Search

This paper proposes a service-oriented manufacturing strategy in a two-echelon supply chain with random demand. When there is no inventory for the retailer to meet demand, customers can choose to order products through the retailer and products will be delivered to them by the manufacturer directly in the promised time, and we call this delivery service in service-oriented manufacturing. We employ a Stackelberg game-theoretic model to analyse the procurement quantity and delivery time decisions by the manufacturer as the leader and the retailer as the follower. Using the performance of the pure-manufacturing strategy as a benchmark, our main results follow. First, the equilibrium strategies of the two players are obtained. Clear decision rules are also given. Second, delivery service in service-oriented manufacturing can be an effective way to reduce the holding cost of the retailer. Third, we describe the customer loss caused by waiting and find how the manufacturer influences procurement quantity of the retailer through the decision of delivery time. Finally, delivery service in service-oriented manufacturing cannot always improve the profit of the manufacturer and the retailer. We give guidance for managers to provide it. [ABSTRACT FROM AUTHOR]

Behaviour-based pricing (BBP) is commonly observed in the information goods industry. Information goods providers typically sell multi-version products and have implemented different BBP strategies for their products. However, previous research on BBP is mostly limited to single-version products, which may not effectively guide practical decision-making regarding multi-version products. This gap sparks interest in how BBP can be applied to multi-version information goods. We show that the equilibrium pricing strategy depends on the quality gap between the products and how much consumers and the firm value future benefits. The firm should implement BBP for both high- and low-quality products if it values future benefits more than consumers do. If consumers value future benefits more than the firm, the firm should not implement BBP for either product. Moreover, when the firm and consumers value future benefits equally, the firm should only practice BBP for the low-quality product. Our calculations of consumer surplus and social welfare reveal that the absence of BBP doesn't always lead to the maximum benefits for consumers and society. Interestingly, if the firm values future benefits more than its consumers do, applying BBP only to high-quality products or both products may lead to higher consumer surplus and social welfare. [ABSTRACT FROM AUTHOR]

The emergence of blockchain creates a new possibility to solve the fraudulent problem of financial supply chain. We construct a game model to verify the strategic choice of the financial supply chain in an uncertain environment. We derive the equilibrium results and investigate the strategic choice of blockchain service for the financial supply chain. We also study the product price, product quantity, financing interest rate, and supply chain risk transmission, respectively. Specifically, when the blockchain is not considered, the financial model of supply chain led by core enterprises depends on firms' reputation. The retail and wholesale prices increase when fraud occurs or consideration payment increases. Besides, the market stability reduces price performance. In the blockchain environment, the strategic choices are divided into two cases: when choosing the core enterprise model and the third-party service model, the third-party service model is the equilibrium strategy; when choosing the third-party service model and the platform model, the platform model is the equilibrium strategy. [ABSTRACT FROM AUTHOR]

The circular economy has become a promising solution to electronic product reuse. The advanced technology of Industry 4.0 drives the recycling platform (RP) to carry out a variety of new business modes, which are important ways to realise a circular economy. In this article, we consider a monopolistic RP under trade-in for new mode, leasing mode, or their mixed mode, respectively. We develop a discrete-time game with infinite periods and use the Markov decision and Bellman equation to obtain the consumer's perfect equilibria consumption strategies. By comparing different modes under RP's profits, consumer surplus, and environment impact perspectives, we find that when the durability of leased refurbished product exceeds that of the resold refurbished ones, the mixed mode is better than the trade-in for new mode, which can achieve more platform profits, more consumer surplus, and less environment impact. By considering the three perspectives together, the mixed mode is not always better than the other two modes. The mixed mode is optimal only when the durability of leased refurbished one is relatively high. We also conduct extensive numerical research, show optimal service mode under three perspectives, and extend the effects of different qualities of recycled used products on optimal mode. [ABSTRACT FROM AUTHOR]

We prove that the PoA of First Price Auctions is 1 -- 1/e² ≈ 0.8647, closing the gap between the best known bounds [0.7430, 0.8689]. [ABSTRACT FROM AUTHOR]

In this article, an H∞$$ {H}_{\infty } $$ optimal preview repetitive control (OPRC) scheme is proposed to deal with the disturbance attenuation problem for continuous‐time linear systems with external unknown disturbance and input delay. A general bounded L2$$ {L}_2 $$ gain is applied to the H∞$$ {H}_{\infty } $$ OPRC tracking control problem by introducing a function with discounted performance. First, an augmented system containing system state equation, tracking error dynamics, and modified repetitive control output equation is constructed, which is then transformed into a non‐delayed one by state transformation. Next, the OPRC controller is given and the game algebraic Riccati equation (GARE) is derived by transforming the H∞$$ {H}_{\infty } $$ tracking problem into a 2‐player zero‐sum game problem to give a Nash equilibrium solution of the associated min–max optimization problem. Besides, a value iteration (VI) algorithm is introduced to optimize the solution of continuous time GARE and ensure its convergence. Furthermore, the bounded‐input bounded‐output stability of the closed‐loop system is obtained by giving an upper bound on the discount factor. Finally, the numerical simulation example is provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Keeping an intelligent agent under surveillance with an autonomous robot as it travels in the environment is one of the essential tasks in robotics. This work addresses a pursuit-evasion surveillance problem between two Differential Drive Robots moving on a plane without obstacles. One of them, the pursuer, is equipped with a limited field-of-view sensor and aims to maintain the other robot (evader) inside its detection region for as much time as possible. On the contrary, the evader wants to escape as soon as possible. Unlike prior studies that address the same problem, based on heuristic approaches, we frame it as a zero-sum differential game, and we use differential game theory to find a solution with theoretical guarantees. In particular, we compute the players' time-optimal motion strategies to achieve their goals near the end of the game and provide analytical expressions describing them. These strategies are in Nash equilibrium, meaning that any unilateral deviation by a player from these strategies does not provide such player benefit toward its goal. Our analysis exhibits the existence of a Transition Surface in which the evader switches its controls. We also determine the game's winner based on the players' initial configurations and velocities. [ABSTRACT FROM AUTHOR]

In this article, we study the M/M/1 queueing systems with negative customers and unreliable repairers. The arrival of a negative customer causes the failure of the server, and the server is repaired immediately. The repairers may successfully repair the server with probability p, and the server continues to serve customers in the system. Otherwise, once the repair fails, the system will be cleared and all customers are forced to leave the system. By analyzing the quasi-birth-and-death process and using the probability generating function method, we obtain the steady-state probability and some performance measures of the system. Then, based on the reward-cost structure, we discuss the strategic behavior of customers and consider the equilibrium strategy. Finally, the effects of system parameters on the performance measures, individual benefit, and social benefit are analyzed by numerical examples. [ABSTRACT FROM AUTHOR]

A mechanism allocates one unit of an infinitely divisible commodity among agents reporting a number between zero and one. Nash, Pareto optimal Nash, and strong equilibria are analyzed for the case where the agents have single-dipped preferences. One main result is that when the mechanism satisfies anonymity, monotonicity, the zero–one property, and order preservation, then the Pareto optimal Nash and strong equilibria coincide and result in Pareto optimal allocations that are characterized by so-called maximal coalitions: members of a maximal coalition prefer an equal coalition share over obtaining zero, whereas the outside agents prefer zero over obtaining an equal share from joining the coalition. A second main result is an axiomatic characterization of the associated social choice correspondence as the maximal correspondence satisfying minimal envy Pareto optimality, equal division lower bound, and sharing index order preservation. [ABSTRACT FROM AUTHOR]

This paper investigates a two-player contest with a multiplicative sabotage effect, showing it can be converted into a standard Tullock contest with a nonlinear, endogenous cost function. We prove the existence and uniqueness of a pure strategy equilibrium. Our findings suggest that sabotage activities can be more pronounced when the productivity difference between players is small, and the more productive player might not necessarily undergo more attacks. Lazear and Rosen (1981) first-best outcome is attainable for symmetric players if sabotage is sufficiently ineffective or costly. When it is unattainable, optimal pay difference induces positive sabotage only if sabotage is ineffective but relatively inexpensive. Optimal pay difference decreases with effectiveness and increases with the marginal cost of destructive effort, exhibiting a non-monotonic relationship with productive-effort effectiveness. This non-monotonicity contrasts with the monotonicity of the first best pay difference when sabotage is infeasible. [ABSTRACT FROM AUTHOR]

We consider a stylized model for investment into renewable power plants under long-term uncertainty. We model risk-averse agents facing heterogeneous weather conditions and a common noise including uncertainty on demand trends, future fuel prices and the average national weather conditions. The objective of each agent is to maximize multistage profit by controlling investment in discrete time steps. We analyze this model in a noncooperative game setting with N players, where the interaction among agents occurs through the spot price mechanism. Our model extends to a mean field game with common noise when the number of agents is infinite. We prove that the N-player game admits a Nash equilibrium. Moreover, we prove that under appropriate assumptions, any sequence of Nash equilibria to the N-player game converges to the unique solution of the MFG game. Our numerical experiments highlight the impact of the risk aversion parameter and the importance of correctly specifying the distribution of the heterogeneity among agents. Moreover, we demonstrate that the results obtained by our model cannot be replicated by a model based on a representative agent with a unique parameter that would represent homogenized weather conditions. This emphasizes the importance of including explicit modeling of heterogeneity in prospective models when a heterogeneous parameter is expected to have a significant influence on the outcomes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a queueing-game-theoretic model and analyze the strategic behavior of customers and social optimization in an observable $M/G/1$ M / G / 1 queue, in which arriving customers decide whether to join the system or balk based on a new binary and random reward-cost structure. Each incoming customer to the queue has a relative tolerance time. If the customer's service does not begin (or end) within his or her relative tolerance time, the customer will incur a cost for his or her waiting. We first derive closed-form solutions for customers' equilibrium and socially optimal joining strategies using the technique of the Laplace-Stieltjes transform. Furthermore, some representative numerical experiments are performed to visualize the theoretical results. The numerical scenarios illustrate the influence of the relative tolerance time on equilibrium strategy and socially optimal strategy. Finally, we compare the effect of relative tolerance time on social welfare in observable and unobservable queues. The numerical results show that observable queues lead to higher social welfare. This study provides guidance for system providers in designing more economical and sustainable public service systems. [ABSTRACT FROM AUTHOR]

This article considers a quantity‐setting duopoly (Cournot rivalry) in which firms adopt an abatement technology as a device to improve the quality of products. Consumer preferences capture vertical product differentiation (quality) towards "green" products. This introduces a trade‐off on the production side, as firms that do not abate, in turn, do not sustain any abatement cost but the demand for their product is low. On the contrary, firms that choose to abate incur abatement costs, but the demand for their product is high. The article aims to study and understand whether this kind of preference may lead firms to strategically invest in green technology and introduces a new, private‐based (that contrasts the well‐known public‐based) mechanism through which pollution abatement can emerge as a sub‐game perfect Nash equilibrium (SPNE) of a non‐cooperative abatement decision game with product quality and complete information. The model is developed in a parsimonious way to pinpoint the main determinants of the endogenous market outcomes ranging from an anti‐prisoner's dilemma in which self‐interest and mutual benefit of non‐abatement do not conflict to an anti‐prisoner's dilemma in which self‐interest and mutual benefit of abatement do not conflict, passing through to an anti‐coordination scenario. Additionally, the welfare analysis reveals the existence of a win‐win solution from a societal perspective. The article shows that the results obtained in the Cournot setting also hold considering a Bertrand duopoly. [ABSTRACT FROM AUTHOR]

We consider a supplier selling substituted products to an e‐retailer through wholesale selling mode or mixed use of wholesale and agency selling mode (hybrid contracts). This paper explains the curious failure of the agency model and investigates the contract selection problem. We build the stylized models that consider consumer formats preferences and distribution sequence. We find that an excessively high agency fee can lead to failure but this is on the condition of substituted products simultaneously distributed. However, we find no failure phenomenon in sequential distribution scenarios. Because it is optimal to mark down the price of the delayed product that prevents inefficiency with a lower price in the agency model than in the wholesale model. Furthermore, in the e‐books industry, when consumers prefer digital (traditional) formats, the equilibrium strategy is that the e‐retailer claims a low agency fee, and the supplier chooses simultaneous (sequential) distribution through hybrid contracts. Besides, the equilibrium is robust. [ABSTRACT FROM AUTHOR]

ABSTRACT This article investigates a multiplayer nonzero‐sum differential game (MNDG) with switching topology and input delay. The Hamilton–Jacobi–Isaacs (HJI) equation is used to derive the optimal control strategies. To solve the issue of switching topology, the control strategies are decoupled with the communication topology. Then, the stability of the system can be achieved regardless of changes in communication topology with the designed control strategies. This does not require a restriction on the dwell time, making the system stability conditions more relaxed. The Lyapunov–Krasovskii functional (LKF) is constructed to prove the stability of the system in the presence of time‐varying input delay. To cope with the real‐world situation, the input delay is considered to be disturbed by a time‐varying perturbation in this article. With these treatments, the system is more in line with real‐world applications. Simulation examples are given to validate the efficiency of the presented methods. [ABSTRACT FROM AUTHOR]

Coordinating with others is part of our everyday experience. Previous studies using sensorimotor coordination games suggest that human dyads develop coordination strategies that can be interpreted as Nash equilibria. However, if the players are uncertain about what their partner is doing, they develop coordination strategies which are robust to the actual partner's actions. This has suggested that humans select their actions based on an explicit prediction of what the partner will be doing—a partner model—which is probabilistic by nature. However, the mechanisms underlying the development of a joint coordination over repeated trials remain unknown. Very much like sensorimotor adaptation of individuals to external perturbations (eg force fields or visual rotations), dynamical models may help to understand how joint coordination develops over repeated trials. Here we present a general computational model—based on game theory and Bayesian estimation—designed to understand the mechanisms underlying the development of a joint coordination over repeated trials. Joint tasks are modeled as quadratic games, where each participant's task is expressed as a quadratic cost function. Each participant predicts their partner's next move (partner model) by optimally combining predictions and sensory observations, and selects their actions through a stochastic optimization of its expected cost, given the partner model. The model parameters include perceptual uncertainty (sensory noise), partner representation (retention rate and internale noise), uncertainty in action selection and its rate of decay (which can be interpreted as the action's learning rate). The model can be used in two ways: (i) to simulate interactive behaviors, thus helping to make specific predictions in the context of a given joint action scenario; and (ii) to analyze the action time series in actual experiments, thus providing quantitative metrics that describe individual behaviors during an actual joint action. We demonstrate the model in a variety of joint action scenarios. In a sensorimotor version of the Stag Hunt game, the model predicts that different representations of the partner lead to different Nash equilibria. In a joint two via-point (2-VP) reaching task, in which the actions consist of complex trajectories, the model captures well the observed temporal evolution of performance. For this task we also estimated the model parameters from experimental observations, which provided a comprehensive characterization of individual dyad participants. Computational models of joint action may help identifying the factors preventing or facilitating the development of coordination. They can be used in clinical settings, to interpret the observed behaviors in individuals with impaired interaction capabilities. They may also provide a theoretical basis to devise artificial agents that establish forms of coordination that facilitate neuromotor recovery. Author summary: Acting together (joint action) is part of everyday experience. But, how do we learn to coordinate with others and collaborate? Using a combination of experiments and computational models we show that through multiple repetitions of the same joint task we select the action which represents the 'best response' to what we believe our opponent will do. Such a belief about our partner (partner model) is developed gradually, by optimally combining prior assumptions (how repeatable or how erratic our opponent behaves) with sensory information about our opponent's past actions. Rooted in game theory and Bayesian estimation, the model accounts for the development of the mutual 'trust' among partners which is essential for establishing a mutually advantageous collaboration, and explains how we combine decisions and movements in complex coordination scenarios. The model can be used as a generative tool, to simulate the development of coordination in a specific joint action scenario, and as an analytic tool to characterize the individual traits or defects in the ability to establish collaborations. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper studies the design of resilient distributed Nash equilibrium (NE) algorithm for second‐order multi‐agent systems in the presence of Denial‐of‐Service (DoS) attacks, which prevent information transmission in the network with limited bandwidth and transmission rate. Resilient communication schemes, integrating periodic/event‐triggered schemes and a dynamic quantized transmission method, are designed in a distributed NE seeking algorithm to ensure the convergence of agents' strategies to the NE of the game. For the designed two resilient communication schemes, the sufficient conditions for the convergence of the algorithm are established, and the tradeoff between the sampling period and the frequency and duration of DoS attacks is analyzed. Cournot competition with distributed energy resources is used as an example to verify the proposed methods. [ABSTRACT FROM AUTHOR]

Summary Trait‐based ecology has become a popular phrase. But all species have traits, and their contributions to ecological processes are governed by those traits. So then, is not all ecology trait‐based? Actually, there do exist areas of ecology that are consciously trait‐free, such as neutral theory and species abundance distributions. But much of ecology could be considered actually or potentially trait‐based. A spectrum is described, from trait‐free through trait‐implicit and trait‐explicit to trait‐centric. Trait‐centric ecology includes positioning ecological strategies along trait dimensions, with a view to inferring commonalities and to generalizing from species studied in more detail. Trait‐explicit includes physiological and functional ecology, and areas of community ecology and ecosystem function that invoke traits. Trait‐implicit topics are those where it is important that species are different, but formulations did not initially characterize the differences via traits. Subsequently, strands within these trait‐implicit topics have often moved towards making use of species traits, so the boundary with trait‐explicit is permeable. Trait‐based ecology is productive because of the dialogue between understanding processes in detail, via traits that relate most closely, and generalizing across many species, via traits that can be compared widely. An enduring key question for trait‐based ecology is which traits for which processes. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are fundamental to modern applications like face recognition and autonomous driving. However, their security is a significant concern due to various integrity risks, such as backdoor attacks. In these attacks, compromised training data introduce malicious behaviors into the DNN, which can be exploited during inference or deployment. This paper presents a novel game-theoretic approach to model the interactions between an attacker and a defender in the context of a DNN backdoor attack. The contribution of this approach is multifaceted. First, it models the interaction between the attacker and the defender using a game-theoretic framework. Second, it designs a utility function that captures the objectives of both parties, integrating clean data accuracy and attack success rate. Third, it reduces the game model to a two-player zero-sum game, allowing for the identification of Nash equilibrium points through linear programming and a thorough analysis of equilibrium strategies. Additionally, the framework provides varying levels of flexibility regarding the control afforded to each player, thereby representing a range of real-world scenarios. Through extensive numerical simulations, the paper demonstrates the validity of the proposed framework and identifies insightful equilibrium points that guide both players in following their optimal strategies under different assumptions. The results indicate that fully using attack or defense capabilities is not always the optimal strategy for either party. Instead, attackers must balance inducing errors and minimizing the information conveyed to the defender, while defenders should focus on minimizing attack risks while preserving benign sample performance. These findings underscore the effectiveness and versatility of the proposed approach, showcasing optimal strategies across different game scenarios and highlighting its potential to enhance DNN security against backdoor attacks. [ABSTRACT FROM AUTHOR]

Summary: In non‐cooperative multi‐player games, an average sub‐gradient (ASG) strategy for finding an ϵ$$ \epsilon $$‐Nash equilibrium. For convex (but not necessarily strictly convex) individual cost functions, the function convergence of the multi‐player game is demonstrated. Applying Tanaka's formula that characterizes the Nash equilibrium leads to finding the strategy for each player. A min‐max formulation defines the corresponding solution for each player. The main result is proving the reachability of the desired regime, obtaining an explicit upper bound for Tanaka's function decrement. Detailed analyses for two‐player and multi‐player games are developed. For a class of two‐player games with a convex performance function, a set of numerical studies shows the applicability of the proposed method. So, the tracking of the arm in 3‐links robot is considered as an illustrative example. [ABSTRACT FROM AUTHOR]

In this paper, the Nash equilibrium (NE) seeking problem is investigated for a class of n$$ n $$‐integrator multi‐agent system with uncertain parameter in the cost function. In the game setup, each agent only accesses its neighbors' information. Without knowing the probability distribution of the uncertain parameter, the robust counterpart of the studied game is considered. Due to the nonsmoothness of the robust game, a smooth game that consists of real agents as well as fictive agents dealing with uncertainty is formulated. In contrast to conventional robust convex‐concave games, the assumption on the uncertain parameter is relaxed to Polyak‐Łojasiewicz (PL) inequality. Based on the relationship between the robust game and its reformulated version, a distributed NE seeking strategy is developed such that the states of the non‐cooperative agent can converge to the NE solution. At the end, numerical examples are given to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

We investigate the equilibrium strategies for customers arriving at overtaking-free queueing networks and receiving partial information about the system's state. In an overtaking-free network, customers cannot be overtaken by others arriving after them. We assume that customer arrivals follow a Poisson process and that service times at any queue are independent and exponentially distributed. Upon arrival, the received partial information is the total number of customers already in the network; however, the distribution of these among the queues is left unknown. Adding rewards for being served and costs for waiting, we analyze the economic behavior of this system, looking for equilibrium threshold strategies. The overtaking-free characteristic allows for coupling of its dynamics with those of corresponding closed Jackson networks, for which an algorithm to compute the expected sojourn times is known. We exploit this feature to compute the profit function and prove the existence of equilibrium threshold strategies. We also illustrate the results by analyzing and comparing two simple network structures. [ABSTRACT FROM AUTHOR]

Under the background of the Energy Internet and the shared economy, it is of great significance to explore the collaborative planning strategies of multi-energy microgrids (MEMGs) and a shared energy storage operator (SESO) supported by shared energy storage resources. In this context, a joint planning method of SESO and MEMG alliances based on a dynamic game with perfect information is proposed in this paper. First, an upper-level model for energy storage capacity configuration and pricing strategy planning of SESO is proposed to maximize the total planning and operational income of SESO. Then, a lower-level model for the optimal configuration of MEMGs' alliance considering SES is proposed to minimize the total planning and operational costs of the MEMG alliance. On this basis, a solving algorithm based on the dynamic game theory with perfect information and the backward induction method is proposed to obtain the Nash equilibrium solution of the proposed bi-level optimization models. Finally, a case study with one SESO and an alliance consisting of five MEMGs is conducted, and the simulation results show that the proposed bi-level optimization method can increase SESO's net income by 1.47%, reduce the average planning costs for each MEMG at least by 1.7%, and reduce model solving time by 62.9% compared with other counterpart planning methods. [ABSTRACT FROM AUTHOR]

In a third-country market model in which two export countries adopt environmental policies (taxes and subsidies), this article analyses how an abatement ("green") subsidy can become a potential strategic trade policy tool. When governments set the optimal policy considering their local environmental damages, a rich set of equilibria arises. In contrast to the standard result, it is shown that subsidising pollution abatement can 1) emerge as the unique Pareto-efficient Nash equilibrium of the policy game, 2) be the only feasible environmental policy when environmental awareness is low, irrespective of the efficiency of the cleaning technology, and 3) emerge as the unique Pareto-inefficient Nash equilibrium of the policy game at the end of the ecological transition. The article also tackles some dynamic issues that the policy game implies. [ABSTRACT FROM AUTHOR]

There are many real life applications where data can not be effectively represented in Hilbert spaces and/or where the data points are uncertain. In this context, we address the issue of binary classification in Banach spaces in presence of uncertainty. We show that a number of results from classical support vector machines theory can be appropriately generalized to their robust counterpart in Banach spaces. These include the representer theorem, strong duality for the associated optimization problem as well as their geometrical interpretation. Furthermore, we propose a game theoretical interpretation of the class separation problem when the underlying space is reflexive and smooth. The proposed Nash equilibrium formulation draws connections and emphasizes the interplay between class separation in machine learning and game theory in the general setting of Banach spaces. [ABSTRACT FROM AUTHOR]

We study the Tullock contest model with loss aversion and endogenously formed reference points. In a contest with n possibly heterogeneous players and convex effort costs, we establish sufficient conditions for a unique Nash equilibrium in pure strategies. Subsequently, we analyze the impact of loss aversion on players' spending behavior, probability of winning, and rent dissipation. [ABSTRACT FROM AUTHOR]

This paper considers a class of distributed aggregative games for first-order and second-order integrator-type multi-cluster systems, respectively. Two Nash equilibrium seeking algorithms are designed based on a distributed aggregation observer which is used to estimate the aggregations of the clusters. The convergence is proved by the input-to-state stable method. Moreover, the algorithms are applied to a class of location control problems, that is, distributed nonlinear placement problems for multi-cluster systems. Finally, some simulations are presented to illustrate the effectiveness of the designed algorithms. [ABSTRACT FROM AUTHOR]

The basic requirement for cognitive radio networks is that secondary users sense spectrum to detect whether primary users have arrived the system. However, imperfect sensing always occurs due to the interference of unreliable channel and signal, which can negatively affect the performance of both primary users and secondary users. To better study the practical problems of cognitive radio networks, we combine queueing theory with it. In this paper, we consider cognitive radio networks with imperfect sensing based on retrial queues with feedback and priority. Using the method of probability generating function, the performance measures are solved. A bi-objective optimization model is established to minimize both system cost and users' sojourn time. From the perspective of secondary users, we analyze both individual equilibrium strategy and socially optimal strategy. Meanwhile, in order to eliminate the differences between two strategies, we impose admission fees on secondary users and obtain the optimal pricing strategy. [ABSTRACT FROM AUTHOR]

To improve the performance of Peer-to-Peer (P2P) networks and reduce energy consumption while minimizing "free-riding" behavior, a queuing model is utilized to abstract dynamic changes of peers in hybrid P2P networks. Requesting peers are categorized differently and a discrete-time Geo/Geo/1 + 1 queuing model is established with simultaneous multiple working vacations, waiting threshold N, and two types of servers with different service rates. The steady-state distribution of the system is obtained by using the matrix-geometric solution method, and performance measures such as average queue length, average delay, and total energy consumption of the two types of requesting peers are calculated. Numerical experiments are conducted to analyze the effects of different parameters on the performance measures. Finally, the optimal strategies and Nash equilibrium points of requesting peers are studied, which provide a theoretical basis to suppress "free-riding" behavior. [ABSTRACT FROM AUTHOR]

Our research question is whether it is possible and how to counteract "the tragedy of the commons" if facing various limitations of real‐world economies. To answer it, we derive regulatory tax–subsidy systems and self‐enforcing environmental agreements in a problem of extraction of common renewable resources. The first considered limitation is that the feasible class of tax–subsidy systems may have a compl icated form, for example, there are transition periods for smooth reduction of fishing. The alternative limitation is that there is no institution that can impose taxes or subsidize, so sustainability can be achieved only by self‐enforcing international agreements. The next limitation is in those agreements: we assume that it takes time to detect a defection. We study these enforcement tools in a continuous‐time version of a Fish War type game with n $n$ countries, with fish indispensable for their economies. We calculate the social optimum, a Nash equilibrium, and partial cooperation equilibria. The Nash equilibrium leads to the depletion of fish, while the social optimum typically results in sustainability. For partial cooperation, only two‐country coalitions are stable. We calculate tax–subsidy systems that enforce maximization of joint payoff, also if there are additional constraints, and we propose an algorithm that looks for such a system in an arbitrary class of regulatory tax–subsidy systems. For the international agreement with imperfect monitoring, we are interested in the maximal detection delay for which the agreement remains self‐enforcing. Counterintuitively, the more the players, the more stable the agreement. [ABSTRACT FROM AUTHOR]

In this article, a game theoretic non‐cooperative dynamic target tracking algorithm that empowers defensive unmanned aerial vehicles (UAVs), with directional sensing capabilities to track and collect information on intrusive UAVs, is proposed. Specifically, defenders aim to maximize the collection of identity information from intruders possessing anti‐tracking and evading capabilities, while simultaneously preventing their entry into protected areas. Game theory is employed to determine the optimal confrontation paths for defenders against the intruders. The probability perception model is utilized for evaluating the dynamic target tracking capability and designing a tracking merit function to assess tracking performance, taking into account both the target's position and the perception relative angle. Furthermore, considering the dynamic interactive behaviors between intruders and defenders, the iterative linear quadratic game (ILQG) algorithm is employed to solve the Nash equilibrium of the non‐cooperative target tracking game. Through simulation experiments, the effectiveness of the proposed algorithm in accomplishing multi‐agent dynamic target tracking tasks is demonstrated and the performance of the algorithm under varying parameters in the intruder's cost function is evaluated, which represent different intrusion intentions. [ABSTRACT FROM AUTHOR]

Traditional jamming technologies have become less effective with the development of anti-jamming technologies, especially with the appearance of intelligent transmitters, which can adaptively adjust their transmission strategies. To deal with intelligent transmitters, in this paper, a game-based intelligent jamming scheme is proposed. Considering that the intelligent transmitter has multiple transmission strategy sets whose prior probabilities are unknown to the jammer, we first model the interaction between the transmitter and the jammer as a dynamic game with incomplete information. Then the perfect Bayesian equilibrium is derived based on assumptions of some prior information. For more practical applications when no prior information about the transmitter is available at the jammer, a Q-learning-based method is proposed to find an intelligent jamming strategy by exploiting the sensing results of the wireless communications. The design of the jammer's reward function is guided by the game utility and the reward is calculated based on the Acknowledgement/Negative Acknowledgement feedback of the receiver. Simulation results show that the proposed scheme has only 0.5 % loss in jamming utility compared to that of the perfect Bayesian equilibrium strategy. Compared to existing jamming schemes, a higher packet error rate can be achieved by the proposed scheme by consuming less jamming power. [ABSTRACT FROM AUTHOR]

With the rapid advancement of Internet of Vehicles (IoV) technology, the demands for real-time navigation, advanced driver-assistance systems (ADAS), vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, and multimedia entertainment systems have made in-vehicle applications increasingly computing-intensive and delay-sensitive. These applications require significant computing resources, which can overwhelm the limited computing capabilities of vehicle terminals despite advancements in computing hardware due to the complexity of tasks, energy consumption, and cost constraints. To address this issue in IoV-based edge computing, particularly in scenarios where available computing resources in vehicles are scarce, a multi-master and multi-slave double-layer game model is proposed, which is based on task offloading and pricing strategies. The establishment of Nash equilibrium of the game is proven, and a distributed artificial bee colonies algorithm is employed to achieve game equilibrium. Our proposed solution addresses these bottlenecks by leveraging a game-theoretic approach for task offloading and resource allocation in mobile edge computing (MEC)-enabled IoV environments. Simulation results demonstrate that the proposed scheme outperforms existing solutions in terms of convergence speed and system utility. Specifically, the total revenue achieved by our scheme surpasses other algorithms by at least 8.98%. [ABSTRACT FROM AUTHOR]

This paper analyzes the social dilemma arising when a large population of individuals with differing incomes have concerns over relative deprivation in terms of visible or conspicuous consumption. These relative concerns are cardinal—people care about the size of the gap between own and others' consumption—and include inequity aversion, where negative comparisons are more important than positive, rivalrous preferences, and comparison with mean consumption. The resulting Nash equilibrium is inefficient, with consumption generally exceeding the socially efficient level. In this model, the income distribution has a direct effect on behavior and under rivalrous preferences, an increase in incomes for the rich can raise consumption at all income levels and make almost everyone worse off. [ABSTRACT FROM AUTHOR]

Bilateral conflict involves an attacker with several alternative attack methods and a defender who can take various actions to better respond to different types of attack. These situations have wide applicability to political, legal, and economic disputes, but they are particularly challenging to study empirically because the payoffs are unknown. Moreover, each party has an incentive to behave unpredictably, so theoretical predictions are stochastic. This article reports results of an experiment where the details of the environment are tightly controlled. The results sharply contradict the Nash equilibrium predictions about how the two parties' choice frequencies change in response to the relative effectiveness of alternative attack strategies. In contrast, nonparametric quantal response equilibrium predictions match the observed treatment effects. Estimation of the experimentally controlled payoff parameters across treatments accurately recovers the true values of those parameters with the logit quantal response equilibrium model but not with the Nash equilibrium model. [ABSTRACT FROM AUTHOR]

We consider a novel class of portfolio liquidation games with market drop‐out ("absorption"). More precisely, we consider mean‐field and finite player liquidation games where a player drops out of the market when her position hits zero. In particular, round‐trips are not admissible. This can be viewed as a no statistical arbitrage condition. In a model with only sellers, we prove that the absorption condition is equivalent to a short selling constraint. We prove that equilibria (both in the mean‐field and the finite player game) are given as solutions to a nonlinear higher‐order integral equation with endogenous terminal condition. We prove the existence of a unique solution to the integral equation from which we obtain the existence of a unique equilibrium in the MFG and the existence of a unique equilibrium in the N‐player game. We establish the convergence of the equilibria in the finite player games to the obtained mean‐field equilibrium and illustrate the impact of the drop‐out constraint on equilibrium trading rates. [ABSTRACT FROM AUTHOR]

To address omnichannel retailers' return challenges, considering consumers' brand and channel preferences. Four subgame models for retailers' Experience‐in‐Store‐and‐Buy‐Online (ESBO) return strategies in a double oligopoly market are developed. Using an optimization model and inverse induction method, equilibrium strategies under different conditions are determined. Finally, simulation analyzes parameter sensitivity and equilibrium strategies. Findings show that when consumers' brand preference is low, the (Y,Y) subgame is the unique equilibrium solution, but profits do not significantly increase and are always lower than the (N,N) subgame, leading to a Prisoner's Dilemma. With a clear brand preference, both retailers always obtain the maximum profit under the (Y,Y) subgame. In markets with a high proportion of omnichannel consumers and a large number of high‐cost‐to‐store consumers, the party implementing the ESBO strategy benefits more in a fiercely competitive market, while the profit situation of the party not implementing the ESBO strategy is less optimistic. [ABSTRACT FROM AUTHOR]

This article introduces a class of Nash games among Stackelberg players (NASPs), namely, a class of simultaneous noncooperative games where the players solve sequential Stackelberg games. Specifically, each player solves a Stackelberg game where a leader optimizes a (parametrized) linear objective function subject to linear constraints, whereas its followers solve convex quadratic problems subject to the standard optimistic assumption. Although we prove that deciding if a NASP instance admits a Nash equilibrium is generally a Σ2p -hard decision problem, we devise two exact and computationally efficient algorithms to compute and select Nash equilibria or certify that no equilibrium exists. We use NASPs to model the hierarchical interactions of international energy markets where climate change aware regulators oversee the operations of profit-driven energy producers. By combining real-world data with our models, we find that Nash equilibria provide informative, and often counterintuitive, managerial insights for market regulators. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03418. [ABSTRACT FROM AUTHOR]

This paper models games where the strategies are nodes of a graph G (we denote them as G-games) and in presence of coalition structures. The cases of one-shot and repeated games are presented. In the latter situation, coalitions are assumed to move from a strategy to another one under the constraint that they are adjacent in the graph. We introduce novel concepts of pure and mixed equilibria which are comparable with classical Nash and Berge equilibria. A Folk Theorem for G-games of repeated type is presented. [ABSTRACT FROM AUTHOR]

Infrastructure networks are critical components of contemporary society, and numerous approaches have been suggested for the selection of strategies to protect these networks. However, for uncertain environments, research on attack and defense game models for infrastructure networks is limited. Therefore, after reviewing the existing approaches, a method based on interval-valued intuitionistic fuzzy set (IVIFS) theory is proposed for attack and defense games in critical infrastructure networks. First, we present the process of constructing the game model proposed in this paper, which mainly includes the formulation of the cost model, the strategies, and the method of generating IVIFS payoffs. Next, the Nash equilibria of the game are identified by a pair of nonlinear programming models based on IVIFS theory. Finally, experiments are conducted on a target scale-free network, and an investigation into the variation patterns of the Nash equilibria under different circumstances is also conducted. We provide explanations for these variation patterns by considering payoffs from the perspective of mathematical programming models. Furthermore, we find that compared to the existing attack and defense game model with crisp payoffs, the model proposed in this paper leads to superior Nash equilibria. Our work is a preliminary attempt to analyse attack and defense games for infrastructure networks based on IVIFS theory, providing a method for assessing payoffs in uncertain environments for the attacker and defender. This topic deserves further study. [ABSTRACT FROM AUTHOR]

With the rapid development of technologies and applications, extending from trusted environments to untrusted environments is the development trend of Internet of Things (IoT) applications. Due to the limited resources of nodes and the rise of edge computing, more and more IoT applications are required to have task delegation functions. Delegating IoT tasks in untrusted environments has to solve two problems: First, how to evaluate the trustworthiness of nodes so that requesters can delegate tasks to trusted providers; the second is how to ensure the successful implementation of the task delegation between the requester and the provider. While trust models are often used to solve the first problem, the lack of ability to describe transactions between the requester and the provider makes them unsuitable for solving the second problem. In recent years, game theory has been more applied to task delegation. By setting appropriate utility functions, game theory can ensure the successful implementation of task delegation. However, since it is unable to evaluate the trustworthiness of nodes, game theory cannot solve the first problem. This paper combines the advantages of trust models and game theory to construct a game of three-party task delegation based on trust to explore a solution to problems of IoT task delegation in an untrusted environment. Finally, aiming at the problem that setting essential parameter values of trust models is empirical and experimental, this paper theoretically discusses how to set these values in an untrusted environment. [ABSTRACT FROM AUTHOR]

Summary: Due to geographical factors, it is impossible to build large‐scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay‐sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space‐air‐ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay‐sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space‐ground integrated network and insufficient energy of local user equipment, firstly, a satellite‐UAV cluster‐ground three‐layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non‐cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO‐SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO‐SG reduces the total system latency during task offloading by about 13%$$ \% $$ and the energy consumption of the edge server by about 35%$$ \% $$. [ABSTRACT FROM AUTHOR]

To alleviate the pressure of economic development on the marine environment, the Marine Ecological Compensation (MEC) has become a major policy tool for the Chinese government to reconcile the contradiction between economic development and the marine environment. In this paper, we propose a novel two-stage bargaining game data envelopment analysis (DEA) model to evaluate the performance of MEC under the cooperative structure. The proposed model considers the link between marine economic development (MED) and marine environmental protection (MEP). Meanwhile, the equivalent form, Nash equilibrium solution, sensitivity and stability of the model are as well documented to further analyze MED and MEP. Eventually, a case study of 30 coastal cities in China serves to verify the practicable effectiveness of the foregoing model combined with numerical simulation and support key insights as below: (i) According to the results evaluated by the decentralized DEA model, we find that if one party of MED and MEP takes priority, the efficiency score of the other party will be severely affected; (ii) the evaluation results of sensitivity and stability demonstrate that inputs and outputs have different impact degrees on the efficiency scores of MED and MEP, which provide directions for improving the efficiency of both systems; (iii) The proposed model addresses the limitation of the conventional two-stage DEA model that cannot handle uncertain variables, thus revealing the influence of uncertainty on MEC efficiency. The compelling evidence presented in the case study solidifies the effectiveness of the proposed model, establishing its promising prospects for application in evaluating the performance of DMUs with a two-stage structure. [ABSTRACT FROM AUTHOR]

This study presents a novel Buyback Rail Freight Option (BRFO), leveraging Stackelberg game theory to enhance the strategic management of rail freight transactions. By integrating traditional buyback theory with a multi-phase trigeminal tree pricing model and parameter identification through a nonparametric Ito stochastic method, the research addresses key challenges of information asymmetry and market uncertainty. The proposed methodology emphasizes dynamic pricing strategies and market adaptation, constructing a Nash equilibrium framework within railway freight pricing. The findings suggest significant strategic benefits for railway enterprises, positioning BRFO as a crucial tool for improving competitiveness in the face of alternative transport options. [ABSTRACT FROM AUTHOR]

Thanks to reinforcement learning (RL), decision‐making is more convenient and more economical in different situations with high uncertainty. In line with the same fact, it is proposed that prosumers can apply RL to earn more profit in the transactive energy market (TEM). In this article, an environment that represents a novel framework of TEM is designed, where all participants send their bids to this framework and receive their profit from it. Also, new state‐action spaces are designed for sellers and buyers so that they can apply the Soft Actor‐Critic (SAC) algorithm to converge to the best policy. A brief of this algorithm, which is for continuous state‐action space, is described. First, this algorithm is implemented for a single agent (a seller and a buyer). Then we consider all players including sellers and buyers who can apply this algorithm as Multi‐Agent. In this situation, there is a comprehensive game between participants that is investigated, and it is analyzed whether the players converge to the Nash equilibrium (NE) in this game. Finally, numerical results for the IEEE 33‐bus distribution power system illustrate the effectiveness of the new framework for TEM, increasing sellers' and buyers' profits by applying SAC with the new state‐action spaces. SAC is implemented as a Multi‐Agent, demonstrating that players converge to a singular or one of the multiple NEs in this game. The results demonstrate that buyers converge to their optimal policies within 80 days, while sellers achieve optimality after 150 days in the games created between all participants. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of tariff escalation on multinational suppliers relocating their production capacity to tariff-preferential regions with unreliable supply caused by low-production technology. We build a game theory model to analyze this issue based on three decisions for supplier-capacity relocation: no relocation, partial relocation, and full relocation. Our analysis finds that when tariffs are low or the production technology of the base in a preferential tariff region is not advanced, the supplier tends to adopt a partial-relocation strategy, but this strategy may be hindered by a manufacturer's order-allocation decision, leading to a no-relocation strategy as the supply chain's equilibrium. This may result in greater losses for the supplier. When tariffs are high or the production technology of the base in the preferential tariff region is advanced, the equilibrium strategy for the supply chain shifts to a full-relocation strategy. Interestingly, in the partial-relocation strategy, the higher production technology in the preferential tariff region negatively impacts the manufacturer's expected profits but benefits the supplier's expected profits due to the increased double marginalization. Finally, we find that the supplier can reduce the impact of tariffs by relocating their production capacity, especially with the partial-relocation strategy, as the supplier is always motivated to improve the production technology of the base in the preferential tariff region, with a potential purpose of transferring tariff costs to the manufacturer and consumers. [ABSTRACT FROM AUTHOR]

The proposed study introduces a novel anti-jamming approach for cognitive radio networks (CRNs) by integrating the Stackelberg game model with direct sequence spread spectrum (DSSS) techniques. This innovative combination enhances the security and performance of CRNs by optimizing resource allocation and fortifying network resilience against jamming attacks. The Stackelberg game model provides a strategic framework where the Defender and Adversary dynamically adjust their strategies to achieve Nash equilibrium, ensuring strategic stability. The application of DSSS further improves signal robustness, mitigating interference from jamming attempts. Simulation results demonstrate significant improvements in network security, resource utilization, and overall performance, validating the efficacy and advantages of the proposed scheme in maintaining reliable communication in the presence of adversarial threats. [ABSTRACT FROM AUTHOR]

AbstractThis article aims to understand the shared micromobility firms’ operations under competition and provide managerial guidance to the firms and the regulator. We consider two shared micromobility firms competing in the same service area, each providing micromobility vehicles to satisfy uncertain demands. Concerning allocation restrictions by the regulator, we propose an innovative capacity-sharing agreement between the two firms. Each firm solves an integrated vehicle allocation and relocation problem, modeled as a two-stage stochastic program on a spatial-temporal network. We explore the optimality condition of each firm’s decision-making and seek a Nash equilibrium by optimizing certain objectives over the joint optimality conditions of both firms. We prove that capacity sharing helps reduce the total demand loss in the system. We perform extensive numerical experiments based on real data to obtain managerial insights. We find that regulator restrictions impact firms’ profitability and service level. After introducing capacity sharing, one firm may act like a free rider that relies on the vehicles transferred from her opponent. Meanwhile, many vehicles are shared in periods and regions with high trip demands. Capacity sharing can reduce the number of relocated vehicles by serving as a substitution for relocation and also improves the firms’ profitability. [ABSTRACT FROM AUTHOR]

This study investigates a class of congestion games with resource failures (CGRF). Since the CGRF is not necessarily equivalent to a potential game, it is difficult to discuss the existence of Nash equilibrium (NE). To improve this case, a sufficient condition is proposed that CGRF must be a potential game. Then the following studies are all based on the idea that CGRF is a potential game. Firstly, all NEs of CGRF are found with the help of the potential function. Secondly, the dynamic convergence problem of evolutionary CGRF is considered by means of the semi-tensor product of matrices. A necessary and sufficient condition to determine whether the evolutionary CGRF can converge globally to the set of NEs is derived. Furthermore, an event-triggered controller is designed to ensure the global convergence of the evolutionary CGRF. Finally, the effectiveness of the obtained results is verified by an illustrative example. [ABSTRACT FROM AUTHOR]