Principles Of Game Theory Research Articles

Under the context of competition between platform sales and live streaming sales, who has the pricing power of products?

The rapid rise of live-streaming selling and the increasing diversity of e-commerce channels have sparked ongoing debates regarding product pricing power. This price competition behaviour of online dual channels triggered by the supply-side pricing dilemma is a topic of significant research interest and forms the focal point of this paper. Therefore, we leverage the principles of game theory to construct a three-party evolutionary game model consisting of a brand, a steamer, and a traditional e-commerce platform. Within this framework, the brand holds the primary pricing authority. Our analysis examines the equilibrium strategies adopted by the various players under diverse scenarios and explores how the evolving behaviours of these parties impact the overall game system. Additionally, we employ the “people-goods-scene” perspective to deconstruct the intricate dynamics within this complex supply chain ecosystem. Our findings reveal two distinct evolutionary stable strategies: one in which brands maintain a stance against low-price negotiation, streamers avoid bargaining, and platforms avoid providing subsidies; the other in which brands are open to negotiating low prices, streamers engage in bargaining, and platforms provide subsidies. Notably, in most cases, brands tend to avoid explicitly stating their opposition to low-price negotiation. However, if a brand’s strategy shifts towards rejecting such negotiation, the negative implications of price wars across channels become a critical consideration. Conversely, when a brand opts for a more concessive pricing strategy, we need to consider the potential positive impacts of low-price competition.

The Stochastic Evolution Game of Knowledge-Sharing Incentive in Virtual Communities under Intelligent Recommendation Systems

To effectively solve the issue of unsmooth knowledge sharing in virtual communities under intelligent recommendation systems, we analyzed the impact of key factors on the strategy selection and evolution path in different scenarios.Based on the principles of bounded rationality and benefit maximization, we consider the principles of evolutionary game theory and the influence of random interference. A random evolutionary game model is constructed to analyze strategy selection in the process of knowledge sharing incentives between virtual community platforms and users. We obtained the evolutionary equilibrium strategies under different parameter restrictions and analyzed the evolutionary stability of the dynamic game process of knowledge sharing incentives.The research shows that interference from random factors affects both the speed and the trend of strategy evolution in virtual community platforms and users. In order to improve the enthusiasm of users to share, virtual community need to increase the proportion of users with positive feedback, improve the recommendation incentive mechanism, and reduce the user loss coefficient. Based on the above research conclusions, some countermeasures and suggestions for improving the performance of virtual community knowledge sharing under intelligent recommendation systems are proposed, which provides theoretical guidance for knowledge sharing among virtual community members.

Dynamic event-driven optimal consensus control for state-constrained multiagent zero-sum differential graphical games

In this paper, a dynamic event-driven optimal control scheme is proposed for the zero-sum differential graphical games in nonlinear multiagent systems with full-state constraints. Initially, to address the dual demands of optimality and state constraints, a set of system transformation functions are introduced to satisfy the state constraints of the agents. Then, by applying the principle of differential game theory, the distributed optimal control problem affected by external disturbances is formulated as a zero-sum differential graphical game, and the performance index function related to neighbor informations and disturbances is designed for each follower. Afterwards, to enhance the utilization of communication resource, a novel dynamic event-triggered mechanism characterized by a dynamic threshold parameter and an auxiliary dynamic variable is developed, which not only exhibits greater flexibility but also diminishes the frequency of triggers. Furthermore, the approximate optimal control strategies are obtained by employing an event-driven adaptive dynamic programming algorithm. Ultimately, a simulation example is presented to verify the applicability of the proposed control approach.

Competitive Analysis at the Cournot-Nash Equilibrium of an Interconnected Network

The electric power network has long been subject to monopolization, encompassing production, transportation, and distribution sectors. However, recent liberalization efforts have introduced competition into the electricity market. To understand and manage this competition, game theory, a prominent tool in economics, is frequently employed. Specifically, competition within the electricity market has been analyzed through various game-theoretical models, including Bertrand's atomicity, Cournot's homogeneity, and Nash's research competition. These models aim to achieve the Cournot-Nash equilibrium, where each participant in the market makes optimal decisions given the strategies of others. To effectively allocate production and ensure a balance between supply and demand, as well as to maintain the stability of the interconnected network, one has adopted a method that combines Load Flow techniques with game theory principles. This hybrid approach enables a strategic distribution of power production, taking into account the competitive dynamics of the market. By integrating these methodologies, one can address the complexities of competition while ensuring efficient and stable operation of the power grid. This innovative approach not only enhances the management of electricity production and distribution but also fosters a more competitive and resilient power network. Moreover, the application of game theory in this context allows for a deeper understanding of strategic interactions among market participants. It helps in predicting behaviors, formulating strategies, and anticipating market changes, thus providing a robust framework for decision-making. This is particularly crucial in a liberalized market where multiple entities vie for market share and profitability. By employing game-theoretical insights, one can simulate various market scenarios, optimize resource allocation, and enhance overall market efficiency. Furthermore, this approach supports the integration of renewable energy sources by ensuring that their variable nature is accommodated within the grid's operational dynamics. In summary, the intersection of game theory and load flow methods offers a comprehensive solution to the challenges posed by a competitive electricity market, paving the way for a sustainable and efficient energy future.

Game Theory in Action: Exploring Planetary Chess as a Tool for Social Change

This paper introduces Planetary Chess, an innovative system aimed at dismantling systemic racism, that includes an educational gaming platform. Still in the conceptual stage, the Planetary Chess game leverages game theory principles to enhance the learning experience, drawing inspiration from strategic decision-making concepts. Operating within the metaverse and integrating NFTs, the game offers an immersive environment where players engage in traditional chess gameplay. Throughout the experience, players encounter questions from planetary chess literature, promoting critical thinking and knowledge acquisition. Planetary Chess features both two-player and single-player modes, with the latter incorporating a play-for-pay concept for users to earn and upgrade chess sets. Additionally, NFT integration allows for the acquisition of unique chess sets with dynamic graphics depicting animated exchanges between pieces. By integrating game theory principles, Planetary Chess presents a unique approach to educational gaming, although it remains in the development stage, potentially impacting learning outcomes and the gaming landscape.

A game theory competitive intelligence solution stimulated from a Stackelberg game: A three players scenario

Competitive Intelligence (CI) involves systematically gathering information about the competitive landscape to anticipate rivals’ decisions and secure a strategic advantage. As academic interest in CI grows, the existing literature reflects a diverse body of knowledge. However, few works address the practical aspects of CI comprehensively, presenting viable solutions to its challenges. The motivation behind this paper is not only the scarcity of practical studies on CI but also the importance of CI and its benefits within companies, including improved decision-making and its positive impact on performance and sustainable growth. This study aims to contribute to the literature by proposing a CI solution grounded in game theory. We assert that applying game theory to CI is both intuitive and natural, given that the competitive environment readily lends itself to a game model. Viewing competitors as players, each striving to maximize profit, aligns seamlessly with game theory principles. We chose to consider a scenario with one initiator and multiple followers and model it as a Stackelberg game within the extensive form game framework. The literature review showed that the computational complexity of Stackelberg Equilibrium becomes NP-hard in most game classes with more than two players. This challenge encouraged us to contribute to the literature and focus on a scenario with two followers (three players), modeling the game in the extensive game form. To find a solution to this challenge, we used a modified version of the backward induction algorithm. To assess our solution’s efficacy, we conduct tests on 26 games (26 data instances), comparing results with those obtained using CPLEX and Decision Tree, and MiniMax methods. The ensuing pairwise sign test establishes the competitiveness of our outcomes. This research significantly contributes to the CI literature, offering a game theory-based solution and demonstrating its practical applicability through evaluation.

Game Theory in the Realm of Pop Culture: A Novel Approach to Social Media Strategy

Abstract This paper explores the intersection of game theory and pop culture as a potent strategy for amplifying social media engagement. A methodological analysis demonstrates how game theory principles can be effectively applied to social media campaigns by leveraging the universal appeal of pop culture. The study introduces a simple yet illustrative model that outlines the strategic use of pop culture elements to attract new followers and maximise net profit, considering variables such as potential follower base, cost of pop culture integration, and the preferences of different audience segments towards pop culture-infused products. Findings underscore the untapped potential of conscious game theory application in social media strategy design, offering a fresh perspective on achieving optimal engagement through a balance of appeal to pop culture enthusiasts and those favouring traditional content. This contribution is novel in its approach. It provides a comprehensive review of game theory’s utility in pop culture and presents a detailed framework for its application in social media campaigns. By bridging theoretical concepts with practical application, this research opens new avenues for strategic engagement in the digital age, making a significant addition to both academic research and practical marketing strategies.

Enhancing vaccination strategies for epidemic control through effective lockdown measures

Evolutionary epidemiology models have substantially impacted the study of various infections and prevention methods in the biology field. These models are called Susceptible, Lockdown, Vaccinated, Infected, and Recovered (SLVIR) epidemic dynamics. We explore how human behavior, particularly in the context of disease transmission, is influenced by two intervention strategies: vaccination and lockdown, both of which are grounded in the principles of evolutionary game theory (EGT). This comprehensive study using evolutionary game theory delves into the dynamics of epidemics, explicitly focusing on the transition rate from susceptibility to immunity and susceptibility to lockdown measures. Our research involves a thorough analysis of the structural aspects of the SLVIR epidemic model, which delineates disease-free equilibria to ensure stability in the system. Our investigation supports the notion that implementing lockdown measures effectively reduces the required level of vaccinations to curtail the prevalence of new infections. Furthermore, it highlights that combining both strategies is particularly potent when an epidemic spreads rapidly. In regions where the disease spreads comparatively more, our research demonstrates that lockdown measures are more effective in reducing the spread of the disease than relying solely on vaccines. Through significant numerical simulations, our research illustrates that integrating lockdown measures and efficient vaccination strategies can indirectly lower the risk of infection within the population, provided they are both dependable and affordable. The outcomes reveal a nuanced and beneficial scenario where we examine the interplay between the evolution of vaccination strategies and lockdown measures, assessing their coexistence through indicators of average social payoff.

“The day my son dies, I will die, too.” Shared decision-making through game theory in paediatric palliative care: A clinical case scenario

Shared decision-making processes are complex, especially in paediatric palliative care. Giving a voice to each actor -the child and his parents- are key to find the best outcome. We suggest that applying the principles of game theory, aiming to reach an equilibrium for each of the family members could be an innovative approach to tackle the challenges of shared decision-making processes. Through this clinical case scenario, for which we applied the principles of the game theory, we show how shared decision making might be more balanced to reach the best outcome. Moreover, we hypothesize that using the game theory approach might help clinicians and researchers in the field of paediatrics to optimize clinical outcomes in the best interest of children and parents.

Evolutionary dynamic of division of labor games

The ubiquity of labor division within diverse social collectives is a topic well captured by evolutionary game theory. This work offers an integrative review of the evolutionary dynamics underpinning such division of labor from a tripartite standpoint —commencing with a theoretical exposition on numerous archetypes of labor division games. Subsequently, we delineate a suite of control strategies formulated to not only realize but also sustain the phenomenon of division of labor. This is followed by an elucidation of practical implementations pertaining to the allocation of tasks and labor division, grounded in the principles of game theory. We culminate with the proposition of prospective avenues and insightful trajectories for future investigations, cultivating a frontier for the continued exploration within this field.

Quantitative Research on “Blockchain+Supply Chain” Financing Based on Game Theory Modeling

In the 21st century, with globalization and the “Belt and Road” economic activities. As an important financial means, supply chain financing plays a key role in trade transactions between enterprises. Supply chain financing refers to providing financial support and services to different participants in the entire supply chain through financial instruments to meet the financial needs of their production and trade activities. To address the issue of “blockchain+supply chain” financing, specifically in the traditional supply chain, the downstream of the SMEs financing difficulties has been one of the factors plaguing its development. Still, also one of the constraints on the overall supply chain operational efficiency, we use the principles of game theory to establish a game theory model between financial institutions and SMEs and use Matlab software to solve the simulation data. We use the principle of game theory to establish a game theory model between financial institutions and MSMEs, use Matlab software to solve the simulation data and analyze the financial institutions to adopt the “blockchain+supply chain” financing strategy, and MSMEs to adopt the trustworthy strategy so that the two sides can maximize the benefits, and also maximize the benefits for the whole supply side. In addition, the research process and results can provide references for relevant state departments and various social enterprises and organizations.

Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis

Game theory, as an analytical tool, is frequently utilized to analyze human behavior in social science research. With the high alignment between the behavior of Large Language Models (LLMs) and humans, a promising research direction is to employ LLMs as substitutes for humans in game experiments, enabling social science research. However, despite numerous empirical researches on the combination of LLMs and game theory, the capability boundaries of LLMs in game theory remain unclear. In this research, we endeavor to systematically analyze LLMs in the context of game theory. Specifically, rationality, as the fundamental principle of game theory, serves as the metric for evaluating players' behavior --- building a clear desire, refining belief about uncertainty, and taking optimal actions. Accordingly, we select three classical games (dictator game, Rock-Paper-Scissors, and ring-network game) to analyze to what extent LLMs can achieve rationality in these three aspects. The experimental results indicate that even the current state-of-the-art LLM (GPT-4) exhibits substantial disparities compared to humans in game theory. For instance, LLMs struggle to build desires based on uncommon preferences, fail to refine belief from many simple patterns, and may overlook or modify refined belief when taking actions. Therefore, we consider that introducing LLMs into game experiments in the field of social science should be approached with greater caution.

Adaptive Therapy: Using Evolutionary Game Theory to Combat Cancer Treatment Resistance

Adaptive therapy is a cancer treatment based upon the principles of evolutionary game theory (EGT) utilized to minimize treatment resistance as a cause of tumor evolution. This treatment strategy incorporates a foundation of Darwinian evolutionary principles with mathematical modeling and clinical data. As a result, adaptive therapy ameliorates the negative effects of traditional cancer treatments such as therapy resistance and a debilitated quality of life. By eliminating the use of maximum tolerated dose (MTD), the hallmark of traditional therapies, the evolutionary properties of tumors to develop drug resistance are circumvented and the probability of a prolonged life for patients is significantly increased. This paper explores the effectiveness of an expanded usage of adaptive therapy through an evaluation of current oncological research.

Legal and ownership unbundling in the Turkish natural gas market: A comparative analysis

This study introduces a theoretical framework for the Turkish natural gas market based on the principles of game theory and industrial organization. It investigates the effects of the legal and ownership unbundling on consumer surplus, social welfare, and competition. The model considers a mixed oligopoly with a transmission system operator (TSO), a state-owned incumbent, and a private firm. The state-owned incumbent is assumed to maximize consumer surplus and its own profit, while the private firm is assumed to be profit-maximizing. Additionally, the state-owned incumbent is assumed to be less efficient than the private firm. The game consists of three stages. In the first two stages, the state-owned incumbent and the private firm sequentially choose contract sizes in the upstream market. In the last stage, a contract size-restricted Cournot game is played. The findings of the study suggest that legal unbundling appears to offer greater advantages for consumer surplus and social welfare compared to ownership unbundling, particularly when considering key factors such as third-party access, non-tariff discrimination, and import liberalization. The results indicate that adopting the role of a Stackelberg follower by the state-owned incumbent in the upstream market is advantageous in terms of consumer surplus, social welfare, and competition under both unbundling approaches.

Poker's Paradox: The Interplay of Skill, Chance, and Game Theory

The intricate dance between skill and chance in poker has been a topic of enduring debate, resonating not just among game enthusiasts but also in legal, economic, and cultural corridors. Drawing from historical underpinnings and foundational principles of game theorypioneered by the likes of Emile Borel and notably advanced by John Von Neumannthis paper delves into the intricate interplay of skill and chance in poker. By introducing a comprehensive model that employs game theory principles, we navigate poker's multidimensional landscape, demonstrating that these elements are inextricably intertwined. Our analysis reveals that while the draw of cards introduces an inherent element of chance, a player's prowess in decision-making, strategizing, bluffing, and opponent interpretation is paramount to long-term success. This dual nature of poker where both skill and chance operate in tandem is what makes it a subject of such profound interest and complexity. By presenting poker in this nuanced light, our study seeks to break down traditional dichotomies and offers a more holistic understanding.

An efficient secure and energy resilient trust-based system for detection and mitigation of sybil attack detection (SAN).

In the modern digital market flooded by nearly endless cyber-security hazards, sophisticated IDS (intrusion detection systems) can become invaluable in defending against intricate security threats. Sybil-Free Metric-based routing protocol for low power and lossy network (RPL) Trustworthiness Scheme (SF-MRTS) captures the nature of the biggest threat to the routing protocol for low-power and lossy networks under the RPL module, known as the Sybil attack. Sybil attacks build a significant security challenge for RPL networks where an attacker can distort at least two hop paths and disrupt network processes. Using such a new way of calculating node reliability, we introduce a cutting-edge approach, evaluating parameters beyond routing metrics like energy conservation and actuality. SF-MRTS works precisely towards achieving a trusted network by introducing such trust metrics on secure paths. Therefore, this may be considered more likely to withstand the attacks because of these security improvements. The simulation function of SF-MRTS clearly shows its concordance with the security risk management features, which are also necessary for the network's performance and stability maintenance. These mechanisms are based on the principles of game theory, and they allocate attractions to the nodes that cooperate while imposing penalties on the nodes that do not. This will be the way to avoid damage to the network, and it will lead to collaboration between the nodes. SF-MRTS is a security technology for emerging industrial Internet of Things (IoT) network attacks. It effectively guaranteed reliability and improved the networks' resilience in different scenarios.

The use of game theory in building sustainable competitive advantage

Creating sustainable competitive advantages is central to both the success and longevity of firms competing in today’s business environment. One powerful tool that businesses can learn from and utilize to potentially foster such advantages is game theory. Further, we have thoroughly researched previous studies and determined three areas in which competitive advantages can be derived from the implementation and understanding of game theory principles. These include improved decision-making, supply chain management, and employee management. Our goal is to analyze the works of experts on the subject to further our understanding of the topic. This research will provide a foundation for the subsequent justifications on how game theory can be utilized to create the competitive advantages we specified. Reviewing our findings has led the researcher to believe that firms of any variety looking to improve their decision-making, supply chain management, and/or employee management could find great value, if not create fully realized competitive advantages, from taking game theory and its observations into consideration.

A novel optimization approach based on unstructured evolutionary game theory

Proposing new metaheuristic methods is crucial for continuous algorithmic improvement and the ability to effectively address increasingly complex real-world optimization problems. On the other hand, Evolutionary Game Theory analyzes how trough competition is possible to modify the strategies of individuals within a population in order to spread successful mechanisms and reduce or remove less successful strategies. This paper introduces a novel optimization approach based on the principles of evolutionary game theory. In the proposed method, all individuals are initialized using the Metropolis–Hasting technique, which sets the solutions at a starting point closer to the optimal or near-optimal regions of the problem. An original strategy is then assigned to each individual in the population. By considering the interactions and competition among different agents in the optimization problem, the approach modifies the strategies to improve search efficiency and find better solutions. To evaluate the performance of the proposed technique, it is compared with eight well-known metaheuristic algorithms using 30 benchmark functions. The proposed methodology demonstrated superiority in terms of solution quality, dimensionality, and convergence when compared to other approaches.

Enhancing System Safety and Reliability through Integrated FMEA and Game Theory: A Multi-Factor Approach

This study aims to address the limitations of traditional Failure Mode and Effect Analysis (FMEA) in managing safety and reliability within complex systems characterized by interdependent critical factors. We propose an integrated framework that combines FMEA with the strategic decision-making principles of Game Theory, thereby enhancing the assessment and mitigation of risks in intricate environments. The novel inclusion of the Best Worst Method (BWM) and Pythagorean fuzzy uncertain linguistic variables refines the accuracy of risk evaluation by overcoming the inherent deficiencies of conventional FMEA approaches. Through sensitivity analysis, the framework’s efficacy in identifying and prioritizing failure modes is empirically validated, guiding the development of targeted interventions. The practical application of our methodology is demonstrated in a comprehensive healthcare system analysis, showcasing its versatility and significant potential to improve operational safety and reliability across various sectors. This research is particularly beneficial for systems engineers, risk managers, and decision-makers seeking to fortify complex systems against failures and their effects.

Pursuit Problem of Unmanned Aerial Vehicles

The study examines scenarios involving a single pursuer tracking a single evader, as well as situations where multiple pursuers are involved in chasing multiple evaders. We formulate this problem as a search and pursuit problem for unmanned aerial vehicles (UAVs). Game theory offers a mathematical framework to model and examine strategic interactions involving multiple decision-makers. By employing game theory principles to address the search and pursuit problem, our objective is to optimize the efficiency of strategies for detecting and capturing unmanned aerial vehicles (UAVs).