Game Theory Approach Research Articles

Strategic Management of Workforce Diversity: An Evolutionary Game Theory Approach as a Foundation for AI-Driven Systems

In the complex organisational landscape, managing workforce diversity effectively has become crucial due to rapid technological advancements and shifting societal values. This study explores strategic workforce management through the novel methodological framework consisting of the evolutionary game theory concept integrated with replicator dynamics and traditional game theory, addressing a notable gap in the literature and suggesting an evolutionarily stable workforce structure. Key findings indicate that targeted rewards for the most Enthusiastic employee type can reduce overall costs and enhance workforce efficiency, although managing a diverse team remains complex. The study reveals that while short-term incentives boost immediate productivity, long-term rewards facilitate favourable behavioural changes, which are crucial for sustaining organisational performance. Additionally, the role of artificial intelligence (AI) is highlighted, emphasising its potential to integrate with these theoretical models, thereby enhancing decision-making processes. The study underscores the importance of strategic leadership in navigating these dynamics, suggesting that leaders must tailor their approaches to balance short-term incentives and long-term rewards to maintain an optimal workforce structure.

The impact of vaccination and social distancing on COVID-19: A compartmental model and an evolutionary game theory approach

The spread of COVID-19 disease is a worldwide issue that has impacted many countries. Mathematical modeling can help us better understand this disease and predict how different interventions and behavioral changes will affect disease progression. This study presents a compartmental model that corresponds to the COVID-19 trend in some countries. The compartments are: susceptible–exposed–unvaccinated infectious-partially vaccinated infectious–hospitalized–recovered–death–vaccinated (with at least three doses of vaccine). In this model, we have compartments such as individuals who have recovered but may re-infect if they come into contact with infection after completing the post-recovery immunity course, and individuals who have received one or two doses of vaccine are assumed to be partially immune and may infect. Individuals who have received at least three doses of vaccine are considered fully immune and will no longer be infected. The results show this model fits the data from the World Health Organization (WHO) for Finland, Denmark, the Netherlands, the United Kingdom, Italy, and Canada. Furthermore, the basic reproduction number of the proposed model is calculated, and we can compare it to the one released by the WHO to analyze the relative risk of infection based on the amount of investment in social distancing. People in Denmark, according to the study’s findings, invested less in social distancing than people in the other five countries. Finally, we use evolutionary game theory to examine the impact of changing strategy from not-vaccinated to partially vaccinated (with one or two doses of vaccine) in a population that is not fully immune.

Optimizing healthcare workforce for effective patient care: a cooperative game theory approach

Optimizing healthcare workforce for effective patient care: a cooperative game theory approach

Optimizing Wind Farm Efficiency through Active Yaw Control: A Neural Network-Aided Game Theory Approach

This research investigates the potential of a game-theoretic-based Active Yaw Control (AYC) strategy to enhance power generation in wind farms. The proposed AYC strategy in this study replaces traditional look-up tables with a trained Artificial Neural Network (ANN) that determines the optimal yaw misalignment for turbines under time-varying atmospheric conditions. The study examines a hypothetical 3x2 rectangular arrangement of NREL 5-MW wind turbines. The FAST.Farm simulation tool, utilizing the dynamic wake meandering (DWM) model, is employed to assess both the power performance and structural load on the wind turbines. When tested with two different inflow directions and ambient turbulence (10%), the AYC strategy demonstrated a maximum increase in total power output of 2.6%, although it affected individual turbines differently. It also exhibits an increase in some structural loads, such as tower-top torque, while some components experience a slight reduction in load. The results underscore the effectiveness of the ANN-guided game-theoretic algorithm in improving wind farm power generation by mitigating the negative impact of wake interference, offering a scalable and efficient method for optimizing large-scale wind farm. However, it is essential to evaluate the overall impact of AYC on wind farm efficiency in terms of both Annual Energy Production (AEP) and structural loading under various atmospheric conditions.

Integrated nonurban-urban flood management using multi-objective optimization of LIDs and detention dams based on game theory approach

Flood events in urban and non-urban areas have long-term devastating impacts on communities resulting in casualties and economic damages. Different interconnected concepts of flood management lead decision-makers to broaden their focus from traditional points of view to transcend the disciplinary boundaries of hydrological modeling. So, evaluating interconnected aspects of flood control requires configuring an Integrated Flood Management (IFM) system. In this research, a novel comprehensive framework is proposed to control floods in urban and non-urban areas using Low-Impact Development (LID) practices and detention dams, simultaneously. In the first step, urban and non-urban areas of the Darakeh catchment in Tehran, Iran, as the study area, are modeled using Storm Water Management Model (SWMM). Next, by coupling the SWMM with Pareto Envelope-based Selection Algorithm II (PESA-II), optimal management scenarios are generated considering four hydrological-economic objectives. Each management scenario specifies the optimal sites and dimensions of detention dams and the optimal location and area of LIDs in non-urban and urban areas, respectively. Indeed, the innovative IFM framework provides a multi-dimensional trade-off between upstream and downstream flood protection. Then, interactions among the main stakeholders with conflicting interests are incorporated into utility functions. Ultimately, using COmplex PRoportional ASsessment (COPRAS) and Nash bargaining methods the most capable management scenarios are selected based on stakeholders' preferences. Results showed that the IFM framework can be significantly effective in flood severity attenuation. Based on the findings, proposed method warrants peak flow reduction up to 60.04 and 53.50 percent at the inlet and outlet of the urban area. Besides, the Nash bargaining approach tends to select a scenario with a higher peak flow reduction compared to the COPRAS method. Also, results signify that each stakeholder's relative importance and preferences can remarkably impact the decision-making process. In addition to resolving conflicts arising from implementing flood control measures, this framework can incorporate divergent aspects of flood management into a reliable system.

Uncovering milestone papers: A network diffusion and game theory approach

Methods to rank documents in large-scale citation data are increasingly assessed in terms of their ability to identify small sets of expert-selected papers. Here, we propose an algorithm for the accurate identification of milestone papers from citation networks. The algorithm combines an influence propagation process with game theory concepts. It outperforms state-of-the-art metrics in the identification of milestone papers in aggregate citation network data, while potentially mitigating the ranking's temporal bias compared with metrics that have similar milestone identification performance. The proposed method sheds light on the interplay between ranking accuracy and temporal bias.

A strategic framework for optimal decisions in football 1-vs-1 shot-taking situations: an integrated approach of machine learning, theory-based modeling, and game theory

Complex interactions between two opposing agents frequently occur in domains of machine learning, game theory, and other application domains. Quantitatively analyzing the strategies involved can provide an objective basis for decision-making. One such critical scenario is shot-taking in football, where decisions, such as whether the attacker should shoot or pass the ball and whether the defender should attempt to block the shot, play a crucial role in the outcome of the game. However, there are currently no effective data-driven and/or theory-based approaches to analyzing such situations. To address this issue, we proposed a novel Shooting Payoff Computation (SPC) framework to analyze such scenarios based on game theory, where we estimate the expected payoff with machine learning (ML) models, and additional features for ML models were extracted with a theory-based shot block model. Conventionally, successes or failures (1 or 0) are used as payoffs, while a success shot (goal) is extremely rare in football. Therefore, we proposed the Expected Probability of Shot On Target (xSOT) metric to evaluate players’ actions even if the shot results in no goal; this allows for effective differentiation and comparison between different shots and even enables counterfactual shot situation analysis. In our experiments, we have validated the SPC framework by comparing it with baseline and ablated models. Furthermore, we have observed a high correlation between the xSOT and existing metrics. This alignment of information suggests that xSOT provides valuable insights. Lastly, as an illustration, we studied optimal strategies in the World Cup 2022 and analyzed a shot situation in EURO 2020.

GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models

This article introduces GeoShapley, a game theory approach to measuring spatial effects in machine learning models. GeoShapley extends the Nobel Prize–winning Shapley value framework in game theory by conceptualizing location as a player in a model prediction game, which enables the quantification of the importance of location and the synergies between location and other features in a model. GeoShapley is a model-agnostic approach and can be applied to statistical or black-box machine learning models in various structures. The interpretation of GeoShapley is directly linked with spatially varying coefficient models for explaining spatial effects and additive models for explaining non-spatial effects. Using simulated data, GeoShapley values are validated against known data-generating processes and are used for cross-comparison of seven statistical and machine learning models. An empirical example of house price modeling is used to illustrate GeoShapley’s utility and interpretation with real-world data. The method is available as an open source Python package named geoshapley.

Expression of Concern: "Green tax as a path to greener economy: A game theory approach on energy and final goods in Iran" [Renew Sustain Energy Rev, 156 (2022) 111968]

Expression of Concern: "Green tax as a path to greener economy: A game theory approach on energy and final goods in Iran" [Renew Sustain Energy Rev, 156 (2022) 111968]

Metaheuristic optimization with dynamic strategy adaptation: An evolutionary game theory approach

Most metaheuristic methods employ a strategy designed to be generical and fixed. As a result, these methods often lack the capability to adaptively modify their performance in response to different scenarios or challenges encountered during the search process. This paper presents a new metaheuristic algorithm designed to dynamically adjust its search strategy throughout the optimization process for increased efficiency. This algorithm is based on Evolutionary Strategies (ES) due to their notable self-adaptive features. To further enhance its efficiency, we have incorporated elements from Evolutionary Game Theory (EGT). This integration ensures a more comprehensive strategy adaptation process, taking into account not only the information from the specific agent but also insights from other population members. Additionally, our approach alters the conventional EGT mechanism by including not just pairwise evaluations but also data from the top-performing individuals in the population, based on their outcomes. This broader adaptation strategy allows for a faster convergence to the most effective dominant strategy. To demonstrate the effectiveness of our method, we compared it against several established metaheuristic algorithms using 28 diverse test functions. Our findings reveal that this approach produces competitive results, delivering higher-quality solutions and faster convergence rates.

Modelling the triple helix system innovation of the main economies from Latin America: a coalitional game theory approach

Modelling the triple helix system innovation of the main economies from Latin America: a coalitional game theory approach

Energy exchange optimization for a building-to-building grid: A non-cooperative game-theory method

The growing demand for green energy solutions has led to an increase in innovative distributed energy generation (DEG) architectures, such as buildings with photovoltaic (PV) generation and energy storage systems (ESS). In these distributed grids, buildings with renewable energy generation capabilities can exchange surplus energy to optimize overall system efficiency, increase their profit, and reduce reliance on the main grid. This paper proposes a game-theory method to optimize energy exchange within a building-to-building (B2B) grid. The proposed method formulates the energy exchange problem as a non-cooperative game, where each building owner aims to maximize their welfare by strategically determining their energy trading decisions, including pricing. The game-theory approach considers energy supply and demand, ESS capacity, and cost considerations, including ESS degradation cost. The proposed approach is demonstrated through simulations, showing significant improvements in cost savings compared to building-to-grid (B2Grid) alternatives. Three cases are compared: (1) B2B grid without considering ESS, (2) B2B grid with ESS without considering the ESS degradation cost (ideal case), and (3) B2B grid with ESS considering the ESS degradation cost. The results show the potential of the proposed game-theory in optimizing energy exchange within the B2B grid with daily and operational hours saving of 2.12−6.37 % and 4.57−165.93 %, respectively.

Application of Game Theory in Solving Urban Water Challenges in Ibadan-North Local Government Area, Oyo State, Nigeria

The significance of water in human life cannot be ignored, but in most of the developing countries; availability, provision and access to good water source to the citizens has post major challenges. The nature of water scarcity has been majorly attributed to seasonal factor and high population pressure. This study therefore aimed at developing a practical approach and mechanism to solving water challenges in urban areas in Nigeria by means of game theory. The findings revealed that majority of respondents lack access to portable or drinkable water from water agency and that regular water supply sources have been traced to be borehole or well which was provided by citizens in the community at a cost. Applying the game theory approach, the outcome of the game indicates that the equilibrium value for the two players (the water agency and the residents) is N2,000 for 1,000 liters of water supply monthly. However, the payoff can be maiximised by every player (monetary values for the water agency and satisfactory water consumption for the residents). It is therefore importance that water agency should improve in the quality of their work in providing adequate water supply to the community to reduce or eradicate ailment arises from non availability of quality water.

“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.

A Two-stage Service-oriented Task Offloading Framework with Edge-cloud Collaboration: A Game Theory Approach

A Two-stage Service-oriented Task Offloading Framework with Edge-cloud Collaboration: A Game Theory Approach

Bridging Information Gaps: Public Sector Intervention in Tourism Promotion during Crises – A Game Theory Approach

Abstract The present study applies Game Theory to study public investment at tourist destinations to determine the ‘best action’ for tourist destinations in competitive scenarios. This study proves that the higher the probability that the public sector of the destination will invest in tourism promotion, the greater the expected financial benefits for the destination. Furthermore, the research results show that the higher the probability of tourists traveling to the destination, the greater the expected financial return for the Destination’s Public Sector (DPS) investing. Notably, the findings prove the positive impact of reducing information asymmetry between tourists and destinations. Results from applying Game Theory to the tourist industry show that spending on advertising may be motivated by more than just increasing visitor numbers. Finally, this article’s main contribution is offering a theoretical-mathematical framework applicable to any tourist destination.

A cooperative game-theory approach for incentive systems in local energy communities

Prosumers have a central role in the context of smart grids, and in particular within local energy communities (LECs), as they are capable of being both energy producers and consumers. In a scenario where peer-to-peer (P2P) energy trading is allowed, prosumers can exchange the energy they produce with other prosumers: the primary outcome of this is the improvement of energy self-consumption across the grid, which leads to decreased transmission losses, as well as lower energy costs and diminished long-term damage to the grid itself. Previous work proposed a mechanism to achieve multiple objectives for a cooperative game theory perspective for small coalitions, but its behavior for coalitions of arbitrary size remains unexplored, and it does not consider the objective of peak shaving. This paper aims to (i) design an algorithm for calculating schedules for coalitions of arbitrary size, (ii) analyze the behavior of this mechanism for large coalitions, (iii) create a new incentive mechanism by proposing new selling functions that ensure that the resulting mechanism would optimize for the objective of peak shaving when all the prosumers work together in one large coalition, and (iv) demonstrate the performance of the existing mechanism in terms of peak shaving, by comparing against the mechanism specifically optimized for this objective. Simulations conducted on data from a grid in Cardiff, UK, reveal that the existing mechanism works particularly well for the non-cooperative game, achieving results for cost reduction and self-consumption almost identical to the cooperative game, no matter the size of the coalitions. More precisely, although all mechanisms achieve optimal peak shaving for the grand coalition, the existing mechanism achieves this objective even within the framework of the selfish game, resulting in a reduction of the peak by approximately 29% compared to alternative methods. Furthermore, the mechanism is proven to optimally achieve peak shaving in both cooperative and non-cooperative cases.

Newborn vaccination game model with potentially periodic behavior

We explore the complex dynamics of epidemics, considering the significant effects of disease transmission, newborn individuals, and mortality rate. Employing an evolutionary game theory approach, we model the vaccination aspect for newborns, incorporating potential periodic behavior. The goal is to create an engaging simulation that underscores the criticality of timely infant vaccinations. By introducing periodic elements, the game adds complexity, fostering player participation and awareness of vaccination schedules. In contrast to traditional intervention game models focused on adult vaccine behavior, our approach recognizes the unique challenges posed by newborn vaccination. We address the limitations of existing models by developing a generalized compartmental model, accommodating infection duration distribution, and accurately capturing both periodic and non-periodic disease trajectories. The results show that higher efficacy in a newborn vaccine program can potentially eradicate the disease, but higher effectiveness and cost may deter vaccination. Increased motivation cost boosts vaccination rates without impacting epidemic size, whereas lower costs stabilize vaccination rates. Lower vaccine costs call for increased government investment to counter hesitancy. However, reduced efficacy and lower motivation lead to higher Vaccine Fear Scores, discouraging participation. Our work also offers a unique perspective on the dilemma of newborn vaccination, providing solutions with closed-form expressions that may exhibit periodic or non-periodic behavior. This approach facilitates disease modelers in exploring the potential periodicity of newborn vaccination programs, aiding efforts to prevent recurrent outbreaks.

Dynamic Awareness and Strategic Adaptation in Cybersecurity: A Game-Theory Approach

Awareness and human factors are becoming ever more important in cybersecurity, particularly in the context of small companies that may need more resources to deal with cybersecurity effectively. This paper introduces a theoretical framework for game analysis of the role of awareness in strategic interactions between the manager and a hacker. A computable approach is proposed based on Bayesian updating to model awareness in a cybersecurity context. The process of gaining awareness considers the manager’s perception of the properties of the hacker’s actions, game history, and common knowledge. The role of awareness in strategy choices and outcomes is analyzed and simulated, providing insights into decision-making processes for managers and highlighting the need to consider probabilistic assessments of threats and the effectiveness of countermeasures. The accuracy of the initial frequencies plays a significant role in the manager’s success, with aligned frequencies leading to optimal results. Inaccurate information on prior frequencies still outperforms complete uncertainty, emphasizing the value of any available intelligence. However, the results suggest that other awareness modeling approaches are necessary to enhance the manager’s agility and adaptiveness when the prior frequencies do not reflect the immediate attacker’s type, indicating the need for improved intelligence about cyber-attacks and examinations of different awareness modeling approaches.

An Islamic Game Theory approach for overcoming base erosion profit shifting challenges for the Organization of Islamic Cooperation (OIC)

An Islamic Game Theory approach for overcoming base erosion profit shifting challenges for the Organization of Islamic Cooperation (OIC)