Non-cooperative Behavior Research Articles

A nonparametric test for cooperation in discrete games

Summary We propose a nonparametric test for cooperative behaviour among players in discrete, static games. Assuming that certain exchangeability conditions hold if we match observable characteristics across all players, we obtain testable implications for cooperative (coalitional) behaviour, which we define as occurring when players maximise an unknown joint objective function that is symmetric conditional on the matching. Cooperation implies the existence of a class of outcomes $\mathcal {Y}$ such that, conditional on the matching, the probability of observing an outcome $y\in \mathcal {Y}$ must be equal to the probability of observing any permutation of y. We present a nonparametric econometric test and we characterise its asymptotic properties. We apply our test to expansion/entry decisions of Lowe’s and Home Depot in the contiguous United States and we find that, while outcomes are consistent with noncooperative behaviour in larger markets, we fail to reject cooperation in smaller markets.

Non-Cooperative Behavior Management in Large-Scale Group Decision-Making Considering the Altruistic Behaviors of Experts and Its Application in Emergency Alternative Selection

Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns, leading to non-cooperative behaviors during the consensus-reaching process. Many studies on non-cooperative behavior management assumed that the maximum degree of cooperation of experts is to totally accept the revisions suggested by the moderator, which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process. In addition, when grouping a large group into subgroups by clustering methods, existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously. In this study, we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensus model taking into account the altruistic behaviors of experts. First, we cluster experts into subgroups by a constrained K-means clustering algorithm according to the opinion similarity and trust relationship of experts. Then, we calculate the weights of experts and clusters based on the centrality degrees of experts. Next, to enhance the quality of consensus reaching, we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts. A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making. The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.

Fiscal Decentralization, Political Heterogeneity and Welfare

This paper contributes to the literature on fiscal decentralization by presenting a formal model of the interaction between the central and local governments (CG and LGs, respectively) where LGs may differ in their degree of political alignment with CG. The non-cooperative optimal behaviour of the agents reveals that optimal tax increases with the extent of fiscal decentralization (FD), political unison and spillovers across localities, while LGs' optimal tax collection effort is negatively associated with all of these parameters. The first novel finding of our study is that both welfare peaks and income distribution are more equitable at a lower level of FD in the case of spillovers than in the case of no spillovers, which supports the decentralization theorem. The second novel finding is that both the amount of redistributable income and central government utility increase with the degree of political unison.

The Attitude of the General Dentist in the Republic of Croatia toward Treating Children.

The aim of this study was to determine the attitude of general dentists in the Republic of Croatia toward working with children. The questionnaire survey involved 361 general dentists. The questionnaire was divided into three parts and contained 34 questions. The first part of the questionnaire survey contained demographic data questions. In the second part, dentists answered questions about the characteristics of the dental offices where they work, the materials that they mostly use, and how they work with children. The third part of the questionnaire referred to attitudes about working and treating young children and consisted of 12 statements, to which the answers were offered on a Likert scale from one to five. This study showed that only 12.46% of dentists have a positive attitude toward treating children and 30.19% of dentists have a negative attitude toward treating children. In addition, the attitude toward working with children correlates with both the knowledge they acquired during dental studies (R = 0.355; p ≤ 0.001) and gender (R = -0.103; p = 0.035). This study confirmed that women have a more often positive attitude toward treating young children. There was a major correlation between the level of education and positive attitudes toward treating children. The child's non-cooperative behavior was the main reason why general dentists refuse to work with children.

A Large Group Decision Making Method Considering Experts’ Non-cooperative Behavior for Investment Selection of Renewable Energy Projects

The rapid expansion of renewable energy has attracted the attention of investors, which makes the evaluation of renewable energy projects a momentous issue. As the investment selection of renewable energy projects requires the joint discussion of experts from different professional backgrounds (such as energy, transportation, construction, economy, environment, etc.), it belongs to the category of large group decision-making (LGDM). Therefore, this paper is devoted to propose a novel LGDM method considering experts’ non-cooperative behavior for investment selection of renewable energy projects. First, considering that the complexity of renewable energy projects makes it difficult for experts to express their views in a single linguistic word, the hesitant fuzzy linguistic term set is used as the tool for expert evaluation in this paper. Second, since the assessment information provided by experts from different fields are often heterogeneous, a consensus-reaching process with a feedback mechanism is introduced which comprehensively considers three reliable sources: the experts’ trust relationship in the social trust network, the consensus contribution in the subgroup and the opinions’ similarity among experts. Further, to improve the efficiency and rationality of decision-making, an experts’ historical adjustment data-based non-cooperative behavior management method is proposed. Finally, the effectiveness and innovation of the proposed method are verified by a case of renewable energy power generation project investment selection in Qingdao, China and a series of comparative analysis.

Consensus reaching mechanism with parallel dynamic feedback strategy for large-scale group decision making under social network analysis

With the scale of group decision making increasing, it is a crucial issue to get the utmost of collective intelligence for seeking the optimal solution. In this study, we propose an automatic consensus reaching process (CRP) for large-scale group decision making (LSGDM) based on parallel dynamic feedback strategy and two-dimensional scenario-based social network analysis (SNA) model. Firstly, individuals express their preferences by distributed preference relations (DPRs) which could keep the uncertainty of assessment and allow multi-attribute comparison. Secondly, SNA based on trust relationship and connection strength is implemented. Then a two-dimensional scenario-based SNA model is established, and a fuzzy clustering algorithm based on connection strength is designed to reduce the scale of decision makers (DMs). Finally, a two-phase CRP with identification rules and feedback strategy is constructed. Identification rules are used for activating different kinds of feedback mechanisms by identifying whether it reaches acceptable local or global consensus. The rules also identify which kind of social relationship for internal or external subgroups and what dominance does individual or subgroup has. Feedback strategy with parallel dynamic adjustment process is further designed based on opinion and trust adjustment factors and non-cooperative behaviors. A real illustrative case for selecting the optimal carbon footprint management provider is presented to demonstrate the validity of our proposed method, and further compare it with other current methods.

A large-scale consensus model to manage non-cooperative behaviors in group decision making: A perspective based on historical data

In the large-scale group decision making (LSGDM) problems, it is common that some decision makers might refuse to modify their preferences to achieve a consensus or even form an alliance to exhibit non-cooperative behaviors for their own interests. These non-cooperative behaviors might bias or hinder the consensus reaching process (CRP). In this paper, we propose a large-scale consensus model to manage non-cooperative behaviors based on the clustering method using the historical preference data of decision makers. In the proposed framework, all the historical preference data of decision makers are used for clustering to catch their features, by which the decision makers with the three defined non-cooperative behaviors will be more centralized. Then, the clusters with non-cooperative behaviors will be detected better through their preference adjustments. On this basis, the penalty strategy of the decision makers in clusters with non-cooperative behaviors would be more effective. Simulation experiments and comparison studies are presented to demonstrate the validity of the proposed consensus framework against traditional frameworks for coping with non-cooperative behaviors.

نشوز زوجہ کی اصلا ح کا قرآنی تصور؛ روایتی اور معاصر آرا کا خصوصی مطالعہ

According to Islamic law, the exercise of the powers acquired as a result of a conjugal contract is not based on absolute monopoly. In its view, it is necessary for the couple to exercise their powers within the scope and spirit of the marriage contract. Exceeding the limits of these powers, non-cooperative behavior and rebellious attitude is called "Nushūz" in the Qur'anic terminology. The Holy Qur'an uses this word for each of the spouses. The Qur'an has also pointed out three ways to remove the non-cooperative attitude of a wife, i.e. wa'ẓ (admonishing), ḥajr fi almaḍhaji' (refuse to share their beds) and ḍarb. In particular, the concept of "ḍarb" has been much debated. A large number of Quranic commentators present the Quranic verse in the context of the husband's authority, so in their case the husband has the right to discipline the wife with beating her lightly i.e. without harming (ḍarb ghair mubarraḥ) but this concept is objected to by a number of contemporary of Muslim scholar. The question is also discussed here whether the subject of the verse is a piece of advice or a preferred rule. The following article discusses the traditional way of thinking and contemporary perspectives in the Qur'anic context of the addressing wife's disobedience.

Exploring a Balinese irrigation water management system using agent-based modeling and game theory

In the irrigation sector, water-related conflict has been compounded due to social issues such as climate change and urbanization-induced population dynamics. Although Irrigation Management Transfer (IMT) and Participatory Irrigation Management (PIM) were implemented for sustainable irrigation infrastructure management, results of PIM/IMT implementations exhibit conflicts between WUAs and the lack of water users’ associations’ (WUAs) participation in management. To unfold PIM/IMT issues, Agent-Based Model (ABM) helps analyze conflicts, and game theory helps analyze governing concepts underlying cooperative management such as rules. This study discusses the applicability of ABM and game theory to analyze conflicts over surface irrigation water and harvesting labor and also aims to present solutions to increasing conflicts due to labor resource depletion owning to urbanization. To investigate cooperative WUAs amid the pressure of social changes, we chose WUAs in Bali, Indonesia, known as “subaks”. Rice cultivation management practices of five subaks in the study area were identified by stakeholder analysis approach, and we developed an ABM to model water management of six agents representing subaks. To examine influence of harvesting labor supply on water management, noncooperative game theory was applied, and three harvesting labor scenarios and two strategies were generated. Scenario analysis indicates that with severe shortages in the harvesting labor supply, harvesting labor allocation orders could compound the decline in annual rice production. This study also establishes that although current management practices maintain cooperative relationships between subaks and flexible management practices likely strengthen cooperative relationships, social changes could accelerate farmers’ noncooperative behavior. To resolve conflicts, subaks need to develop cooperative relationships on a larger scale, but social issues go beyond the intra-community level and will require government intervention to efficiently use deceasing resource supply. This method using ABM and game theory will contribute to the development of socio-hydrological analysis approach to sustainable resource allocation in PIM/IMT study.

Does the Costless Identification of Contributors Affect the Intuitiveness of Prosocial Behavior?

Introduction/ Main Objectives: This study investigates whether relaxing the assumption of the non-anonymity of participants in an experiment of a finitely repeated public-good game with randomly matched players affects the intuitiveness of prosocial behavior. Background Problems: Various studies show that, in general, participants of social dilemma game experiments tend to be intuitive in choosing prosocial behaviour, whereas non-cooperative behavior tends to be slow. Other studies show that experiments which induced the non-anonymity of participants promote prosocial behavior, however, these studies did not impose non-anonymity on the participants. Novelty: This study aims to fill the literature gap on whether introducing non-anonymity of participants in a social dilemma game experiment may affect the intuitiveness of prosocial behavior. Research Methods: This study used a laboratory experiment of finitely repeated public-good games with randomly matched players in each stage. The main difference between the control and the treatment groups lay in the anonymity of participants in the interaction, where participants in treatment group are exposed to the photo of their opponent. Finding/Results: The results show that relaxing the anonymity setting improved the participants’ contributions to the public-good game; however, their prosocial behavior became less intuitive than non-cooperative behavior. Conclusion: This paper demonstrated an attempt to fill the gap in the literature between the intuitiveness of prosocial behaviour and the role of identity in prosocial behavior.

New success likelihood index model for large group human reliability analysis considering noncooperative behaviors and social network

Human reliability analysis (HRA) is a systematic method to qualitatively and quantitatively assess and analyze potential human errors for enhancing the safety and reliability of complex socio-technical systems. As a representative HRA approach, the success likelihood index method (SLIM) has been widely applied to quantify the effects of performance shaping factors on human performance and derive the human error probabilities of operational tasks. The influence quantification of performance shaping factors mainly relies on experts’ subjective judgments, where conflict opinions inevitably exist because of their different knowledge and experiences. Besides, the current SLIM studies often involved a small group of experts and assumed them to be independent, which is insufficient to tackle increasingly complicated HRA problems. In this article, a large group SLIM model is developed to calculate the human error probabilities of operational tasks considering experts’ noncooperative behaviors and social relations. Specifically, a large group of experts are included to provide task state assessments under complex uncertain linguistic environment, and clustered into subgroups based on a social network analysis. Then a consensus reaching process is performed to tackle the noncooperative behaviors of experts to obtain a consensus human error probability estimation. Finally, a case study of cargo loading process is provided to illustrate the practicality and effectiveness of the proposed large group SLIM model.

PINE: Post-Quantum Based Incentive Technique for Non-Cooperating Nodes in Internet of Everything.

The Internet of Everything (IoE) is a smart system that interconnects smart entities by incorporating low-cost or low-energy gadgets that are useful for communication with people, processes, data, and devices/things. In such an instantaneously connected environment, network-enabled heterogeneous devices may exhibit non-cooperative behaviour which may lead to the degradation of the network. To address this performance degradation, the proposed Post-quantum based Incentive technique for Non-cooperating nodes in internet of Everything (PINE) protocol provides an end-to-end reliable solution by incorporating location-aware post-quantum encryption in these networks while addressing the non-cooperative behaviour of the nodes by employing an effective strategy in a bi-directional multi-hop relay environment. This proposed protocol further aims to evaluate the consequences of non-cooperative nodes by considering various metrics, namely, number of nodes, message size, execution time, memory consumption, average residual energy, percentage of selfish nodes, and blackhole nodes detection, aiming to achieve significant accuracy in an IoE environment.

Large-scale group decision-making to facilitate inter-rater reliability of human-factors analysis for the railway system

The interdependency analysis of human factors usually needs to identify and label factors from accident/incident reports using a binary framework. The labeling procedure involves enormous knowledge and requires a large group of raters, but the raters’ opinions usually highly contradict each other. Thus, inter-rater reliability should be implemented to improve the labeling consistency. However, the inter-rater reliability coefficient is commonly computed on a category-by-category basis, which is inefficient especially when there are many raters. To overcome this deficiency, a feedback-autonomy-based consensus model is proposed to determine the inter-rater reliability of human-factors labeling results involving a large group of raters. The proposed model can manage various non-cooperative behaviors and provide feedback adjustment suggestions to raters for the labeling procedure. Notably, the autonomy mechanism allows raters to freely choose and adjust labels referring to the reference opinions without causing over-adjustment issue. The inter-rater reliability and the interdependency results are performed on 279 accident/incident reports. We apply a commercial tool to obtain the reference results, which are consistent with the unanimous group opinions. Besides, other comparative studies clarify the advantages of the proposed consensus model. The proposed model is convenient for reducing conflicts among raters that usually exist in human-factors analysis.

Risk Assessment of Open-Pit Slope Based on Large-Scale Group Decision-Making Method Considering Non-Cooperative Behavior

Risk Assessment of Open-Pit Slope Based on Large-Scale Group Decision-Making Method Considering Non-Cooperative Behavior

A non-cooperative behavior management method for multi-attribute large group decision-making

In multi-attribute large group decision-making (MALGDM), the ideal state indicates a high degree of consensus among a set of decision-makers (DMs). It is complex to reach consensus because the number of decision attributes and DMs increases. Thus, we developed a novel consensus model to manage the decision-making in large group based on the non-cooperative behavior. The improved clustering method takes account of the similarities among different DMs. Similar DMs will be grouped into the same group. The consensus threshold is determined from an objective and subjective aspect to judge whether the consensus reaching process continues. With the introduction of three non-cooperative behaviors, we investigated a non-cooperative behavior detection method under the change of consensus level. Base on the number of DMs who are willing to change their preliminary views and the change value of consensus level, the non-cooperative degree of subgroup can be computed. According to the non-cooperative degree, the subgroups’ weight can be modified to raise the consensus level. Meanwhile, the subgroup is allowed to change. Based on the adjustment amount of DMs’ opinions, whether decision maker (DM) belongs to this subgroup is recalculated. Finally, an emergency decision-making problem in flood disaster is applied to manifest the feasibility and distinctive features of the proposed method.

A large-scale equilibrium model of energy emergency production: Embedding social choice rules into Nash Q-learning automatically achieving consensus of urgent recovery behaviors

A large-scale energy emergency production plan driven by extreme events in energy supply chain networks is the low-probability/high-consequence event that is difficult to prepare. One of the most prominent challenges is efficiently computing the equilibrium point characterized with more frequently involved in common resource conflicts due to different player behaviors in energy supply chain networks. In this paper, a novel large-scale equilibrium model of energy emergency production: embedding social choice rules into Nash Q-learning automatically achieving consensus of urgent recovery behaviors, is proposed to tackle this challenge. The main contributions of this work are that firstly set up a large-scale equilibrium model of energy emergency production to formulate energy emergency production plans by modifying the large-scale energy equilibrium model, and the computational limitations of Generalized Nash Equilibrium are overcame by combination of Nash Q-learning methods and individuals’ preferences reaching a collective decision which guarantees uniqueness of the large-scale Nash equilibrium to achieve both system-level efficiency and maximum fairness. Simulations results show that the generalized Nash bargaining solution can be implemented by the proposed large-scale equilibrium model of energy emergency production, in which outcome of the game is the emergency production stable equilibria alternative with no chance moves in a given consensus level, and compared with the existing techniques considering non-cooperative behaviors, it has a significantly lower minimisation of time for the energy restoration by twenty-nine percent, and reduces minimisation of cost for the energy restoration by seventeen percent and minimisation of carbon dioxide emissions by twenty-three percent with disaster recovery.

Heterogeneous large-scale group decision making with subgroup leaders: An application to the green supplier selection

Under the mainstream development trend of energy conservation, environmental protection and sustainability, green supplier selection has become a common concern of many enterprises and scholars. Meanwhile, more and more attention has been paid to the problem of large-scale group decision making (LSGDM) which requires the participation of multiple decision makers (DMs). As the factors to be considered in selecting the optimal green supplier become increasingly complex, DMs with different professional backgrounds may give different forms of assessment. Therefore, it is of great significance to study the application of heterogeneous LSGDM in green supplier selection (GSS). In this article, a new heterogeneous LSGDM method based on subgroup leaders (SLs) is proposed. First, a novel determination method of SLs is presented based on the interaction between DMs. Then, according to the heterogeneous preference information of SLs, the solution method of the collective priority vector is given, and then an approach to determine the overall consensus level is presented by considering the consensus degree within and between subgroups. Subsequently, a consensus building method is proposed to manage the non-cooperative behaviour of DMs and reduce the divergences among DMs. Finally, an example of GSS is given to demonstrate the applicability and effectiveness of the proposed method.

Mondgezondheid en autismespectrumstoornis

Oral care for children with autism spectrum disorder (ASD) can be challenging at home and in the dental offi ce. Next to the common problems such as communication and interaction problems, other autism-related factors are associated with non-cooperative behavior during the oral care process. Th is article discusses factors that aff ect oral health in children with ASD.

A right to pollute versus a duty to mitigate: on the basis of emissions trading and carbon markets

ABSTRACT Emissions trading, also known as cap-and-trade systems, has not yet fulfilled its function of mitigating overall global greenhouse gas (GHG) emissions. The reasons for this failure are manifold and have been broadly discussed at political and empirical levels in the last decades. However, much can still be said from a philosophical perspective. Such an analysis is not limited to the evaluation of cap-and-trade systems’ lack of efficiency and the consequences arising from it but goes deeper into the moral questions underlying cap-and-trade systems. This is how this paper attempts to contribute to and expand the debate on emissions trading at different levels. By examining a popular analogy between traditional and climate commons, I challenge some of the economic and normative assumptions at the core of cap-and-trade systems. I argue that these assumptions lead to misguided conclusions in responding to the causes of climate change. This will partly explain why, although emissions trading is intended to fulfil a duty to mitigate greenhouse gases, we should not pin all our hopes on it just yet. Key policy insights The problems associated with free access to the traditional and climate commons can be conceived of in an analogous way, but the solutions to address them cannot. These include market-based policies focusing on the distribution of property rights over the atmosphere’s capacity to absorb GHGs. Like other common resources, the atmosphere’s capacity to absorb GHGs is limited and subject to deterioration due to free access and competing consumption. Unlike other common resources, there is no ability to restrict, control or sanction non-cooperative behaviour that continues to pollute the atmosphere. This poses a major challenge to the effectiveness of cap-and-trade systems. Emissions trading systems can be attractive mechanisms for pursuing GHGs targets by minimizing the costs of pollution while limiting the impact of mitigation on the overall economy. Nevertheless, even efficient emissions trading systems fail to fully address the moral concerns that lie at the heart of our current climate crisis.

Cooperative and Competitive Multi-Agent Systems: From Optimization to Games

Multi-agent systems can solve scientific issues related to complex systems that are difficult or impossible for a single agent to solve through mutual collaboration and cooperation optimization. In a multi-agent system, agents with a certain degree of autonomy generate complex interactions due to the correlation and coordination, which is manifested as cooperative/competitive behavior. This survey focuses on multi-agent cooperative optimization and cooperative/non-cooperative games. Starting from cooperative optimization, the studies on distributed optimization and federated optimization are summarized. The survey mainly focuses on distributed online optimization and its application in privacy protection, and overviews federated optimization from the perspective of privacy protection mechanisms. Then, cooperative games and non-cooperative games are introduced to expand the cooperative optimization problems from two aspects of minimizing global costs and minimizing individual costs, respectively. Multi-agent cooperative and non-cooperative behaviors are modeled by games from both static and dynamic aspects, according to whether each player can make decisions based on the information of other players. Finally, future directions for cooperative optimization, cooperative/non-cooperative games, and their applications are discussed.