Game Theory Strategy Research Articles

Management of the Traffic Networks Transportation in Supplier-Customers Services by Strategic Games

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

Government and Polluting Companies Game Strategies in the Context of Green Finance A Prisoners Dilemma Game Model

Promoting the development of green industries to facilitate the growth of green finance has become a consensus among various sectors of society. This article focuses on the game theory strategies between governments and polluting companies in the context of green finance. Taking the Prisoner's Dilemma game model as an example, we discuss if polluting companies, under different types of governments, would choose to allocate loans towards improving production and environmental projects. Furthermore, we analyze whether the preferences of the general public towards eco-friendly products would influence the game strategies of governments and polluting companies. Through solving the model, we find that when governments increase penalties for polluting projects, reduce the costs of environmental project research and development for polluting companies, enhance environmental awareness among the general public, and stimulate their preference for eco-friendly products, they can strengthen corporate social responsibility and promote green investments. Consequently, polluting companies are more likely to invest in environmental projects to gain greater returns.

REGIONAL DIMENSION OF DIGITAL TRANSFORMATION: THE INTERESTS OF THE EUROPEAN UNION IN THE COUNTRIES OF CENTRAL ASIA

Today, digital transformation leads to the acceleration of processes and at the same time to the complication of relations between actors in world politics. The countries of the Central Asian region (CAR) in today's geopolitical realities are faced with a new challenge - the timely introduction of digital solutions to the dynamics of these conditions. In this context, the purpose of this study is to identify the main directions, problems and prospects of digital transformation of the CAR countries in the new geopolitical conditions. In this study, the method of game theory is used in the context of international relations on the example of the interaction of the CAR countries and the EU. The authors have carried out work on finding compromise strategies of interaction between the parties leading to certain Nash equilibrium. The results of the study showed that the implementation of joint projects of the CAR and the EU on digital transformation will make it possible not only to update outdated digital platforms, but also create conditions for increasing the competitiveness of transport hubs on the world market. This is especially relevant in today's unstable political and socio-economic conditions. The results revealed the problems of interregional cooperation based on game theory strategies in the field of digital transformation. The application of the game theory method helped to analyze the possibilities of regulating the state's position in the international arena. As a result, the authors managed to find a solution to the conflict of interests between the players and establish the optimality of their strategies.

Evolution of cooperation in games with dual attribute strategy

Cooperation and defection are two common strategies in game theory. Considering that there may be a kind of middle state between the two as a third option in the real dilemma, we introduce the dual attribute strategy. Compared with cooperation, this strategy possesses the defection attribute, while compared with defection, this strategy possesses the cooperation attribute. Here, it is assumed that players holding the dual attribute strategy play snowdrift games with cooperators based on their defection attribute, meanwhile, they play another snowdrift game with defectors based on their cooperation attribute. Furthermore, we explore the evolution of the three strategies in the well-mixed population and the structured population, respectively. The results show that the dual attribute strategy is more likely to survive. Additionally, the smaller the two kinds of cost-to-benefit ratios are, the more likely cooperation is to exist. Moreover, in the well-mixed population, the existence of cooperation is independent of the temptation to defect. In the structured population, cooperation is more favorable when the cost-to-benefit ratio between the cooperation strategy and the dual attribute strategy is moderate, regardless of the temptation to defect. The dual attribute strategy is actually the embodiment of the simultaneous weakening of cooperation and defection.

Game theory system optimization in a supplier-customer transport network

The development of modern IT technologies and resources, offer in many economic-industrial applications, efficient solutions for planning and managing of specific activities. New research directions in the field of management and optimization of supply-customer (SC) systems are defined and evaluated. The paper proposes a management model based on a causal graph structure, which expresses the connection and interdependence between the two entities involved in the specific activities of the field, the suppliers of goods and services and the consumers of these goods and products. Information related to production and storage capacity, up-to-date requirements for customer needs, delivery planning, production and consumption dynamics are considered. The transport between the actors involved in the system is an important component with weight in the costs of supply-sale activities. The optimal solution for the supply-customer transport system is proposed based on different strategies of game theory.

Priority Wise Electric Vehicle Charging for Grid Load Minimization

The number of Electric vehicle (EV) users is expected to increase in the future. The driving profile of EV users is unpredictable, necessitating the design of charging scheduling protocols for EV charging stations servicing multiple EVs. A large EV charging load affects the grid in terms of peak load demand. Electric vehicle charging stations with solar panels can help to reduce the grid impact of EV charging events. With reference to the increasing number of EVs, new technology needs to be developed for charging station and management to create a stable system for users, and electric utilities. The load of a total EV charge can affect the grid, degrading quality and system stability. In this paper, a charging station scheduling strategy is proposed based on the game theoretic approach. In the proposed strategy, with respect to the grid load demand minimization, charging stations have scheduled EV charging times to prevent sudden peak load on the grid the proposed game theory strategy is sudden peak load on the grid. The proposed game theory strategy is defined on the basis of priority so that both grid operators and EV users can maximize their profit by setting priorities for charging and discharging. This work provides a strategy for grid peak load minimization.

Game Theory Analysis on Marketing Strategy Determination of KAI Access and Traveloka based on Usability of HCI (Human-Computer Interaction)

In the era of digitalization, KAI finally transformed train ticket booking transactions by launching the KAI Access application. By applying the optimal strategy of Game Theory, KAI Access will be able to compete with marketplaces such as Traveloka to be able to represent itself to the community as a credible company. Game Theory was chosen to analyze the existing competition to find the optimum strategy. Because both applications sell the same product, the strategy will be seen from the services provided by both applications using the usability of HCI. The 5 strategies are Learnability, Efficiency, Memorability, Errors, and Satisfaction. The best strategy used by KAI Access to obtain maximum profit is the low error rate (errors) strategy, while for Traveloka the strategy used to minimize loss is the ease of application (learnability).

A Low-Complexity Detector for Uplink SCMA by Exploiting Dynamical Superior User Removal Algorithm

Sparse Code Multiple Access (SCMA) is capable of ameliorating the performance of wireless communication systems for massive connectivity. The sparsity of the codeword allows SCMA to employ the message-passing algorithm (MPA) at the receiver to address the connection overload. The detection complexity of traditional MPA, on the other hand, is exponentially exploding with the increasing number of connected devices. In this paper, a low-complexity MPA scheme is developed with the threshold and dynamical user removal algorithm (TB-RMPA) based on shuffled MPA (SMPA). By introducing the concept of Iterative Elimination of Dominated Strategies (IEDS) in game theory, a novel method of assigning different dominance levels to users is proposed hereby, and a threshold criterion is discussed to judge the reliability of user decoding. Inspired by this, users with reliable decoding in the iterative process will be dynamically eliminated in each iteration. The numerical analysis revealed that the new TB-RMPA detector’s implementation complexity decreases significantly compared with SMPA, while the symbol error ratio performance degrades unnoticeably.

Linja: A Mobile Application Based on Minimax Strategy and Game Theory

This article presents an application of Minimax strategy and game theory to implement the Linja mobile game. This game theory strategy applies collaborative learning to determine the winner of a game between two opponents, thus determining the optimal move in complex environments. In the development of the collaborative game, different game learning scenarios are proposed where competition between a player and the machine, and competitions against other players, intervene. In the learning process, moves are proposed that allow the maximum gain and the minimum loss among the competitors. In this case, the methodological approach was carried out towards the move that allows maximizing the profit and minimizing the loss, based on the application of the Mini/Max algorithm in search of determining the optimal solution of the game. The process is obtained from the adaptation of mathematical models for the development of games, using specialized tools that support a multi-paradigm programming language working together with the tools that the same language provides and that potentially serve as a contribution to the development of the game. In the search for an intelligent and autonomous system. The intelligent system correctly finds the winner of a game, showing the course of the game move by move. The results show that the game developed with the Minimax strategy allows automatic learning in multiuser environments, correctly identifying the winner of a game, generating the most optimal route of the game from move to move.

Solving narrow Konane boards

In this paper we investigate the game of Konane, using Combinatorial Game Theory and game-specific solving strategies. We focus on narrow rectangular boards ( m × n boards with m ⩽ 4). These are dubbed as Linear Konane, Double Konane, Triple Konane, and Quadruple Konane, respectively. The initial board contains black and white stones in a checkered pattern, with a gap of two adjacent empty squares to enable moving. Only capture moves are possible. Depending on the exact location of the initial gap (the setup) we have four classes of initial Konane boards (two for Linear Konane), namely all combinations of a horizontal or vertical setup in the middle of the board or at a corner. For solving narrow Konane boards two notions proved very useful. First, we define moves that cannot be prevented by the opponent as safe moves of a player. Second, two fragments are independent if there is no way they can ever interact. Using these two notions Linear and Double Konane have been completely solved. Triple Konane was solved except for the horizontal corner setup. For Quadruple Konane only the vertical setup in the middle of the board was solved.

Database Recovery Technique for Mobile Computing: A Game Theory Approach

Contact between mobile hosts and database servers presents many problems in the Mobile Database System (MDS). It is harmed by a variety of causes, including handoff, inadequate capacity, frequent transaction updates, and repeated failures, both of which contribute to serious issues with the information system’s consistency. However, error tolerance technicality allows devices to continue performing their functions in the event of a failure. The aim of this paper is to identify the optimal recovery approach from among the available state-of-the-art techniques in MDS by employing game theory. Several of the presented recovery protocols are chosen and evaluated in order to determine the most critical factors affecting the recovery mechanism, such as the number of processes, the time required to deliver messages, and the number of messages logged-in time. Then, using the suggested payout matrix, the game theory strategy is adapted to choose the optimum recovery technique for the specified environmental variables. The NS2 simulator was used to carry out the tests and apply the chosen recovery protocols. The experiments validate the proposed model’s usefulness in comparison to other methods.

Correlation Filters Based on Multi-Expert and Game Theory for Visual Object Tracking

Most existing correlation filters based tracking algorithms only use the linear additive features or simple fusion features, which are unable to fully learn the appearance information of the target. In this paper, a multi-expert and game theory correlation filter (MEGTCF) algorithm is proposed to treat the aforementioned problems. The algorithm extracts a variety of handcrafted features and deep features of the target and forms multiple experts in different combinations to track the target in parallel. In addition, the game theory strategy is introduced to obtain more reliable decisions. The expert evaluation mechanism evaluates and selects the most discriminative experts as game participants and fully explores the complementary information among different expert features through the iterative game, so as to realize the information interaction between expert features. We also propose a confidence indicator to calculate the weights of the experts with higher scores and adaptively update the expert model to avoid expert corruption. Extensive experiments are conducted on five popular benchmarks, including OTB2013, OTB2015, VOT2018, LaSOT, and the UAV123 tracking benchmark. The success rate and precision of the MEGTCF tracker on the OTB2015 reach 67.8 % and 91.4 %, which outperforms other state-of-the-art trackers based on correlation filters. In addition, the MEGTCF tracker is also highly suitable for challenging scenarios such as object deformation, background clutter, and occlusion.

Equity and Game-Theory Strategies to Promote Gender Diversity and Inclusion in an Academic Health Science Centre

BackgroundAchieving diversity, inclusion, and gender equity remains an elusive challenge for many institutions worldwide and is understudied in Canadian academic health science centres. MethodsMcMaster University’s Department of Medicine undertook surveys and analyses to determine whether there was inequity in leadership positions and salaries, or unprofessional behaviour within the department. Measures of academic productivity in relation to gender for both educators and researchers were analyzed. The department began shifting policies to foster greater gender diversity and inclusion. A revision of the leadership selection process, incorporating tenets of equity and a new game theory–based strategy called Diversitive Agreement Versus Nash Equilibrium (DAvNE) was evaluated. ResultsThe department’s survey revealed underrepresentation of women and people of colour in leadership positions, with perceived barriers to their promotion. Both women and people of colour reported experiencing unprofessional behaviour directed toward them. A gender gap in base salary was observed, with female full professors being paid less. No difference in academic productivity was seen between male and female educators or researchers. The leadership competitions conducted under new selection processes emphasizing diversity resulted in 66% of participating women securing a leadership position, in comparison to 25% of participating men. People of colour made up 27% of members participating in these leadership competitions, but none was successful in obtaining a position. ConclusionsDiversity and inclusion disparities in the Department of Medicine at McMaster University indicate a need for further efforts and innovation to bring about greater gender and racial equity.

Stability analysis of networked control systems under DoS attacks in frequency domain via game theory strategy

In this paper, the stability analysis of networked control systems (NCSs) under Denial-of-Service (DoS) attacks is studied. Firstly, the stability analysis for NCSs free of DoS attacks is investigated. Considering the DoS attacks, the game theory is introduced to establish the non-cooperative game between defender and attacker of NCSs. At the same time, the stability of NCSs is analysed in frequency domain, and the signal-to-noise ratio (SNR) expression is given by using spectral decomposition technique and Nash equilibrium (NE) strategy. The packet dropout phenomenon caused by DoS attacks is described by Bernoulli distribution. By virtue of the NE strategy, the stability of NCSs can be optimised. In the obtained results, the influence of the traditional constraints and DoS attacks is illustrated. Finally, some numerical examples are given to show the effectiveness of the derived theoretical results.

Game theory‐based metaheuristics for structural design optimization

AbstractThis paper presents game theory‐based strategies incorporated with metaheuristics to improve the computation efficiency and effectiveness in design optimization of civil engineering structures. The idea is that a structural optimization problem can be treated as a multiplayer game with optimization algorithms as the players. This study presents an innovative mixed game strategy and provides insights into domain decomposition schemes. Based on game theory strategies, six modified metaheuristic algorithms are proposed and demonstrated through a case study of a 30‐story mega‐braced frame‐core building with a complex configuration. Jaya algorithm is selected as the control to evaluate the proposed methods. The results show that the mixed game strategy helps reduce the computation cost by about 50%, while reducing the weights of the building by 4%, compared with the Jaya algorithm.

A Multi-Agent Based Optimization Model for Microgrid Operation with Hybrid Method Using Game Theory Strategy

Owing to the increases of energy loads and penetration of renewable energy with variability, it is essential to determine the optimum capacity of the battery energy storage system (BESS) and demand response (DR) within the microgrid (MG). To accomplish the foregoing, this paper proposes an optimal MG operation approach with a hybrid method considering the game theory for a multi-agent system. The hybrid method operation includes both BESS and DR methods. The former is presented to reduce the sum of the MG operation and BESS costs using the game theory, resulting in the optimal capacity of BESS. Similarly, the DR method determines the optimal DR capacity based on the trade-off between the incentive value and capacity. To improve optimization operation, multi-agent guiding particle swarm optimization (MAG-PSO) is implemented by adjusting the best global position and position vector. The results demonstrate that the proposed approach not only affords the most economical decision among agents but also reduces the utilization cost by approximately 8.5%, compared with the base method. Furthermore, it has been revealed that the proposed MAG-PSO algorithm has superiority in terms of solution quality and computational time with respect to other algorithms. Therefore, the optimal hybrid method operation obtains a superior solution with the game theory strategy.

ПРИМЕНЕНИЕ ТЕОРЕТИКО-ИГРОВОГО МОДЕЛИРОВАНИЯ В СОВРЕМЕННЫХ БИЗНЕС-ПРОЦЕССАХ И ЭКОНОМИКЕ

Executives and managers who use business intelligence tools can benefit by using the available information from a game theory perspective to develop a game that is appropriate for their organizations. The results can be much more useful than when the organization maintains the current state of affairs. No particular game theory strategy is perfect, because different situations or problems within an organization must be solved in different unique ways. The bottom line is that game theory can be used very effectively as a decisionmaking tool in political, psychological, economic, personal, or business settings. However, this should not be used as a substitute for common sense when making strategic decisions; it is simply a tool for critical thinking. Game theory only sheds light on why companies behave the way they do, and why they use specific strategies that are accessible through transparency.

Optimality analysis of locality-aware tit-for-tat-based P2P file distribution

In the current Internet, periodic software update is essential to maintain systems secure from malicious attacks. In case of proliferated software, e.g., Operating System (OS), the distribution server for update tends to be a bottleneck, due to the concentration of users’ requests. To tackle this problem, several systems, e.g., Windows Update, have been applying the Peer-to-Peer (P2P) file distribution technique where clients called peers upload retrieved fragments of the original file, i.e., pieces, to others. However, peers may not be willing to upload pieces to others, due to their own communication overhead. BitTorrent has adopted the Tit-for-Tat (TFT) strategy in game theory, which encourages peers to exchange an equivalent number of pieces among each pair of peers. In recent years, the optimality of TFT-based P2P content distribution, i.e., file distribution and streaming, has been analyzed with the help of Integer Linear Programming (ILP). In this paper, considering the fact that the communication overhead inside a group, e.g., LAN or Autonomous System (AS), is much less than that of between groups, we model locality-aware TFT-based P2P file distribution where the TFT constraint is relaxed for intra-group communications. We further formulate the optimal piece flow determination problem as ILP in the similar way to the existing work. Through numerical results, we show that the locality-aware TFT-based P2P file distribution can achieve quasi-optimal average file download time when the upload capacity of the server is a bottleneck and the number of groups is moderate.

Performance Evaluation of Game Theory Based Efficient Task Scheduling For Edge Computing

The edge paradigm that is intended as prominent computing due to its low computation latencies faces multiple issues and challenges due to the restrictions in the computing capabilities and its resource availability especially in the huge populace scenarios. To examine the problems faced during the task scheduling when the edge computing is called up by multiple users at time, the paper puts forward the game theory approach. Utilizing the game theory strategy the paper puts forth the a novel multi-tasking scheduling in the edge computing from the user perception developing an algorithm taking into consideration the consistency of the stable tasks. The analysis of the proposed algorithm used in the allocation of the tasks is done on terms of average time consumed for the execution of the task and the waiting time. The results acquired showed that the proposed method provides a maximized throughput, minimizing the waiting time compared to the conventional methods used in optimizing the parameters of scheduling.

Dynamics of Strategy Distributions in a One-Dimensional Continuous Trait Space for Games with a Quadratic Payoff Function

Evolution of distribution of strategies in game theory is an interesting question that has been studied only for specific cases. Here I develop a general method to extend analysis of the evolution of continuous strategy distributions given a quadratic payoff function for any initial distribution in order to answer the following question—given the initial distribution of strategies in a game, how will it evolve over time? I look at several specific examples, including normal distribution on the entire line, normal truncated distribution, as well as exponential and uniform distributions. I show that in the case of a negative quadratic term of the payoff function, regardless of the initial distribution, the current distribution of strategies becomes normal, full or truncated, and it tends to a distribution concentrated in a single point so that the limit state of the population is monomorphic. In the case of a positive quadratic term, the limit state of the population may be dimorphic. The developed method can now be applied to a broad class of questions pertaining to evolution of strategies in games with different payoff functions and different initial distributions.