Системный анализ и теоретико-игровой инструментарий взаимодействия экономической, правовой и социальной национальных макросистем

Nowadays, games exist everywhere in people's daily life, and game theory methods should not be just formulas and words in books, but also brought into people's daily life, helping people to solve many practical problems in life. This paper selects the most common problem in the life of postgraduate students - the choice of thesis direction between postgraduate students and their supervisors, takes Nash equilibrium as the theoretical support point, analyses the "prisoner's dilemma" model in game theory, combines with the payment matrix, and finds the solution to the problem. The solution to the problem is the application of game theory in daily life, which demonstrates the close connection between games and life.

This paper develops two models to investigate the impact of a tariff on logistics strategies in an international e-commerce trade, where the methods of game theory and agent-based model are utilized. We consider a foreign supplier and a domestic e-tailer. There are two logistics strategies: the supplier undertakes the logistics and the e-tailer shoulders the logistics. First, two strategies are compared under the game theory model. Then, we capture the uncertainties of market demand and tariff. An agent-based model is adopted to further study how the tariff affects the supply chain’s decisions and strategies. Some meaningful results are obtained. (i) The supplier should provide the logistics service when market demand and tariff are certain. (ii) Under demand uncertainty, the supplier and the e-tailer can reach an agreement on logistics strategies when the tariff is small. (iii) When tariff uncertainty is moderate, two players both abandon the logistics intention..

Purpose This paper aims to present various three-level service contracts among the following three participants: a manufacturer, an agent and a customer. The interaction between the aforementioned participants will be modeled using the game theory approach. Under non-cooperative and cooperative games, the optimal sale price, warranty period and warranty price for the manufacturer and the optimal maintenance cost (repair cost) and marketing expenditure for the agent are obtained by maximizing their profits. The satisfaction of the customer is also maximized by being able to choose one of the suggested options from the manufacturer and the agent, based on the risk parameter. Design/methodology/approach Three-echelon supply chains with marketing and warranty services are studied. Game-theoretic approaches (non-cooperative and cooperative) are presented. The non-cooperative approaches are static (NE) and dynamic (Stakelberg) models. The cooperative approach is related to bargaining models (Nash bargaining games). The authors develop a sensitivity analysis of some parameters and their effect. Findings Based on the mentioned drawbacks (i.e. lack of a model containing warranty, marketing and pricing), despite their importance, a developed model is proposed in this research to cover one of the research gaps. In addition, main contributions of this paper that differentiate it from the existing papers are regarding inventory, lost sale and lost goodwill, which are significant in the comparison environment. Another advantage of this study is related to the solution approach, the game theory. Twofold of the games theoretical, i.e. cooperative (in three forms) and non-cooperative are considered, because of their importance. Three types of non-cooperative games are presented as follows: Nash equilibrium – each echelon decides respectively and simultaneously; manufacturer-Stackelberg – the manufacturer has more power than the agent and the agent has more power than the customer; and customer-Stackelberg – customer is leader of the agent and the agent is the leader of manufacturer. The involved cooperative game in this paper is the bargaining problem that the participants can determine how to share the additional profits. Originality/value In this paper, various three-level service contracts will be presented among the following three participants: a manufacturer, an agent and a customer. The interaction between the aforementioned participants will be modeled using the game theory approach. Under non-cooperative and cooperative games, the optimal sale price, warranty period and warranty price for the manufacturer and the optimal maintenance cost (repair cost) and marketing expenditure for the agent are obtained by maximizing their profits. The satisfaction of the customer is also maximized by being able to choose one of the suggested options from the manufacturer and the agent, based on the risk parameter. Several numerical examples are used to illustrate the models presented in this paper. Finally, the authors develop a sensitivity analysis of some parameters and their effects on the objective functions.

In Malaysia, there are plentiful of palm biomass but not always these resources are fully utilised. In the perspective of biomass industry owner, there are three factors that affect the business decision, (i) constant and good quality supplies, (ii) process efficiency, (iii) market demand and price. The strategy considerations that respond to these factors are based on other players’ competition, plantation output, mill output, logistics matter, government policy and weather. In order to model the real situation in a particular palm plantation area, game theory approach is used to analyse the best strategy of biomass industry owner. Since every players are act on their self-interest based on maximum profit, this is a non-cooperative game study. A case study is modelled on two industry players, two oil mills and two plantations. Nash equilibrium is achieved by analysing the best strategies that are the best responses to other parties in the scenario. By analysing this scenario using game theory approach, an optimal non- cooperative strategy can be derived. In future works, it can be applied into the decision support framework for the biomass industry management team.

SummaryEnergy consumption in wireless sensor network (WSN) is one of the important issues as tiny sensor nodes are equipped with a non‐replaceable batteries. Clustering is one of the effective means to prolong the network lifetime in WSN by reducing the number of packets to be sent from cluster heads (CHs) to base station (BS). Recently, game theory techniques have been applied in the clustering process for optimal CH election at each round. However, none of them have considered the energy parameter in their game strategy and multihop routing for data transmission. In this manuscript, an energy‐efficient game‐theoretic clustering and multi‐hop routing scheme (EGCR) has been proposed where the non‐cooperative approach of game theory is used to elect optimal CHs. In the proposed game theoretic framework, each node behaves in a selfish manner while considering the residual energy of nodes and focuses on increasing the overall lifetime of the network. Nash equilibrium is determined among a set of pure and mixed strategies, and then the expected payoff of a node is determined for Nash equilibria. Apart from the energy‐efficient clustering, the EGCR also proposes a multi‐hop routing solution for data transmission that further enhances the lifetime of whole network. In the multi‐hop routing approach, inter‐cluster and intra‐cluster routing techniques are proposed to identify optimal routes from nodes to CHs and from CHs to BS by using various factors such as node centrality, distance from the node to BS, and residual energy. The proposed algorithm is compared with traditional clustering schemes like low‐energy adaptive clustering hierarchy (LEACH) and efficient energy‐aware game theory‐based clustering (EEGC) and also with existing game theory‐based algorithms such as clustered routing for selfish sensors (CROSS), localized game theoretical clustering algorithm (LGCA), and energy‐efficient clustering algorithm based on game theory (ECAGT). Simulation experiments validate that the proposed algorithm enhances the network's lifetime up to 47.49% compared to existing clustering algorithms.

PurposeAdopting efficient marketing strategies is a challenging task in a competitive market place involving complex marketing planning, techniques and mechanisms to identify the best course of action under these circumstances and finding optimal solutions or stable outcomes. Decisions and strategies of competitors in the market influence the selection of the appropriate marketing strategy. The main purpose of this paper is to develop a mathematical methodology based on the game theory approach for planning optimal marketing-mix strategies in dynamic competitive markets, taking into account strategic foresight and interaction effects.Design/methodology/approachThe game theory approach, as a decision-making tool in conflict situations, is suggested for planning and adopting optimal marketing strategy. The main intellectual attraction of the game theory is essentially a question of how to act in gaming situations against highly rational opponents A kind of static, finite and non-cooperative game analytics approach has been developed for this issue, and the proposed model has been implemented to design optimal marketing strategies for two top brands of the automotive parts market in Iran.FindingsThe findings of this study show that the optimal marketing-mix strategy for brand A is pricing and for brand B is the product strategy.Practical implicationsGame theory and the Nash equilibrium model can provide a practical approach to find and adopt the right strategy, know competitors' movements and strategies and get more profit.Originality/valueThe integration of the game theory approach into the marketing mix framework has been adopted as a generalized model for marketing strategy planning and analysis as well as to resolve some shortcomings of the marketing mix framework. The Nash equilibrium model has been used to analyze the results. The incorporation of game theory into marketing models has the potential to enrich the scope of marketing modeling.

Due to the individualized consumer needs, cloud manufacturing (CMfg) has been widely used in the optimization of available manufacturing resource allocation to enhance resource utilization and reduce energy consumption. However, efficient scheduling of tasks and subtasks under dynamic CMfg environments to these re-sources are challenging problems. This paper proposes a game theory based on task scheduling and model selection for effectively exploiting distributed manufacturing resources in CMfg, and the Nash equilibrium (NE) in this game theory is implemented by a double ant colony optimization (DACO) algorithm. Through this model, services provided by different providers can handle a batch of tasks in real-time. Besides, to satisfy different service providers and demanders, the proposed approach considers multiple task attributes simultaneously, including completion time, cost, service quality, service composition capability, service availability, energy consumption, service sustainability, service maintainability, and service trust. Simulation results demonstrate that the proposed method is not only effective for the relevant optimization objective but also can achieve great performance under real-time CMfg environments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —To provide the best production guides, the efficiency of configuration optimization of manufacturing resources is critical to the control and management of smart manufacturing systems. This paper investigates the dynamic scheduling problem for manufacturing services in CMfg. Previous task scheduling approaches fail to evaluate multiple factors together, like completion time, cost, and energy consumption. Also, the traditional scheduling method cannot respond to requests caused by service state changes in an efficient way. Therefore, in this paper, a game theory model that consists of a static scheduling sub-game and a dynamic selection sub-game is presented. This model is achieved by adopting a proposed double ant colony optimization algorithm that solves constrained non-linear programming. Simulation experiments shown in this paper prove that the proposed method outperforms existing scheduling methods in multiple aspects, including completion time and energy consumption. Also, this method can be readily implemented and incorporated into real production environments. Future work can improve the proposed method by analyzing the uncertainty during scheduling tasks and sharing the logistics resources on the same routes.

One of the famous economic models in game theory is the duopoly Stackelberg model, in which a leader and a follower firm manufacture a single product in the market. Their goal is to obtain the maximum profit while competing with each other. The desired dynamics for a firm in a market is the convergence to its Nash equilibrium, but the dynamics of real-world markets are not always steady and can result in unpredictable market changes that exhibit chaotic behaviors. On the other hand, to approach reality more, the two firms in the market can be considered heterogeneous. The leader firm is bounded rationale, and the follower firm is adaptable. Modifying the cost function that affects the firms' profit by adding the marginal cost term is another step toward reality. We propose a Stackelberg model with heterogeneous players and marginal costs, which exhibits chaotic behavior. This model's equilibrium points, including the Nash equilibrium, are calculated by the backward induction method, and their stability analyses are obtained. The influence of changing each model parameter on the consequent dynamics is investigated through one-dimensional and two-dimensional bifurcation diagrams, Lyapunov exponents spectra, and Kaplan-Yorke dimension. Eventually, using a combination of state feedback and parameter adjustment methods, the chaotic solutions of the model are successfully tamed, and the model converges to its Nash equilibrium.

Game theory has been widely used in economic life, and has had an important impact on economic development. This paper will analyze and explain some basic internal logic of game theory through some concrete cases related to the Game of Nim. It is found that the strategies of some theoretical models in game theory can be applied to the real game of chance, so these cases can be widely applied to actual economic life and social life.

Bounded rationality aims to understand the effects of how limited rationality affects decision-making. The traditional models in game theory and multiagent system research, such as finite automata or unrestricted Turing machine, fall short of capturing how intelligent agents make decision in realistic applications. To address this problem, we model bounded rational agents as restricted Turing machines: restrictions on running time and on storage space. We then study our model in two-person repeated games. In the case where the running time of Turing machines is restricted, we show that computing the best response of a given strategy is much harder than the strategy itself. In the case where the storage space of the Turing machines is restricted, we show the best response of a space restricted strategy can not be implemented by machines within the same size (up to a constant factor). Finally, we study how these restrictions affect the set of Nash equilibria in infinitely repeated games. We show restricting the agent's computational resources will give rise to new Nash equilibria.

Bounded rationality aims to understand the effects of how limited rationality affects decision-making. The traditional models in game theory and multiagent system research, such as finite automata or unrestricted Turing machine, fall short of capturing how intelligent agents make decision in realistic applications. To address this problem, we model bounded rational agents as restricted Turing machines: restrictions on running time and on storage space. We study our model under the context of two-person repeated games. In the case where the running time of Turing machines is restricted, we show that computing the best response of a given strategy is much harder than the strategy itself. In the case where the storage space of the Turing machines is restricted, we show the best response of a space restricted strategy can not be implemented by machines within the same size (up to a constant factor). Finally, we study how these restrictions affect the set of Nash equilibria in infinitely repeated games.We show restricting the agent’s computational resources will give rise to new Nash equilibria.

Controlling the polarization states of transmit waveforms can improve the performance of radar systems, especially for main lobe jamming suppression applications. In this paper, we consider the design of optimal transmit polarizations for deceptive jamming suppression in the main lobe using a game theory framework. We propose a co-located polarization multiple-input multiple-output (MIMO) radar system that combines the advantages of MIMO radar and those offered by optimally choosing the transmit polarization to improve the jamming suppression performance. In the polarization MIMO radar, polarization diversity is employed in the transmit array, and 2-D vector sensors are adopted in the receive array to separately measure the horizontal and vertical components of the received signals. Furthermore, based on the concepts and advantages of game theory, we formulate a polarization design problem for this radar system as a two-player zero-sum (TPZS) game between the radar and jammers. Additionally, we propose two design methods for different cases: a unilateral game for dumb jammers, and a strategic game for smart jammers. The optimal strategy and Nash equilibrium solution for two cases are presented. The simulation results demonstrate that jamming can be effectively suppressed with the proposed radar configuration and that improved jamming suppression performance can be achieved when the transmit polarization scheme is designed using the game theory approach.

In this paper, we have introduced two models for spectrum management (spectrum trading and spectrum competition) in cognitive radio network. The first model is without game theory and the second one is with game theory. The first model for spectrum management without game theory, called SMWG, which has been designed to provide an efficient and dynamic equations to enhance the network performance. SMWG provides a novel function, called QoS function, to support three levels of QoS. In addition, it provides a novel factor, called Competition Factor, to control the behaviors among spectrum owners (primary users). In the second model, we have applied the game theory concept into spectrum management (SMG) to compare the network performance in both cases (SMWG and SMG). SMG provides two games, the first one is to model the dynamic behavior of spectrum competition among primary users, a Bertrand game is formulated where the Nash equilibrium is considered as the solution. The second game is to model the spectrum trading between the primary user and the secondary user, a Stackelberg game is formulated where the Nash equilibrium is again considered as the solution. Basically, the solution is found in terms of the size of the offered spectrum to the secondary users with regards to the offered spectrum price. We compare SMWG with conventional scheme, also compare SMG with conventional scheme, and finally compare SMWG with SMG in terms of network performance.

To align a post-mining site to the wants and needs of local communities and stakeholders, mine closure needs to consider criteria which may be difficult to value using conventional cost–benefit analysis. For example, criteria associated with the environment, socio-environmental relationships, health, and long-term sustainability. Game theory techniques are implemented to investigate how different stakeholder groups could act during mine closure planning and decision-making. Using game theory with sustainability criteria, this study proposes an innovative approach to analyze some key decisions throughout the mine life that affect closure. Non-cooperative game theory models are investigated by developing Nash equilibrium equations which are based on the change in environmental risk, economic potential, and impacts on company reputation. The modified equilibrium formulas can highlight the key sustainability criteria for multi-stakeholder closure planning and complex decision-making. The discussion explores how the developed model can assist mining stakeholders in understanding their position during the mine closure process. In all, the game theory and multiple criteria models can help structure and manage the complex closure risks throughout the mine life cycle.

: The authors' aim in this article was to show how the use of classroom experiments may be a good pedagogical tool to teach the Nash equilibrium (NE) concept. The basic game is a version of the beauty contest game (BCG), a simple guessing game in which repetition lets students react to other players' choices and converge iteratively to the equilibrium solution. The authors perform this experiment with undergraduate students with no previous training in game theory. After four rounds, they observe a clear decreasing tendency in the average submitted number in all groups. Thus, the findings show that by playing a repeated BCG, students quickly learn how to reach the NE solution.