Framework Of Game Theory Research Articles

A game theory-based analysis of merchants’ mobile payment adoption using hybrid SEM-neural network approach

PurposeDriven by the evidence from the literature on the significance of mobile (m-)payment in economic growth and productivity and at the same time the relative dismal adoption of this service, the purpose of present paper is to elucidate the merchants’ m-payment adoption from the perspective of trust, drawing upon the game theory framework, in the Malaysian context.Design/methodology/approachAn online survey consisting of 302 respondents was carried out to investigate the impact of trust and opportunism on merchants’ perceived trustworthiness using a two-staged structural equation modeling–neural network approach to determine the significance and relative importance of variables. This study also applies a game-theoretic approach to analyze the impact of trust on the relationship between merchants and m-payment service providers.FindingsThe results indicate a positive and statistically significant relationship between merchant trust, merchant opportunism and perceived trustworthiness, and a statistically significant negative relationship was found between m-payment provider opportunism and perceived trustworthiness. The findings from the prisoner’s dilemma two-player model indicate that the scenarios of mutual trust and mutual opportunism as paradigmatic of cooperation and defection produce the best and worse outcomes, respectively. An intriguing result was the positive impact of merchant opportunism on perceived trustworthiness, which indicates a very calculative orientation of merchants in m-payment contracting.Originality/valueTo the best of the authors’ knowledge, this is among the first attempts to propose a game theory approach to the interaction between merchants and m-payment providers under the framework of trust and opportunism. A game theory study in the context of m-payment adoption can contribute to the theoretical literature by providing insights into the decision-making processes of merchants. By incorporating trust and opportunism into the game theory model, we can gain a better understanding of how they affect the decision-making process and overall adoption rates. The conclusions and implications provide useful insights for managers of both m-payment platforms and merchants in this relational exchange. The results of the present research can provide insights into the factors that influence merchant decisions and guide them toward suitable partnerships for successful adoption and can guide authorities for policy interventions and supporting adoption efforts.

Governance of risky public goods under the threat of ostracism

How to enforce cooperation among unrelated agents is one of the main concerns in distributed artificial intelligence. The limited success of existing efforts to achieve large-scale cooperation is partly related to the lack of strong sanctioning mechanism to restrain defective agents who undermine cooperation. Here we use the framework of evolutionary game theory to investigate the emergence of cooperation in collective risk dilemma (CRD) through the implementation of instrumental ostracism on defective agents. By analyzing the stochastic model, we find that instrumental ostracism can better promote the emergence of widespread cooperation than punishment. Such advantage is more obvious when the risk is relatively low. Furthermore, we verify that the polycentric sanctioning institutions including punishment and ostracism are more effective than a single global one in improving the level of cooperation. Our model can be applied to many collective action problems, such as climate change and vaccination in Covid-19, so as to further provide some suggestions for decision makers.

Modeling Government Subsidy Strategies for Railway Carriers: Environmental Impacts and Industry Competition

In this paper, we investigate the government’s optimal subsidy strategy for the China–Europe Railway Express (CERE) considering environmental impacts and industry competition. Specifically, we consider three subsidy options: no subsidies, subsidies to CERE carriers, and subsidies to shippers. A game theory framework is developed to analyze the problem of developing a sustainable supply chain consisting of the government, competitive carriers, and shippers. First of all, we find that for the government, indirect subsidies to CERE carriers and direct subsidies to shippers lead to the same total social welfare. We then examine the conditions for phasing out government subsidies. Our results indicate that the government’s optimal subsidy strategy switches at a threshold level of CERE’s environmental advantage. In particular, when the environmental advantage of CERE is high, the government should subsidize CERE by subsidizing either the carrier or shipper. In contrast, when the environmental advantage of CERE is low, the government should opt out of subsidies. At last, we find that this threshold of CERE’s environmental advantage is further impacted by CERE’s capacity and marginal operating costs. This study differs from prior research by investigating various subsidy strategies while taking into account CERE’s emission advantage and the timing of subsidy withdrawal.

The dynamics of corruption under an optional external supervision service

External supervision services play an important role in combating corruption by detecting potential collusive bribery. This work aims at studying the dynamics of collusive bribery when participants have the option of engaging the external supervision services. To do so, we construct a basic model where collusive bribery can happen between the defecting participants who aim to escape from a punishment by offering a bribe to rule enforcers who monitor interactions among all participants. Among rule enforcers, only the corrupt ones accept the bribe and ignore the violations. The cooperative participants can engage the external supervision service at a certain cost to avoid the risk of potential collusive bribery. Under the framework of evolutionary game theory, we find that a higher initial fraction of honest enforcers is more likely to lead to a trusting cooperating equilibrium. We also find that, when allowing random exploration of available strategies, increasing the exploration rate of rule enforcers is effective in combating corruption for both infinite and finite populations. Lastly, we find that minimizing the cost of external supervision services is not always good. When the system evolves into a cooperating equilibrium, a low cost of external supervision service induces unnecessary costs of seeking external supervision. When the strategy profiles exhibit stable oscillations, there exists an optimal cost of external supervision, considering the trade-off between minimizing the chance of exposing cooperative participants to collusive bribery and strengthening the punishment on the corrupt enforcers. Premised on the results, we discuss practical management suggestions.

Bank Run Model: The Application of Game Theory

The term "bank run" describes the mass withdrawal of funds from a bank by its customers. With cash on hand, the bank might be able to handle the situation. As more consumers pull their money out of the company's bank accounts, the likelihood of the business collapsing increases. Based on this, this paper studies the bank run in game theory. It was found that: within the framework of game theory, this situation might be represented as a "coordination game," the latter being a kind of game with two distinct and stable equilibrium states: If enough people keep their money in the bank, the bank won't be able to default on its responsibilities, thus keeping your money there makes sense. On the other hand, if many customers withdraw their deposits, the bank will collapse, and at that time, it is logical for all of the customers to seek to withdraw their money. To clarify the phenomenon of bank runs, this study will use the Diamond-Dybvig model. This theory holds that a bank's primary function is facilitating long-term loans for investment purposes and short-term deposits. A bank run occurs when too many individuals take their money from a financial institution before the long-term debts are returned.

Fair travel distances in tournament schedules: A cooperative game theory approach

The most studied problem in sports scheduling, so-called traveling tournament problem (TTP), aims at finding schedules minimizing the total distance traveled by the teams. While minimizing all the traveling between games is efficient from the overall perspective, it overlooks the distribution of the travel among the teams. Consequently, some teams may end up better than others with respect to their individual goals, an imbalance which may affect teams’ often-limited resources or preparedness for the games. This article adopts a cooperative game theory framework to obtain tournament schedules where the distances traveled by the teams are allocated according to fairness criteria. The approach consists of three steps. First, the scheduling problem is reformulated as a transferable utility game. Second, by means of well-established allocation methods, an ideal distance distribution among the teams is determined. Third, we introduce fairness measures to produce a schedule which approximately resembles the ideal distribution. We also discuss the case of not pursuing fairness, but rather a compromise between fairness and minimum total distance. We illustrate the approach by a numerical example in one of the classic TTP data instances.

Trust and Reciprocity in Firms’ Capacity Sharing

Problem definition: We study the use of nonmonetary incentives based on reciprocity to facilitate capacity sharing between two service providers that have limited and substitutable service capacity. Academic/practical relevance: We propose a parsimonious game theory framework, in which two firms dynamically choose whether to accept each other’s customers without the capability to perfectly monitor each other’s capacity utilization state. Methodology: We solve the continuous-time imperfect-monitoring game by focusing on a class of public strategy, in which firms’ real-time capacity-sharing decision depends on an intuitive and easy-to-implement accounting device, namely the current net number of transferred customers. We refer to such an equilibrium as a trading-favors equilibrium. We characterize the condition in which capacity sharing takes place in such an equilibrium. Results: We find that some degree of efficiency loss (as compared with a central planner’s solution) is necessary to induce reciprocity. The efficiency loss is small when the two firms have similar traffic intensity even if they are different in service-capacity scale, whereas the efficiency loss can be considerably large when the two firms have significantly different traffic intensities. The trading-favors mechanism, surprisingly, can outperform the perfect-monitoring benchmark when the two firms exhibit high asymmetry in terms of service-capacity scale or traffic intensity because the smaller firm tends to deviate from collaboration. Managerial implications: Firms should consider engaging in nonmonetary reciprocal capacity sharing if regulations, transaction costs, or other market and operational frictions make it difficult to use a capacity-sharing contract based on monetary payments. The trading-favors collaboration can improve the firms’ payoff close to the centralized upper bound when the firms have similar traffic intensities. However, when their traffic intensities are highly different, firms are better off with a monetary-payment contract to induce more capacity sharing and are worse off investing in increasing their visibility to each other’s real-time available capacity, namely investing in perfect monitoring. Funding: X. Hu thanks Faculty of Business and Economics of the University of Hong Kong and R. Caldentey thanks the University of Chicago Booth School of Business for financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1203 .

Recent Developments in Game-Theory Approaches for the Detection and Defense against Advanced Persistent Threats (APTs): A Systematic Review

Cybersecurity has become a prominent issue in regard to ensuring information privacy and integrity in the internet age particularly with the rise of interconnected devices. However, advanced persistent threats (APTs) pose a significant danger to the current contemporary way of life, and effective APT detection and defense are vital. Game theory is one of the most sought-after approaches adopted against APTs, providing a framework for understanding and analyzing the strategic interactions between attackers and defenders. However, what are the most recent developments in game theory frameworks against APTs, and what approaches and contexts are applied in game theory frameworks to address APTs? In this systematic literature review, 48 articles published between 2017 and 2022 in various journals were extracted and analyzed according to PRISMA procedures and our formulated research questions. This review found that game-theory approaches have been optimized for the defensive performance of security measures and implemented to anticipate and prepare for countermeasures. Many have been designed as part of incentive-compatible and welfare-maximizing contracts and then applied to cyber–physical systems, social networks, and transportation systems, among others. The trends indicate that game theory provides the means to analyze and understand complex security scenarios based on technological advances, changes in the threat landscape, and the emergence of new trends in cyber-crime. In this study, new opportunities and challenges against APTs are outlined, such as the ways in which tactics and techniques to bypass defenses are likely to evolve in order to evade detection, and we focused on specific industries and sectors of high interest or value (e.g., healthcare, finance, critical infrastructure, and the government).

Effects of the limited incentive pool on cooperation evolution in public goods game

Cooperative behavior is prevalent in human societies, yet it is often undermined by selfish individuals. Within the framework of evolutionary game theory, pro-social incentives are often considered to be an effective means of promoting cooperation. In fact, however, the resources (including human resources, budget, etc.) to implement incentives are often limited for the institutions. Based on the public goods game, this paper introduces the concept of limited (negative or positive) incentive pool, in which the number of punishers/rewarders that the incentive pool can accommodate is limited. When the capacity of the incentive pool reaches the maximum, the remaining individuals who want to perform the punish/reward behavior will automatically withdraw from the game. Experimental simulations show that the introduction of positive/negative incentive facilitates the promotion and maintenance of cooperation, and that negative incentive is more effective in promoting cooperation and inhibit defection than positive incentives.

A Deep Learning Game Theoretic Model for Defending Against Large Scale Smart Grid Attacks

Power grids that are interdependent with communication networks create more possible modes of failure (e.g., cyberattacks) as well as more complex propagation of failure through the coupled networks. To ensure robust defense of smart grids, it is important to model both attacker and defender as intelligent, a scenario that the framework of game theory provides methods to analyze. However, prior works in applying game theoretic models to smart grid security limit the problem space to a small number targets under threat due to the inability of state-of-the-art methods to scale to large networks. Our method scales to large networks by combining neural networks that use featurized action representations with an approximation of large combinatorial actions to generalize knowledge about the best targets to attack/defend across graphs of various topologies and sizes. Our model’s invariance to the size of the input graph allows us to transfer knowledge from games played on small graphs during training to large graphs during evaluation. Our experiments show that our method can learn Nash equilibrium strategies on small networks, and demonstrate low exploitability when generalized to large networks, especially compared to the common heuristics currently used to simulate attacks on large graphs.

Pricing lane changes

Risky and aggressive lane changes on highways reduce capacity and increase the risk of collision. We propose a lane-changing pricing scheme as an effective tool to penalize those maneuvers to reduce congestion as a societal goal while aiming for safe driving conditions. In this paper, we first model driver behavior and their payoffs under a game theory framework and find optimal lane-changing strategies for individuals and their peers in multiple pairwise games. Payoffs are estimated for two primary evaluation criteria: efficiency and safety, which are quantified by incorporating driver tradeoffs. After that, the discretionary lane-changing (DLC) model is calibrated and validated by real-world vehicular trajectory data. To manipulate drivers’ DLC behaviors, two types of lane-changing tolls based on local-optimal and global-optimal rules are introduced to align individual preferences with social benefits. We find prices can close this gap and achieve ‘win-win’ results by reducing drivers’ aggressive lane changes in the congested traffic. Meanwhile, the tolls collected can be used to compensate drivers who get delayed when yielding, to encourage appropriate yielding behavior and a pseudo-revenue neutral tolling system.

Consumers' purchasing decisions of a dual-channel supply chain system under return and warranty policies

ABSTRACT With the rapid growth of internet technology, particularly during the COVID-19 pandemic, a major portion of consumers have intended to do online shopping. Also the use of eco-friendly products is essential in today's environment and human health. Thus this paper investigates the consumers' purchasing behaviour towards substitutable non-green and eco-friendly products in a dual-channel (offline and online channels) supply chain system. The manufacturer offers a novel combination of promotions such as a return policy in the online channel and a warranty policy in the offline channel depending on the position and situation of consumers. Here, the environmental burden is reduced by considering remanufacturing/refurbishing used products during the warranty period. Therefore, consumers' demand depends on price, eco-friendliness level, warranty and return agreements. The entire problem is modelled under centralised and decentralised decision-making scenarios. Finally, the profit maximisation problem is formulated and solved in the game theory framework. A series of sensitivity analyses of various parameters is conducted numerically to validate the problem. It is observed that, due to the instant return facility in the online channel, online demand is higher than the offline with a higher warranty period.

Backhaul-Aware User Association and Throughput Maximization in UAV-Aided Hybrid FSO/RF Network

Free-space optical (FSO) communication is expected to play an indispensable role with high data rates and low system complexity in beyond fifth-generation (B5G) networks. However, infrequent adverse weather conditions can incapacitate its performance. The combination of FSO and radio frequency (RF) has emerged as an effective alternative for meeting the growing need for high data rates in wireless communication networks. Unmanned aerial vehicles (UAVs) are also anticipated to play an instrumental role in B5G networks due to their flexible movement and deployment. In this paper, a UAV-aided hybrid FSO/RF backhauling system using a matching game theory (GT) and reinforcement learning (RL) framework is investigated. We deploy a UAV to provide a user offloading service to an already existing ground base station (GBS), which is facing a reduced backhaul capacity due to weather attenuation (e.g., fog). It is considered that the GBS has a pre-installed FSO backhaul connection to a macro-base station (MBS). However, during adverse weather conditions, the FSO backhaul is severely affected, compromising the reliability of the FSO link. With the reduced FSO backhaul capacity, the GBS needs an additional backhaul link to support its backhaul data transmission to the destination MBS. As a result, instead of building an expensive permanent parallel RF link for the rare foggy situation, a UAV can be hired to serve a portion of the users, thereby reducing the GBS load. The users perform a matching game-based procedure to select the base station (BS) of their choice to maximize their utility. The UAV is deployed at an optimal altitude, and the bandwidth partition between the GBS and the UAV is optimized to maximize the system throughput using RL. Real weather data from the cities of Edinburgh and London in the U.K. are used to evaluate the performance of the system. The numerical results show the superiority and effectiveness of the proposed scheme compared to conventional methods.

A game theory approach for joint blind deconvolution and inpainting

In this paper we propose a new mathematical model for joint Blind Deconvolution and Inpainting. The main objective is the treatment of blurred images with missing parts, through the game theory framework, in particular, a Nash game, we define two players: Player 1 handles the image intensity while Player 2, operates on the blur kernel. The two engage in a game until the equilibrium is reached. Finally, we provide some numerical examples: we compare the efficiency of our proposed approach to other existing methods in the literature that deals with Blind Deconvolution and Inpainting separately.

A Novel Label Propagation Method for Community Detection Based on Game Theory

Community is a mesoscopic feature of the multi-scale phenomenon of complex networks, which is the bridge to revealing the formation and evolution of complex networks. Due to high computational efficiency, label propagation becomes a topic of considerable interest within community detection, but its randomness yet produces serious fluctuations. Facing the inherent flaws of label propagation, this paper proposes a series of solutions. Firstly, this paper presents a heuristic label propagation algorithm named Label Propagation Algorithm use Cliques and Weight (LPA-CW). In this algorithm, labels are expanded from seeds and propagated based on node linkage index. Seeds are produced from complete subgraph, and node linkage index is related to neighboring nodes. This method can produce competitive modularity Q but not Normalized Mutual Information (NMI), and compensate with existing methods, such as Stepping Community Detection Algorithm based on Label Propagation and Similarity (LPA-S). Secondly, in order to combine the advantages of different algorithms, this paper introduces a game theory framework, design the profit function of the participant algorithms to attain Nash equilibrium, and build an algorithm integration model for community detection (IA-GT). Thirdly, based on the above model, this presents an algorithm, named Label Propagation Algorithm based on IA-GT model (LPA-CW-S), which integrates LPA-CW and LPA-S and solves the incompatibility between modularity and NMI. Fully tested on both computer-generated and real-world networks, this method gives better results in indicators such as modularity and NMI than existing methods, effectively resolving the contradiction between the theoretical community and the real community. Moreover, this method significantly reduces the randomness and runs faster.

A game theory framework for newly launched greening product in two-echelon supply chain

A two-echelon supply chain model is considered where one manufacturer and one retailer reduce the customers' sensitivity to the product price and stimulate the demand of the product by introducing facilities such as the product's greenness, service level and promotional effort. They share the costs jointly on the above facilities following some agreements. The models are formulated as profit maximisation problems for the whole system and the individual chain members. The game theoretical approach is used to solve the models under centralised and decentralised decision scenarios. Several particular models are studied by considering one or two facilities (greenness, service level and promotional effort) at a time. Finally, numerical experiments are conducted and some managerial decisions are presented from the optimal results and sensitivity analyses. It is demonstrated that the greenness, service level and promotional effort activities stimulate higher demand and fetch more profits for the supply chain and its members.

Multi-robot task allocation clustering based on game theory

A cooperative game theory framework is proposed to solve multi-robot task allocation (MRTA) problems. In particular, a cooperative game is built to assess the performance of sets of robots and tasks so that the Shapley value of the game can be used to compute their average marginal contribution. This fact allows us to partition the initial MRTA problem into a set of smaller and simpler MRTA subproblems, which are formed by ranking and clustering robots and tasks according to their Shapley value. A large-scale simulation case study illustrates the benefits of the proposed scheme, which is assessed using a genetic algorithm (GA) as a baseline method. The results show that the game theoretical approach outperforms GA both in performance and computation time for a range of problem instances.

Evolutionary dynamics of sustainable blockchains

The energy sustainability of blockchains, whose consensus protocol rests on the Proof-of-Work, nourishes a heated debate. The underlying issue lies in a highly energy-consuming process, defined as mining, required to validate crypto-asset transactions. Mining is the process of solving a cryptographic puzzle, incentivized by the possibility of gaining a reward. The higher the number of users performing mining, i.e. miners, the higher the overall electricity consumption of a blockchain. For that reason, mining constitutes a negative environmental externality. Here, we study whether miners’ interests can meet the collective need to curb energy consumption. To this end, we introduce the Crypto-Asset Game, namely a model based on the framework of Evolutionary Game Theory devised for studying the dynamics of a population whose agents can play as crypto-asset users or as miners. The proposed model, studied via numerical simulations, reveals a rich spectrum of possible steady states. Interestingly, by setting the miners’ reward in the function of the population size, agents reach a strategy profile that optimizes global energy consumption. To conclude, can a Proof-of-Work-based blockchain become energetically sustainable? Our results suggest that blockchain protocol parameters could have a relevant role in the global energy consumption of this technology.

Revisiting implementation of multiple natural enemies in pest management

A major goal of biological control is the reduction and/or eradication of pests using various natural enemies, in particular, via deliberate infection of the target species by parasites. To enhance the biological control, a promising strategy seems to implement a multi-enemy assemblage rather than a single control agent. Although a large body of theoretical studies exists on co-infections in epidemiology and ecology, there is still a big gap in modelling outcomes of multi-enemy biological control. Here we theoretically investigate how the efficiency of biological control of a pest depends on the number of natural enemies used. We implement a combination of eco-epidemiological modelling and the Adaptive Dynamics game theory framework. We found that a progressive addition of parasite species increases the evolutionarily stable virulence of each parasite, and thus enhances the mortality of the target pest. However, using multiple enemies may have only a marginal effect on the success of biological control, or can even be counter-productive when the number of enemies is excessive. We found the possibility of evolutionary suicide, where one or several parasite species go extinct over the course of evolution. Finally, we demonstrate an interesting scenario of coexistence of multiple parasites at the edge of extinction.

Belief-Dependent Motivations and Psychological Game Theory

The mathematical framework of psychological game theory is useful for describing many forms of motivation where preferences depend directly on one’s own or others’ beliefs. It allows for incorporating, for example, emotions, reciprocity, image concerns, and self-esteem in economic analysis. We explain how and why, discussing basic theory, experiments, applied work, and methodology. (JEL C70, D83, D91)