufacturer affords a higher production cost, Cournot competition is more conducive to coordinate chains when competition is weak or relatively fierce, whereas Bertrand competition will be more effective when competition is moderate or very fierce. [ABSTRACT FROM AUTHOR]

To achieve a competitive advantage, corporations are growingly adopting strategies to effectively promote their market demand. Trade credit payment and pricing strategies provided by corporates can efficiently influence customers' purchasing behavior. Although granting a trade credit strategy can increase corporations' market share, such a strategy is a risk-based financing program for corporations. Therefore, corporates should choose whether to use trade credit financing in their long-term. This paper proposes an analytical model to investigate the evolutionary behaviors of retailers regarding pricing and trade credit strategies in the long term. In the study under investigation, retailers can use two financing strategies: risk-based trade credit and non-trade credit (i.e., pricing). This study provides both numerical and analytical findings. Our findings demonstrate that the risk-based trade credit strategy is the stationary financing solution for retailers in the long term. The result indicates that when customers are financially constrained, providing a trade credit scheme to customers is a successful marketing policy in both short-term and long-term frameworks. [ABSTRACT FROM AUTHOR]

We consider a distributed computing setting wherein a central entity seeks power from computational providers by offering a certain reward in return. The computational providers are classified into long-term stakeholders that invest a constant amount of power over time and players that can strategize on their computational investment. In this paper, we model and analyze a stochastic game in such a distributed computing setting, wherein players arrive and depart over time. While our model is formulated with a focus on volunteer computing, it equally applies to certain other distributed computing applications such as mining in blockchain. We prove that, in Markov perfect equilibrium, only players with cost parameters in a relatively low range which collectively satisfy a certain constraint in a given state, invest. We infer that players need not have knowledge about the system state and other players' parameters, if the total power that is being received by the central entity is communicated to the players as part of the system's protocol. If players are homogeneous and the system consists of a reasonably large number of players, we observe that the total power received by the central entity is proportional to the offered reward and does not vary significantly despite the players' arrivals and departures, thus resulting in a robust and reliable system. We then study by way of simulations and mean field approximation, how the players' utilities are influenced by their arrival and departure rates as well as the system parameters such as the reward's amount and dispensing rate. We observe that the players' expected utilities are maximized when their arrival and departure rates are such that the average number of players present in the system is typically between 1 and 2, since this leads to the system being in the condition of least competition with high probability. Further, their expected utilities increase almost linearly with the offered reward and converge to a constant value with respect to its dispensing rate. We conclude by studying a Stackelberg game, where the central entity decides the amount of reward to offer, and the computational providers decide how much power to invest based on the offered reward. [ABSTRACT FROM AUTHOR]

Most entrepreneurs seek VC funding after the reward-based crowdfunding campaign succeeds, and venture capitalists (VCs) can contribute to the project in two aspects: investment and operational expertise. With a game-theoretical model, we find that entrepreneurs face twelve possible scenarios contingent on the mass market revenue and revenue share, including the six scenarios wherein neither side exerts effort to complete the project. To attract VC funding and ensure the project completion after a campaign success, entrepreneurs should set the funding goal above a certain threshold. Specifically, we identify three ranges of the revenue share and derive the lower bound for the funding goal in each range. However, we notice that entrepreneurs prefer a low funding goal to promise the campaign success and the optimal goal will be the lower bound in each range. Moreover, we show that the revenue share is decisive to the role of VCs in the project. If the entrepreneur's share exceeds a high threshold, the venture capitalist becomes a pure investor with no incentive to exert effort, similar to the role of banks; if the share is less than a low threshold, the entrepreneur won't follow up but transfers the project, and the VC investor will be a project owner; if the share stays medium, the VC investor acts as a partner and there may exist "free-riding". In the extension, we consider the revenue share an endogenous and analyse the role of VCs further. Interestingly, the VC investor prefers to own the project and occupy all revenue in mass market, while the entrepreneur treats the inefficient venture capitalist as a pure investor and the efficient one as a partner. In addition, when cooperating with efficient VCs, the entrepreneur is more likely to enlarge her share as the mass market revenue increases. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates optimizing the collaborative energy power consumption between nodes with energy harvesting (EH) capability at the fusion center (FC) and multi‐antenna sensors using game theory. Using multi‐antennas at FC on sensor networks (SNs), we can benefit from the spatial diversity in receiving the data and using multi‐antenna sensors and energy to transfer the data and optimize the SN, respectively. Also, in this paper, the data transmission in the network is formulated by considering the limitations of storage resources of each sensor, minimum data receiving, minimum EH, and maximum collaboration among sensors. In this paper, some modes are suggested to minimize the collaborative energy among sensors for transmitting the data and energy among sensors in the collaboration of sensors to optimize the power control and allocation power policy. Suggestions have been studied based on the number of antennae to transmit, using the cooperative games in sharing the data and the energy and auction game in transmitting to FC. The simulation results of the proposed problem using MATLAB software show optimization in participation function, EH, allocation in cooperation time, and transferring to FC to improve the detection of the transmitted data based on the increasing number of antennas in FC. [ABSTRACT FROM AUTHOR]

The growing water demand and decreasing groundwater recharge have made groundwater management one of the most severe challenges in most countries of the world, and Iran is no exception. This study aims to examine the optimal groundwater exploitation in three cropping years (2020–2021, 2021–2022, and 2022–2023) in a study of the Silakhor plain, Iran, by use of game theory. Game theory problems involve multidecision-making to address conflicting objectives. Thus, farmers and environmentalists were considered as game theory 'players' and their strategies were examined. Two groups of groundwater exploitation scenarios were considered based on both groundwater recharge and the current exploitation. Optimal groundwater exploitation was determined. The results of determining exploitation scenarios based on the current exploitation show that the optimal groundwater exploitation in the Silakhor plain is 103.9, 101, and 99 million m3 in the next 3 years, respectively; these values decrease by 6.7, 7, and 7.4%, respectively, by determining exploitation scenarios based on groundwater recharge. The second major finding is that the farmers' net benefit will increase by 18% by applying the optimal cropping pattern. Taken together, the results show that the design of the game structure is very important and the basis of the players' strategies must be determined before using conflict resolution methods. [ABSTRACT FROM AUTHOR]

Scientific and technological advancements over the last three decades have failed to reduce the widespread prevalence of academic dishonesty in higher education, in large part because institutional barriers prevent faculty from adopting existing tools to curb cheating. We conducted a systematic literature review of research on cheating and found that the majority of studies propose new tools without advancing theory or even utilizing existing theory. Although some studies note the systemic nature of academic misconduct, the academic integrity literature requires a robust theoretical framework to model its systemic nature and derive practical strategies. Building on theory from several domains, we propose a systemic model of academic (mis)conduct which predicts group-level effects on students and offers practical guidance for faculty overcoming institutional barriers to curb cheating. We leverage game theory for useful models of systemic, group-level phenomena in this context, and we leverage education reform literature for insights on how to support instructors' adoption of new tools. Our model, the spectrum of academic conduct, identifies trust as a single dimension governing both cheating behaviors and productive learning behaviors. Integrating insights from pedagogy, conflict management, and organizational psychology, we discuss multiple practical strategies to lower students' opportunity, motivation, and rationalization to cheat. [ABSTRACT FROM AUTHOR]

Online retail platforms such as Amazon and Tmall have the ability to create personalized recommendations based on the consumer's browsing history, purchase history, and preferences by investing in data analytics capability. In practice, manufacturers may encroach on the retail market through the agency channel that sells products directly to online consumers in addition to wholesale products to retail platforms through the reselling channel. In this study, we develop a game-theoretic model to study the interplay between the manufacturer's encroachment and the online retail platform's data analytics capability investment. Our outcomes reveal that the conditions for the manufacturer to encroach become more lenient if the platform invests in data analytics capability, and we show that the investment in data analytics capability can lead to a Pareto improvement and the manufacturer can free ride on the platform's investment. Moreover, we found that the manufacturer's encroachment always creates more incentives for the platform to enhance the investment level in data analytics capability. Our research in this study provides useful insights for managers to make encroachment decisions and data analytics capability investment decisions with the manufacturer who sells through the online retail platform. [ABSTRACT FROM AUTHOR]

Scheduling workload is a demand response strategy for data centers to reshape electricity usage, which provides an opportunity for them to utilize renewable energy. Enhancing the flexibility of workload scheduling would promote the data center to integrate renewable energy. Considering that the improvement of flexibility in workload scheduling is tightly related to the Quality of Service (QoS) required by IT consumers (ITCs), it becomes desirable for the data center to collaborate with them to further facilitate renewable energy integration in demand response programs. This paper proposes a QoS‐based bi‐level demand response model for the data center, where game theory is adopted to resolve the conflict of interest in the collaboration between the data center and ITCs. At the upper level, the data center is a leader who provides differential monetary incentives to ITCs while dispatching workload to integrate renewable energy. At the lower level, ITCs act as followers who adjust the workload's QoS to enhance the flexibility of workload scheduling. Simulation results show that the proposed model can decrease the operating cost of the data center while increasing the utilization of renewable energy. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Mathematical models describing biological phenomena comprise powerful tools for their understanding and provide further insight regarding their behavior. Modeling complex processes, such as a cancer tumor, can provide detailed description of the mechanisms governing the function of unique cancer cells, as well as their integrative function in a tumor as a heterogeneous cellular system. Furthermore, mathematical modeling of the results of in vitro experiments can highlight new hypotheses that can be tested experimentally, enriching thus, the value of the results of an in vitro study. In the current review, some of the most frequent and important techniques of mathematical modeling used in biology and medicine, as well as in the study of cancer, are described: differential equations, that can be either ordinary or partial, game theory and its most specialized form for biological phenomena -- evolutionary game theory, agent-based modeling and finally cellular automata and dynamic cellular automata. The main points of each technique are discussed along with their advantages and limitations. Subsequently, specific examples of published research studies focusing on the investigation of cancer systems that make use of modeling methods are provided. The aim of this review is to provide an understanding of the value of mathematical modeling in cancer research and the way that it can integrate and predict experimental evidence that derive from in vitro studies. [ABSTRACT FROM AUTHOR]

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP. [ABSTRACT FROM AUTHOR]

The AB Game is a game similar to the popular game Mastermind. We study a version of this game called Static Black-Peg AB Game. It is played by two players, the codemaker and the codebreaker. The codemaker creates a so-called secret by placing a color from a set of c colors on each of p ≤ c pegs, subject to the condition that every color is used at most once. The codebreaker tries to determine the secret by asking questions, where all questions are given at once and each question is a possible secret. As an answer the codemaker reveals the number of correctly placed colors for each of the questions. After that, the codebreaker only has one more try to determine the secret and thus to win the game. For given p and c , our goal is to find the smallest number k of questions the codebreaker needs to win, regardless of the secret, and the corresponding list of questions, called a (k + 1) -strategy. We present a (⌈ 4 c / 3 ⌉ − 1) -strategy for p = 2 for all c ≥ 2 , and a ⌊ (3 c − 1) / 2 ⌋ -strategy for p = 3 for all c ≥ 4 and show the optimality of both strategies, i.e., we prove that no (k + 1) -strategy for a smaller k exists. [ABSTRACT FROM AUTHOR]

Most nuclear dyads are characterized by some degree of nuclear and conventional asymmetry. This paper argues that these asymmetries create an environment in which deliberate nuclear first use (DNFU) can be rational. This possibility has been discarded in the formal literature on nuclear escalation because of the common reliance on the assumption of mutually assured destruction (MAD). This paper develops a formal model that traces how and under what circumstances two types of DNFU are rational. First, nuclear imbalances and advancements in counterforce technologies create a damage limitation incentive for a strong actor. Second, conventional asymmetry creates an incentive for the coercive use of nuclear weapons by the weaker player. Moreover, this paper illustrates that these asymmetric conditions are a relevant characteristic in important and very different nuclear dyads: DPRK–US, Pakistan–India, and Russia–US. Thus, the model demonstrates the potential core drivers of DNFU in today's nuclear landscape. [ABSTRACT FROM AUTHOR]

Order execution is an important operational level of activity encountered in portfolio investment and risk management. We study a sequential Stackelberg order execution game which arises naturally from the practice of algorithm trading in financial markets. The game consists of two risk-neutral traders, one leader and one follower, who compete to maximize their expected payoffs respectively by trading a single risky asset whose price dynamics follows a linear-price market impact model over a finite horizon. This new Stackelberg game departs from the Nash games which have been the main focus in the algorithm trading literature. We derive a closed-form solution for the unique open-loop Stackelberg equilibrium by exploiting the special structures of the model. This analytic solution enables us to develop new and complementary managerial insights by looking at both players' equilibrium behavior in terms of trading speeds and positions, expected price dynamics, price of anarchy, first mover's advantage, and trading horizon effect. [ABSTRACT FROM AUTHOR]

We consider the general problem of a set of agents trading a portfolio of assets in the presence of transient price impact and additional quadratic transaction costs and we study, with analytical and numerical methods, the resulting Nash equilibria. Extending significantly the framework of Schied and Zhang (2019) and Luo and Schied (2020), who considered the single asset case, we prove the existence and uniqueness of the corresponding Nash equilibria for the related mean-variance optimization problem. We then focus our attention on the conditions on the model parameters making the trading profile of the agents at equilibrium, and as a consequence the price trajectory, wildly oscillating and the market unstable. While Schied and Zhang (2019) and Luo and Schied (2020) highlighted the importance of the value of transaction cost in determining the transition between a stable and an unstable phase, we show that also the scaling of market impact with the number of agents J and the number of assets M determines the asymptotic stability (in J and M) of markets. [ABSTRACT FROM AUTHOR]

This paper considers a resource allocation problem involving servers and mobile users (MUs) operating in a serverless edge computing (SEC)-enabled Internet of Things (IoT) network. Each MU has a fixed budget, and each server is powered by the grid and has energy harvesting (EH) capability. Our objective is to maximize the revenue of the operator that operates the said servers and the number of resources purchased by the MUs. We propose a Stackelberg game approach, where servers and MUs act as leaders and followers, respectively. We prove the existence of a Stackelberg game equilibrium and develop an iterative algorithm to determine the final game equilibrium price. Simulation results show that the proposed scheme is efficient in terms of the SEC's profit and MU's demand. Moreover, both MUs and SECs gain benefits from renewable energy. [ABSTRACT FROM AUTHOR]