Game-theoretic Model Research Articles

Blockchain adoption and mode selection strategies for remanufacturing supply chain under cap-and-trade policy

Environmental conservation and economic advantages have propelled the swift expansion of the remanufacturing industry. Many original equipment manufacturers (OEMs) delegate remanufacturing operations to third-party remanufacturers (TPRs) through outsourcing and authorization. Furthermore, blockchain technology enhances consumers’ intention to purchase remanufactured products by disclosing more product information. To investigate the impacts of blockchain on remanufacturing mode selection, this paper focuses on a competitive remanufacturing supply chain comprising an OEM and a TPR under a cap-and-trade policy. Further, four game-theoretic models are derived from whether to adopt blockchain technology and which remanufacturing mode to select. Three major findings are obtained by solving and comparing the four models: (1) Adopting blockchain does not consistently result in advantages for both the OEM and TPR, and they should adopt blockchain for information disclosure in instances where the unit blockchain adoption cost is low, and the disclosure degree of remanufactured product information is also low. (2) Compared with not adopting blockchain technology, if the unit blockchain adoption cost is low, blockchain technology will increase the remanufactured product sales, thereby fostering the advancement of remanufacturing and contributing to reducing the environmental impact. (3) Whether blockchain is adopted or not, the OEM always tends to be in the outsourcing remanufacturing mode. The TPR only opts for the outsourcing remanufacturing mode when consumers’ preference for remanufactured products is high. More importantly, adopting blockchain will likely enhance consumers’ preference for remanufactured products, potentially shifting the TPR’s inclination for remanufacturing mode from authorization to outsourcing in some instances.

Strategic Queueing Behavior of Two Groups of Patients in a Healthcare System

Long waiting times and crowded services are the current medical situation in China. Especially in hierarchic healthcare systems, as high-quality medical resources are mainly concentrated in comprehensive hospitals, patients are too concentrated in these hospitals, which leads to overcrowding. This paper constructs a game-theoretical queueing model to analyze the strategic queueing behavior of patients. In such hospitals, patients are divided into first-visit and referred patients, and the hospitals provide patients with two service phases of “diagnosis” and “treatment”. We first obtain the expected sojourn time. By defining the patience level of patients, the queueing behavior of patients in equilibrium is studied. The results suggest that as long as the patients with low patience levels join the queue, the patients with high patience levels also join the queue. As more patients arrive at the hospitals, the queueing behavior of patients with high patience levels may have a negative effect on that of patients with low patience levels. The numerical results also show that the equilibrium behavior deviates from a socially optimal solution; therefore, to reach maximal social welfare, the social planner should adopt some regulatory policies to control the arrival rates of patients.

Blockchain adoption to eliminate dispute in platform-supplier cooperative delivery

Inefficient delivery services undermine consumers’ online shopping experience, posing a crucial challenge for e-commerce platforms and suppliers. In a platform-supplier cooperative delivery system, the issue of unclear responsibility for quality defects often arises. By utilizing real-time traceability enabled by blockchain, the platform can promptly identify supplier delivery accidents and return defective products directly to the supplier. This blockchain-enabled return mechanism could significantly reduce disputes in cooperative delivery processes. We develop a game theoretical model to analyze delivery strategies in supply chains with and without blockchain, and further examine the impact of blockchain on members’ delivery services and their profits. Our analysis indicates that blockchain incentivizes the platform to conduct high-level delivery services. However, it may either encourage or discourage the supplier’s provision of high-level delivery services. Moreover, blockchain acts as a double-edged sword in the supply chain, enhancing consumer satisfaction while also increasing wholesale prices. The interaction between these two effects determines whether blockchain negatively impacts on the supply chain’s profit, with the disposal cost of returned products by the supplier playing a crucial role. When the supplier’s disposal cost is relatively high, blockchain may result in supply chain members being worse off, despite its enhancement of consumer satisfaction. Furthermore, we find that blockchain enhances both the delivery service level and the profit of the supply chain if neither the supplier’s disposal cost nor the platform’s cost of high-level service are high. This study provides valuable insights into the potential benefits and challenges associated with blockchain adoption in cooperative delivery systems.

Verifiable Crowd Computing: Coping with bounded rationality

In this paper we use the repeated-games framework to design and analyze a master-worker (MW) mechanism, where a master repeatedly outsources computational tasks to workers in exchange for payment. Previous work on MW models assumes that all workers are perfectly rational and aim to maximize their expected utility. Perfect rationality is a strong behavioral assumption because it requires that all workers follow the prescribed equilibrium. Such a model may be unrealistic in a practical setting, where some agents are not perfectly rational and may deviate from the equilibrium for short-term gains. Since the correctness of these MW protocols relies on workers following the equilibrium strategies, they are not robust against such deviations.We augment the game-theoretical MW model with the presence of such bounded-rational players or deviators. In particular, we show how to design a repeated-games based MW Verifiable Crowd Computing mechanism that incentivizes the rational workers (or followers) to effectively punish such deviators through the use of terminal payments. We show that the master can use terminal payments to obtain correct answers to computational problems even in this more general model. We supplement our theoretical results with simulations that show that our mechanism outperforms related approaches.

A game theoretic decision forest for feature selection and classification

Abstract Classification and feature selection are two of the most intertwined problems in machine learning. Decision trees (DTs) are straightforward models that address these problems offering also the advantage of explainability. However, solutions that are based on them are either tailored for the problem they solve or their performance is dependent on the split criterion used. A game-theoretic decision forest model is proposed to approach both issues. DTs in the forest use a splitting mechanism based on the Nash equilibrium concept. A feature importance measure is computed after each tree is built. The selection of features for the next trees is based on the information provided by this measure. To make predictions, training data is aggregated from all leaves that contain the data tested, and logistic regression is further used. Numerical experiments illustrate the efficiency of the approach. A real data example that studies country income groups and world development indicators using the proposed approach is presented.

Should competing sellers adopt the data analytics service to enable product improvement in online marketplace

More and more marketplace platforms choose to use the data gathered from consumers (e.g., customer search terms, demographics) to provide a data analytics service for third-party sellers, both encouraging innovations and improving the operations of the latter. Through adopting the data analytics service, a seller can enhance its competitiveness by improving the quality of its products when there is more than one seller offering substitutes. This study develops a game-theoretic model to characterize a scenario with a marketplace platform and two competing sellers that sell substitutable products and decide whether to adopt the data analytics service provided by the platform. Then, we find that a seller's decision of whether or not to purchase the service depends on the competitor's decision and the two sellers' absorptive capacities of knowledge. Furthermore, a seller can benefit from adopting the data analytics service only if the seller has a high absorptive capacity, which can help increase product quality. When only one seller adopts, the platform and competing sellers can benefit from the adoption if this seller's absorptive capacity is high and the other's is moderate. The sellers' interests and social welfare can be aligned unless both sellers' absorptive capacities are low or high.

When ownership and copyright are separated: Economics of non-fungible token marketplaces with secondary markets

Creators have long strived to secure royalties for their works but with little success. In the digital realm, monetization presents an even greater challenge, as traditional digital assets frequently suffer from piracy issues, primarily due to the lack of verifiable ownership. Recently, non-fungible token (NFT), a blockchain-enabled tradable digital asset, has aroused great public attention for its potential to address this long-standing issue. Specifically, NFTs empower creators by enabling them to earn resale royalties from post-primary-sale transactions and by providing verifiable ownership that facilitates consumer trading in a secondary market. In this paper, we employ a two-stage game-theoretic model to examine this innovative business model. Here, an NFT creator makes optimal pricing and royalty rate decisions, while consumers make their purchase and resale decisions accordingly. Our study reveals that the creator inevitably reduces the selling price when a secondary market is introduced, even without imposing royalties. Moreover, contrary to conventional wisdom that NFT-enabled royalties always benefit creators, our research uncovers that the introduction of a secondary market leads to unintended revenue loss for the creator, particularly when most consumers only engage in secondary transactions. Furthermore, we find that the platform's profitability diminishes with the introduction of a secondary market, especially when most consumers are uninformed and the platform relies primarily on the primary market for commission collection. Finally, we find that the introduction of a secondary market may leave consumers worse off, despite the resale opportunities it offers. Our findings carry crucial managerial implications for platforms, creators, consumers, and policymakers.

How power structure and markup schemes impact supply chain channel efficiency under price-dependent stochastic demand

Although considerable attention has been separately given to factors such as power structures, price-dependent demand, and markup pricing schemes, there has been limited exploration of the combined effects of these factors on supply chain efficiency and the leader’s advantage. We propose a game theoretic model in which a manufacturer sells a single product to a newsvendor retailer who sets both optimal order quantity and selling price under uncertain price-dependent demand. Furthermore, we examine a supply network wherein a single retailer fulfills orders using a global manufacturer for regular orders and a local manufacturer to clear any shortages. Through numerical analysis, we show that the retailer always prefers to charge a percentage markup. In a two-player game, channel efficiency is higher when the retailer is the leader under linear demand; however, under iso-elastic demand, the manufacturer being a leader brings a higher channel efficiency. When a local manufacturer is involved as a second manufacturer, channel efficiency is higher when the retailer remains a follower, as this induces more fierce wholesale price competition between the two manufacturers. Additionally, when demand uncertainty is high in the two-player game with linear demand, the retailer as a follower can achieve higher profits, whilst high uncertainty under iso-elastic demand decreases both players’ profits. Moreover, it becomes advantageous for the retailer to have a local manufacturer as demand uncertainty increases, even when the local manufacturer announces the wholesale price first.

Competition between New Energy and Fuel Vehicles with Behavior-Based Pricing Strategies When Considering Environmental Concerns and Green Innovation

Environmental sustainability is an important issue in supply chain management (SCM). New energy vehicles (NEVs) have significant environmental value when compared to traditional fuel vehicles (FVs). Currently, there is intense competition between fuel and new energy vehicles, owing to differentiated pricing strategies. This paper focuses on behavior-based pricing (BBP) strategies between energy vehicles and fuel vehicles in a two-echelon supply chain wherein consumers are environmentally conscious. A two-period game-theoretic model is built to examine the effect of consumers’ environmental concerns on competition between fuel and energy vehicles, behavior-based pricing strategies, supply chain efficiency, and social welfare. The analytical results indicate that consumers’ environmental concerns facilitate the market share competition by new energy vehicle firms in the second period. If consumers care more about the environment, supply chain efficiency is improved in cases of retail as well as wholesale-and-retail behavior-based pricing strategies. Wholesale-and-retail behavior-based pricing strategies benefit all members of the supply chain, but this is not the case for retail behavior-based pricing strategies. If consumers are sufficiently concerned about the environment and new energy vehicle firms are more efficient, a win–win–win scenario for firms, consumers, and social welfare occurs in the two behavior-based pricing strategies. Counterintuitively, green innovation improves new energy vehicle, fuel vehicle as well as overall supply chain efficiency, in three cases.

Game theoretical analysis in two-echelon sustainable supply chains to manage and coordinate strategic decisions

Business firms and consumers are increasingly and rapidly realizing the benefits of adopting environmental responsibility. The business firms stress including it in their supply chain strategy and/or operations. This paper explores the environmental consciousness and quality attentiveness of consumers in a supply chain. Demand for the product depends on its price, eco-friendly level, and quality of the product. Three decentralized models are developed under three different game theoretical settings. A comparative study of the three game-theoretical models reveals several characteristics. It determines conditions under which channel members can decide the policy for the best possible benefit. The present work explores the effect of customer environmental awareness on the optimal decision. Production of eco-friendly items is also the prime consideration of our paper. It reveals that the manufacturer gets the most profit in the manufacturer Stackelberg game model, but the retailer gets the maximum profit from the retailer Stackelberg game model. Although channel members prefer their leading position to make the game theoretical decision-making process, the most decentralized channel profit is obtained from the Nash game model. Thus, consumers will also benefit, and the best channel profit can be obtained if the channel members agree to make decentralized decisions using the Nash game model. The centralized channel profit outperforms all three decentralized channel profits. The paper applies the concepts of sub-game perfect equilibrium and alternating offer bargaining to coordinate the channel and distribute surplus profit. Finally, analytical results are verified using numerical examples.

Nonlinear dynamics and game-theoretic modeling in economics and finance

In this foreword to the Special Issue “Nonlinear dynamics and game-theoretical modeling in economics and finance” we review the contributions in the issue highlighting the economic results and the connections with aspects of dynamic analysis. Indeed, the common theme of the contributions is the focus on system dynamics and the latest analytical techniques. This issue is devoted to celebrating the 70th birthday of Professor Laura Gardini, who inspired a generation of scholars in the study of discrete-time systems and global methods of analysis.

Committing to the wrong artificial delegate in a collective-risk dilemma is better than directly committing mistakes

While autonomous artificial agents are assumed to perfectly execute the strategies they are programmed with, humans who design them may make mistakes. These mistakes may lead to a misalignment between the humans’ intended goals and their agents’ observed behavior, a problem of value alignment. Such an alignment problem may have particularly strong consequences when these autonomous systems are used in social contexts that involve some form of collective risk. By means of an evolutionary game theoretical model, we investigate whether errors in the configuration of artificial agents change the outcome of a collective-risk dilemma, in comparison to a scenario with no delegation. Delegation is here distinguished from no-delegation simply by the moment at which a mistake occurs: either when programming/choosing the agent (in case of delegation) or when executing the actions at each round of the game (in case of no-delegation). We find that, while errors decrease success rate, it is better to delegate and commit to a somewhat flawed strategy, perfectly executed by an autonomous agent, than to commit execution errors directly. Our model also shows that in the long-term, delegation strategies should be favored over no-delegation, if given the choice.

Navigating the recycling intention of electronics manufacturers in collaborating with a third-party collector: A Stackelberg game analysis

Despite recycling being perceived as essential for sustainable development in the electronics industry, two critical issues have yet to be thoroughly investigated: the role of government intervention and contract design on improving recycling intentions of manufactures. This study employs game-theoretic models to examine the impact of government intervention and various contractual arrangements on the strategic decision-making of electronics manufacturers regarding recycling. Our findings reveal that: 1) the manufacturers' recycling intentions increase when tax rate is within certain range, as their recycling effort, under such a circumstance, contributes to improvements in their profitability; 2) when collaborating with a third-party collector (3 PR), the 3 PR may exhibit passive resistance by reducing recycling efforts upon detecting manufacturers' insufficient incentives for recycling. Intriguingly, the 3 PR will increase the price offered to consumers for recycled materials in order to stimulate greater quantities of recycling; (3) the different designs of recycling contracts can incentivize manufacturers to engage in recycling activities in various ways. Overall, the findings presented herein provide new insights into examining the incentive mechanisms of manufacturers’ recycling intentions and identifying potential challenges and opportunities for policy implementation. This study also offers managerial implications for decision-makers to enhance the efficiency of their recycling efforts and contribute to a more sustainable production system.

Evolution of Cooperation in Spatio-Temporal Evolutionary Games with Public Goods Feedback.

In biology, evolutionary game-theoretical models often arise in which players' strategies impact the state of the environment, driving feedback between strategy and the surroundings. In this case, cooperative interactions can be applied to studying ecological systems, animal or microorganism populations, and cells producing or actively extracting a growth resource from their environment. We consider the framework of eco-evolutionary game theory with replicator dynamics and growth-limiting public goods extracted by population members from some external source. It is known that the two sub-populations of cooperators and defectors can develop spatio-temporal patterns that enable long-term coexistence in the shared environment. To investigate this phenomenon and unveil the mechanisms that sustain cooperation, we analyze two eco-evolutionary models: a well-mixed environment and a heterogeneous model with spatial diffusion. In the latter, we integrate spatial diffusion into replicator dynamics. Our findings reveal rich strategy dynamics, including bistability and bifurcations, in the temporal system and spatial stability, as well as Turing instability, Turing-Hopf bifurcations, and chaos in the diffusion system. The results indicate that effective mechanisms to promote cooperation include increasing the player density, decreasing the relative timescale, controlling the density of initial cooperators, improving the diffusion rate of the public goods, lowering the diffusion rate of the cooperators, and enhancing the payoffs to the cooperators. We provide the conditions for the existence, stability, and occurrence of bifurcations in both systems. Our analysis can be applied to dynamic phenomena in fields as diverse as human decision-making, microorganism growth factors secretion, and group hunting.

Games, Norms, and Utterances

AbstractA body of work proposes that social-norm change can be explained in terms of game theory. These game theoretic models, however, don't fully account for how and why utterances are used to change social norms. This paper describes the problem and some of the solution elements. There are three existing, relevant, game-based models. The first is a game theoretic model of social norm change (Bicchieri, 2005, 2016). This accounts for how individuals make decisions to adhere to or violate norms, based on empirical expectations of how others will behave. The second is the idea of a conversational game (Lewis, 1979) and its extensions. This posits that speech acts are accommodated in a conversation to make what is said correct play. This feature can explain how some speech acts, such as slurring utterances, change the dynamics of a conversation. The third is a theory of pragmatic inference, known as Rational Speech Act theory (Goodman and Frank, 2016). This is a computational theory of pragmatics, of how listeners interpret utterances and how speakers construct utterances that can be understood. This paper proposes, without setting out the full formal model, that elements of these three theories need to be incorporated together into a game theoretic model of how utterances change long-term social norms.

Retail Category Management with Slotting Fees

Problem definition: Slotting fees are lump-sum payments retailers demand from manufacturers to include manufacturers’ products in their assortments. Although retailers regard slotting fees as part of doing business, some manufacturers claim that slotting fees limit their ability to compete on a level playing field with other manufacturers. Considering these conflicting views, we study the role of manufacturer competition in the emergence of slotting fees and how slotting fees affect retailers’ category management (i.e., assortment and pricing) decisions. Methodology/results: We consider a game-theoretic model with a single retailer and two competing manufacturers, each offering a single product. The retailer makes slotting fee, assortment, and pricing decisions in the presence of an operational cost term that increases in the assortment size. The manufacturers that can afford the slotting fee set the wholesale prices for their products. This study leads to three key findings. First, slotting fees can be suboptimal when their absence would trigger intense wholesale price competition. Second, depending on the retailer’s operational cost and the intensity of manufacturer competition, slotting fees create three distinct effects -category expansion, rent extraction, and competitive exclusion under which product variety (i.e., the retailer’s assortment size) increases, remains unchanged, and decreases, respectively. Third, slotting fees are most (least) beneficial for the retailer when they lead to a decrease (increase) in product variety. Managerial implications: This study not only illustrates that retailers can use slotting fees as a strategic tool to control the intensity of manufacturer competition but also reveals how slotting fees impact retailers’ assortment and pricing decisions, with implications for manufacturers and policy makers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0143 .

Unlocking Flexible Electric Vehicle Charging via New Rate Design

A high penetration of electric vehicles (EVs) will deeply impact the management of electric power systems. The risk of not providing adapted EV pricing signals can lead to inefficient investments in grid infrastructure. To avoid costly grid reinforcements and to ensure proper guidance for EV charging, a solution allowing customers to access EV-only rates without installing a separate meter, which we refer to as submetering, is an attractive option for EV owners and grid operators. We develop a game-theoretical model expressed and treated as a mathematical program with equilibrium constraints (MPEC) to capture the interaction between a national regulatory authority (NRA) designing these tariffs and heterogeneous agents. This framework represents a stylized regulatory setup applicable to several European countries. First, we analyse the conditions in which EV-only tariffs can be applied for domestic charging by comparing different energy profiles. Second, we study the impact of EV charging on different tariff structures to identify the most efficient way of recovering network costs. We found that the adoption of submetering under a pure volumetric tariff can bring yearly gains varying from [Formula: see text] to [Formula: see text] to consumers with EV. Finally, we derive policy implications from the results.

Video platforms’ advertising and pricing decisions in the presence of derivative videos

Derivative videos generated based on copyright videos are a common type of user-generated content (UGC) on today’s video platforms. Given the role of derivative videos in resolving consumers’ uncertainty about the quality of paid copyright videos, and their potential to generate advertising revenue, we develop a game-theoretic model to investigate a video platform’s optimal joint-decisions on pricing and advertising to maximize the utilization of derivative videos. We find that including advertisements into derivative videos to derive advertising revenue is not always advantageous for platforms. Under certain conditions, it is more beneficial for platforms to exclude advertisements from derivative videos. The presence of a derivative video may not affect the optimal price of the copyright video. Furthermore, a higher consumer initial quality expectation for the copyright video does not necessarily lead to a greater platform profit in the presence of a derivative video.

Game-theoretic modeling in regulating greenhouse gas emissions

This research introduces an innovative framework for addressing the escalating issue of greenhouse gas emissions through the integration of game theory with differential equations, proposing a novel model to simulate the regulatory dynamics between emission sources and legislative actions. By blending advanced mathematical modeling with environmental science, this paper underscores the critical necessity for pioneering, proactive strategies in environmental management and policy formulation. Central to our approach is the simulation of interactions within a game-theoretic context, aiming to delineate optimal strategies for emission sources and regulatory bodies, factoring in legislative constraints and environmental ramifications. The methodology employs a system of ordinary differential equations, capturing the dynamic, non-stationary nature of atmospheric processes and offering a realistic portrayal of the challenges in mitigating greenhouse gas emissions. Furthermore, the study introduces a fee-based regulatory mechanism designed to encourage emission reductions, highlighting the economic implications of such strategies. Significantly contributing to environmental management, this research presents a detailed model capable of predicting the trajectory of greenhouse gas emissions over a decade, considering the potential impact of technological innovations in emission control. The conclusion emphasizes the promising role of artificial intelligence in refining environmental governance, acknowledging the complexities and limitations inherent in predictive modeling. Aimed at policymakers and environmental scientists, this paper serves as a strategic tool for informed decision-making, advocating for a multidisciplinary approach to develop sustainable, effective solutions to combat one of the most critical environmental challenges facing the globe today.

Investment in Data Analytics with Manufacturer Encroachment

Online retail platforms such as Amazon and Tmall have the ability to create personalized recommendations based on the consumer’s browsing history, purchase history, and preferences by investing in data analytics capability. In practice, manufacturers may encroach on the retail market through the agency channel that sells products directly to online consumers in addition to wholesale products to retail platforms through the reselling channel. In this study, we develop a game-theoretic model to study the interplay between the manufacturer’s encroachment and the online retail platform’s data analytics capability investment. Our outcomes reveal that the conditions for the manufacturer to encroach become more lenient if the platform invests in data analytics capability, and we show that the investment in data analytics capability can lead to a Pareto improvement and the manufacturer can free ride on the platform’s investment. Moreover, we found that the manufacturer’s encroachment always creates more incentives for the platform to enhance the investment level in data analytics capability. Our research in this study provides useful insights for managers to make encroachment decisions and data analytics capability investment decisions with the manufacturer who sells through the online retail platform.