Non-cooperative Scenario Research Articles

Coopetition strategies in a two-stage, multi-agent supply chain under hybrid carbon trading mechanisms

ABSTRACT Managing a multi-agent supply chain within a hybrid carbon trading framework, which integrates Certified Emission Reductions (CERs) and Carbon Quota Trading (CQT), presents a significant challenge. This complexity is further amplified by the interplay of competition and cooperation among agents. Motivated by this, we develop a biform game-based cooperative mechanism for a two-stage supply chain that includes a high-emission manufacturer, a negative-emission manufacture and a downstream retailer. This mechanism is designed to encapsulate both the non-cooperative and cooperative game elements. The former features a product competition game among the manufacturers and retailer, setting the stage for the cooperative game without directly yielding equilibrium solutions. The latter involves a cost-sharing agreement for the CER project's initial investment, informed by various coalitional zero-sum games. Our analytical findings, corroborated by numerical simulations, reveal that the biform game coordination mechanism significantly enhances the volume of CERs traded and consumer surplus, compared to a non-cooperative scenario. It effectively aligns the collaborative efforts of all agents within the supply chain. This research contributes substantially to the literature on coordinating multi-stage, multi-agent supply chains, and the strategic deployment of emission reduction initiatives in supply chains. It also offers valuable managerial insights for the design of government regulations in carbon markets.

DRL-Tomo: a deep reinforcement learning-based approach to augmented data generation for network tomography

Abstract Accurate and current comprehension of network status is crucial for efficient network management. Nevertheless, direct network measurement strategies entail substantial traffic overhead and demand intricate coordination among network entities, making them impractical. Network tomography, an indirect measurement approach, utilizes insights garnered from measured parts to deduce characteristics of the entire network. Past studies frequently depend on acquiring challenging-to-access information, such as the complete network topology or support from specialized protocols. Unfortunately, these constraints pose challenges in non-cooperative scenarios where obtaining such information is difficult. Recent endeavors pursue emancipating tomography from dependence on copious information, striving to predict unmeasured path performance using limited data. Nevertheless, the disparity between the measured data and actual performance has hindered the accuracy. In response, we introduce an innovative tomography framework named DRL-Tomo, designed to alleviate potential biases. DRL-Tomo initiates by generating augmented data through deep reinforcement learning, gradually approximating the genuine performance of unmeasured paths. Subsequently, a neural network model is trained using this augmented data, enabling precise inferences. Our experiments, encompassing both real-world and synthetic datasets, vividly demonstrate DRL-Tomo’s remarkable enhancement. Specifically, it achieves a substantial 10%–67% improvement in path delay prediction and an impressive 30%–98% enhancement in path loss rate prediction.

Second‐best socially optimal R&D under output spillovers

AbstractUsing the standard two‐stage game of process R&D and Cournot competition with R&D output spillovers, this paper provides a thorough second‐best welfare analysis. The planner's solution is compared with the standard noncooperative scenario, the R&D cartel, the cartelized research joint venture, and the social research joint venture solution in terms of propensities for R&D as well as welfare levels. The main result is that, when spillovers are not too high, a cartelized joint venture unexpectedly outperforms the planner's solution in terms of propensities for R&D and resulting welfare level, though it is the only market scenario to do so. We also assess the performance of a social joint venture, relative to all the well‐known scenarios for the organization of R&D. Finally, we observe that the gap between market outcomes and planner's solutions, in terms of welfare levels, increases as R&D becomes less appropriable.

Designing profitable supply chains for lithium-ion battery recycling in the United States

<abstract> <p>Recycling spent lithium-ion batteries (LIBs) has attracted lots of attention recently, due to the increasing demand for critical materials contained in LIBs, putting high pressure on their geological reserves. We evaluated the potential of bioleaching technology as a sustainable solution for recycling spent LIBs to help inform decision-making processes for stakeholders involved in LIB recycling supply chains. A supply chain model was developed to include required upstream processes with the objective of maximizing economic feasibility of LIB recycling through the technology. The model has been applied to the U.S. and an optimal supply chain configuration was identified, considering the major factors affecting the economic viability of the technology. The net present value of the supply chain was estimated to be ＄18.4 billion for operating over 10 years, achieving the maximum processing capacity of 900,000 tons of black mass per year. The economic viability of the technology was identified to be highly sensitive to the cost associated with purchasing black mass, which accounted for more than 60% of the total supply chain cost. The breakeven price of black mass was identified as ＄8.7/kg over which the supply chain was not economically sustainable. Additionally, we examined the non-cooperative scenarios where each tier tries to maximize its own profit to demonstrate how the overall profitability of the supply chain changes with different pricing strategies of sortation facilities and acid producers. We estimated that the maximum prices of non-recyclable paper and acid that the supply chain could tolerate were ＄0.89/kg and ＄8.5/kg, respectively, beyond which the supply chain was no longer sustainable.</p> </abstract>

Impact analysis of a construction and demolition waste dumping quota trading scheme in uncertain, cooperative, and non-cooperative scenarios

Construction and demolition waste (C&DW) has emerged as a significant environmental challenge, demanding urgent attention and innovative solutions. In response, this paper introduces a novel approach, the C&DW Dumping Quota Trading Scheme (CDWTS), and presents an optimization model to gauge its deep-seated implications on construction project investments and environmental pollution mitigation. This model considers both collaborative and non-collaborative interactions among decision-makers and effectively addresses the uncertainties inherent in the evaluation process through a proposed Hybrid Defuzzification Strategy (HDS). The HDS integrates distributed robust optimization, fuzzy set theory with risk-oriented, and sensitivity analysis to attain risk hedging that balances economy and robustness in the assessment and analysis of the CDWTS. Numerical testing of the model using data from Nanjing, China offers valuable perspectives for CDWTS stakeholders. Specifically, the CDWTS serves as a powerful tool for encouraging enterprises to internalize the external costs of pollution, resulting in significant environmental improvements and the promotion of cleaner production processes within the construction industry. Notably, the allotment and pricing of quotas are identified as pivotal factors in optimizing environmental benefits. Future implementations of the CDWTS should prioritize the development of equitable and efficient quota allocation methods while ensuring stability in quota pricing. Furthermore, it's vital to recognize the potential environmental risks associated with collusion among enterprises and the utilization of rudimentary recycling technologies. Thus, it is recommended to augment subsidies for technological investments in the recycling process and to enhance supervision and management of enterprise alliances. In conclusion, unlike prior studies that mainly delved into dissecting CDWTS concepts and designing its framework, this paper conducts a comprehensive analysis of CDWTS's economic and environmental benefits while addressing uncertainties. Policymakers should consider adopting the CDWTS and HDS into their waste management toolkits, as well as fully assessing the potential concerns identified in this study. These tools and insights not only assist the construction industry in moving toward cleaner production on its own, but they also address potential barriers for policymakers on a path to "waste-free cities".

On the optimisation of detector threshold for energy efficient CSS over fading channels

ABSTRACT Non-cooperative scenarios in cognitive wireless sensor network (CWSN) are often encountered with shadowing and hidden terminal issues. Cooperative spectrum sensing (CSS) can solve these issues but at the expense of large overhead. It is necessary to optimise the energy of battery-operated unlicensed users, also known as secondary users (SUs). The effect of fading and noise uncertainty is often overlooked when determining CSS performance. Energy efficiency (EE) is a comprehensive parameter that gives a complete picture of the overall performance of the CSS. There are several parameters that affect the EE of CSS. In this paper, the detector threshold is optimised to maximise the EE of the system. A system model is proposed to determine the EE of centralised CSS over different fading channels in noisy reporting conditions. An iterative algorithm is presented which determines the optimum detector threshold for which the EE is maximum. Results show that the optimum value of the detector threshold from the analytical model matches with simulation data.

A Modulation Recognition System for Underwater Acoustic Communication Signals Based on Higher-Order Cumulants and Deep Learning

Underwater acoustic channels, influenced by time-varying, space-varying, frequency-varying, and multipath effects, pose significant interference challenges to underwater acoustic communication (UWAC) signals, especially in non-cooperative scenarios. The task of modulating and identifying distorted signals faces huge challenges. Although traditional modulation recognition methods can be useful in the radio field, they often prove inadequate in underwater environments. This paper introduces a modulation recognition system for recognizing UWAC signals based on higher-order cumulants and deep learning. The system achieves blind recognition of received UWAC signals even under non-cooperative conditions. Higher-order cumulants are employed due to their excellent noise resistance, enabling the differentiation of OFDM signals from PSK and FSK signals. Additionally, the high-order spectra differences among signals are utilized for the intra-class recognition of PSK and FSK signals. Both simulation and lake test results substantiate the effectiveness of the proposed method.

To Compete or to Collaborate? Logistics Service Sharing and Retailers’ Resale in Competitive Online Channels

The prosperity of e-commerce has made more and more businesses willing to enter the e-commerce market, which has also brought a series of strategic collaboration between firms. This study considers game models with and without collaboration between the platform and the retailer. An e-commerce platform has relative logistics service sharing advantages while the retailer has relative procurement advantages. We formulated a multichannel supply chain consisting of a manufacturer and two retailers to explore the feasibility of the above strategic collaboration model. We utilized the Stackelberg game and Nash game approaches to obtain equilibrium solutions under both cooperative and noncooperative scenarios. Through a further analysis, we determined the impacts of the logistics sensitivity, the cost of the unit logistics service effort, the price of shared logistics service per unit, and the price competition intensity on optimal prices, the logistics service efforts, and the profits. Moreover, the collaborative exchange of advantages between the platform and the retailer needs to consider the interests of participating manufacturers in the game. Our extension suggests all three firms should actively promote deeper collaboration.

Multi-Unit Auctions for Allocating Chance-Constrained Resources

Sharing scarce resources is a key challenge in multi-agent interaction, especially when individual agents are uncertain about their future consumption. We present a new auction mechanism for preallocating multi-unit resources among agents, while limiting the chance of resource violations. By planning for a chance constraint, we strike a balance between worst-case approaches, which under-utilise resources, and expected-case approaches, which lack formal guarantees. We also present an algorithm that allows agents to generate bids via multi-objective reasoning, which are then submitted to the auction. We then discuss how the auction can be extended to non-cooperative scenarios. Finally, we demonstrate empirically that our auction outperforms state-of-the-art techniques for chance-constrained multi-agent resource allocation in complex settings with up to hundreds of agents.

Public Subsidies and Cooperation in Research and Development. Evidence from the LAB

Abstract We implement an experimental design based on a duopoly game in order to analyse the impact of public subsidies on the willingness to cooperate in research and development (R&D) activities. We first implement six experimental markets by exogenously varying the level of knowledge spillovers (low or high) and the intensity of competition in the product market (low, intermediate, or high). We find that the probability of cooperation increases in the level of spillovers and decreases in that of market competition. We then replicate the six experimental markets by subsidising subjects who cooperate. Whenever they are sufficient to change the incentive structure of the game, subsidies substantially increase the probability of cooperation, causing, however, a reduction of R&D investments. Overall, our evidence suggests that, depending on the characteristics of the market, the provision of subsidies is not always desirable. These might be redundant because firms have sufficient private incentives to invest cooperatively in R&D, or even counterproductive, as they might induce firms to significantly reduce R&D investments compared to the noncooperative scenario.

Dual-branch time–frequency domain anti-interference method for ship radiated noise signal

Sensing the target ship using the radiated noise of the ship is the main means of sensing the target ship. In the adversarial and non-cooperative scenarios, explosion interference is a typical interference source used to mask the target ship and confuse the underwater acoustic sensing system to effectively perceive the target. In this paper, we try to use the idea of signal enhancement to deal with the anti-explosion interference. we try to enhance the ship radiated noise signal with extremely low signal-to-noise ratio, so as to achieve anti-explosion signal interference. We propose a deep learning approach with encoder–decoder as the basic framework. We design a decoder with two branches, one for estimating the he mask of the noise signal amplitude spectrogram and the other for estimating the complex spectrogram. We add the attention mechanism to the model to select features relevant to each branch task. Finally, we use the real experimental data to test, the experimental results show that our method can enhance the ship radiation noise signal to 2 dB under the condition of −20 dB to −25 dB very low signal-to-noise ratio. The experimental results fully prove that our method can achieve anti-explosion interference of ship radiated noise.

Should fiscal policies be centralized in a monetary union? A dynamic game approach

In this paper we analyze dynamic interactions in a monetary union with three fiscal players (the governments of the countries concerned) and a common central bank in the presence of exogenous shocks. The model is calibrated for the euro area and includes a fiscally more solid core block denoted as country 1 as well as a fiscally less solid periphery block represented by countries 2 and 3. Introducing two periphery countries allows us to capture different attitudes of the periphery countries towards the goal of sustainable fiscal performance. Moreover, different coalition scenarios are modelled in this study including a fiscal union, a coalition of periphery countries and a coalition of fiscal-stability oriented countries. The exogenous shocks are calibrated in such a way as to describe the last major crises in the euro area, namely the financial crisis, the European sovereign debt crisis, the Covid-19 crisis, and the Ukraine war (energy price) crisis. Using the OPTGAME algorithm we calculate a cooperative Pareto and non-cooperative feedback Nash equilibrium solutions for the modelled scenarios. The fully cooperative solution yields the best results. The different non-cooperative scenarios allow insights into the underlying trade-off between economic growth, price stability and fiscal stability.

Solving routing problems for multiple cooperative Unmanned Aerial Vehicles using Transformer networks

Missions involving Unmanned Aerial Vehicle usually consist of reaching a set of regions, performing some actions in each region, and returning to a determined depot after all the regions have been successfully visited or before the fuel/battery is totally consumed. Hence, planning a route becomes an important task for many applications, especially if a team of Unmanned Aerial Vehicles is considered. From this team, coordination and cooperation are expected to optimize results of the mission. In this paper, a system for managing multiple cooperative Unmanned Aerial Vehicles is presented. This system divides the routing problem into two stages: initial planning and routing solving. Initial planning is a first step where the regions to be visited are grouped in multiple clusters according to a distance criterion, with each cluster being assigned to each of the Unmanned Aerial Vehicles. Routing solving computes the best route for every agent considering the clusters of the initial planning and a variant of the Orienteering Problem. This variant introduces the concept of shared regions, allowing an Unmanned Aerial Vehicle to visit regions from other clusters and compensating for the suboptimal region clustering of the previous stage. The Orienteering Problem with shared regions is solved using the deep learning architecture Transformer along with a deep reinforcement learning framework. This architecture is able to obtain high-quality solutions much faster than conventional optimization approaches. Extensive results and comparison with other Combinatorial Optimization algorithms, including cooperative and non-cooperative scenarios, have been performed to show the benefits of the proposed solution.

A Novel Method for Few-Shot Specific Emitter Identification in Non-Cooperative Scenarios

Obtaining larger category-label-containing training signal datasets in non-cooperative scenarios is difficult. Moreover, employing smaller labeled signal datasets for specific emitter identification is technically challenging. Therefore, we propose a novel method for few-shot SEI. We first design a bispectral analysis and Radon transformation-based signal preprocessing scheme to obtain feature vectors that effectively characterize the radio frequency fingerprints. The feature vectors are then fed to a network model for feature learning. Moreover, a meta-learning algorithm is applied to the network model to adapt to few-shot feature learning. The conventional meta-learning algorithm is improved to develop a novel algorithm involving latent embedding optimization for meta-learning. The proposed method extracts low-dimensional key features from high-dimensional input data and evaluates the distance and degree of feature dispersion. The resulting information is employed in sample point prediction. The algorithm effectively achieves few-shot SEI and offers stable and efficient recognition after training with a minimum of forty samples. This method identifies emitter individuals under multiple modulation types and exhibits scalability in identifying the emitter numbers. Moreover, it offers adaptability in identifying the emitter individuals under multiple propagation channel types.

Deep Reinforcement Learning Based Decision Making for Complex Jamming Waveforms

With the development of artificial intelligence, intelligent communication jamming decision making is an important research direction of cognitive electronic warfare. In this paper, we consider a complex intelligent jamming decision scenario in which both communication parties choose to adjust physical layer parameters to avoid jamming in a non-cooperative scenario and the jammer achieves accurate jamming by interacting with the environment. However, when the situation becomes complex and large in number, traditional reinforcement learning suffers from the problems of failure to converge and a high number of interactions, which are fatal and unrealistic in a real warfare environment. To solve this problem, we propose a deep reinforcement learning based and maximum-entropy-based soft actor-critic (SAC) algorithm. In the proposed algorithm, we add an improved Wolpertinger architecture to the original SAC algorithm in order to reduce the number of interactions and improve the accuracy of the algorithm. The results show that the proposed algorithm shows excellent performance in various scenarios of jamming and achieves accurate, fast, and continuous jamming for both sides of the communication.

Identification of WFRFT signals' parameter based on multi‐domain statistical features

An efficient approach to recognize the parameter of the weighted-type fractional Fourier transform signals based on the higher-order cumulant is proposed. Due to the index additivity of weighted-type fractional Fourier transform, the statistical features on different weighted fractional domains could be jointly considered, to overcome the problem of parameter ambiguity arising from the existing estimation methods based on higher-order cumulant. This method can improve the ability of blind signal recognition no matter in a cooperative or non-cooperative scenario.

Few-Shot Unsupervised Specific Emitter Identification Based on Density Peak Clustering Algorithm and Meta-Learning

Deep-learning methods are becoming increasingly popular for improving the efficiency of specific emitter identification (SEI). However, in noncooperative scenarios, it is difficult to both obtain a large number of signal samples for the training of deep-learning models and assign labels to the acquired samples. To solve these two problems, a few-shot unsupervised SEI method is proposed in this study. First, the original radio-frequency (RF) signal is preprocessed based on the Hilbert–Huang transform (HHT) to obtain the Hilbert time–frequency spectrum, which can highlight RF fingerprints (RFFs) that can be used as signal training samples. Then, the Hilbert time–frequency spectrum is input to an improved autoencoder network for training to obtain the latent vector that can represent hidden features of the received signal. Next, the clustering by fast search and find of a density peaks [i.e., density peak clustering (DPC)] algorithm is used to cluster and label the latent vector, which is later reconstructed by the improved autoencoder network to obtain training samples with labeled information. Finally, the meta-learning algorithm is used to train the few-shot SEI network under the few-shot condition, so that it can distinguish different types of RF signals, corresponding to specific emitters. The experimental results show that the unlabeled signal training samples can be clustered and labeled accurately with the proposed method to perform SEI efficiently in few-shot scenarios. In comparison with state-of-the-art solutions, the proposed method in this study exhibited its superiority with regard to few-shot unsupervised SEI.

Learning-Based UAV Path Planning for Data Collection With Integrated Collision Avoidance

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectory in multi-UAV noncooperative scenarios while collecting data from distributed Internet of Things (IoT) nodes is a challenging task. In this article, we consider a path-planning optimization problem to maximize the collected data from multiple IoT nodes under realistic constraints. The considered multi-UAV noncooperative scenarios involve a random number of other UAVs in addition to the typical UAV, and UAVs do not communicate or share information among each other. We translate the problem into a Markov decision process (MDP) with parameterized states, permissible actions, and detailed reward functions. Dueling double deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (D3QN) is proposed to learn the decision-making policy for the typical UAV, without any prior knowledge of the environment (e.g., channel propagation model and locations of the obstacles) and other UAVs (e.g., their missions, movements, and policies). The proposed algorithm can adapt to various missions in various scenarios, e.g., different numbers and positions of IoT nodes, different amount of data to be collected, and different numbers and positions of other UAVs. Numerical results demonstrate that real-time navigation can be efficiently performed with high success rate, high data collection rate, and low collision rate.

Few-Shot Specific Emitter Identification Based on Variational Mode Decomposition and Meta-Learning

Recently, deep learning has become the mainstream solution to solve specific emitter identification (SEI) problems. However, because large amounts of labeled signal samples cannot be obtained in noncooperative scenarios, the performance of deep learning-based data-driven methods for SEI was limited. As a result, a novel SEI method targeted on few-shot was proposed in this study. First, the received signal was preprocessed based on variational mode decomposition and the Hilbert analysis to obtain the Hilbert time-frequency spectrum. Subsequently, a classification neural network model was built and trained with a small number of Hilbert time-frequency spectrum samples through meta-learning. This model could identify specific emitters with limited training samples. The experimental results showed that this method accomplishes network training with as few as 80 training samples while obtaining a good level of generalization and effectively identifying different emitter individuals. In addition, this method exhibits a strong robustness to noise by maintaining an identification accuracy of more than 80% in channels with low signal-to-noise ratios. Finally, the proposed method demonstrated better identification performances than other existing methods with its capability to effectively solve SEI problems in the few-shot scenario.

Meta learning-based open-set identification system for specific emitter identification in non-cooperative scenarios

Meta learning-based open-set identification system for specific emitter identification in non-cooperative scenarios