Non-cooperative Behavior Research Articles

A charge-discharge optimization strategy considering the spatiotemporal distribution of electric vehicles and the operational safety of the power distribution network in the power-transportation coupling network

An electric vehicle (EV) charge-discharge optimization (CDO) strategy that accommodates both grid-side and user-side demands is conducive to mitigating the adverse effects of disordered EV charging on the power distribution network (PDN). To tackle the issue of inaccurate estimation of the schedulable capacity of EVs in existing research, a high temporal resolution dynamic spatiotemporal distribution simulation model for EVs is developed. Furthermore, leveraging the characteristics of the power-transportation coupling network (PTCN), a sub-districted dynamic electricity pricing (DEP) is proposed to assist in improving the PDN node voltage distribution. Subsequently, an incentive coefficient is introduced to incentivize the discharge of EVs during potential peak periods. The proposed CDO strategy, considering the non-cooperative behavior of EV owners (EVOs), can provide personalized charge-discharge plans for them. The temporal sequence experiments reveal a significant decrease in the peak-to-valley disparity of the load, achieving an optimization rate of load mean square error that exceeds 80%. The sub-districted DEP exhibits a significant advantage in maintaining PDN voltage stability compared to traditional time-of-use electricity pricing (TOUEP) and DEP, while also achieving reductions in active power losses of more than 15% and 10% for PDN, respectively. Moreover, in comparison to the conventional scenario, the CDO strategy leads to a maximum reduction of up to 942.5% and 22.3% in the total economic cost for electric private cars and electric taxis respectively. Lastly, the impact of seasonal factors is discussed. Numerical results indicate effective alleviation of load peak-valley differences, load fluctuations, and voltage drop phenomena in both winter and summer. Additionally, the maximum reduction in active power losses reaches 20.1% and 21.9%, respectively, while the total economic cost for EVOs participating in the CDO strategy is reduced by up to 97.7% and 114.7%.

북·중·러 연합의 형성과 전망

This study analyzes the North Korea, China, and Russia coalition amidst complex global crises like the US-China strategic competition, the US-Russia crisis, and the North Korean nuclear issue, using coordination games and problems as analytical tools. In the ‘New Cold War’ context, they identify a focal point, form coalitions, and coordinate strategies based on reactive anti-US policies and confrontations with coalitions involving ROK, the US, and Japan. However, differing perspectives on the ‘New Cold War’ and external factors leading to divergent focal points resulted in strategic uncertainty. Forming a stable grand coalition as an institution is challenging, and there is a risk of abandonment when forming a specific coalition based on interests. Moreover, the emergence of spoilers within the coalition breeds apprehension among other nations, rendering the coalition unsustainable. Consequently, the sustainability of the North Korea-China-Russia coalition diminishes, fostering ongoing non-cooperative behavior amidst a temporary phase of adhesion.

A game-theoretical approach for the adoption of government-supported blockchain application in the IoT-enabled agricultural supply chain

Establishing a reliable, cost-effective, and data-accessible supply chain is a requirement for all administrations. The significance of such a supply chain lies in its ability to strengthen national resiliency, ensure economic security, and foster sustainable growth. Governments know financial investments and subsidies in infrastructure, logistics, and technology are necessary for a robust supply chain. This paper proposes an IoT-enabled agricultural supply chain that integrates with a cooperative Nash game between two parties: telecommunications operators and agricultural enterprises. The aim is to establish cooperative decision-making procedures in advance of the integration of blockchain technology into the supply chain, which is regulated by the subsidy policy of the government. The Nash game yields a unique Nash equilibrium, indicating that both parties can engage in cooperative negotiations regarding their participation in the blockchain application to maximize their financial profits. To illustrate the efficacy of Nash collaboration in blockchain adoption, this study presents two scenarios where non-cooperative behavior is explored to analyze stakeholder responses in competitive contexts. Numerical results justify the theoretical findings and suggest Nash cooperation among stakeholders instead of competition in the blockchain-based agri-food market, which leads to a more accessible market environment that ultimately benefits consumers. Moreover, the positive and negative aspects of the deployment of the government-supported blockchain application are derived through sensitivity analyses conducted on subsidy rate, agricultural market price, subsidy amount, and blockchain market. Practical managerial implications are derived from the findings and observations to help policymakers formulate blockchain policies in agricultural supply chains supported by governments.

Influence measure-based large-scale group decision making with linear uncertain preference relations

This research focuses on the problem of large-scale group decision-making (LSGDM) based on influence measure under linear uncertain preferences. The value of this research is that it improves the performance of the current clustering algorithms, increases the efficiency of consensus reaching for major decision-making events, thus reducing the cost of feedback adjustments, and at the same time reflecting the risk attitude of the decision-makers (DMs) during situations of uncertainty. First, an approach for measuring the influence of the DMs is presented. Based on the influence measure, a clustering method is proposed to classify the experts into subgroups. Then, a method for determining the weights of the subgroups is developed by combining the influence and credibility of the subgroup leader as well as the intra-subgroup consensus level. Subsequently, a feedback adjustment method is provided to reduce the disagreement among the DMs by considering the unit adjustment cost and the corresponding adjustment willingness of the DMs. Further, based on the trust risk and credibility of the DMs, a dual management mechanism of non-cooperative behavior is established to hasten consensus. Finally, a case study is presented to demonstrate the practicality of the suggested approach, while simulation experiments and comparative analysis are conducted to verify the model.

Modeling and Control Strategies for Flight Service Vehicle Queue Behavior in Non Cooperative Games

From the perspective of the running process for service vehicles, based on the analysis of the benefits maximization of the special needs vehicles joining the queue as the scheduling goal, and aiming at the shortcomings of traditional task scheduling strategies, methods and models, a non-cooperative game behavior optimization scheduling model with security, economy and environmental protection as the optimization goals was proposed by using game theory. At the same time, the vehicle service capability, service times and import Angle are limited to ensure that the model is more in line with the actual operation scenario. In order to judge the deviation between the game result and the actual operation, the game efficiency matrix is constructed, and Nash equilibrium is used to prove that the model has an equilibrium point. On the basis of the game model, the strategy layer, the game layer and the optimization layer are divided into queue behavior control strategy methods. Finally, this paper verifies the feasibility of the model by using the actual parameter changes to determine the best parameter combination suitable for the model.

Impact of Blockchain on Improving Taxpayers’ Compliance: Empirical Evidence from Panel Data Model and Agent-Based Simulation

ABSTRACT The purpose of this study is to examine the impact of blockchain technology on taxpayer compliance among U.S. taxpayers, using it as a case study. It aims to explore the critical factors affecting blockchain technology applications in tax compliance systems. We first utilized a panel data model to establish empirical parameters linking audit intensity and qualification rates of Internal Revenue Service (IRS) tax returns. We then applied these parameters to an agent-based simulation model powered by artificial intelligence. We showed that integrating blockchain technology can effectively address noncooperative behavior and reduce the tax gap. Moreover, we identified two key factors—the improvement of the IRS’s efficiency and increased punishment—that can accelerate the development of blockchain technology in the tax compliance system. Our research adds to the existing literature on applications of agent-based simulation models in tax compliance systems and provides policy implications for promoting the use of blockchain technology. Data Availability: Data are available from the public sources cited in the text.

Robust fake-post detection against real-coloring adversaries

We design warning mechanisms for detecting fake-post on online social networks using crowd signals without significantly affecting the real-post propagation. Building on a recent algorithm where all users assign a real or fake tag to any post, we now consider that users can exhibit adversarial or non-cooperative behavior while tagging. To analyze this system, we introduce and study the total-current population-dependent branching process with multiple death types. We highlight the limitations of the existing mechanism in handling adversaries and propose improved mechanisms that perform effectively even with limited user-specific parameter knowledge. Our theoretical findings are validated through Monte-Carlo simulations.

A large-scale group decision-making approach based on hesitancy degrees and non-cooperative behaviors with picture fuzzy information

Large-scale group decision-making (LSGDM) problems involve two processes: a clustering process is first implemented to break down a larger group into several smaller subgroups for simplification purposes, after which a consensus reaching process (CRP) is utilized to eliminate information conflicts among large-scale decision-makers (DMs). Opinion conflicts are inevitable in large DM groups due to self-interest biases and information blind spots. To address these limitations, this study proposes a twofold feedback mechanism to address both non-cooperative behaviors and information hesitation. Firstly, based on detailed review of existing similarity measures, we defined a novel picture fuzzy Chi-square similarity measure (NPFCS) to enhance accuracy. To distinguish subgroups, the second procedure was developed to establish a weight assignment model following the implementation of the developed similarity-based fuzzy clustering algorithm. In the third step, decision opinions are examined from two distinct perspectives: the hesitancy degrees and non-cooperative behaviors, to ensure favorable decision outcomes. Alongside the definition of a hierarchical mechanism for detecting DMs with information blind spots, the identified DMs were mandated to resubmit individual matrices. This measure aims to prevent the aggregation of negative impacts on information credibility caused by the major principle. Furthermore, non-cooperative behaviors were identified at the element level of multi-criteria matrices with corresponding adjustments performed automatically. Next, a real-world site selection problem involving thirty DMs was solved using the proposed method to illustrate its application. Moreover, the results of sensitivity analysis demonstrate that our proposal is robust to changes in model parameters, and several comparisons were conducted from two perspectives, thus confirming the superiority of the proposed approach.

Self-Confidence Rationality Identification of Group Decision Making Based on Consensus Analysis

This article focuses on the consensus-reaching process (CRP) for group decision-making with self-confidence (SC) and considers personalized individual semantics (PIS). To identify the rationality of SC, this article first uses PIS to transform linguistic variables into numerical variables, by which initial SC-based adjustment intervals are derived. Further, maximum and minimum programming models at three layers are conducted to derive adjustment intervals in view of consensus analysis, by which SC-based revision adjustment intervals are obtained. Moreover, the rationality of SC-based revision adjustment intervals is judged by consensus models. When irrationality is identified, the model for minimizing consensus adjustment under the given consensus level is built. In this process, if corresponding decision-makers show non-cooperative behaviors, different penalty rules are recommended in various cases. Finally, the proposed method is applied to select ideal medical care and nursing institutions, and sensitivity and comparative analyses are performed.

Residual Q-Networks for Value Function Factorizing in Multiagent Reinforcement Learning.

Multiagent reinforcement learning (MARL) is useful in many problems that require the cooperation and coordination of multiple agents. Learning optimal policies using reinforcement learning in a multiagent setting can be very difficult as the number of agents increases. Recent solutions such as value decomposition networks (VDNs), QMIX, QTRAN, and QPLEX adhere to the centralized training and decentralized execution (CTDE) scheme and perform factorization of the joint action-value functions. However, these methods still suffer from increased environmental complexity, and at times fail to converge in a stable manner. We propose a novel concept of residual Q-networks (RQNs) for MARL, which learns to transform the individual Q -value trajectories in a way that preserves the individual-global-max (IGM) criteria, but is more robust in factorizing action-value functions. The RQN acts as an auxiliary network that accelerates convergence and will become obsolete as the agents reach the training objectives. The performance of the proposed method is compared against several state-of-the-art techniques such as QPLEX, QMIX, QTRAN, and VDN, in a range of multiagent cooperative tasks. The results illustrate that the proposed method, in general, converges faster, with increased stability, and shows robust performance in a wider family of environments. The improvements in results are more prominent in environments with severe punishments for noncooperative behaviors and especially in the absence of complete state information during training time.

Managing non-cooperative behaviors in consensus reaching processes: a comprehensive self-management weight generation mechanism

Managing non-cooperative behaviors in consensus reaching processes: a comprehensive self-management weight generation mechanism

Selection of landslide treatment alternatives based on LSGDM method of TWD and IFS

The disaster caused by landslide is huge. To prevent the spread of the disaster to the maximum extent, it is particularly important to carry out landslide disaster treatment work. The selection of landslide disaster treatment alternative is a large scale group decision-making (LSGDM) problem. Because of the wide application of social media, a large number of experts and the public can participate in decision-making process, which is conducive to improving the efficiency and correctness of decision-making. A IF-TW-LSGDM method based on three-way decision (TWD) and intuitionistic fuzzy set (IFS) is proposed and applied to the selection of landslide treatment alternatives. First of all, considering that experts and the public participate in the evaluation of LSGDM events, respectively, the method of obtaining and handling the public evaluation information is given, and the information fusion approach of the public and experts evaluation information is given. Second, evaluation values represented by fuzzy numbers are converted into intuitionistic fuzzy numbers (IFNs), and the intuitionistic fuzzy evaluation decision matrix described by IFNs is obtained. Then, a new LSGDM method of alternatives classification and ranking based on IFS and TWD is proposed, the calculation steps and algorithm description are given. In this process, we first cluster the experts, then consider the identification and management of non-cooperative behavior of expert groups. This work provides an effective method based on LSGDM for the selection of landslide treatment alternatives. Finally, the sensitivity of parameters is analyzed, and the feasibility and effectiveness of this method are compared and verified.

БИРЖЕВАЯ ТОРГОВЛЯ И ЗАКЛЮЧЕНИЕ ЧАСТНЫХ КОНТРАКТОВ НА ГЛОБАЛЬНОМ НЕФТЯНОМ РЫНКЕ

The most important element of the global economic system research is the study of the trends in the development of the global oil market. The aim of the work is to build a mathematical model suitable for researching this market in modern conditions. A theoretical game model has been developed, which provides for the possibility of describing oil trade, both through commodity exchanges and under separate agreements between producers and consumers. The parameters of stable states of this market have been determined, both in conditions of non-cooperative behavior of participants, and in the devel-opment of joint strategies for their behavior. With the help of the proposed model, the possibilities of concluding coalition agreements were investigated, both between producers regulating oil supplies on the exchange, and between producers and consumers of oil, concluding private contracts for the purchase of oil bypassing the exchange. The effectiveness of such agreements in the global oil market was assessed. It was concluded that it is possible to use the model to describe the oil market in the presence of sanctions restrictions.

The dual interactions-driven consensus model: Probing the non-cooperative behavior through order effects

An improved consensus mechanism is designed with decision-makers (DMs) to pursue the possibility of working with different partial agreements on the evaluations of alternatives. Existing works focus on static cooperation and non-cooperation behaviors in the consensus-reaching process but ignore the individual dynamic cooperation degree caused by trusted information. Since the cooperation and non-cooperation behaviors of DMs affect each other, a quantum model is developed to manipulate such a phenomenon, with an emphasis on the order effects that occur when DMs contact the interference information with different orderings. Four cases are analyzed to determine how the individual state vectors about cooperation evolve and are structured, and a series of optimization models are built to obtain the updated cooperation degrees of DMs. To reflect the effect of interaction between evaluation criteria and mutual inference between DMs on the consensus, a multi-objective consensus model with a two-fold consensus level based on the Choquet integral is constructed to obtain the optimized modified information. The built models can be applied in decision problems with interactions between criteria and between DMs when individual cooperative attitudes in group negotiations change dynamically due to information interference. In the constructed consensus model, the willingness of individual modification and the satisfaction of each DM with the group consensus are fully integrated. Therefore, it is seen as an expansion of the conventional consensus model in the quantum decision-making environment. Furthermore, result and comparison analysis are performed to illustrate the advantages of the proposed model.

Group efficiency and individual fairness tradeoff in making wise decisions

In group decision making, the consensus model with minimum cost has been researched with the aim of improving group efficiency and saving resources. However, one limitation of the minimum cost consensus model is that the reach of consensus is usually at the expense of some group members. We consider two issues that we see as keys in group consensus: efficiency and fairness. We propose the price of fairness in the opinion revision process and give two kinds of fairness schemes. According to the individual's perception of inequity, we introduce inequity aversion parameters and classify experts into two types: experts with non-cooperative behaviors and with altruistic behaviors. Experts with altruistic behaviors will be allowed to contribute more than the recommended number of modifications. Then, we discuss how to achieve the tradeoff between efficiency and fairness. Furthermore, with the rapid development of social media, cloud, and e-government platforms, collective intelligence (CI), i.e., groups of individuals doing things collectively that seem intelligent, has been a hot topic. We expand our work to a crowd context with many individuals. We investigate how the opinion revision process and fairness schemes can influence the emergence of CI. Results suggest that the proportional fairness and max-min fairness have similar performance in stimulating CI. Moreover, the improvement of group accuracy is mainly related to two factors: the group consensus level of initial opinions and the relative distance between group aggregated opinion and the ground truth.

Managing non-cooperative behaviors and ordinal consensus through a self-organized mechanism in multi-attribute group decision making

In consensus-based multi-attribute group decision making (MAGDM) problems, decision makers (DMs) may exhibit non-cooperative behaviors since they usually have different individual interests (sometimes conflicting) or limited knowledge, which strongly affects the efficiency of consensus and the quality of decision outcomes. Additionally, the existing MAGDM studies mainly focus on the cardinal consensus, and the ordinal consensus is ignored. Thus, this paper proposes a self-organized mechanism based framework to manage non-cooperative behaviors and ordinal consensus in MAGDM. First, a dual-membership function based on the basic idea of synergy theory is designed to detect non-cooperative behaviors at the element level of the multiple attribute evaluation matrix (MAEM), and then the weights of elements with non-cooperative behaviors are penalized automatically. In this way, the negative effects of non-cooperative behaviors can be eliminated. Next, a novel preference ranking-based ordinal consensus approach is proposed, which calculates an ordinal consensus based on the preference rankings of alternatives between individuals and the group. If the pre-defined consensus level is not reached, the feedback adjustment is used to help DMs modify their MAEMs to improve the consensus level; otherwise, the selection process is utilized to choose the optimal alternative(s). Finally, detailed simulation experiments and comparative analysis are designed to show the properties and effectiveness of the proposed framework, and an illustrative angel investment case is presented to demonstrate the calculation process and usability.

Managing non-cooperative behaviors in consensus reaching process: A novel multi-stage linguistic LSGDM framework

Large-scale group decision-making (LSGDM) is a complex process involving numerous decision-makers (DMs). However, considering such a large number of DMs increases the complexity of the process. it seems necessary to pay much more attention to aspects such as a proper dimensionality reduction for scalability, consensus processes with automatic feedback, and effective management of non-cooperative DMs. To address such aspects, this paper presents a novel framework for LSGDM, based on Extended Comparative Linguistic Expressions With Symbolic Translation (ELICIT). We first extend the K-means clustering algorithm by incorporating individual assessments and trust relationships to classify DMs into subgroups, enhancing decision-making efficiency. We then develop a feedback mechanism based on two optimization consensus models for ELICIT information, that automatically provides optimal recommendations. An essential aspect of our proposal is the management of non-cooperative behaviors by utilizing the normal distribution to detect and penalize misbehaviors. Furthermore, we introduce a Data Envelopment Analysis (DEA) cross-efficiency method based on ELICIT values to rank all alternatives once an acceptable group consensus degree is reached. The framework’s effectiveness is demonstrated through a practical application case study, accompanied by a parametric analysis. Comparisons with existing LSGDM methods highlight the superiority of our proposal in terms of efficiency.

Trust and calculation in international negotiations: how trust was lost after Brexit

Abstract How does a loss of trust affect international negotiations? We know that trust enables cooperation and comes with the risk of betrayal. Yet the effects of lost trust are not well understood, with most research in International Relations (IR) focusing on how trust is initially built in adversarial relations. In this article, we conceptualize trust as the eclipsing of possible futures in which others do not behave cooperatively. When trust is lost, actors include further possibilities of non-cooperative behavior in their calculations and adjust their negotiating demands accordingly. We use this framework to explain the European Commission's response to Boris Johnson's government breaking its trust during the Brexit negotiations by deviating from the Political Declaration, introducing the Internal Market Bill, and not conducting border checks in the Irish Sea. Drawing on elite interviews, we trace how the Commission altered its expectations and consequently sought negotiated outcomes with tighter contracting, reduced discretion and retaliatory mechanisms. Analysing the interplay of trust and calculation on a granular level, we show that breaches of trust do not preclude future cooperation per se, but shape its form and extent in important ways.

Why don’t People Cooperate? Exploring the Lack of Cooperation Through the use of Experiments

Objective: This study sought to investigate, from the perspective of Game Theory, the reasons for the “non-cooperation” of certain groups that depend on a specific reservoir for water supply, considering the influence of economic incentives. Method: Public good game experiments were carried out with 60 recyclable material collectors, 63 indigenous people, 55 university students and 68 professional fishermen. Subsequently, semi-structured interviews complemented the data collection strategy. Results and conclusion: Among the main results, we can mention the opportunistic behavior in the group of collectors, sociocultural issues in the indigenous context, the perception of inequality of earnings among students, individual work and independence from the group, in this case of fishermen. It appears that understanding this multiplicity of factors involved in the decision not to cooperate can encourage new discussions regarding the formulation of policies aimed at preserving public goods, going beyond the mere use of punishment and reward based on inspection actions. Originality/value: The need to understand the context of these groups, and others equally benefiting from certain public goods, would be valuable to reduce non-cooperative behavior.

An adaptive consensus model in large-scale group decision making with noncooperative and compromising behaviors

In recent years, different feedback mechanisms have been reported in many consensus models to improve decision levels. However, the improvement of decision level often leads to the reduction of decision efficiency, which has been rarely considered in existing consensus models. This paper proposes an adaptive consensus model consisting of automatic strategy and interactive strategy, which are implemented in different consensus stages to balance decision efficiency and decision level. In addition, the behavior diversity of decision makers (DMs) is often unavoidable, such as noncooperative behavior, which brings greater complexity to the consensus reaching. Meanwhile, cooperative behavior is usually accompanied by compromise behavior. Considering that the compromise behavior of DMs will change subgroup structure, dynamic cluster analysis is performed in the consensus reaching process. On the premise of dynamic clustering, the traditional weight penalty mechanism will fail to manage the noncooperative behaviors of subgroups. To this end, this paper proposes a new penalty mechanism. The proposed adaptive consensus model is applied to the selection of cities for establishing the freight hub. Finally, some numerical simulations and comparative analyses are presented to verify the effectiveness of the proposed model.