Non-cooperative Approach Research Articles

The Complexity of LTL Rational Synthesis

In rational synthesis , we automatically construct a reactive system that satisfies its specification in all rational environments, namely environments that have objectives and act to fulfill them. We complete the study of the complexity of LTL rational synthesis, when the objectives are given by formulas in Linear Temporal Logic. Our contribution is threefold. First, we tighten the known upper bounds for settings that were left open in earlier work. Second, our complexity analysis is parametric, and we describe tight upper and lower bounds in each of the problem parameters: the game graph, the objectives of the system components, and the objectives of the environment components. Third, we generalize the definition of rational synthesis by adding hostile players to the setting and by combining the cooperative and non-cooperative approaches studied in earlier work.

Multi-objective optimization for energy efficient cooperative communication in energy-constrained overlay Cognitive Radio Networks

With the rapid proliferation of wireless devices, energy efficiency and addressing spectrum scarcity have become critical global concerns. To address these challenges, this paper explores radio-frequency energy harvesting in Cognitive Radio Networks (CRNs) as a solution which can significantly extend the battery life of mobile devices. In this setup, energy-constrained secondary users (SUs) use Simultaneous Wireless Information and Power Transfer (SWIPT) technology to harvest energy from primary signals and aid primary transmission. We propose a joint time-switching and power-splitting technique to maximize energy harvesting, achieving a 97.7% accurate sub-optimal solution compared to the optimal (benchmark) result. Furthermore, we investigate optimal power allocation during cooperative communication and secondary transmission, resulting in a near-optimal solution with 98.5% accuracy. We also introduce a cooperative communication framework using the Hedonic game concept, enabling SUs to establish preferred partnerships with suitable primary users (PUs). Numerical results indicate that this cooperative framework enhances the secondary network’s overall utility, average SU satisfaction, and SU participation when compared to the non-cooperative approach.

Signaling, screening, and core stability

This paper provides a noncooperative approach to core stability in an economy with incomplete information. We study the perfect Bayesian equilibria of an extensive form mechanism that extends the one used by Serrano and Vohra (1997) to implement the core of a complete-information economy. This leads to a version of the core that we refer to as the sequential core, which allows for information to be transmitted among the agents during the process of coalition formation. Such information flows include proposals that can be viewed as signaling devices and/or screening contracts. Equilibrium refinements are then used to provide justifications for the coarse core and the fine core. As a robustness check, we show that the sequential core corresponds to stationary PBE outcomes of an infinite horizon bargaining game.

Modeling the exploitation of common oil and gas resources under different conditions of resource distribution and extraction power: A game theory approach

Oil and gas are the driving forces behind many economic and productive activities, so they hold a crucial place in economic growth and development. The South Pars/North Dome field, the world's largest gas field, needs to be adequately developed and utilized. Iran and Qatar jointly own this field, and they have the responsibility of developing appropriate strategies for oil and gas exploitation. This study analyzes four static game models of the Nash equilibrium and optimal strategies between the two countries to exploit their shared resource. It assumes that a country's extraction power relies on international sanctions, which leads to unequal extraction power. The various conditions of the model include “equal resource distribution and equal extraction power”, “unequal distribution with equal extraction power”, “equal distribution with unequal extraction power”, and “unequal distribution with unequal extraction power”. The study's findings indicate that in the first two scenarios in which no country faces sanctions, both countries should adopt a non-commitment to cooperation. However, in the latter two scenarios in which one country is under sanctions and has weaker extraction power, the sanctioned country should commit to a cooperative policy, while the other country should adopt a non-cooperative approach. Therefore, the decision of a country to participate in the exploitation of common resources depends on its extraction power rather than resource distribution. Consequently, it can be argued that Iran and Qatar's common gas field, which is unequally divided, is significantly affected by sanctions. Due to Iran's sanctions, its extraction power has decreased, and it has adopted a cooperative strategy with Qatar. Meanwhile, Qatar's dominant strategy is non-cooperation. As a result, Iran suffers from the exploitation of shared resources under sanctions.

Learning Decentralized Power Control in Cell-Free Massive MIMO Networks

This paper studies learning-based decentralized power control methods for cell-free massive multiple-input multiple-output (MIMO) systems where a central processor (CP) controls access points (APs) through fronthaul coordination. To determine the transmission policy of distributed APs, it is essential to develop a network-wide collaborative optimization mechanism. To address this challenge, we design a cooperative learning (CL) framework which manages computation and coordination strategies of the CP and APs using dedicated deep neural network (DNN) modules. To build a versatile learning structure, the proposed CL is carefully designed such that its forward pass calculations are independent of the number of APs. To this end, we adopt a parameter reuse concept which installs an identical DNN module at all APs. Consequently, the proposed CL trained at a particular configuration can be readily applied to arbitrary AP populations. Numerical results validate the advantages of the proposed CL over conventional non-cooperative approaches.

A non-cooperative approach to the folk rule in minimum cost spanning tree problems

This paper deals with the problem of finding a way to distribute the cost of a minimum cost spanning tree problem between the players. A rule that assigns a payoff to each player provides this distribution. An optimistic point of view is considered to devise a cooperative game. Following this optimistic approach, a sequential game provides this construction to define the action sets of the players. The main result states the existence of a unique cost allocation in subgame perfect equilibria. This cost allocation matches the one suggested by the folk rule.

Help and hindrance: family roles concerning small businesses of women in Bangladesh during COVID-19 pandemic

PurposeAlthough the extant literature has already recognised the negative impact of homebound responsibilities on women's entrepreneurship during the COVID-19 pandemic, it is yet to know whether and how the family has any other role in women's businesses during this critical period. This research aims to explore the patronising and patriarchal roles of the family regarding women's small businesses in a developing nation during the pandemic.Design/methodology/approachThis feminist study is based on the interviews of women business-owners of a highly patriarchal developing nation, Bangladesh. During the period of the interview, Bangladesh was one of the top ten regions of the world in terms of the identified coronavirus cases.FindingsThe research unveils work-family enrichment by illustrating the help of family members in meeting the challenges of the pandemic period regarding women's certain business activities, such as the innovative production process. Besides, the study reveals the assisting and, in some cases, the non-cooperative approaches of family members concerning additional homebound responsibilities that affect work-family conflict during the COVID-19 pandemic.Originality/valueWhereas the existing literature on women's entrepreneurship regarding the family revolves around work-family conflict due to maternal or caregiving responsibilities during the COVID-19 period, this feminist study substantially contributes to the understanding by revealing how family members help women by getting involved in business activities. It further enriches the prevailing knowledge regarding assisting or hindering activities of family members concerning domestic activities that affect women's businesses during the pandemic.

Ruthenium-Catalyzed Dehydrogenative Functionalization of Alcohols to Pyrroles: A Comparison between Metal-Ligand Cooperative and Non-cooperative Approaches.

Herein, we report the synthesis and characterization of two ruthenium-based pincer-type catalysts, [1]X (X = Cl, PF6) and 2, containing two different tridentate pincer ligands, 2-pyrazolyl-(1,10-phenanthroline) (L1) and 2-arylazo-(1,10-phenanthroline) (L2a/2b, L2a = 2-(phenyldiazenyl)-1,10-phenanthroline; L2b = 2-((4-chlorophenyl)diazenyl)-1,10-phenanthroline), and their application in the synthesis of substituted pyrroles via dehydrogenative alcohol functionalization reactions. In catalyst [1]X (X = Cl, PF6), the tridentate scaffold 2-pyrazolyl-(1,10-phenanthroline) (L1) is apparently redox innocent, and all the redox events occur at the metal center, and the coordinated ligands remain as spectators. In contrast, in catalysts 2a and 2b, the coordinated azo-aromatic scaffolds are highly redox-active and known to participate actively during the dehydrogenation of alcohols. A comparison between the catalytic activities of these two catalysts was made, starting from the simple dehydrogenation of alcohols to further dehydrogenative functionalization of alcohols to various substituted pyrroles to understand the advantages/disadvantages of the metal-ligand cooperative approach. Various substituted pyrroles were prepared via dehydrogenative coupling of secondary alcohols and amino alcohols, and the N-substituted pyrroles were synthesized via dehydrogenative coupling of aromatic amines with cis-2-butene-1,4-diol and 2-butyne-1,4-diol, respectively. Several control reactions and spectroscopic experiments were performed to characterize the catalysts and establish the reaction mechanism.

A New Cooperative Game—Theoretic Approach for Customer-Owned Energy Storage

The increasing demand for energy storage systems (ESSs) alongside the continuous enhancements to storage technology have been of great positive impact on the electric grid. Their unceasing development has been driven by the need to accommodate increased penetration of renewable energy resources and defer capital investments, among other benefits. Moreover, ESSs have played a key role in the grid’s ability to cope with its ever-shifting load profiles, resulting in large economic gain for ESS owners. For this reason, this prospective study was designed to investigate privately-owned energy storage hubs (ESHs) and their interactions with potential customers as well as with the electric grid. This research examined two contrasting interaction approaches for customer-owned stationary energy storage hubs: a cooperative and a non-cooperative game-theoretic approach. The goal of the cooperative technique is to conduce to a correlated equilibrium increasing the social welfare of all players involved using a regret matching algorithm. On the other hand, in the non-cooperative approach, modeled as an ascending price-clinching auction, each player acts greedily, maximizing only their individual welfare. Implementing both case studies resulted in important insights into ESH players’ interactions and provided contrasting methods of modeling their behaviors. Finally, depending on the application at hand, the choice of one approach may be more realistic than the other.

Evaluation of cooperative and non-cooperative game theoretic approaches for water allocation of transboundary rivers

Efficient water allocation in a transboundary river basin is a complex issue in water resources management. This work develops a framework for the allocation of transboundary river water between the countries located in the river basin to evaluate the characteristics of allocation approaches. The allocation of river water is obtained based on initial-water conditions, cooperative, and non-cooperative game-theoretic approaches. The initial-conditions water allocation approach assigns 34, 40, and 26% of the Harirud River flow to Afghanistan, Iran, and Turkmenistan, respectively. The game-theoretic cooperative approach assigns 36, 42, and 22% of the river flow to Afghanistan, Iran, and Turkmenistan, respectively. The non-cooperative game-theoretic approach establishes that the most stable water allocation was 42, 38, and 20% of the Harirud River flow for Afghanistan, Iran, and Turkmenistan, respectively. Human and agricultural water-stress criteria are used to evaluate the water allocations in the Harirud River basin. The criterion of human water stress has the largest influence in Iran, and the criterion of agricultural water stress has the smallest influence in Afghanistan. This work’s results indicate the initial-conditions water allocation approach favors Turkmenistan, whereas the cooperative and the non-cooperative game-theoretic approaches favors Iran and Afghanistan, respectively. The results show that the priorities of each country governs water allocation, and cooperation is shown to be necessary to achieve sustainable development.

An Efficient Method for Cooperative Multi-Target Localization in Automotive Radar

We consider the problem of locating multiple targets using automotive radar by exploiting a pair of cooperative vehicles, which form a mono- and bi-static sensing system to provide spatial diversity for localization. Each of the two sub-systems can measure the target echoes. The problem is to determine the locations of multiple targets in the surrounding area. A conventional approach is to directly estimate the target locations from the joint distribution of the mono- and bi-static observations, which is computationally prohibitive. In this paper, we propose a efficient two-step method that first uses the delay and angle estimates from each individual system to determine initial target locations, which are subsequently refined via an association and fusion step. Specifically, we use a 2-dimensional (2-D) fast Fourier transform (FFT) based approach to obtain the delay and angle estimates of each target in a sequential manner. The delay/angle estimates obtained by mono-static and bi-static systems lead to two sets of initial target location estimates, which are then sorted and paired via a minimum distance criterion. Finally, the initial location estimates are fused/weighted according to the target strength observed by each system. Simulation results show that our cooperative approach yields significant improved performance over non-cooperative approaches using only the mono-static or bi-static sensing system.

Cooperative Multiple Rigid Body Localization via Semidefinite Relaxation Using Range Measurements

This paper investigates the multiple rigid body localization (MRBL) problem using range measurements. To address the MRBL problem, we propose the cooperative approach by availing of not only the sensor-anchor but also the sensor-sensor measurements. The performance of cooperative MRBL is proven to be better than that of the noncooperative MRBL approach, in which only sensor-anchor measurement is used to locate the rigid bodies. Considering two different cases of 2-D and 3-D, we also propose a cooperative semidefinite programming (CSDP) solution to determine the rotation matrices and positions of multiple rigid bodies. At low noise levels, the performance of the CSDP solution is proven to sufficiently approach the Cramér-Rao Lower Bound (CRLB) accuracy due to its tight CSDP form. In addition, a set of determinant constraints is developed and included in the CSDP to improve the performance of the moderate noise levels. The simulated results show that the cooperative CSDP has significantly improved performance compared with the noncooperative approach. At moderate noise levels, the CSDP performs better than the CSDP-NDet, which does not include the determinant constraints. In the simulations, we also verify the conditions of the tight CSDP form. Besides, we also demonstrate that the CSDP solution outperforms the existing methods at varying noise levels.

On the use of public goods

We state and analyze a non-cooperative approach to the provision of public goods where agents decide simultaneously not only their private contribution but also their level of utilization which may differ among consumers (contributors and non-contributors). We show that the distribution of intensities of use matters and allows us to deepen the analysis of altruistic behaviors, neutrality results, congestions issues, and further externalities captured within our framework.

Two-level supply chain models with imperfect quality items when demand influences price and marketing promotion

The present paper studies a supply chain model with items that are of imperfect quality and with the assumption that end demand is responsive to price and promotion cost. The seller delivers items to the buyer in a lot. After an inspection process, it is observed that few articles produced are not of perfect quality. These defects might be the result of common operations or static maintenance. These defective items are then collected and are sold at a lower/discounted price. In this paper, supply chain models are developed to approve the interaction among the players, in the supply chain channel. This interaction between the players is demonstrated by non-cooperative and cooperative game theoretical approaches. In non-cooperative approach, optimal solutions are attained by game theoretic approaches named as Seller–Stackelberg and Buyer–Stackelberg. In the cooperative approach, a Pareto efficient solution is outlined. In the last, numerical illustrations with sensitivity scrutiny are presented to support the theory of the present paper.

Multi‐agent decentralized microgrids planning considering long‐term demand response model

Demand-side management resources, such as demand response programs (DRPs) are useful options for the reduction of energy cost according to the viewpoint of department of energy. Utilizing these resources in long-term studies like generation and transmission expansion planning (GTEP) needs long-term modeling. In this article, the GTEP was developed considering long-term model of DRPs using a cooperative game theory approach to minimize the total cost of microgrids (MGs). A two-level decision-making method was used to develop this model. On the top level, investment decisions were cooperatively made by MGs and a stochastic chance-constrained mixed integer linear programming formulation was modeled for investment decisions with operational uncertainties on the bottom level. The self-sufficiency index was also considered that guarantees supply of sensitive loads in islanding mode. Effectiveness of the proposed model was shown by numerical studies in three cases where in Case 1, load of each smart MG (SMG) will be supplied using its own resources and trading with the retail market. In Case 2, SMGs can also trade with each other in non-cooperative (profitable) approach and in Case 3, SMGs can trade with each other in cooperative approach. The total cost of SMGs was decreased in Case 2 compared to the previous case by 2.62%, which was due to the use of other SMGs' resources to provide load. Also, the total cost of SMGs was decreased in Case 3 compared to the two previous cases (2.92% and 0.3% decrease compared to the Cases 1 and 2, respectively), which was due to cooperative approach taken by SMGs in this case. In this approach, SMGs provide each other's load without additional cost and try to minimize the total cost of all the SMGs.

An axiomatic and non-cooperative approach to the multi-step Shapley value

Inspired by the two-step Shapley value, in this paper we introduce and axiomatize the multi-step Shapley value for cooperative games with levels structures. Moreover, we design a multi-step bidding mechanism, which implements the value strategically in subgame perfect Nash equilibrium for superadditve games.

Maximizing network throughput by cooperative reinforcement learning in clustered solar-powered wireless sensor networks

Power management in wireless sensor networks is very important due to the limited energy of batteries. Sensor nodes with harvesters can extract energy from environmental sources as supplemental energy to break this limitation. In a clustered solar-powered sensor network where nodes in the network are grouped into clusters, data collected by cluster members are sent to their cluster head and finally transmitted to the base station. The goal of the whole network is to maintain an energy neutrality state and to maximize the effective data throughput of the network. This article proposes an adaptive power manager based on cooperative reinforcement learning methods for the solar-powered wireless sensor networks to keep harvested energy more balanced among the whole clustered network. The cooperative strategy of Q-learning and SARSA( λ) is applied in this multi-agent environment based on the node residual energy, the predicted harvested energy for the next time slot, and cluster head energy information. The node takes action accordingly to adjust its operating duty cycle. Experiments show that cooperative reinforcement learning methods can achieve the overall goal of maximizing network throughput and cooperative approaches outperform tuned static and non-cooperative approaches in clustered wireless sensor network applications. Experiments also show that the approach is effective in response to changes in the environment, changes in its parameters, and application-level quality of service requirements.

Performance estimation for Kalman filter based multi-agent cooperative navigation by employing graph theory

Cooperative Navigation (CN) exploits inter-agent relative measurement and communication to achieve navigation performance improvement. This technique has attracted worldwide interest in many multi-agent (e.g., multiple unmanned air vehicles) applications, due to its significant advantage over non-cooperative approaches. Comparing to prior studies which mainly focused on performance analysis under a fixed CN framework, this work aims at establishing the theoretical basis to quantify the navigation performance for different CN integration architectures. Two graph variables are defined to describe the architecture: relative measurement graph and communication & fusion graph. The measurements are integrated through extended Kalman filters, and the state covariance matrices are rigorously derived and bounded to establish the relationship between the navigation performance and the integration architecture. Simulations are carried out to demonstrate and validate the proposed framework, and the results show its feasibility and effectiveness.

Multi-Robot Sensor Fusion Target Tracking With Observation Constraints

In Mobile Robotics, visual tracking is an extremely important sub-problem. Some solutions found to reduce the problems arising from partial and total occlusion are the use of multiple robots. In this work, we propose a three-dimensional space target tracking based on a constrained multi-robot visual data fusion on the occurrence of partial and total occlusion. To validate our approach we first implemented a non-cooperative visual tracking where only the data from a single robot is used. Then, a cooperative visual tracking was tested, where the data from a team of robots is fused using a particle filter. To evaluate both approaches, a visual tracking environment with partial and total occlusions was created where the tracking was performed by a team of robots. The result of the experiment shows that the non-cooperative approach presented a lower computational cost than the cooperative approach but the inferred trajectory was impaired by the occlusions, a fact that did not occur in the cooperative approach due to the data fusion.

Non-cooperative Operation of Transmission and Distribution Systems

The increased penetration of distributed energy resources (DERs) has enhanced the importance of coordinated operation of transmission system operator (TSO) and distribution system operators (DSOs) in order to better exploit DERs. When TSO and DSOs coordinately operate to optimize overall performance of the system, it does not ensure that the benefit is attained by all participants. To ensure entering TSO and DSOs as subjective and self-interested agents’ coordinated operation, this article introduces a non-cooperative framework for coordinated operation of TSO and DSOs. In the proposed non-cooperative operation approach, TSO and DSOs are considered as subjective agents whose interest is to optimize their own objective functions selfishly while they are interactive and modify their solutions through exchanged boundary signals until the equilibrium is obtained. The effectiveness of the proposed non-cooperative operation approach has been demonstrated through many studies on two integrated test systems. Comparison of simulation results of applying the proposed non-cooperative approach with those of the cooperative ones demonstrates that although the optimal solution is achieved for the whole system in the cooperative operation, some agents cannot obtain a satisfactory solution. Thus, these agents prefer to enter a non-cooperative game with other agents. The proposed non-cooperative game leads to relative benefits for all agents.