Game Theoretic Approach Research Articles

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.

A game theoretic approach for spectral and energy efficient D2D communication in 5G-IoT networks

In the context of fifth generation (5G) technology, Device-to-Device (D2D) communication plays a pivotal role, requiring swift and intelligent decision-making in mode selection and device discovery. This study addresses the challenge of rapid mode selection and device discovery within 5G communication networks, focusing specifically on enhancing spectral and energy efficiency for Internet of Things (IoT) applications. A novel self-centered game theory-based algorithm is introduced to optimize spectral efficiency and support intelligent mode selection. Additionally, the utilization of the support vector machine (SVM) expedites mode selection decisions. For D2D discovery, the Frank-Wolfe method is adopted, significantly improving the differentiation between D2D and Cellular users based on signal strength and interference, thereby enhancing spectral efficiency. The proposed approach maximizes spectral efficiency while adhering to strict power and interference constraints, intelligently partitioning bandwidth into two subparts using game theoretic principles to amplify spectral efficiency. Furthermore, the emphasis on energy efficiency is underscored through iterative calculations to achieve maximum energy-efficient spectral allocation. Numerical analyses validate the efficacy of the proposed technique, revealing substantial improvements in accurately predicted labels. As the number of devices increases, precision and recall rates experience noteworthy enhancements, ultimately leading to superior bandwidth utilization. This research presents a significant contribution to the field of 5G communication, particularly concerning energy efficiency, which is paramount for IoT applications. By accelerating D2D connectivity and optimizing energy and spectrum resources, it advances the goals of energy-efficient D2D communication within 5G-IoT networks.

Game-theoretic agent-based modelling of micro-level conflict: Evidence from the ISIS-Kurdish war.

This article delves into the dynamics of a dyadic political violence case study in Rojava, Northern Syria, focusing on the conflict between Kurdish rebels and ISIS from January 1, 2017, to December 31, 2019. We employ agent-based modelling and a formalisation of the conflict as an Iterated Prisoner's Dilemma game. The study provides a nuanced understanding of conflict dynamics in a highly volatile region, focusing on microdynamics of an intense dyadic strategic interaction between two near-equally- powered actors. The choice of using a model based on the Iterated Prisoner's Dilemma, though a classical approach, offers substantial insights due to its ability to model dyadic, equally-matched strategic interactions in conflict scenarios effectively. The investigation primarily reveals that shifts in territorial control are more critical than geographical or temporal factors in determining the conflict's course. Further, the study observes that the conflict is characterised by periods of predominantly one-sided violence. This pattern underscores that the distribution of attacks, and target choices are a more telling indicator of the conflict nature than specific behavioural patterns of the actors involved. Such a conclusion aligns with the strategic implications of the underlying model, which emphasises the outcome of interactions based on differing aggression levels. This research not only sheds light on the conflict in Rojava but also reaffirms the relevance of this type of game-theoretical approach in contemporary conflict analysis.

Dynamic vaccination strategies in dual-strain epidemics: A multi-agent-based game-theoretic approach on scale-free hybrid networks

This paper proposes a multi-agent-based game-theoretic framework to explore the dynamics of decision-making among agents in a situation involving a dual-strain epidemic, where the emergence of the second strain is directly related to the first strain. It considers the impact of imperfect vaccination on dual strains within the SIRV framework, in a hybrid network that combines scale-free and grid-based structures. This research presents three models based on the level of decision-making of agents: Individual Decision Making (IDM), Collective Decision Making (CDM), and Stochastic Decision Making (SDM) model. In the IDM model, agents can continuously update their strategies based on the infection and vaccination statuses of their immediate neighbors. The CDM model offers a macro-level approach where agents’ willingness to vaccinate varies uniformly, while the SDM model maintains a constant level of willingness for vaccination across agents. This study demonstrates that agents using the IDM model tend to initiate vaccination earlier in scenarios of high infection rates, effectively mitigating the spread of the disease in subsequent epidemic seasons. However, when both infection and vaccination rates heavily influence decision-making, there is a noticeable decrease in vaccination uptake, thereby aggravating the spread of infection. Additionally, the authors found that in scenarios of high infection, an over reliance on vaccination without adequate initial uptake can jeopardize disease control efforts. Another important finding of this study is that despite the CDM and SDM models showing a quicker emergence of the second strain and reaching equilibrium points faster than the IDM model, the overall infection rate remains lower in the IDM model. This research underscores the complexity of managing dual-strain epidemics through vaccination strategies and highlights the significance of micro-level decision-making in epidemic control.

Distributed and Fair Beacon Power and Beaconing Rate Adaptation Based on Game Theoretic Approach for Connected Vehicles

In vehicular ad hoc networks, vehicles regularly transmit information through beacons to raise awareness among nearby vehicles about their presence. However, as the number of beacons increases, the wireless channel becomes congested, resulting in packet collisions and the loss of numerous beacons. This paper addresses the challenge of optimizing joint beaconing power and rate in VANETs. A joint utility-based beacon power and rate game is formulated, treated both as a non-cooperative and a cooperative game. To compute the desired equilibrium, three distributed and iterative algorithms (Best Response Algorithm, Cooperative Bargaining Algorithm) are introduced. These algorithms simultaneously update the optimal values of beaconing power and rate for each vehicle in each step. Extensive simulations showcase the convergence of the proposed algorithm to equilibrium and offer insights into how variations in game parameters may affect the game's outcome. The results demonstrate that the Cooperative Bargaining Algorithm is the most efficient in converging to equilibrium.

Mean-variance portfolio with wealth and volatility dependent risk aversion

Risk aversion rate plays a significant role in mean-variance portfolio selection. Most existing literature assumes it to be constant or wealth dependent, which is unrealistic. In this study, I contend that it is both wealth and volatility dependent because it varies across economic status: either steady or fluctuated. In addition, I decompose the risk aversion rate into a wealth prudence rate and a volatility prudence rate and investigate their mutual effect on portfolio selection under a continuous-time mean-variance framework. Using Game theoretic approach and asymptotic expansion, I derive the optimal trading rule analytically.

Effects of governmental supportive policies on waste management for two substitutable products made of virgin and waste materials: A game-theoretic approach.

In recent years, the concept of sustainability has attracted a great amount of attention, due to increasing energy resources scarceness. Waste recycling is known as an efficient approach to improve sustainability and save energy. In this view, a sustainable supply chain is established in the current study to investigate the effects of waste recycling on sustainable development. The considered supply chain consists of the government, two manufacturers, a supplier, a waste depot, and a recycler. Under this structure, two substitutable products are made of the virgin and recyclable waste materials. The supplier provides the virgin materials for the first product. The waste depot collects the non-recycled waste, whereas the recycler recycles it and supplies the recycled waste for the second product. Also, the government supports the second product to provide an incentive for its members to collect and recycle more waste. Then, the game theory is applied to make decisions under the considered structure. Finally, the results are revealed and some managerial insights are provided. It is derived that the governmental supportive policies play a significant role in resources conservation and energy storage. Moreover, increasing the quality of the product made of the recyclable waste improves the government's utility.

Coordination of interests between local environmental protection departments and enterprises under China’s environmental regulation policies: an evolutionary game theoretical approach

The Central Inspection for Ecological and Environmental Protection (CIEEP) and Environmental Vertical Management Reform (EVMR) policies have reshaped the relationships among stakeholders and the power structure in local environmental governance systems. To reveal the current local environmental collaborative governance mechanism, this study placed CIEEP and EVMR policies within the same research framework and considered multiple governance entities, including the central government, higher-level environmental protection departments, local environmental protection departments, enterprises, and the public, employing evolutionary game theory and numerical simulation techniques to explore the boundary conditions, key influencing factors, influence mechanisms, and realization paths of local environmental collaborative governance from a dynamic process perspective. The results show that: 1) The cost of enterprise environmental behavior, intensity of reward and punishment for enterprise environmental behavior, government subsidies, supervision cost of local environmental protection departments, and intensity of reward and punishment for local environmental protection departments all have important effects on local environmental governance; 2) Compared with the cost of enterprise environmental behavior, enterprise environmental behavior is more sensitive to changes in supervision cost of local environmental protection departments, and the intensity of reward and punishment for enterprise environmental behavior has the same major impact on enterprise environmental behavior as that of local environmental protection departments; 3) The influence of CIEEP policy on enterprise environmental behavior is unstable, showing signs of strategic response. Therefore, local environmental governance systems should be further optimized by encouraging local environmental protection departments to actively supervise and designing differentiated policy tools. This study provides a reference for solving local environmental governance problems under current environmental regulation policies.

Three-stage energy trading framework for retailers, charging stations, and electric vehicles: A game-theoretic approach

Electric vehicle (EV) load integration and environmental concerns in recent years have motivated energy users to step forward in reducing carbon footprints by promoting energy trading. In the energy trading market, different energy stakeholders participate in trading energy with each other to maximize their utilities and improve trading efficiency. The social welfare (SW) of the energy market plays a significant role in keeping the market going and motivating new users to engage in energy trading. The SW of the energy market depends on its structure, where energy flows from producer to end user in a fair way. This paper introduces a hierarchical energy trading framework, formulated as a three-stage Stackelberg game, to enhance SW. In the first stage of this hierarchy, the retailer, acting as the sole leader, seeks to maximize profits through optimized energy sales to charging stations (CS). Subsequently, in the second stage, each CS operates as a leader for EVs, aiming to maximize profits by minimizing energy trading costs, while also acting as a follower in response to the retailer’s strategies. The third and final stage involves EVs collaboratively acting as a unified strategic agent to minimize their trading costs through optimized charging and discharging schedules. This paper also introduces an average price penalty function to further enhance SW in the energy market. The proposed model, addressing the complexities of energy trading interactions, is formulated as a non-linear optimization problem with constraints. It has been implemented in a Python environment and solved using the Gurobi optimization solver, demonstrating its potential to improve the efficiency, fairness, and overall social welfare of the energy trading market.

Game Theoretic Approaches to Modelling Cooperative and Competitive Dynamics for Enhanced Routing and Resource Allocation

The goal of the field of game theory is to simulate scenarios in which decision-makers must choose between courses of action that may or may not conflict and in which the choices made by one element may affect the choices made by other elements. Techniques from game theory have been widely used to solve a variety of engineering design issues. Game theory may be applied to wireless networks to create cooperative strategies between nodes, terminals and network providers, among other organizations. The majority of the time, game theory has been used in networking to address routing and resource allocation issues in competitive settings. Because radio communication channels are typically shared in wireless networks, the actions of one wireless device may have an impact on the communication capabilities of a neighboring device. Game theory may be used to simulate situations such as these. A multi-layered viewpoint is presented in Applications of Game Theory in Wireless Networking, highlighting the domains in which game theory might find practical application. This explains how different wireless network interactions may be represented as games and how game-theoretical methods can accurately mimic or forecast genuine user behavior in cooperative or competitive settings. Because of these similarities, it is possible to grasp the intricate relationships between nodes in this highly dynamic and dispersed environment by using a strong mapping between classical game theory components and network parts

Low carbon strategy and coordination for a two-echelon automotive supply chain considering dual credit policy

The dual-credit policy currently plays a crucial role in the automobile industry and significantly contributes to low-carbon operations in the automotive supply chain. This paper employs the game-theoretic approach to build a supply chain comprising a supplier (leader) and an automaker (follower), considers the impact of dual-credit policy, and incorporates vertical spillovers into the supply chain abatement. Both parties lower carbon emissions through vertical spillovers while maximizing profits, thereby studying the low-carbon strategy of the automobile supply chain. Our findings reveal that (1) increased vertical spillover rate reduces the prices of fuel vehicles (FVs) and new energy vehicles (NEVs), raises the low-carbon levels and profits of automakers and suppliers. (2) An increase in the NEV credit ratio under the centralized system raises both price and demand for NEVs, while the low-carbon levels of automotive enterprises are reduced. (3) The carbon emissions of each vehicle under the centralized system will be lower than those of the decentralized system, and the order of total carbon emission levels under both systems is closely related to the credit trading price and credits per NEV. Moreover, we explore the automotive supply chain coordination and find that the Nash bargaining model fully coordinates the supply chain, while the cost-sharing and revenue-sharing contracts do not. Also, numerical analysis shows that the optimal system profit under revenue-sharing is greater than cost-sharing.

An evolutionary game-theoretic approach to analysis the green innovation chain dynamics under government policies

Continuous improvement of the green innovation chain is essential for promoting sustainable entrepreneurship and addressing environmental issues. In this paper, we developed a tripartite evolutionary game model involving tech firms, startups, and accelerators. Our goal is to examine the impact of both static and dynamic government interventions on the evolution of the green innovation chain. After determining the evolutionary stable strategies, we examine government interventions through a system dynamics simulation using three different scenarios: (1) Government support downstream of the innovation chain, (2) government support upstream of the innovation chain, and (3) government support throughout the chain. In the first scenario, it is observed that although it promotes green technology innovation (GTI), the necessary support for startups and accelerators to scale up green innovations is lacking. Consequently, the overall level of GTI is limited. Conversely, in the second scenario, there is a lack of incentives for tech firms to invest in GTI, resulting in a stagnation in the innovation chain. Nevertheless, with government support extended throughout the chain, a synergistic effect emerges between all players, leading to a higher level of collaboration and resulting in a more efficient and effective green innovation chain.

Analyzing the blockchain mining strategy in Industrial Internet of Things: A game theoretical approach

Deploying Proof-of-Work (PoW) consensus based blockchain systems in Industrial Internet of Things (IIoT) scenarios that rely on wireless channels for data transmission faces new challenges. An IIoT device working in industrial production may participate in the operation of blockchain as a miner. These devices not only need to consume their own computation capability to compete for the right to generate blocks, but also need to transmit newly generated blocks to other devices through wireless channel to continuously maintain the global ledger. Asynchronous mining and data transmission by IIoT devices in wireless environments may significantly interfere with the propagation of blocks in the system and result the reduction of mining payoff, thereby affecting the willingness of IIoT devices to participate in maintaining blockchain system. This paper considers the behavior of independent mining between multiple IIoT devices as a non-cooperative game, which we call the Wireless Mining Game (WMG). Markov chain is adopted to derive the block propagation model under the wireless channel, and then the payoff model of IIoT device is formulated. IIoT devices can make reasonable mining decisions by evaluating the computation capability of other IIoT devices and the probability of successful block transmission. We analyze the Nash equilibrium of the WMG and the corresponding mining behavior set between two-players scenario through theoretical modeling and experiments. We also analyzed the expected mining payoff of the IIoT devices in the multiple devices scenario.

Manufacturer vs. Retailer: A Comparative Analysis of Different Government Subsidy Strategies in a Dual-Channel Supply Chain Considering Green Quality and Channel Preferences

As escalating environmental pollution results from economic development, the green supply chain is vital in enhancing ecological quality. In light of the rapid growth of online shopping, the dual-channel supply chain has gained significant popularity. Governments have implemented policies to achieve carbon peaks and neutrality worldwide. Considering green quality and channel preferences, this study constructs a Stackelberg model led by the manufacturer within a dual-channel supply chain to compare the effects under two government subsidy strategies. A Stackelberg game-theoretic approach is applied to analyze the model. The findings indicate the following: (1) Increases in the subsidy coefficient can increase product greenness and the overall profit in both subsidy strategies, which makes sense; (2) When direct sales channels become dominant, subsidizing the manufacturer proves superior to subsidizing the retailer in terms of promoting green quality and overall profit, which extends existing studies of government subsidy decisions. Furthermore, subsidizing the retailer may negatively affect the total supply chain profit; (3) Consistent with previous literature, intense competition between manufacturers and retailers is expected to enhance the overall profit of the supply chain; (4) Interestingly, customer preference for online channels does not influence product green quality under manufacturer subsidization strategies but exhibits a negative impact under retailer subsidization strategies. Finally, this research provides managerial implications for decision-makers and potential issues for future research.

Developing sustainable and efficient customer-centric strategies for a three-stage dual-channel green supply chain: a game-theoretic approach with two-part tariff contract

Developing sustainable and efficient customer-centric strategies for a three-stage dual-channel green supply chain: a game-theoretic approach with two-part tariff contract

Cooperation in combinatorial search

AbstractIn the game theoretical approach of the basic problem in Combinatorial Search an adversary thinks of a defective element d of an n-element pool X, and the questioner needs to find x by asking questions of type is $$d\in Q?$$ d ∈ Q ? for certain subsets Q of X. We study cooperative versions of this problem, where there are multiple questioners, but not all of them learn the answer to the queries. We consider various models that differ in how it is decided who gets to ask the next query, who obtains the answer to the query, and who needs to know the defective element by the end of the process.

Logistics strategies of food delivery O2O supply chain with anti-food waste regulation

With the ever-growing popularity of food deliver, more and more consumers are embracing this convenience as part of their lifestyle. However, the issue of food waste created by the food deliver industry has become a pressing concern in society. This paper aims to examine the link between food waste and food delivery services, and investigate the effects of anti-food waste regulations on the generation of food waste and the choice of logistics strategies in an Online-to-Offline (O2O) supply chain. Using game-theoretical approach, we focus on two prominent logistics strategies—the restaurant-free self-logistics strategy (RF strategy) and the platform-charge logistics strategy (PC strategy). Our research results show that anti-food waste regulation can effectively reduce food waste in food delivery service under the PC logistics strategy. The choice of logistics strategy is constrained by the online market potential, the relative logistics costs of platform logistics, and anti-food waste regulations. If the anti-food waste regulation is strict, as long as the size of the food delivery market and the relative logistics costs of platform logistics are not simultaneously small, the RF strategy will be the equilibrium strategy, whereas the supply chain members should choose the PC strategy. The study thus offers useful inferences for theory and practice.

Testimonial justification under epistemic conflict of interest

Can a hearer be rationally justified to have beliefs based on testimony alone when the source of his information is known to have conflicting epistemic goals? When it comes to belief justification, existing theories either recommend avoiding epistemic conflicts of interest or ignoring them. This is an important epistemological limitation. A theory that comes in degrees, capable of explaining what beliefs we are justified to hold and why, despite epistemic conflict of interest, is still lacking. Building on a game-theoretical approach, I suggest such a theory and argue that the hearer can justify some beliefs on testimony alone. This justification relies on an equilibrium concept, which is only reached in the long run. In addition, the hearer’s justified beliefs will always be less accurate than those held by the original source. For instance, assume the speaker is a climate scientist who has good reasons to believe that a 2∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}C increase in temperature will lower the current global GDP by 10 percentage points. Under epistemic conflict of interest, a hearer will typically be justified to a belief close to that value, but not equal to it. The smaller the epistemic conflict of interest, the closer, on average, the hearer’s and speaker’s belief. These results highlight the importance of scientific norms which, in practice, are the embodiment of these equilibrium mechanisms and thus of scientific credibility.

The Influence of ESG Reporting on Competitive Dynamics in Business: Insights from a Game Theoretical Approach

As the basis of ESG rating, ESG information disclosure has gradually moved from a niche investment concept to a mainstream trend in recent years, which plays a key role in the ESG investment process. However, many enterprises still choose not to disclose relevant information for reasons such as controlling costs and reducing negative information output. For enterprises in the same industry with competitive relations, different choices made by both parties may bring about impacts on enterprise competitiveness, financing cost, strategic transformation, and regulation and policy formulation. This paper, from the perspective of enterprises and their competitors in the same industry, uses the game theory model to put forward suggestions for improving enterprises' strategic choices. By establishing a game model to promote the establishment or improvement of ESG management systems within enterprises, enterprises can seek the balance between cooperation and competition in the game, and finally achieve the overall sustainable development in their respective industries.

The evolutionary stability of partial migration with Allee effects

An Allee effect is a density-dependent phenomenon in which population growth or individual components of fitness increase as population density increases. Understanding the density-dependent effects is vital to elucidate how populations evolve and to investigate evolutionary stability. Partial migration, where a proportion of a population migrates while other individuals remain resident, is widespread across most migratory lineages. However, the mechanism still needs to be better understood in most taxa, especially those experiencing positive density-dependent effects. Here we investigate the evolutionary stability of a partial migration population with only the migrant population experiencing Allee effects. Using the Evolutionary Game Theoretic (EGT) approach, we prove the existence and uniqueness of an evolutionary stable strategy (ESS). EGT provides a mathematical framework for understanding and modelling Darwinian evolution by natural selection. We also show that the ESS is the only Ideal Free Distribution (IFD) that arises in the context of a partially migrating population in a two-habitat environment.