Search Results (8)

Green co-creation plays a vital role in promoting sustainability by engaging both firms and consumers in value creation, yet most studies examine competition and cooperation separately without considering their interplay. This study investigates the dynamics of coopetition in green co-creation by developing a two-stage biform game that integrates competitive interaction and cooperative bargaining within a unified framework. The results show that (1) greater green co-creation efforts, representing deeper firm–customer interactions, improve both parties’ equilibrium outcomes; (2) cooperation leads to greater green effort investment than pure competition; and (3) when Nash bargaining conditions are satisfied, coopetition improves both individual profits and total welfare compared with sole competition. These findings highlight that coopetition not only strengthens mutual economic benefits, but also enhances sustainability performance by balancing competitive and cooperative forces. This study provides an analytical foundation for understanding firm–customer coopetition and offers actionable insights for advancing sustainable value creation in green supply chain management. Full article

From the interdisciplinary perspective of industrial economics and behavioral finance, this study establishes a noncooperative-cooperative biform game model between new energy enterprises and traditional energy enterprises. In this model, sales price is considered the non-cooperative strategy, while R&D expenses borne forms the basis of cooperative alliances. The Shapley value is applied to allocate profits, and numerical analysis is conducted to analyze the impact of factors, such as government subsidies and competitive intensity, on optimal strategies. The findings reveal the following: (1) Government subsidies effectively increase energy sales volume, promote technological advancements in new energy enterprises, and reduce the traditional energy enterprises’ proportion of R&D expenses borne. Moderate increases in competitive intensity are conducive to expanding market size, thereby enhancing both energy sales volume and profits. (2) Reasonably increasing the executive risk preference of energy enterprises encourages traditional energy enterprises to bear a higher proportion of R&D expenses and stimulates new energy enterprises to improve their production level, leading to increased sales value of energy. (3) Rising investment and production costs result in a higher proportion of R&D expenses borne for new energy enterprises. Consequently, the shrinking of new energy value reduces their profits, while the profits of traditional energy enterprises increase correspondingly. Full article

Green product innovation in aluminum building material supply chains is critical for sustainability, particularly amid growing economic and environmental pressures. However, effective coordination is challenged by the presence of multiple agents with divergent interests and heterogeneous innovation capacities. This study proposes coordination mechanisms based on a biform game that integrates both non-cooperative and cooperative elements. Key findings include the following: (1) Greater innovation capability heterogeneity promotes green innovation investment by the stronger manufacturer and enhances overall welfare, but reduce the supplier’s profit. (2) Biform game-based decision making supports the triple bottom line more effectively than decentralized models and offers greater flexibility than centralized ones. (3) A multi-perspective compensation contract, incorporating three decision-making modes, is developed within the biform game. Exogenous decision making helps resolve the endogenous game dilemma, improving coordination outcomes. (4) The coordination framework allows firms to dynamically adjust compensation parameters in response to environmental changes, thereby enhancing supply chain resilience. Our main contribution lies in applying a novel biform game approach to address coordination challenges in green product innovation under innovation capability heterogeneity. In addition, a multi-perspective contract coordination paradigm is proposed to support triple bottom line sustainability. Full article

The traditional non-cooperative and cooperative game methods have limitations in solving the traffic problems of autonomous or assisted driving vehicles using vehicle-to-everything communication. In this paper, the biform game method is introduced to optimize the lane-changing behavior of autonomous or assisted driving vehicles in highway on-ramp areas based on vehicle-to-everything. Considering the lane-changing and speed adjustment needs of autonomous vehicles in high-speed scenarios, a forced lane-changing framework was constructed, and the speed gain allocation was determined based on the target vehicle lane-changing time, and a speed increase was regarded as a benefit. Through the constructed biform game model, research was carried out on conflicting and cooperative vehicles. A strategy combination is first constructed in the non-cooperative situation, and then the cooperative game competition stage begins. The Shapley value is used to deduce the distribution value of each participant in the cooperative game stage, which is the profit value in the non-cooperative stage, and then the pure-strategy Nash equilibrium solution is calculated. The interaction with other vehicles in the lane-change process is based on maximizing the benefit to all the vehicles participating in the lane change, and the optimal speed solution of the biform game model when changing lanes is obtained. Numerical examples were used to verify the validity and feasibility of the model and broaden the application range of the biform game method. In future research, this method will be applied to more complex traffic models, such as driving models in emergency situations and research from the perspective of road infrastructure designers, providing new ideas and directions for optimization strategies for autonomous vehicle lane changes in the Internet of Vehicles. Full article

In this paper, we try to study a class of biform games with the coalition function from the cooperation of players. For this purpose, we interpret the biform games as cooperative games by defining a characteristic function of minimax representation based on the coalition function and giving the core and Shapley value as cooperative solutions. The relations between the coalition function and the characteristic function are investigated in terms of additivity and convexity, and the properties associated with the characteristic function, such as individual rationalities and cores, are compared with the corresponding results. The relations among the solutions of the normal-form game, biform game, and cooperative game are discussed with several examples. Full article

We define the mixed strategy form of the characteristic function of the biform games and build the Shapley allocation function (SAF) on each mixed strategy profile in the second stage of the biform games. SAF provides a more detailed and accurate picture of the fairness of the strategic contribution and reflects the degree of the players’ further choices of strategies. SAF can guarantee the existence of Nash equilibrium in the first stage of the non-cooperative games. The existence and uniqueness of SAF on each mixed strategy profile overcome the defect that the core may be an empty set and provide a fair allocation method when the core element is not unique. Moreover, SAF can be used as an important reference or substitute for the core with the confidence index. Full article

Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today’s mobile networks. Such a communication paradigm requires a certain level of intelligence at the device level, thereby allowing it to interact with the environment and make proper decisions. However, decentralizing decision-making may induce paradoxical outcomes, resulting in a drop in performance, which sustains the design of self-organizing yet efficient systems. We propose that each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. In the first part of this article, we describe a biform game framework to analyze the proposed self-organized system’s performance, under pure and mixed strategies. We use two reinforcement learning (RL) algorithms, enabling devices to self-organize and learn their pure/mixed equilibrium strategies in a fully distributed fashion. Decentralized RL algorithms are shown to play an important role in allowing devices to be self-organized and reach satisfactory performance with incomplete information or even under uncertainties. We point out through a simulation the importance of D2D relaying and assess how our learning schemes perform under slow/fast channel fading. Full article

Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today’s mobile networks. Such a communication paradigm requires implementing a certain level of intelligence at device level, allowing to interact with the environment and select proper decisions. However, decentralizing decision making sometimes may induce some paradoxical outcomes resulting, therefore, in a performance drop, which sustains the design of self-organizing, yet efficient systems. Here, each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. Given the set of active devices and the channel model, we derive the outage probability for both cellular link and D2D link, and compute the system throughput. We capture the device behavior using a biform game perspective. In the first part of this article, we analyze the pure and mixed Nash equilibria of the induced game where each device seeks to maximize its own throughput. Our framework allows us to analyse and predict the system’s performance. The second part of this article is devoted to implement two Reinforcement Learning (RL) algorithms enabling devices to self-organize themselves and learn their equilibrium pure/mixed strategies, in a fully distributed fashion. Simulation results show that offloading the network by means of D2D-relaying improves per device throughput. Moreover, detailed analysis on how the network parameters affect the global performance is provided. Full article