Game Theory Research Articles

Integrated evaluation of the performance of phosphogypsum recycling technologies in China

The Chinese government is implementing policies, such as the “Guidance on comprehensive utilization of bulk solid waste for the 14th Five-Year Plan period”, to stimulate phosphogypsum (PG) reduction and recycling. Thus, the comprehensive evaluation of PG recycling technologies for sustainable development is crucial. This study proposes a novel multi-criteria decision analysis (MCDA) method that considers the criteria of resources, environment, economy, and society and risk attitudes of decision-makers and integrates game theory (GT) and utility theory for criteria weighting and ranking to assess industrial-scale PG recycling technologies in China. The results demonstrate that GT provides more reasonable criteria weights than individual weighting methods. PG-based lightweight plaster is the top performer in the resource and environmental dimensions owing to its exceptional resource and energy efficiency. PG utilized for dry-mix mortar and organic fertilizer production exhibited the best utility performance of 0.74 and 0.73, respectively. Measures, such as subsidies and product publicity, should be implemented to promote these technologies. However, technologies with poor performance, such as PG used for the co-production of sulfuric acid and fertilizer or cement, may require optimization or substitution for the sustainable recycling of PG. The proposed MCDA method is robust and can serve as a reliable decision-making tool for other waste-recycling technologies. However, caution must be exercised when determining risk attitude using the MCDA method as it may vary with the number of technologies and affect the final rankings.

The mechanisms of labor division from the perspective of task urgency and game theory

Labor division is a prevalent phenomenon in nature, drawing the attention of researchers across various fields owing to its inherent cooperative dilemmas. The study of how groups of self-interested individuals maintain a harmonious division of labor is both fascinating and significant. However, the majority of existing game models, which are rooted in two-player interactions, often fail to accurately represent real-world scenarios where interactions between individuals are not widespread. To bridge this gap, we present the Division of Labor Game within Community (DOLC) model. This paper begins with an overview of the problem's background, progressing to the development of the DOLC mathematical model. Subsequent sections delve into theoretical analyses and simulations of the two-task variant of the DOLC, before exploring its extension to multi-task scenarios and investigating the impact of network structures on cooperative dynamics. Our research reveals critical insights: in the absence of the urgency coefficient, members of the community invariably choose the same task, hindering cooperative efforts. Conversely, the urgency coefficient positively influences the evolution of intra-group labor division, while the selection intensity tends to undermine it. These dynamics persist even in multi-task configurations of the DOLC model, and network circulation often hampers group collaboration. These findings have promising applications in enhancing the efficiency of unmanned systems and optimizing strategies for task allocation and scheduling.

Randomized Strategies for Robust Combinatorial Optimization with Approximate Separation

In this paper, we study the following robust optimization problem. Given a set family representing feasibility and candidate objective functions, we choose a feasible set, and then an adversary chooses one objective function, knowing our choice. The goal is to find a randomized strategy (i.e., a probability distribution over the feasible sets) that maximizes the expected objective value in the worst case. This problem is fundamental in wide areas such as artificial intelligence, machine learning, game theory, and optimization. To solve the problem, we provide a general framework based on the dual linear programming problem. In the framework, we utilize the ellipsoid algorithm with the approximate separation algorithm. We prove that there exists an α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document}-approximation algorithm for our robust optimization problem if there exists an α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document}-approximation algorithm for finding a (deterministic) feasible set that maximizes a nonnegative linear combination of the candidate objective functions. Using our result, we provide approximation algorithms for the max–min fair randomized allocation problem and the maximum cardinality robustness problem with a knapsack constraint.

Min–max optimization of node‐targeted attacks in service networks

AbstractThis article considers resilience of service networks that are composed of service and control nodes to node‐targeted attacks. Two complementary problems of selecting attacked nodes and placing control nodes reflect the interaction between the network operator and the network attacker. This interaction can be analyzed within the framework of game theory. Considering the limited performance of the previously introduced iterative solution algorithms based on non‐compact problem models, new compact integer programming formulations of the node attack optimization problem are proposed, which are based on the notion of pseudo‐components and on a bilevel model. The efficiency of the new formulations is illustrated by the numerical study that uses two reference networks (medium‐size and large‐size), and a wide range of the sizes of attacks and controllers placements.

Optimization of construction safety resource allocation based on evolutionary game and genetic algorithm

In the construction industry, ensuring the safety performance of a project relies heavily on the effective allocation of safety resources. As the importance of mental health in the construction industry increases, evolutionary game theory can be used to analyze the interaction mechanism of various factors affecting safety performance during the construction phase. The objective of this paper is to construct an analytical model that combines evolutionary game theory with genetic algorithms from the perspective of Leader-Member Exchange Ambivalence. The model aims to quantify and compare the various factors that influence achieving the expected safety state and identify the specific necessary constraints. Initially, we analyzed the relationships among construction site employees, divided them into superiors and subordinates, and established a game model and payoff matrix based on the research background. Next, we introduced genetic algorithms into the model via the replicator dynamic equation for optimization. We adjusted the coefficients of safety risk level, psychological expected return, moral identity, and other factors to simulate various construction site scenarios. Simulation and optimization results indicate that genetic algorithms provide more accurate reference values for safety resource allocation compared to preset or manually assigned values.

A Game Theoretic Approach to Collaboration in Policy Coordination

Public policies are courses of action by a government in response to public problems in the real world with the aim of meeting the needs of society. Such policies must be coordinated to avoid inefficiencies. Most attempts to model public policy coordination are qualitative and, therefore, do not yield precise conclusions. More accurate modelling attempts are found in game theory, but they are not entirely appropriate as models of policy coordination, because policy coordination involves a high degree of collaboration. There is only one game-theoretic model of collaboration, and it does not model public policy coordination. The aim of this article is to show that a collaboration-based game theory model is not only feasible, but also more realistic than current game theory models of policy coordination. This was performed by adapting Newton’s seminal model to a society capable of formulating and coordinating policies. When this adapted game was compared to alternative games used to explain policy coordination, it was found that the adapted game made more realistic assumptions, the modelling process was simpler, and it can be applied to a broader range of contexts. By demonstrating that the adapted model offers a feasible theoretical foundation for the modelling of policy coordination, this paper provides a starting point for future modelling efforts in this area.

Research on the Dynamic Characteristics of Photovoltaic Power Production and Sales Based on Game Theory

This paper mainly studies the dynamic characteristics of production and sales of distributed photovoltaic power. This is beneficial for the construction and development of a green power system, and it promotes the sustainable development of the social economy. First, the production and sales laws of the photovoltaic power are analyzed, and the trade process between photovoltaic power producers (PVPs) and photovoltaic power demanders (PVDs) is modeled as a game. Different from the existing relevant literature, two kinds of payoff bimatrices are provided, which correspond to the PVD market and PVP market, respectively. Then, the dynamic characteristics of the model are revealed by using the semitensor product method. The results present a more reliable theoretical basis for the sustainable development of the green electricity market. Finally, through an illustrative example, it can be seen that the strategies of all participants are constantly changing to obtain more profits rather than stable at a traditional Nash equilibrium point. It is worth pointing out that the method and results are applicable to other distributed low-carbon energies, contributing to the development of sustainable energy systems.

Investments in energy efficiency with government environmental sensitiveness: An application of geometric programming and game theory

Investments in energy efficiency with government environmental sensitiveness: An application of geometric programming and game theory

Decentralized justice: state of the art, recurring criticisms and next-generation research topics

Decentralized justice is a novel approach to online dispute resolution based on blockchain, crowdsourcing and game theory for adjudicating claims in a neutral and efficient way. Since the launch of the first decentralized justice platform in 2018, the field has attracted wide interest both from practitioners and academics in Web3 and dispute resolution. The decentralized justice approach is based on the ideas of decentralization, economic incentives and a claim to fairness in its decisions. At the current stage of development, decentralized justice is facing a number of technical, market, legal and ethical challenges for further development. This paper provides a review of the short history of decentralized justice, addresses a number of recurrent topics and lays down a path for future exploration.

The metabolomic physics of complex diseases

Human diseases involve metabolic alterations. Metabolomic profiles have served as a vital biomarker for the early identification of high-risk individuals and disease prevention. However, current approaches can only characterize individual key metabolites, without taking into account the reality that complex diseases are multifactorial, dynamic, heterogeneous, and interdependent. Here, we leverage a statistical physics model to combine all metabolites into bidirectional, signed, and weighted interaction networks and trace how the flow of information from one metabolite to the next causes changes in health state. Viewing a disease outcome as the consequence of complex interactions among its interconnected components (metabolites), we integrate concepts from ecosystem theory and evolutionary game theory to model how the health state-dependent alteration of a metabolite is shaped by its intrinsic properties and through extrinsic influences from its conspecifics. We code intrinsic contributions as nodes and extrinsic contributions as edges into quantitative networks and implement GLMY homology theory to analyze and interpret the topological change of health state from symbiosis to dysbiosis and vice versa. The application of this model to real data allows us to identify several hub metabolites and their interaction webs, which play a part in the formation of inflammatory bowel diseases. The findings by our model could provide important information on drug design to treat these diseases and beyond.

Auditing a supply chain with environmental regulations: a game theory approach

ABSTRACT The seriousness of climate change’s impact on people’s lives has compelled policymakers to create rules for businesses, aiming to limit their impact on the environment. Some companies decide to take the risk of breaking these rules instead of spending resources to follow them. In this study, we investigate the decision-making challenge that arises when a company interacts with a recycler. We expand the original model by including a second recycler. The company can decide to audit the recyclers or skip the audit and save resources. The recyclers can choose whether to follow environmental rules or not. The company wants to minimise costs, while the recyclers aim to maximise profits from recycling. We use game theory to represent how the company and the recyclers interact. The study’s findings reveal that adding the second recycler decreases the willingness of the first recycler to take risks and break environmental regulations.

The US/India Civil Nuclear Agreement of 2008: A Two-Level Game?

Nuclear power relationships between states have historically determined global power structures in a wider context. Following India’s maiden nuclear test in 1974, the international community sidelined the country for a long time. However, within the next three decades, international opinions about the India significantly improved and paved the way forthe country’s first civilian nuclear deal. That said, the circumstances behind this deal and the United States of America (US)’ objectives involved therein merit a review, considering changes in the US’ domestic situation and changing political realities around the world. Did the US make an objective foreign policy decision by pivoting its nuclear policy towards India? Or was the US playing a strategic two-level game with both domestic interests as well as its international agenda at stake? This paper seeks to analyze the different layers of interests that were involved in the US’ decision-making process resulting in the Agreement. It also aims to apply Robert Putnam’s Two-Level Game Theory to understand these interests in a better way.

Interpretable artificial intelligence for advanced oxidation systems: Principle, operations and performance

Advanced oxidation processes have been widely studied and employed due to their potent mineralization capacity for pollutants. However, the intricate reaction mechanisms of these processes pose limitations for fitting and predicting performance. In this study, we comprehensively derived and assessed neural networks for three advanced oxidation methods: catalytic oxidation, catalytic wet air oxidation, and electrochemical oxidation. Our analysis encompassed multilayer perceptron principles, forward and back propagation process, strategies for handling overfitting, and performance evaluation matrices. Additionally, we utilized Bayesian optimization to probe the impact of network architecture on outcomes. Two conventional methods, multiple linear regression and response surface methodology, are employed for comparison. Our results demonstrate that neural networks exhibit more robust performance in fitting and predicting advanced oxidation processes as indicated by statistical indicators. Importantly, we tackle the "black box" issue of neural networks by incorporating the shapley additive explanations interpretable model of game theory to elucidate the impact of advanced oxidation features on outcomes. The superior performance of explainable artificial intelligence techniques implies their vast potential for broad applications in environmental science and technology.

An Evolutionary Game-Theoretic Approach to Unmanned Aerial Vehicle Network Target Assignment in Three-Dimensional Scenarios

Target assignment has been a hot topic of research in the academic and industrial communities for swarms of multiple unmanned aerial vehicle (multi-UAVs). Traditional methods mainly focus on cooperative target assignment in planes, and they ignore three-dimensional scenarios for the multi-UAV network target assignment problem. This paper proposes a method for target assignment in three-dimensional scenarios based on evolutionary game theory to achieve cooperative targeting for multi-UAVs, significantly improving operational efficiency and achieving maximum utility. Firstly, we construct an evolutionary game model including game participants, a tactical strategy space, a payoff matrix, and a strategy selection probability space. Then, a multi-level information fusion algorithm is designed to evaluate the overall attack effectiveness of multi-UAVs against multiple targets. The replicator equation is leveraged to obtain the evolutionarily stable strategy (ESS) and dynamically update the optimal strategy. Finally, a typical scenario analysis and an effectiveness experiment are carried out on the RflySim platform to analyze the calculation process and verify the effectiveness of the proposed method. The results show that the proposed method can effectively provide a target assignment solution for multi-UAVs.

Determination of the potential soil losses and prioritization of sub-watersheds: Insight from North African highland system

Soil erosion has negative effects on people and property, hinders the sustainable development of natural resources, and poses a threat to the economic growth of territories worldwide, and is particularly acute for African countries. Consequently, identifying and prioritizing areas susceptible to this phenomenon is crucial for the implementation of efficient preventative measures. In this study, the empirical Erosion Potential Method (EPM) was used to quantify soil losses, and two Game Theory (GT) algorithms (Borda and Condorcet) were employed to identify locations prone to soil erosion and prioritize sub-watershed (SW) based on hydromorphometric characteristics in a hydrological system from High Atlas in Morocco. For this purpose, 26 factors were calculated, subsequently, a linear correlation analysis was conducted to assess the relationship between potential soil erosion and various influencing factors. The EPM model findings indicated average loss rates of roughly 3356 m3/km2/year, which are corroborated by 217 inventory points gathered in the most productive sediment locations, with an AUC value of 80 %. The results of prioritization using GT algorithms show that the SW6 and SW8 are classified as areas with high vulnerability to soil erosion, and the sub-watersheds located upstream of the Lakhdar river watershed are the units that deserve prioritization in terms of planning intervention. The validation of results indicates that the Borda algorithm performed better (AUC= 92 %) than the Condorcet algorithm (AUC= 72 %). This innovative approach enhances our understanding of erosive processes within specific spatial units, enabling informed decision-making and making a substantial contribution to the sustainable and efficient management of water resources. This represents a significant advancement compared to previous studies in the region.

SURVS: A Swin-Unet and game theory-based unsupervised segmentation method for retinal vessel

Medical images, especially intricate vascular structures, are costly and time-consuming to annotate manually. It is beneficial to investigate an unsupervised method for vessel segmentation, one that circumvents the manual annotation yet remains valuable for disease detection. In this study, we design an unsupervised retinal vessel segmentation model based on the Swin-Unet framework and game theory. First, we construct two extreme pseudo-mapping functions by changing the contrast of the images and obtain their corresponding pseudo-masks based the on binary segmentation method and mathematical morphology, then we prove that there exists a mapping function between pseudo-mappings such that its corresponding mask is closest to the ground true mask. To acquire the best-predicted mask, based on which, we second develop a model based on the Swin-Unet frame to solve the optimal mapping function, and introduce an Image Colorization proxy task to assist the learning of pixel-level feature representations. Third, since to the instability of two pseudo-masks, the predicted mask will inevitably have errors, inspired by the two-player, non-zero-sum, non-cooperative Neighbor's Collision game in game theory, a game filter is proposed in this paper to reduce the errors in the final predicted mask. Finally, we verify the effectiveness of the presented unsupervised retinal vessel segmentation model on DRIVE, STARE and CHASE_DB1 datasets, and extensive experiments show that has obvious advantages over image segmentation and conventional unsupervised models.

Evolutionary Game Analysis of Open Financing Models for Small and Medium-Sized Internet Education Enterprises

The open financing models combine traditional financing and network financing, which plays an important role in alleviating the financing constraints of small and medium-sized education enterprises and is conducive to promoting the growth of small and medium-sized education enterprises. Based on the systematic review of evolutionary game theory, the characteristics of evolutionary game theory and its differences with game theory, this paper first constructs the game process analysis of the open financing mode of small and medium-sized Internet education enterprises, and proposes the stabilization strategies of the participants through the model analysis. On this basis, the financing model is improved by introducing the constraints of individuals, loan companies, government and intermediaries, and the open financing game process is theoretically analyzed and mathematically derived. The conclusions of the study provide an important reference for promoting the open financing model of small and medium-sized Internet education enterprises.

Understanding and extending the geographical detector model under a linear regression framework

The Geographical Detector Model (GDM) is a popular statistical toolkit for geographical attribution analysis. Despite the striking resemblance of the q-statistic in GDM to the R-squared in linear regression models, their explicit connection has not yet been established. This study proves that the q-statistic reduces into the R-squared under a linear regression framework. Under linear regression and moderate-to-strong spatial autocorrelation, Monte Carlo simulation results show that the GDM tends to underestimate the importance of variables. In addition, an almost perfect power law relationship is present between the percentage bias and the degree of the spatial autocorrelations, indicating the presence of fast uplifting bias in response to increasing levels of spatial autocorrelations. We propose an integrated approach for variable importance quantification by bringing together the spatial econometrics model and the game theory based-Shapley value method. By applying our proposed methodology to a case study of land desertification in African, it is found human activity tends to affect land desertification both directly and indirectly. However, such effects appear to be underestimated or undistinguished in the classic GDM.

Trading strategy for virtual power plant clusters based on a multi-subject game model

With the rapid development of renewable energy and the continuous growth of new loads, VPP has become an important form of smart grid and energy internet due to its flexible and effective management of distributed energy. During the operation of Virtual power plant, there is a game relationship between the system operator and VPP, and they are in a non-complete information environment. However, most of the current game optimization modeling is under the condition of complete information, and the game model based on complete information cannot solve this problem. This article focuses on the VPP cluster trading problem based on non-complete information game theory, constructs a Bayesian game model for multiple VPPs with multiple subjects under the master-slave game framework by introducing the Bayesian concept to optimize the cluster transactions within VPPs, and verifies the effectiveness of the model through simulation experiments. The experimental results show that the multi-VPP multi-subject Bayesian game model established in the study can guarantee the privacy of each subject and effectively reduce PAR, thus ensuring the security and stability of the VPP network and reducing cost expenditures, which has practicality in actual VPP cluster transactions.

Voluntary vaccination on hypergraph

In reality, when epidemics spread, higher-order interactions in group settings such as homes and schools, for example, can make infections more likely to occur. People often voluntarily get vaccinated to protect themselves and other members of their group from infection. On the other hand, there are individuals who want to “free-ride” and try to avoid infection through herd immunization. Thus, it is clear that vaccination is influenced by both individuals and groups. However, in existing studies, vaccination behavior is often determined only by individual’s self-payoff, with little consideration of group influences. Here, we propose a model of vaccination on a hypergraph. In our model, self-payoff and group-payoff together influence the vaccination. Evolutionary game theory is used to explore the differences in individual vaccination behavior when group-payoff are prioritized versus self-payoff are prioritized. The mean-field method is utilized for theoretical analysis. The experimental results show that at low adoption strength, vaccine cost determines vaccination. When adoption strength is high, priority is given to group-payoff to promote vaccination. If individuals place a extremely high value on self-payoff and have a high strength of adoption, they only vaccinate when the cost of vaccination is as low as zero. Yet, after the rates of infection is high enough, individuals choose to get vaccinated regardless of whether they care more about self-payoff or group-payoff.