Game-theoretic Framework Research Articles

Anti-Jamming Transmission in NOMA-Based Satellite-Enabled IoT: A Game-Theoretic Framework in Hostile Environments

Anti-Jamming Transmission in NOMA-Based Satellite-Enabled IoT: A Game-Theoretic Framework in Hostile Environments

Is blockchain technology desirable? When considering power structures and consumer preference for blockchain

Blockchain technology is very useful for combating counterfeits and verifying the authenticity of products for consumers. This paper studies blockchain adoption in a two-level supply chain consisting of a brand supplier and a retailer. Our contribution using the game-theoretic framework is to consider the various preferences that consumers may have for blockchain-supported products and to investigate the value and effects of blockchain in a traditional wholesale channel with different power structures and a platform-based-agent selling channel. We confirm that consumer aversion to blockchain will lower retailer incentive to adopt blockchain, while the opposite will occur if consumers are interested in blockchain. We find that the market leader with power advantage is more motivated to adopt blockchain while the follower has less motivation. We also find that the brand supplier will be willing to move to the agent selling channel if the unit cost of the blockchain is low and the platform commission rate is medium. Moreover, an interesting finding is that blockchain can achieve a win–win-win outcome for the brand supplier, retailer, and consumers under a certain critical threshold, which may cause unfair distribution of supply chain profit and may also be detrimental to the establishment of agent selling.

Insight into China’s economically motivated adulteration risk in online agricultural product sales

Quality uncertainty and inspectors’ imperfect testing capability leave agricultural products wide open to economically motivated adulteration (EMA), and online shopping demand for these products in China makes it even worse. We develop a game-theoretic framework to investigate online agricultural product sellers’ EMA behavior on an e-commerce platform (EP). We characterize the sellers’ equilibrium pricing and adulteration decisions and the EP’s optimal take rate decision and analyze how the sampling inspections and adulteration penalty jointly impact these decisions. Moreover, we investigate three managerial levers to deter EMA: (i) penalizing EP’s malpractice regarding food safety management, (ii) claiming a higher-than-law-requires adulteration penalty to consumers, and (iii) adopting the traceability systems to record reliable quality information. Finally, we use real-world data to calibrate our model and derive more managerial insights. We find that the quality-differentiated sellers’ adulteration decisions are symmetric and their ex-post pricing decisions lead them to evenly share the market on the EP. We show that the EP’s higher take rate can inhibit the sellers’ adulteration behavior, however, it may indulge their such behavior by intentionally decreasing this rate. Our results highlight a penalty-inspection-centered approach as essential to combat EMA, and that it can be supplemented by the three levers.

What’s the News About Bad News? A Review of Bad News Games as a Tool to Teach Media Literacy

Abstract This paper examines the Bad News series of games, created by the Cambridge University Social Decision Making Lab and DROG Group, as an educational tool. More specifically, it considers Bad News as a persuasive game, within the umbrella of gamification. After considering the history and context of the game, the educational, motivational, and informational theoretical frameworks of the games, research, and criticisms, this paper concludes that the games are not, in themselves, transformative.

Information Sharing in a Green Supply Chain with a Common Retailer

In the paper, we analyze the problem of information sharing in a green supply chain with two competing manufacturers selling environmentally friendly substitutable products in markets through a common retailer. We develop a game-theoretic framework of a network supply chain structure. The study shows that (a) the manufacturer is better off while the retailer is worse off when the retailer shares his private information with the manufacturer; (b) the equilibrium greening levels are the highest when both the manufacturers are informed; (c) under intense competition, the retailer has an incentive to share the information with the manufacturer; (d) the equilibrium greening levels decrease with information inaccuracy. This study shows the existence of a contracting mechanism the manufacturers can employ to induce information sharing. This study’s results will be helpful to managers of green supply chain structures to make marketing and operational decisions under uncertain situations. The main contribution of this study is that it explores the problem of information sharing in a green supply chain under competition.

A Multi-Vehicle Game-Theoretic Framework for Decision Making and Planning of Autonomous Vehicles in Mixed Traffic

A Multi-Vehicle Game-Theoretic Framework for Decision Making and Planning of Autonomous Vehicles in Mixed Traffic

Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

We aim to preserve a large amount of data generated inside base station-less sensor networks (BSNs) while considering that sensor nodes are selfish. BSNs refer to emerging sensing applications deployed in challenging and inhospitable environments (e.g., underwater exploration); as such, there do not exist data-collecting base stations in the BSN to collect the data. Consequently, the generated data has to be stored inside the BSN before uploading opportunities become available. Our goal is to preserve the data inside the BSN with minimum energy cost by incentivizing the storage- and energy-constrained sensor nodes to participate in the data preservation process. We refer to the problem as DPP: d ata p reservation p roblem in the BSN. Previous research assumes that all the sensor nodes are cooperative and that sensors have infinite battery power and design a minimum-cost flow-based data preservation solution. However, in a distributed setting and under different control, the resource-constrained sensor nodes could behave selfishly only to conserve their resources and maximize their benefit. In this article, we first solve DPP by designing an integer linear programming (ILP)-based optimal solution without considering selfishness. We then establish a game-theoretical framework that achieves provably truthful and optimal data preservation in BSNs. For a special case of DPP wherein nodes are not energy-constrained, referred to as DPP-W, we design a data preservation game DPG-1 that integrates algorithmic mechanism design (AMD) and a more efficient minimum cost flow-based data preservation solution. We show that DPG-1 yields dominant strategies for sensor nodes and delivers truthful and optimal data preservation. For the general case of DPP (wherein nodes are energy-constrained), however, DPG-1 fails to achieve truthful and optimal data preservation. Utilizing packet-level flow observation of sensor node behaviors computed by minimum cost flow and ILP, we uncover the cause of the failure of the DPG-1. It is due to the packet dropping by the selfish nodes that manipulate the AMD technique. We then design a data preservation game DPG-2 for DPP that traces and punishes manipulative nodes in the BSN. We show that DPG-2 delivers dominant strategies for truth-telling nodes and achieves provably optimal data preservation with cheat-proof guarantees. Via extensive simulations under different network parameters and dynamics, we show that our games achieve system-wide data preservation solutions with optimal energy cost while enforcing truth-telling of sensor nodes about their private cost types. One salient feature of our work is its integrated game theory and network flows approach. With the observation of flow level sensor node behaviors provided by the network flows, our proposed games can synthesize “microscopic” (i.e., selfish and local) behaviors of sensor nodes and yield targeted “macroscopic” (i.e., optimal and global) network performance of data preservation in the BSN.

Optimal employment model for an entrant platform in on-demand service market

PurposeFor an entrant platform in the on-demand service market, choosing an appropriate employment model is critical. This study explores how the entrant optimally chooses the employment model to achieve better performance and investigates the optimal pricing strategies and wage schemes for both incumbent and entrant platforms.Design/methodology/approachBased on the Hotelling model, the authors develop a game-theoretic framework to study the incumbent's and entrant's optimal service prices and wage schemes. Moreover, the authors determine the entrant's optimal employment model by comparing the entrant's optimal profits under different market configurations and analytically analyze the impacts of some critical factors on the platforms' decision-making.FindingsThis study reveals that the impacts of the unit misfit cost of suppliers or consumers on the pricing strategies and wage schemes vary with different operational efficiencies of platforms. Only when both the service efficiency of contractors and the basic employee benefits are low, entrants should adopt the employee model. Moreover, a lower unit misfit cost of suppliers or consumers makes entrants more likely to choose the contractor model. However, the service efficiency of contractors has nonmonotonic effects on the entrant's decision.Originality/valueThis study focuses on an entrant's decision on the optimal employment model in an on-demand service market, considering the competition between entrants and incumbents on both the supplier and consumer sides, which has not been investigated in the prior literature.

Competitive integrated airline schedule design and fleet assignment

Airline profits are significantly influenced by competitive timetables that match passenger demand to fleet resources. In this research, we develop an integrated, mixed-integer optimization model to derive a comprehensive flight schedule, fleet assignment, and average airfares jointly. Passenger choice behavior is incorporated through a prospect theory-adjusted, nested, multinomial logit model which estimates market share. The competitive fleet assignment and schedule design (CFSD) formulation is embedded in a differentiated Bertrand game, such that each transport operator optimizes their best response function connected through the market share model. To solve the integrated optimization problem efficiently, a hybrid algorithm is developed that combines stabilized column generation with a large neighborhood search algorithm.The game-theoretic framework is applied to a case study in China involving legacy airlines, a low-cost carrier, and a high-speed rail (HSR) operator. The equilibrium outcomes suggest that the low-cost carrier is likely to stimulate demand and improve the level of service through secondary airports. The further development of high-speed rail service is expected to intensify competition among airlines and reduce the overall fare level. The low-cost carriers and HSR are able to significantly increase overall consumer surplus, providing insights into the potential for low-cost service in the Chinese aviation markets and the entrance of the public service obligation program at minimum cost.

Developing a game-theoretic interactive decision-making framework for urban land-use planning

Land-use changes are the result of individuals' interactive decisions and are directly affected by stakeholders' preferences. For a better understanding of land-use change processes and to support the development of an adequate land-use plan, simulating stakeholders' interactions is a key challenge. This study aims to develop a Game-Theoretic Interactive Decision Making (GTIDM) framework to simulate the interrelated behavior of stakeholders with imperfect information in a land-use change process. GTIDM considers consequences of the any possible decision taken by actors and support the best possible payoff for all decision-makers in conflicted and competitive situations. The proposed framework was implemented and analyzed using data from the 22nd municipal region of Tehran. We compared the results from GTIDM with Multi-Agent System (MAS) and Conventional Multi-Criteria Decision Making-based (CMCDM) frameworks to demonstrate the effects of considering or ignoring the interactive decision-making process. The results obtained indicate that the interactive decision-making situation can be efficiently simulated by GTIDM and the outputs are more similar to stakeholders’ decisions in the real world. The MAS- and CMCDM-based frameworks are less focused on interactive situations. Finally, GTIDM provides insight into interactive decision-making in land-use change process and can lead us toward a feasible solution in urban land-use planning.

Joint impact of CSR policy and market structure on environmental sustainability in supply chains

The integration of Corporate Social Responsibility (CSR) and Smart Manufacturing (SM) has emerged as a promising strategy for addressing carbon emissions. Governments can play a crucial role in promoting sustainable practices and environmental sustainability by implementing targeted policies and regulations. In this study, we examine the sustainability performance of competing smart supply chains that offer substitutable products under different CSR regulatory policies. Specifically, we investigate five CSR policies: Deregulation, Direct Tariff on Market, Sustainability Penalty and Credits, Direct Limitation on Sustainability, and Government Cooperative Sustainability Efforts. Using a game theoretical framework, we model and analyze the effectiveness of each CSR policy within monopoly and oligopoly market structures. Our results uncover the importance of considering the synergistic effects of market structure and CSR when designing sustainability strategies for policymakers and supply chain managers. For instance, the Direct Tariff on Market policy in the monopoly market is shown to be the preferred regulatory approach as it effectively enhances both supply chain profitability and environmental sustainability. Furthermore, the Direct Tariff on Market policy in the oligopoly market, along with the Direct Limitation on Sustainability policy in the monopoly market, results in a greater market share of sustainable products. Understanding these dynamics enables policymakers to make informed decisions that maximize the environmental benefits of CSR practices, considering varying market structures.

Nashian game theory is incompatible with quantum physics

We suggest to look at quantum measurement outcomes not through the lens of probability theory, but instead through decision theory. We introduce an original game-theoretical framework, model and algorithmic procedure where measurement scenarios are multiplayer games with a structure all observers agree on. Measurement axes and, newly, measurement outcomes are modeled as decisions with nature being an action-minimizing economic agent. We translate physical notions of causality, correlation, counterfactuals, and contextuality to particular aspects of game theory. We investigate the causal consistency of dynamic games with imperfect information from the quantum perspective and conclude that counterfactual dependencies should be distinguished from causation and correlation as a separate phenomenon of its own. Most significantly, we observe that game theory based on Nash equilibria stands in contradiction with a violation of Bell inequalities. Hence, we propose that quantum physics should be analyzed with non-Nashian game theory, the inner workings of which we demonstrate using our proposed model.

A Bi-Level Decision Framework for Incentive-Based Demand Response in Distribution Systems

In a growing retail electricity market, demand response (DR) is becoming an integral part of the system to enhance economic and operational performances. This is rendered as incentive-based DR (IBDR) in the proposed study. It presents a bi-level decision framework under the ambit of multiple demand response providers (DRPs) in the retail competition. It is formulated as a multi-leader-multi-follower game, where multiple DRPs, as the DR stakeholders, are strategically interacting to optimize load serving entity cost at the upper level, and individual DRP as the aggregated customers is optimizing its cost at the lower level. The strategic behavior of DRPs is modeled in a game-theoretic framework using a generalized Stackelberg game. Further, the existence and uniqueness of the game are validated using variational inequalities. It is presented as a nonlinear problem to consider AC network constraints. An equilibrium problem with equilibrium constraints is used as a mathematical program to model the multi-leader-multi-follower, bi-level problem, which is simultaneously solved for all DRPs. The diagonalization method is employed to solve the problem. The detailed numerical analyses are conducted on IEEE 33-bus test and Indian-108 bus distribution systems to demonstrate the applicability and scalability of the proposed model and the suggested method.

Decentralized, Privacy-Preserving Routing of Cellular-Connected Unmanned Aerial Vehicles for Joint Goods Delivery and Sensing

Unmanned aerial vehicles (UAVs) have been extensively applied to goods delivery and in-situ sensing. It becomes increasingly probable that multiple UAVs are delivering goods and carrying out sensing tasks at the same time. The destinations of the UAVs are usually required to jointly design their trajectories and sensing selections, leading to privacy concerns for the UAVs. This paper presents a new game-theoretic routing framework for joint goods delivery and sensing of multiple cellular-connected UAVs, where the UAVs minimize their energy consumption and connectivity outage, maximize their sensing reward, and ensure timely goods delivery and trajectory privacy by optimizing their trajectories and sensing task selections in a decentralized manner. The key idea is that we unify routing and sensing in a single task selection process, which is further transformed into routing on a task-time graph. Another important aspect is that we design a non-cooperative potential game for the routing on the task-time graph. A distributed strategy is developed, where each UAV only reports its sensing task selections and withholds its destination information and its best response produced by the Bellman-Ford algorithm. By this means, the destination and trajectory privacy of the UAVs are protected. Simulations show that the new game-theoretic approach can ensure timely delivery and achieve close-to-optimal solutions with significantly lower complexity compared to a centralized brute-force approach.

Incentive contract design and selection for inhibiting unethical collusion in construction projects

Purpose The construction industry has long been criticized for unethical conduct. The owner usually manages the contractor's opportunistic behaviors by employing a professional supervisor, but there is a risk of covert collusion between the supervisor and contractor. Based on the principal–agent theory and collusion theory, this paper aims to investigate optimal collusion-proof incentive contracts.Design/methodology/approachThis paper presents a game-theoretic framework comprising an owner, supervisor and contractor, who interact and pursue maximized self-profits. Built upon the fixed-price incentive contract, cost-reimbursement contract, and revenue-sharing contract, different collusion-proof incentive contracts are investigated. A real project case is used to validate the developed model and derived results.FindingsThis paper shows that the presence of unethical collusion undermines the owner's interests. Especially, the possibility of agent collusion may induce the owner to abandon extracting quality information from the supervisor. Furthermore, information asymmetry significantly affects the construction contract selection, and the application conditions for different incentive contracts are provided.Research limitations/implicationsThis study still has some limitations that deserve further exploration. First, this study explores contractor–supervisor collusion but ignores the possibility of the supervisor abusing authority to extort the contractor. Second, to focus on collusion, this paper ignores the supervision costs. What's the optimal supervision effort that the owner should induce the supervisor to exert? Finally, this paper assumes that the colluders involved always keep their promises. However, what if the colluders may break their promises?Practical implicationsSeveral collusion-proof incentive contracts are explored in a project management setting. The proposed incentive contracts can provide the project owner with effective and practical tools to inhibit covert collusion in construction management and thus safeguard construction project quality.Originality/valueThis study expands the organization collusion theory to the field of construction management and investigates the optimal collusion-proof incentive contracts. In addition, this study is the first to investigate the effects of information asymmetry on contract selection.

Inventory Commitment and Monetary Compensation Under Competition

Problem definition: Inventory commitment and monetary compensation are widely recognized as effective strategies in monopoly settings when customers are concerned about stockouts. To attract more customer traffic, a firm reveals its inventory availability information to customers before the sales season or offers monetary compensation to placate customers if the product is out of stock. This paper investigates these two strategies when retailers compete on both price and inventory availability. Methodology/results: We develop a game-theoretic framework to analyze the strategic interactions among the retailers and customers and draw the following insights. First, both inventory commitment and monetary compensation may lead to a prisoner’s dilemma. Although these strategies are preferred regardless of the competitor’s price and inventory decisions, the equilibrium profit of each retailer could be lower in the presence of inventory commitment or monetary compensation because they intensify the competition between the retailers. Second, we find that market competition may hurt social welfare compared with a centralized setting by reducing the product availability in equilibrium. The inventory commitment and monetary compensation strategies further intensify the competition between the retailers, therefore causing an even lower social welfare. Managerial implications: Our study shows that, although inventory commitment and monetary compensation improve retailers’ profit and social welfare under monopoly, these strategies should be used with caution under competition. Funding: F. Zhang is grateful for the financial support from the National Natural Science Foundation of China [Grants 71929201, 72131004]. R. Zhang is grateful for the financial support from the Hong Kong Research Grants Council General Research Fund [Grant 14502722] and the National Natural Science Foundation of China [Grant 72293560/72293565]. Y. Yu is grateful for the financial support from the National Natural Science Foundation of China [Grant 71921001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0411 .

Game-theoretical approach for task allocation problems with constraints

The distributed task allocation problem, as one of the most interesting distributed optimization challenges, has received considerable research attention recently. Previous works mainly focused on the task allocation problem in a population of individuals, where there are no constraints for affording task amounts. The latter condition, however, cannot always be hold. In this paper, we study the task allocation problem with constraints of task allocation in a game-theoretical framework. We assume that each individual can afford different amounts of task and the cost function is convex. To investigate the problem in the framework of population games, we construct a potential game and calculate the fitness function for each individual. We prove that when the Nash equilibrium point in the potential game is in the feasible solutions for the limited task allocation problem, the Nash equilibrium point is the unique globally optimal solution. Otherwise, we also derive analytically the unique globally optimal solution. In addition, in order to confirm our theoretical results, we consider the exponential and quadratic forms of cost function for each agent. Two algorithms with the mentioned representative cost functions are proposed to numerically seek the optimal solution to the limited task problems. We further perform Monte Carlo simulations which provide agreeing results with our analytical calculations.

A Bayesian Approach with Prior Mixed Strategy Nash Equilibrium for Vehicle Intention Prediction

The state-of-the-art technology in the field of vehicle automation will lead to a mixed traffic environment in the coming years, where connected and automated vehicles have to interact with human-driven vehicles. In this context, it is necessary to have intention prediction models with the capability of forecasting how the traffic scenario is going to evolve with respect to the physical state of vehicles, the possible maneuvers and the interactions between traffic participants within the seconds to come. This article presents a Bayesian approach for vehicle intention forecasting, utilizing a game-theoretic framework in the form of a Mixed Strategy Nash Equilibrium (MSNE) as a prior estimate to model the reciprocal influence between traffic participants. The likelihood is then computed based on the Kullback-Leibler divergence. The game is modeled as a static nonzero-sum polymatrix game with individual preferences, a well known strategic game. Finding the MSNE for these games is in the PPAD cap PLS complexity class, with polynomial-time tractability. The approach shows good results in simulations in the long term horizon (10s), with its computational complexity allowing for online applications.

Running Out of Bank Runs

The theoretical literature on bank runs has modeled depositors’ withdrawal decision as a one-off choice, made simultaneously by all depositors. Our game-theoretic framework gives depositors a heterogeneous, stochastic opportunity to change their minds about withdrawing their money. They can run out of (or run into) the crowd in front of the bank based on their observation of what others have done. Depositors’ opportunity to change their decision supports implicit coordination, which in some circumstances reduces the probability that self-fulfilling bank runs will occur.

Arbitrage Equilibrium, Invariance, and the Emergence of Spontaneous Order in the Dynamics of Bird-like Agents.

The physics of active biological matter, such as bacterial colonies and bird flocks, exhibiting interesting self-organizing dynamical behavior has gained considerable importance in recent years. Current theoretical advances use techniques from hydrodynamics, kinetic theory, and non-equilibrium statistical physics. However, for biological agents, these approaches do not seem to recognize explicitly their critical feature: namely, the role of survival-driven purpose and the attendant pursuit of maximum utility. Here, we propose a game-theoretic framework, statistical teleodynamics, that demonstrates that the bird-like agents self-organize dynamically into flocks to approach a stable arbitrage equilibriumof equal effective utilities. This is essentially the invisible handmechanism of Adam Smith's in an ecological context. What we demonstrate is for ideal systems, similar to the ideal gas or Ising model in thermodynamics. The next steps would involve examining and learning how real swarms behave compared to their ideal versions. Our theory is not limited to just birds flocking but can be adapted for the self-organizing dynamics of other active matter systems.