Game Theory Model Research Articles

Solar–Hydrogen-Storage Integrated Electric Vehicle Charging Stations with Demand-Side Management and Social Welfare Maximization

The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits.

Root foraging strategies and niche segregation of three mediterranean shrub species

Shrubs are usually adapted to stressful environments in which soil resources are limited, and thus, roots are fundamental for their biological success. However, root measures are challenging to collect, especially in field conditions and at the individual level. For this study, we collected data on the three‐dimensional distribution of fine root biomass of twenty‐three individuals belonging to three shrub species in a mediterranean shrubland in central Spain: gum rockrose Cistus ladanifer, rosemary Salvia rosmarinus, and hairy‐fruited broom Cytisus striatus. Our goal was to determine the soil‐foraging strategies adopted by the plants. We hypothesized that plants would show stabilizing niche differences explaining the high plant biodiversity characteristic of mediterranean shrublands and that they would follow the game theory model's prediction of exploitative segregation of roots behaving territorially but also over‐proliferating roots close to their stem and engaging in a root tragedy of the commons. We found that two‐thirds to three‐fourths of the biomass was belowground, and the system's productivity was roughly 500–600 g C m−2 year−1. Only rosemary plants competed with neighbors following the exploitative segregation predictions. Broom plants had the shallowest and most widespread root systems but significantly reduced their root range toward competing neighbors. Gum rockrose presented deep, narrow root systems avoiding extensive overlap with neighbors but did not appear to respond to competitive pressure levels. Shrubs appeared to stratify their roots at different soil depths, supporting the niche segregation hypothesis.

Towards a Sustainable Blockchain: A Peer-to-Peer Federated Learning based Approach

In the rapidly evolving digital world, blockchain technology is becoming the foundation for numerous applications, ranging from financial services to supply chain management. As the usage of blockchain is becoming more prevalent, the energy-intensive nature of this technology has raised concerns about its long-term sustainability and environmental footprint. To address this challenge, we explore the potential of Peer-to-Peer Federated Learning (P2P-FL), a distributed machine learning approach that allows multiple nodes to collaborate without sharing raw data. We present a novel integration of P2P-FL with blockchain technology, aimed at enhancing the sustainability and efficiency of blockchain networks. The basic idea of our approach is the use of distributed learning mechanisms to find the optimal performance parameters of blockchain without relying on centralized control. These parameters are then used by a load-balancing mechanism that prioritizes energy efficiency to distribute loads on different blockchains. Furthermore, we formulate a non-cooperative game theory model to align the individual node strategies with the collective objective of energy optimization, ensuring a balance between self-interest and overall network performance. Our work is exemplified through a case study in the renewable energy sector, demonstrating the application of our model in creating an efficient marketplace for energy trading. The experimentation and results indicate a significant improvement in the execution times and energy consumption of blockchain networks. Therefore, the overall sustainability of the network is enhanced, making our framework practical and applicable in real-world scenarios.

Construction and application of product optimisation design model driven by user requirements

User requirements serve as the primary reference content in product design. The effective capture of crucial user requirements, followed by the development of a product technical solution aligned with these requirements, stands as a pivotal approach to enhancing design efficiency. In order to explore the problem of generating and decision-making of product technical solutions in the case of complex user demands, this study constructs a user requirements driven product optimization design model, which is used to complete the generation and decision-making of product design solutions in a more reasonable way. The model unfolds across three key stages: Firstly, a user requirements importance ranking system is crafted leveraging Kano Model and Pairwise Analysis. Next, employing the Functional Analysis System Techniques (FAST) theory and the Quality Function Deployment (QFD) theory, user requirements undergo transformation into technical solutions. Finally, these technical solutions are amalgamated into diverse technical combinations, with decisions facilitated by a game theory model to yield the optimal overall design solution. The new optimal design model reduces the influence of subjectivity and ambiguity in the process of user requirements analysis, increases the reliability of the transformation of user requirements into technical solutions, and improves the efficiency of the generation and decision-making of product design solutions under multiobjective situations. The model proposed in this study is exemplified through the development of a medicated bath water heater. Results indicate that the technical solution derived from the model surpasses similar products in terms of user satisfaction, thereby validating its feasibility.

A multi-stage techno-economic model for harnessing flexibility from IoT-enabled appliances and smart charging systems: Developing a competitive local flexibility market using Stackelberg game theory

The increasing integration of renewable energy sources with power systems has led to greater uncertainties, heightening the need for more flexible services. Demand-side resources can provide significant flexibility cost-effectively, making it crucial to design new business models to harness their flexible capacities. Hence, this paper introduces a three-stage techno-economic framework designed to enhance the exploitation of flexible capacities from demand-side resources such as Internet-of-Things (IoT)-Enabled Appliances (IEAs), Battery-Integrated Rooftop Solar Panels (BIRSPs), and Electric Vehicle (EV) smart charging systems in the local flexibility market. The proposed framework operates in three stages. First, smart homes with IEAs, BIRSPs, and smart charging systems assess their flexibility and report it to their Microgrid (MG) operators. Next, MG operators optimize strategies to maximize market returns and report capacities to the Distribution System Operator (DSO). Finally, the DSO manages flexibility transactions based on MG inputs. A Stackelberg game theory model using a gradient-based method updates the coupling variables between market parties, ensuring convergence in the second and third stages within a decentralized space with limited information sharing. Implemented on a modified IEEE 69-node distribution system, the model fully utilizes the potential of smart homes, including IEAs, BIRSPs, and smart charging systems in the local flexibility market. It reduces the Upstream Network (UN) share in the local flexibility market by 56.86% and decreases DSO costs for energy balancing by 22.36%.

Preferential evaluation of coal filling mining scheme under building pressure based on improved grey target decision-making model of game theory

The problem of coal compression under buildings is common in underground mining of coal mines in China. The selection of traditional mining programme is subjective and lacks scientific rationality. In order to solve this problem, this paper studies the evaluation index system and model applicable to the selection of coal filling mining scheme under buildings. A multi-objective evaluation index system integrating economic, technical and adaptive factors is constructed. And an integrated optimization model is established, which is based on the traditional grey target model, combining the game theory optimal combination of weights with the hierarchical analysis method, entropy weight method, Critic method to determine the weights, and then introducing the TOPSIS model and the Mars distance to establish an improved grey target decision-making model. The validity of the evaluation index system and model is verified by taking the example of coal mining under pressure of buildings in five mining areas of a coal mine, which provides technical support for decision makers. This study helps to scientifically and reasonably carry out the preferred mining scheme of coal filling under building pressure.

The interaction of upstream encroachment and blockchain technology application in a shipping supply chain

In shipping practice, ocean shipping (OS) companies launch their own cargo-canvassing channel to diversify revenue streams and further develop blockchain technology to improve their shipping service level. This study employs a game theory model to investigate the value of the OS company (i.e., upstream) encroaching and the application of blockchain technology by engaging in strategic interactions. Through our investigation, we find that it is disadvantageous for the OS company to encroach on the shipping market (apply blockchain technology) when the cost of encroachment (blockchain) is relatively high. Interestingly, by analyzing strategic interactions, we find that the application of blockchain technology influences the encroachment pattern, and the encroachment incentivizes the OS company to apply blockchain technology when the market demand for the encroaching channel is moderate. More interestingly, we innovatively propose the synergy, dysergy and mutual exclusivity effect to demonstrate our analysis. Furthermore, we discuss the impacts of the value of OS company’s encroachment and application of blockchain technology on the freight forwarder, and propose a “Blockchain Technology Compensation” (BTC) coordination mechanism to cope with the “win-lose” situation caused by the application of blockchain technology by the OS company.

Channel access mechanism for maximizing throughput with fairness in wireless sensor networks

The spectrum scarcity problem of wireless sensor networks (WSNs) is improved through amalgamation of cognitive radio networks (CRNs) into WSNs. However, spectrum allocation to secondary users (SUs) is challenging in cognitive radio wireless sensor networks (CR-WSNs) as channel is already crowded and at same time should not induce interference to primary users (PUs). In designing efficient spectrum access model for CR-WSNs recent work have adopted machine-learning game theory (GT) and statistical model. However, the major limitation of existing spectrum access model they fail to assure access fairness with maximal throughput with minimal collision. This work presents a maximizing channel access fairness model to handle the research challenges. To boost CR-WSN performance, the throughput maximization using channel access fairness (TMCAF) employs shared and non-shared channel access designs. Experiment outcome shows throughput is improved and collision in network is reduced in comparison with state-of-art channel access models.

Influencers: The Power of Comments

This paper builds a game theory model of influencer endorsements and product comments by their followers.

Analysis of manufacturers' dual‐source and dual‐channel strategies under the trend of brand weakening

AbstractIn the context of the world economic downturn, consumers are showing a trend of brand weakening. In order to explore the product advantages of the source manufacturer, that is, the competitive supplier, we establish a game theory model to study the strategy choice of original equipment manufacturers (OEM), competitive suppliers (CS), and noncompetitive suppliers (NS) based on consumer heterogeneity. By dividing consumers into quality consumers and ordinary consumers and considering horizontal competition (between suppliers) and vertical competition (between OEM and suppliers), it is found that the products of the source manufacturers are becoming more and more popular with consumers. Contrary to the traditional view, we find that under the trend of brand weakening, the products of the source manufacturers should be positioned above the price range acceptable to consumers, and the OEM products of the brand owners should be appropriately reduced. The interesting question is whether CS will discontinue the supply of OEM components once it has its own branded products. Further, we analyze that CS terminating the component sales business is an untrustworthy threat to OEM. Finally, we find that there will be a “win–win” zone between OEM and competing suppliers, and the “win–win” region will decrease as the manufacturing cost of the competing supplier increases and increase as the manufacturing cost of the noncompeting supplier increases.

Strategic interactions and market dynamics in the intelligent connected vehicle industry: An analysis of technology sharing, value co‐creation, and market competition

AbstractThis paper utilizes a game theory model to investigate the interplay of cooperation and competition between smart vehicle companies (the incumbents) and traditional vehicle companies (the entrants) in the intelligent connected vehicle industry. The results show that technology sharing emerges as a critical factor in improving business performance and driving market expansion. Additionally, the collaborative endeavors of competitive firms, coupled with consumer involvement, play a crucial role during the value co‐creation phase of new products, significantly impacting company efficiency and consumer satisfaction. Drawing on case studies such as Tesla further corroborates our management insights.

Value-added services and e-commerce platform competitiveness: a game theoretic approach

PurposeIn response to the intense competition in the platform economy, e-commerce platforms are actively introducing value-added services to maintain their competitiveness. However, how effective these value-added services are in fulfilling this purpose remains unclear. This paper explores how value-added services can enhance e-commerce platform competitiveness, measured by both user scale and reputation, considering the effect of network externalities.Design/methodology/approachA bilateral e-commerce platform with potential high-quality sellers and low-quality sellers on one side and potential buyers on the other side was chosen as research setting. Game theory models are constructed to simultaneously consider the behaviors of all actors (including sellers, buyers and the platform).FindingsOn the one hand, to increase the seller scale, basic services play a substituting role in determining the effect of value-added services. On the other hand, to increase the buyer scale and improve platform reputation, basic services play a fundamental role in determining the effect of value-added services. Furthermore, the higher the loss rate of the product value, the bigger the room for providing value-added services. With increasing loss rate of the product value, participating buyers who are attracted by value-added services are the fastest growing indicators; this indicates that the most significant effect of value-added services is its increase in the buyer scale.Practical implicationsBasic services determine the lower limit of platform competitiveness, while value-added services set the upper limit. The results of this paper can instruct different types of platforms to enhance their competitiveness in different ways.Originality/value(1) While previous studies on how to enhance platform competitiveness only considered scale or reputation separately, this paper applies a new perspective of platform competitiveness, namely the improvement of both the seller scale/buyer scale and platform reputation. (2) According to the characteristics of bilateral platforms, game theory models are constructed to explore how value-added services can enhance platform competitiveness considering both positive and negative network externalities. (3) The existing literature studies basic services and value-added services in a fragmented state; this paper contributes to research on value-added services by considering the mutual effect between basic and value-added services.

The Dynamics of Rewards and Penalties: Governmental Impact on Green Packaging Adoption in Logistics

The widespread use of traditional packaging materials poses significant environmental challenges. Adopting green packaging is essential for reducing pollution and conserving natural resources. This paper aims to examine the effectiveness of government incentives and penalties in promoting the adoption of green packaging by logistics companies. We developed an evolutionary game theory model that involves governments and logistics companies, comparing the impacts of static and dynamic reward and penalty policies. The results indicate that (1) static policies often lead to oscillatory adoption rates of green packaging without achieving a stable equilibrium, while dynamic policies generally promote steadier adoption of sustainable practices. (2) Different combinations of dynamic policies have varying influences on logistics companies’ propensity to adopt green packaging solutions. Specifically, dynamic rewards and static penalties are particularly effective at encouraging logistics companies to adopt green packaging. (3) A combination of dynamic rewards and penalties tends to facilitate more rapid and consistent adoption of green packaging by logistics companies. (4) An increase in government supervision costs is associated with reduced regulatory actions and a lower prevalence of green packaging. These insights are critical for policymakers aiming to craft regulations that successfully encourage sustainability within logistics operations.

Analysis of Unmanned Aerial Vehicle-Assisted Cellular Vehicle-to-Everything Communication Using Markovian Game in a Federated Learning Environment

The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a cluster utilized a strategy-based mechanism to maximize their model completion and transmission probability. We modeled a two-stage zero sum Markovian game with incomplete information to jointly study the utility maximization of the participating vehicles and the UAV in the FL environment. We modeled the aggregating process at the UAV as a mixed strategy game between the UAV and each vehicle. By employing Nash equilibrium, the UAV determined the probability of sufficient updates received from each vehicle. We analyzed and proposed decision-making strategies for several representative interactions involving gross data offloading and federated learning. When multiple vehicles enter a parameter transmission conflict, various strategy combinations are evaluated to decide which vehicles transmit their data to the UAV. The optimal payoff in a transmission window is derived using the Karush–Khun–Tucker (KKT) optimality conditions. We also studied the variation in optimal model parameter transmission probability, average packet delay, UAV transmit power, and the UAV–Vehicle optimal communication probabilities under different conditions.

Has climate change promoted the high-quality development of financial enterprises? Evidence from China

Climate change has become a critical global issue and challenge, with significant implications for financial enterprises as an integral part of economic activities. A thorough analysis of the impact of climate change on the high-quality development of financial enterprises is of great importance for financial sustainability. This paper first conducts an in-depth mathematical analysis of the intrinsic mechanisms through which climate change affects the high-quality development of financial enterprises by establishing a game theory model. Secondly, using data from listed companies for the years 2000–2020, an econometric model is constructed to empirically examine the relationship between climate change and the high-quality development of financial enterprises. The research findings demonstrate that climate change significantly inhibits the high-quality development of financial enterprises, as evidenced by robust results obtained through various methods such as data truncation, variable substitution, and changes in sample periods. Furthermore, this study addresses the endogeneity of the regression model using propensity score matching (PSM), instrumental variable methods, and system generalized method of moments (GMM). Additionally, climate change impacts the high-quality development of financial enterprises through technological innovation. Given the backdrop of climate change, understanding the relationship and logic between climate change and the high-quality development of financial enterprises and discerning the channels and mechanisms through which climate change affects their development are crucial. This research provides a new perspective and expands the research frontier on the high-quality development of financial enterprises, enriching the theoretical foundations in this field.

Equity split evolution in entrepreneurial teams: Assessing financial contributions and human capital

ABSTRACT Proper equity splits play a vital role in the success of entrepreneurial teams, yet the literature lacks a comprehensive understanding of their evolving nature. This study addresses this gap by analyzing the dynamic and evolutionary process of equity splits using longitudinal data. Through the development of a game-theory model incorporating human-capital signals, we illuminate the impact of these factors. Utilizing data from the Kauffman Firm Survey and employing fixed- and mixed-effects modeling, we uncover significant findings. Initially, financial contributions shape equity splits, but their influence diminishes as hidden human capital gains prominence. Education attainment and work experience have no direct effect, while prior entrepreneurial experience influences the initial year but not subsequent equity resplitting. Our research offers practical insights for making informed equity-split decisions in the early and later stages of ventures. Nonetheless, it’s important to note some limitations in our research stemming from the lack of information on individual entrepreneurs’ performance within entrepreneurial teams, which could enrich the measurement of hidden human capital.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

PurposeIn order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.Design/methodology/approachFirst, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.FindingsThe cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/valueThis study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

Is the incumbent crying or laughing: when does the entrant adopt the market expansion entry strategy?

PurposeIt is only logical that a firm aims to make a profit after entering the market. However, some firms enter the market with the goal of market expansion and even burn money to pursue market share, which is counterintuitive in practice. To explore the theoretical foundations behind this rare phenomenon, this paper focuses on discussing the impact of the market expansion entry strategy on the entrant firm and the incumbent firm.Design/methodology/approachUsing a game theory model of a supply chain with an incumbent and an entrant, this paper explores the mathematical conditions for the entrant to adopt either the traditional or the market expansion entry strategy and investigates the incumbent’s benefits and losses under different entry strategies.FindingsThe results show that when the market-expansion effect and the selling price ceiling are moderate, the entrant firm always adopts the market expansion entry strategy, and the incumbent firm obtains a free ride from the entrant firm and benefits from it. The entire industry profits and the industry consumer surplus are increased. In particular, we further investigate the cases in which the incumbent firm has a first-mover advantage or there is a troublesome cost, and the results confirm the aforementioned conclusions.Originality/valueBy considering market share as the entrant’s goal, this paper contributes to the dual-purpose literature. Moreover, based on the model’s mathematical results, this paper offers relevant management insights for the entrant and its stakeholders in the e-commerce platform.

О МОДЕЛИРОВАНИИ ЗАКУПКИ ОБРАЗОВАТЕЛЬНОГО ОБОРУДОВАНИЯ И БЮДЖЕТНОЙ КОНКУРЕНЦИИ ШКОЛ

Educational establishments regularly acquire administrative and pedagogical equipment to ensure a quality educational process. In the context of limited budgets and competition among institutions, optimal resource allocation and efficient procurement management become crucial. This study builds upon approaches by applying game theory to model procurement in the educational sector. The first part extends single-stage auction models of procurement to account for heterogeneous participant information. Acquired knowledge enables participants to reduce uncertainty in procurement outcomes and outmaneuver stronger market participants with more favorable offers. Specifically, an optimal strategy is identified for the scenario where only one player possesses information, along with a quantitative order of neutralizing the undesired advantage through additional player inclusion. The second part of the study examines mechanisms of long-term budgetary competition among schools. Well-known Rubinstein, Baron, and Ferejohn game theory models are employed to investigate this setup. A substantive analogy between competition and multi-stage negotiations is drawn, and key parameters determining competitive outcomes are delineated. The purpose of the work is to evaluate the applicability of game theory models. Materials and methods. For modeling, the mathematical apparatus of dynamic games is used, including methods of probabilistic modeling, game theory and optimization. The determining criterion for the decision of the players is the greatest utility, calculated solely based on available information. Results of the modeling are performed analytically through optimization, considering the discounting of the utility function over time. Results. Novelty of work is coupled with domain interpretation of game theory models. Modeling allows predicting agents behavior under the regulation of the procurement market and the definition of their budgeting. Conclusion. The analysis of models of game theory in relation to the field of education is accomplished. It is shown that the degree of awareness of the players about the value functions can significantly influence their decisions and the results of procurement.

Game Theory in Smart Grids: Strategic decision-making for renewable energy integration

This article explores the application of Game Theory in the context of Smart Grids, focusing on its role in strategic decision-making for the effective integration of renewable energy sources. As the demand for sustainable energy solutions rises, the complexities of managing renewable resources within power grids become increasingly challenging. Game Theory provides a valuable framework for understanding and addressing the strategic interactions among various stakeholders in Smart Grids. The discussion encompasses the fundamentals of Smart Grids, the challenges associated with renewable energy integration, and the crucial role of strategic decision-making. By examining real-world case studies and evaluating the benefits and limitations, this article aims to shed light on the practical implications of employing Game Theory models in optimizing Smart Grids for the seamless integration of renewable energy. Looking forward, it explores future trends and developments, emphasizing the continuing significance of Game Theory in shaping the future of sustainable energy infrastructure. The results underscore the transformative potential of game theory in shaping the future of smart grids and advancing the transition towards a more sustainable and resilient energy infrastructure.