Game Theory Model Research Articles

Platform service decision and selling mode selection under different power structures

PurposeThis paper forms an e-commerce supply chain that include a manufacturer providing products and an online platform providing service. The reselling platform mode and the agent platform mode are considered through an exploration of the manufacturer Stackelberg (MS), vertical Nash (VN), platform Stackelberg (PS) power structures. The purpose of this paper is to explore the pricing and platform service decisions under different platform selling modes and channel power structures.Design/methodology/approachBased on the game theory models, this paper investigates the interaction between the manufacturer and the online platform under four different scenarios. The optimal solutions of four models are provided. Through comparison analyses, this paper evaluates the impacts of platform selling mode and channel power structure on the pricing and platform service decisions and the members’ profits.FindingsThe manufacturer prefers the MS power structure in any platform mode. The online platform prefers the PS (MS) power structure under a low (high) service cost efficiency in the reselling platform mode, while prefers the PS and VN power structures in the agent platform mode. Moreover, the manufacturer prefers the agent (reselling) platform mode under a low (high) service cost efficiency in any power structure. The online platform prefers the reselling platform mode in the MS and PS power structures, while prefers the reselling (agent) platform mode under a low (high) service cost efficiency in the VN power structures.Originality/valueThe analysis result provides important managerial implications that help the supply chain members develop a better understanding of the selection of the platform selling mode and the effect of the channel power structure in the presence of platform service.

Looking at the bid picture: A framework for identifying reverse auctions in ecological systems.

Biological market theory can be used to explain intraspecific cooperation, interspecific mutualism, and sexual selection through models of game theory. These models describe the interactions between organisms as two classes of traders (buyers/sellers) exchanging commodities in the form of goods (e.g. food, shelter, matings) and services (e.g. warning calls, protection). Here, we expand biological market theory to include auction theory where bidding serves to match buyers and sellers. In a reverse auction, the seller increases the value of the item or decreases the cost until a buyer steps forward. We provide several examples of ecological systems that may have reverse auctions as underlying mechanisms to form mutualistic relationships. We focus on the yellow baboon (Papio cynocephalus) mating system as a case study to propose how the mechanisms of a reverse auction, which have the unintended but emergent consequence of producing a mutually beneficial outcome that improves collective reproductive benefits of the troop in this multi-female multi-male polygynandrous social system. For the yellow baboon, we posit that the "seller" is the reproductively cycling female, and the "buyer" is a male looking to mate with a cycling female. To the male, the "item for the sale" is the opportunity to sire an offspring, the price is providing safety and foraging time (via consortship) to the female. The "increasing value of the item for sale" is the chance of conception, which increases with each cycle since a female has resumed cycling post-partum. The female's sexual swelling is an honest indicator of that cycle's probability of conception, and since resident males can track a female's cycle since resumption, there is transparency. The males presumably know the chance of conception when choosing to bid by offering consortship. Across nature, this reverse auction game likely exists in other inter- and intraspecific social relationships. Considering an ecological system as a reverse auction broadens our view of social evolution and adaptations through the lens of human economic structures.

The straight and narrow: A game theory model of broad- and narrow-spectrum empiric antibiotic therapy

Physicians prescribe empiric antibiotic treatment when definitive knowledge of the pathogen causing an infection is lacking. The options of empiric treatment can be largely divided into broad- and narrow-spectrum antibiotics. Prescribing a broad-spectrum antibiotic increases the chances of covering the causative pathogen, and hence benefits the current patient’s recovery. However, prescription of broad-spectrum antibiotics also accelerates the expansion of antibiotic resistance, potentially harming future patients. We analyse the social dilemma using game theory. In our game model, physicians choose between prescribing broad and narrow-spectrum antibiotics to their patients. Their decisions rely on the probability of an infection by a resistant pathogen before definitive laboratory results are available. We prove that whenever the equilibrium strategies differ from the socially optimal policy, the deviation is always towards a more excessive use of the broad-spectrum antibiotic. We further show that if prescribing broad-spectrum antibiotics only to patients with a high probability of resistant infection is the socially optimal policy, then decentralization of the decision making may make this policy individually irrational, and thus sabotage its implementation. We discuss the importance of improving the probabilistic information available to the physician and promoting centralized decision making.

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.

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.

The collective wisdom of behavioral game theory

AbstractWe apply an algorithm from the wisdom-of-crowds literature to optimally combine behavioral game theory models to more accurately predict strategic choice in one-shot, simultaneous-move games. We find that the optimal weighted average of seven behavioral game theory models predicts out-of-sample choice behavior significantly better than any of the individual models. The crowd of behavioral game theory models is wiser than any single one of them. Different strategic choice models complement each other by capturing distinct patterns of behavior. The field of behavioral game theory is enriched by having this diversity of models.

Red light game identifies ineffective criminal deterrence

AbstractThe proceeding investigates the ineffectiveness of law enforcement placement in specific circumstances from the penological framework of rational choice theory. This work employs game theory modeling to determine an individual's propensity to commit a crime. The succeeding illustrates how it is counterproductive to nudge potential criminals where socially preferable behavior corresponds to a pure strategy Nash equilibrium in a simultaneous two‐player game. The result of this analysis demonstrates an addendum to contemporary rational choice theory in criminology. The solutions of these stage games show that increasing the placement of criminal deterrents, like surveillance or fines intended to reduce the number of opportunities to commit deviant behavior, is dysfunctional under particular circumstances. This suggests that law enforcement resources should not be directed to enforcing certain crimes. The implications of this work could help optimize department assets and personnel hours.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Evaluation of Carrying Capacity of Water Resource in the Heihe River Basin Based on Game Theory and Cloud Model

To optimize the allocation of water resources and ensure sustainable water resources management, we evaluated the carrying capacity of water resources in the Heihe River Basin. 16 evaluation indicators were selected from the social, economic, ecological, and water resource systems to construct an evaluation system of the carrying capacity. The combination weight and membership degree were determined using the game theory combined with the weighting method and normal cloud model. The water resource utilization efficiency during 21 years was estimated by analyzing influencing factors and development trends of carrying capacity of water resources. The water use rate was important in the carrying capacity, and indicators such as urbanization rate, per capita Gross Domestic Product (GDP), sewage discharge per 10000 yuan, and water consumption per 10000 yuan of industrial output value were important factors affecting the carrying capacity. The carrying capacity of the Heihe River Basin increased from a severe overload status (Level I) in 2000 to a good status (Level V) in 2021. In the other years, the status remained at Level IV. The carrying capacity was improved. The result of TOPSIS confirmed the consistency of the development trend of the carrying capacity and the appropriateness of the developed evaluation model in this study. The model based on the game theory and cloud model can be used to effectively guide the sustainable development of water resources and improve the carrying capacity of water resources of the Heihe River Basin. The results of this study serve as a reference for establishing policies to save water resources and evaluate the carrying capacity of water resources.

Predicting Real-World Penny Auction Durations by Integrating Game Theory and Machine Learning

Game theory and machine learning are two widely used techniques for predicting the outcomes of strategic interactions among humans. However, the game theory-based approach often relies on strong rationality and informational assumptions, while the machine learning-based approach typically requires the testing data to come from the same distribution as the training data. Our work studies how to integrate the two techniques to address these weaknesses. We focus on the interactions among real bidders in penny auctions, and develop a three-stage framework to predict the distributions of auction durations, which indicate the numbers of bids and auctioneer revenues. Specifically, we first leverage a pre-trained neural network to encode the descriptions of products in auctions into embeddings. Second, we apply game theory models to make preliminary predictions of auction durations. In particular, we tackle the challenge of accurately inferring parameters in game theory models. Third, we develop a Multi-Branch Mixture Density Network to learn the mapping from product embeddings and game-theoretic predictions to the distributions of actual auction durations. Experiments on real-world penny auction data demonstrate that our framework outperforms both game theory-based and machine learning-based prediction approaches.

The emergence of cooperation in the context of prior agreement with threshold and posterior compensation

Previous experience suggests that prior agreement (a mechanism to test how strongly peers are committed to a common goal) and posterior compensation (a way to compensate cooperators by punishing defectors who violate their commitments) together contribute to the flourishing of cooperation. Far less is known, however, how the determination of the optimal threshold for prior agreement in force and the posterior compensation affect collaboration, and the relevance of different strategies. Here, in the context of public goods games and using an evolutionary game theory model, we investigate the evolution of cooperation and the relevance of different strategies by introducing prior agreements and posterior compensation in structured populations. Analytical results show that posterior compensation does not always promote cooperation and that there are sudden increases and decreases in the first-order phase transition. Interestingly, prior agreements without posterior compensation can also effectively restrain defectors, with cooperation abundance remaining at a high level. Furthermore, the threshold of effective agreement has nonlinear effects on cooperation, with cooperation abundance peaking at intermediate thresholds under specific parameters.

Analysis of blockchain introduction strategies in maritime business with game theory modelling

In the last years the blockchain technology has attracted the focus of many companies in maritime business, due to its potential to make a revolution in this field. This technology became especially important in supply chains, where it offers the possibility to increase the transportation effectiveness. It is shown that currently there is no instrument to analyze the strategies on blockchain technology introduction in maritime shipping. In order to solve this problem, it is suggested to use game theory models, which allows you to simulate the relationships among maritime shipping participants. In order to create a model of such type, the description of main participants is represented in the paper and the main schemes of their interaction are analyzed. The structured schemes of the participants introduction, during the organization of maritime shipping in a traditional form and with the utilization of blockchain are suggested in the paper. The structure of basic game theory model of maritime shipping is shown. In order to show the possibilities of the analysis using the proposed model four experiments are considered. The first one is the traditional form of participants interaction. The second one is the interaction of participants fully through the blockchain. The third one is the change of attitude to blockchain depending on economic efficiency. The fourth one is the gradual introduction of blockchain technology. The results show the change of economical effectiveness of business participants in different cases. It is shown in the paper that the introduction of blockchain allows to speed up a company cash-flow, which makes the business management much easier. It is also proved that the suggested model must be improved with introduction of new conditions and motives of the participants.

A Review of Game Theory Models to Support Production Planning, Scheduling, Cloud Manufacturing and Sustainable Production Systems

Cyber-physical systems, cloud computing, the Internet of Things, and big data play significant roles in shaping digital and automated landscape manufacturing. However, to fully realize the potential of these technologies and achieve tangible benefits, such as reduced manufacturing lead times, improved product quality, and enhanced organizational performance, new decision support models need development. Game theory offers a promising approach to address multi-objective problems and streamline decision-making processes, thereby reducing computational time. This paper aims to provide a comprehensive and up-to-date systematic review of the literature on the application of game theory models in various areas of digital manufacturing, including production and capacity planning, scheduling, sustainable production systems, and cloud manufacturing. This review identifies key research themes that have been explored and examines the main research gaps that exist within these domains. Furthermore, this paper outlines potential future research directions to inspire both researchers and practitioners to further explore and develop game theory models that can effectively support the digital transformation of manufacturing systems.

Vibration based single-objective finite element model updating using cooperative game theory approach

This paper introduces a novel approach that focuses on application of cooperative game theory model for automated finite element model updating. The approach involves transforming the conventional single-objective optimization finite element model updating problem into a game theory problem. Among the different alternatives, a cooperative game model is considered to solve the transformed updating problem. This new approach overcomes the limitations of the conventional single-objective optimization formulation, which include challenges related to selecting appropriate weighting factor of residuals and analysing their influence on optimization results. To demonstrate the effectiveness of the proposed approach, an ultralight fibre reinforced polymer footbridge is considered. The numerical model of the footbridge is updated using the experimentally determined inherent properties (natural frequencies and mode shapes) using the proposed approach. A comparison with the conventional sensitivity-based weighted single objective method reveals that the inclusion of the weighting factor in the optimization process under the cooperative game model method allows for more efficient solution of the single-objective updating optimization problem, while maintaining accuracy and quality in the updated model.

Decentralized platoon formation for a fleet of connected and autonomous trucks

This research explores energy-saving potential through platooning, a method aimed at reducing energy consumption, especially for connected and autonomous vehicles allowing shorter inter-vehicle distances. In platooning, energy is saved due to reduced drag, but there is increased energy use during acceleration and deceleration while forming and maintaining platoons. A decentralized game theory model is created to assess energy-saving capabilities in typical traffic scenarios. This model is a dynamic game where drivers act independently, deciding whether to join a platoon to maximize individual gains. A simulation model is developed to replicate vehicle behavior in realistic traffic conditions, estimating energy consumption over time. Findings show that decentralized decision-making leads to system-wide energy savings of 2.2% to 2.6%, contrasting with a 3% saving achieved through centrally mandated platoon formation.

Hierarchical Blockchain Energy Trading Platform and Microgrid Management Optimization

In this paper, we describe a solution that has been developed to create an intelligent software platform for the optimal management of energy trade (specifically a P2P trade) in microgrids. Furthermore, the choice of a solution based on the architecture of two-level hierarchical systems using the restrictions and recommendations of the control center is substantiated. A general functional design of the platform is proposed using blockchain technology and cyber security solutions. The proposed solution includes the technical specification of the presented platform using the game theory model as a special case without loss of generality. The enhanced learning method was used to simulate customer behavior. Simulation of the platform operation was carried out using the example of optimizing the peak-to-average network load by users making individual decisions using game theory algorithms. The simulation results of the implementation of scenarios with and without recommendations confirmed that the proposed model provides a better peak-to-average ratio (PAR) in the network.

Nash equilibrium selection using a hybrid two-player static game with trade-off ranking method

The paper aims to suggest the ranking of an optimal solution when there exists more than one Nash equilibrium in the game theory solution concept. Many studies tend to merge the game theory with the multi criteria decision-making (MCDM) method to cater the real-situation problems. In the paper, a novel hybrid non-cooperative static game in game theory is combines with the trade-off ranking (TOR) method in MCDM. The proposed hybrid method is used to rank multiple Nash equilibria concerning some criteria. The methodology for both static game and TOR method are explained in the paper. The game theory model used is a two-player non-constant-sum static game. The proposed methodology is tested using international cooperation in Iran. The result suggests the ranking of the combined strategies using the proposed method.

Social bond dynamics and the evolution of helping

Empiricists often struggle to apply game theory models to real-life cases of animal cooperation. One reason is that many examples of cooperation occur in stable groups, where individuals form social bonds that influence exchanges of help in ways that are not well described by previous models, including the extent of reciprocity and how relationships are initiated. We present a game theory model exploring the conditions under which social bonds between group members promote cooperation. In the model, bonds build up from exchanges of help in a similar way as the strength of association increases in learning, as in the Rescorla-Wagner rule. The bonds in turn affect partner choice and influence helping amounts. The model has a mechanism of reciprocity for bonded pairs, which can evolve toward either loose or strict reciprocation. Several aspects of the model are inspired by observations of food sharing in vampire bats. We find that small social neighborhoods are required for the evolutionary stability of helping, either as small group sizes, or if bonded members of larger groups can form temporary (daily) smaller groupings. The costs of helping need to be fairly low, while the benefits can be substantial. The form of reciprocity that evolves is neither immediate nor very strict. Individuals in need request help based on bond strength, but there is also an evolved preference for initiating bonds with new group members. In contrast, if different groups come into temporary contact, the evolved tendency is to avoid forming bonds between groups.

A Review of Stackelberg Game Theory Model on Trade Credit

Over the years, game theory has been used extensively to study interactions between the supplier and the retailer in business environment. Recently, a number of researchers have applied Stackelberg game theory on trade credit in centralized and decentralized channels. In this work, we reviewed the assumptions of the Stackelberg game theory model, its solution, its limitations and further extensions were also considered. Furthermore, how the Stackelberg game model applies to trade credit has been analyzed.

Package-type strategies and packaging's carbon reduction decisions in the take-out industry

Using environmentally friendly packaging (E-packaging) instead of non-environmentally friendly packaging (N-packaging) can mitigate pollution and reduce carbon emissions. However, the market penetration rate of E-packaging is low, and the packaging carbon reduction level is uneven. This study uses game theory models to examine the factors affecting packaging carbon reduction levels and fees as well as firms' package-type decisions. First, we obtain the optimal solutions for packaging manufacturers and firms when they use different types of packaging under monopoly and competition, respectively. We find that green competition reduces the carbon reduction level of E-packaging, whereas price competition can increase it under certain conditions. The unit manufacturing cost of N-packaging and cost coefficient of carbon reduction are critical considerations for package-type decisions. When the unit manufacturing cost of N-packaging is low and the cost coefficient is large, no firm uses E-packaging. We also demonstrate that firms in a competitive market may become trapped in a prisoner's dilemma after making package-type decisions. Interestingly, we show that the firms' package-type decisions can hurt consumers and welfare under certain conditions, depending on the N-packaging's unit manufacturing cost and pollution damage factor. Our findings provide insights for packaging manufacturers, firms, and environmental regulatory agencies, and can help promote E-packaging.