Non-cooperative Behavior Research Articles

Efficiency of a common natural forest exploitation by the system of individual transferable quotas with overlapping generations, capital, and monetary inheritance

This article focuses on the economic analysis of the exploitation of a renewable natural resource in common property, namely forests. In the Sahelian zone of West Africa with little forest cover, these natural assets are exposed to overexploitation despite the participation system established as a mode of their management for more than three decades. Thus, another mode of management is necessary to avoid the extinction of forests in certain countries like Niger to continue to benefit from the countless services that they provide. This paper proposes another mode of management by analyzing the effectiveness of the operating system through free distribution of transferable individual quotas with overlapping generations, capital, and monetary inheritance. From a general equilibrium model, the resolutions of the optimization programs using the Lagrangian show that the system in question provides the conditions for optimal management of the forest in common property. Moreover, it allows for an intergenerational transmission of renewable natural resources without any other incentive instrument such as taxes or subsidies despite the non-cooperative behavior of the actors.

Robust fake-post detection against real-coloring adversaries

The viral propagation of fake posts on online social networks (OSNs) has become an alarming concern. The paper aims to design control mechanisms for fake post detection while negligibly affecting the propagation of real posts. Towards this, a warning mechanism based on crowd-signals was recently proposed, where all users actively declare the post as real or fake. In this paper, we consider a more realistic framework where users exhibit different adversarial or non-cooperative behaviour: (i) they can independently decide whether to provide their response, (ii) they can choose not to consider the warning signal while providing the response, and (iii) they can be real-coloring adversaries who deliberately declare any post as real. To analyse the post-propagation process in this complex system, we propose and study a new branching process, namely total-current population-dependent branching process with multiple death types. At first, we compare and show that the existing warning mechanism significantly under-performs in the presence of adversaries. Then, we design new mechanisms which remarkably perform better than the existing mechanism by cleverly eliminating the influence of the responses of the adversaries. Finally, we propose another enhanced mechanism which assumes minimal knowledge about the user-specific parameters. The theoretical results are validated using Monte-Carlo simulations.

Weight Penalty Mechanism for Noncooperative Behavior in Large-Scale Group Decision Making With Unbalanced Linguistic Term Sets

This paper proposes a novel framework to manage subgroups' non-cooperative behavior by weight penalty in largescale group decision making (LSGDM). To do that, a trustconsensus index (TCI) is defined by combining trust score and consensus degree among experts expressed by unbalanced linguistic term sets. A Louvain algorithm clustering process based on undirected graph composed of TCI is introduced to detect the subgroups in large network. Hence, a weight penalty feedback model is established to manage the subgroups detected as discordant and non-cooperative. The proposed method novelty resides in that the minimum adjustment cost can be obtained with respect to the penalty parameter. A detail analysis regarding the computation of the optimal penalty parameter to prevent excessive penalization is reported. Finally, a detailed numerical and comparative analyses are provided to verify the validity of the proposed method.

Aged Care Projects Evaluation Research Based on the Self-Adaptive Consensus Emergence Model Driven by the Social Learning Mechanism

In recent years, the accelerated aging process in China has brought enormous pressure on the traditional aged care system. The private family-based aged care mode cannot resolve the contradiction between the insufficient labor force and a large elderly population. The public community-based aged care mode could effectively integrate various social resources such as housing, healthcare, land, education, etc., which may better meet diverse elderly care demands. The senior residents' evaluation of public aged care services is essential for local pension policy making, determining the local government's investment direction to some extent. Therefore, this research organizes a decision group to conduct the evaluation of alternative aged care service projects with Maslow's hierarchy-based evaluation framework. The decision group consists of senior citizens from the Xiaolingwei community in Nanjing, which is a deeply aging metropolis with the flourishing aged care service market and demand. Considering the aged residents' inherent preference differences rooted in living habits, cultural background, and risk attitude, it is essential to introduce an effective consensus model into the group evaluation to control opinion conflict and reach consensus. The current consensus models still have the following limitations: consensus models depend on the preset empirical consensus threshold, hardly reflecting the spontaneous consensus emergence; the inconsistency of group consensus opinion harms the reliability and effectiveness of group decision; the existing methods generally identify the non-cooperative behavior passively, lacking the adaptive and active management. Therefore, this paper develops the consensus emergence model driven by the social learning mechanism based on the BA scale-free network and the Bayesian learning rule, guiding the self-adaptive opinion evolution and the spontaneous consensus convergence. The novel consensus model includes three crucial parts: the environment network construction, the private cognition formation, and the individual preference modification. The proposed consensus emergence model has outstanding advantages in consensus consistency through spontaneous opinion evolution, the adaptive consensus emergence with greater consensus efficiency, and the consensus robustness under non-cooperators disturbance.

The reconciliation mechanism by cooperative intention index for managing non-cooperative behaviors in social network group decision making

Social network group consensus is a hybrid cooperative and non-cooperative problem, with the management of non-cooperative behavior as a key topic. In response, this study investigates the reconciliation mechanism to manage non-cooperative behaviors based on a cooperative intention index (CII). First, a CII index driven by preference similarity and trust relationships is constructed, which predicts the compromise domain of decision makers (DMs). Subsequently, this study recognizes three types of non-cooperative behaviors from the group and individual proposal perspectives in turn: (i)Type-1 cooperation with the group; (ii)Type-2 non-cooperation with the group but cooperation with the individual; and (iii)Type-3 non-cooperation with both the group and the individual. Then, reconciliation mechanisms applicable to different types of mediation are proposed. Then, group-orientated, individual-orientated and compulsory reconciliation mechanisms are proposed for different types. Finally, a demonstration example and discussions are provided to illustrate the efficacy of the proposed model.

Evolutionary game analysis for non-cooperative behavior of entities in a closed-loop green supply chain under government intervention

Evolutionary game analysis for non-cooperative behavior of entities in a closed-loop green supply chain under government intervention

Scaled consensus for MASs with compound noises over Markovian switched topologies

AbstractThis article aims to address the stochastic scaled consensus issue in almost surely and mean square sense of stochastic multiagent systems (SMASs) with compound noises in a Markovian switching setting. At first, based on stochastic approximation technique and nearest‐neighbor interaction rules, the stochastic scaled consensus controller is built for SMASs in presence of multiplicative and additive noise by designing a time‐varying gain. Meanwhile, some consensus criterions of SMASs are addressed if the union of the graph and time‐varying gain fulfill some mild requirements. Furthermore, the scaled consensus means that the agents' state variables tend to a prescribed proportion rather than the common value, so this behavior can be seen as non‐convergent behavior. The difference with most of excellent literatures is that the coexistence of noncooperative behavior and multiplicative noise generates that it is not easy to transform the multiplicative noise term into the form of an error equation, which means that the Lyapunov methods cannot be directly utilized in our article. To cope with this, we first demonstrate the boundedness for each agent's state, and hence the convergence can be achieved. Finally, in order to reveal the efficiency of our proposed protocol, the corresponding example is given in the simulation part.

A Large-Scale Group Decision-Making Approach to Assess Water Resource Sustainability with Double-Level Linguistic Preference Relation

With the rapid development of science and technology and the continuous progress of society, water resource sustainability has attracted much attention. The assessment process of water resource sustainability has become a hot topic. Because professional models cannot ensure the accuracy of evaluation results, expert judgment techniques are used to perform the assessment process. Meanwhile, to eliminate the biases and consider people’s cognitive processes in complex decision making, this study utilizes a large group of experts to evaluate the sustainability of water resources, which is called a large-scale group water resource sustainability assessment (LGWRSA). This study proposes the double-level linguistic preference relation (DLLPR) to enable experts to present more reasonable and intuitive linguistic opinions. Based on the DLLPR, this study presents a clustering method, a weighting method, and a comprehensive adjustment factor determination method. To prevent minority opinions and non-cooperative behaviors from interfering with the decision-making process and to make the decision-making process develop in an accurate and objective direction, a consensus model is developed to modify minority opinions and suggestions and manage non-cooperative behaviors. After a sufficient number of experts reach a consensus through the method proposed in this article, a more accurate evaluation result can be obtained. Finally, an actual LGWRSA problem is established to derive water resource sustainability expectations for four provinces, which validates the effectiveness of the proposed method. A comparative analysis is performed to illustrate the benefits of these methods and present their shortcomings.

Attitudinal analysis of vaccination effects to lead endemic phases

To achieve endemic phases, repeated vaccinations are necessary. However, individuals may grapple with whether to get vaccinated due to potential side effects. When an individual is already immune due to previous infections or vaccinations, the perceived risk from vaccination is often less than the risk of infection. Yet, repeated rounds of vaccination can lead to avoidance, impeding the establishment of endemic phases. We explore this phenomenon using an individual-based Monte Carlo simulation, validating our findings with game theory. The Nash equilibrium encapsulates individuals' non-cooperative behavior, while the system's optimal value represents the societal benefits of altruistic cooperation. We define the difference between these as the price of anarchy. Our simulations reveal that the price of anarchy must fall below a threshold of 12.47 for endemic phases to be achieved in a steady state. This suggests that for a basic reproduction number of 10, a consistent vaccination rate greater than 89% is required. These findings offer new insights into vaccination-related decision-making and can inform effective strategies to tackle infectious diseases.

Consensus reaching process with noncooperative behaviors in large-scale group social network environment

Large-scale group decision-making (LSGDM) problems have drawn general attention from scholars. The distribution linguistic preference relation (DLPR) is a flexible and practical tool to describe the preferences of decision makers (DMs) in the decision-making process. Opinion conflict is inevitable among large-scale DMs due to self-interest and individual perception. Thus, it is essential to establish a consensus reaching process (CRP), but there may be non-cooperative behaviors for DMs when they are required to accept the opinions adjustment. As the social network becomes more ubiquitous, moreover, the trust relationship among decision members has implications for the consensus reaching process. Hence, this study proposes a new consensus model that manages non-cooperative behaviors from the cooperative degree, trust relationship, and individual self-confidence level three aspects, and discusses the specific influence of these factors on the penalty for non-cooperative behaviors. In addition, hesitancy-based similarity measure of linguistic distributed assessment is proposed for clustering and measuring consensus level. Finally, a numerical example of a price hearing system demonstrates the feasibility and efficacy of the proposed method, and a comparative analysis illustrates its features and advantages.

Optimal taxation in an endogenous fertility model with non-cooperative behavior

Optimal taxation in an endogenous fertility model with non-cooperative behavior

Community-aware empathetic social choice for social network group decision making

To study how interdependent and self-interested decision makers collectively make a choice in highly complex social environments, this paper presents a novel and powerful Community-Aware Empathetic Social Choice (CAESC) model, which considers decision makers in social networks as autonomy-oriented agents, who derive utility based on both their own intrinsic preferences, and empathetic preferences determined by the satisfaction of their intra-community neighbors. We devise optimization approaches for identifying community structures with local (global) social welfare maximizing under two varieties of CAESC models: The local CAESC model first identifies the non-cooperative behaviors by calculating the structure-preference coordination coefficients between pairwise decision makers, then employs the Logit response like dynamics process in a carefully defined potential game to find the optimal community structure that maximizes the local social welfare. To find the optimal community structure that maximizes the global social welfare, the global CAESC model is composed of three coupled phases during each stage: (i) opinion evolution within each community using the classic Jacobi method; (ii) structure-preference coordination matrix updating based on the current opinion vector; and (iii) local optimal community structure updating based on the local CAESC model. Extensive experiments on both randomly generated and real-world social networks with synthetic and real-world preferences confirm that neglecting the empathetic effect and community structure usually yields sub-optimal group decisions which degrade the social welfare of network members. Our experiments also show that, the higher societal empathy, the rougher preference distribution and the denser network structure will facilitate the maximal social welfare reaching in both local and global CAESC models; meanwhile, by comparing with the typical empathetic decision making models and some recent proposed consensus reaching methods, our proposed approaches show relatively significant advantages in terms of candidate ranking and decision efficiency.

Minimum conflict consensus models for group decision-making based on social network analysis considering non-cooperative behaviors

Decision makers (DMs) may exhibit non-cooperative behaviors such as deviating from recommendations or making only minor modifications in consensus-based group decision-making (GDM) problems. These non-cooperative behaviors will lead to high intra-group conflicts and low consensus efficiency. This paper designs a budget-constrained consensus framework with minimum group conflict based on social network analysis to address non-cooperative behaviors. First, the conflict degrees among DMs are quantified by opinion discrepancies and asymmetric 2-tuple linguistic trust values. Then, DMs’ weights are derived by considering degree centrality and node strength indices in the constructed conflict network. To obtain a feasible budget constraint for the consensus, the non-linear optimization models considering non-cooperative behaviors are proposed. Furthermore, two kinds of minimum conflict consensus models are established to deal with general and completely non-cooperative DMs, respectively. The existence of the optimal solutions of these consensus models and the convergence of the consensus reaching process are proved. Finally, the proposed consensus models are illustrated by an example of the negotiation of the sewage discharge standards. Compared with the consensus models based on minimum cost or adjustment, the proposed consensus models could achieve higher consensus and lower group conflict.

Managing non-cooperative behaviors in large-scale group decision making based on trust relationships and confidence levels of decision makers

In large-scale group decision making events, diverse backgrounds of decision makers (DMs) may result in various non-cooperative behaviors as they pursue personal interests, thereby causing significant harm to the decision making process. Instead of solely analyzing DMs’ evaluations in detecting traditional non-cooperative behaviors, this study defines three new categories of non-cooperative behaviors by integrating DMs’ evaluations with their personal attributes, such as confidence levels and trust relationships. These new behaviors include bribery behavior, passive participation behavior, and potential conflict behavior. To address these behaviors, this research proposes a confidence and trust-based consensus reaching process (CT-CRP). Within CT-CRP, three distinct acceptance functions for DMs are introduced, which are utilized to describe the likelihood of DMs accepting recommended plans and determining an optimal modification rate. Lastly, an illustrative example and several experiments are provided to demonstrate the effectiveness and validity of CT-CRP in enhancing consensus among DMs.

Revealing perceived individuals’ self-interest

In non-cooperative game-theory models, the Nash equilibrium concept is used to assess the outcome of rational decision-makers’ strategic involvement. This method allows researchers to look into certain outcomes while keeping in mind people’s self-interest restrictions. Participants, on the other hand, are not bound to provide accurate information when acting in their own self-interest. In this work, we investigate noncooperative behavior in a repeating game and suggest a new method in game theory for designing an observer. An average repeated non-cooperative Markov game with imperfect information is used to determine the categories of participants in this situation. A sequential technique, in which information is revealed a finite number of times, can approximate all of Nash’s equilibria. By computing the derivative of the player’s equilibrium with regard to his or her present type, we explain how to instrument the observer’s design. The dynamic observer creates the imperfect information game. We study a Markov model extension that incorporates a new variable that reflects the product of the observer design and the distribution vector to solve the problem. The strategies and the observation kernel make up the observer design. In this approach, the approaches are first seen as mappings from private and reported states to lotteries over alternatives. We build equations to retrieve the variables of interest using the available type: observer, strategies, observation kernels, and distribution vectors. We provide a numerical example of an industrial organization in which enterprises compete on the market and decide on the amount of production they will produce simultaneously and independently of one another.

A multi-objective grey hierarchical group consensus model and its application

PurposeThe paper attempts to construct a novel multi-objective grey hierarchical group consensus approach to deal with the group consensus problems consisting of hierarchical relationship and non-cooperative behaviors among decision makers (DMs).Design/methodology/approachTo deal with these group consensus problems consisting of hierarchical relationship and non-cooperative behaviors among DMs non-cooperative behavior in uncertain information systems, considering the influence of coordination cost and the degree of group consensus, based on the idea of grey situation decision-making, the authors establish a multi-objective grey hierarchical group consensus model, and design different invalid decision elimination rules for decision-making groups of different sizes, and use a case verifies the effectiveness and feasibility of the model.FindingsWith the continuous improvement of the coordination cost budget, the degree of consensus of all departments and the overall consensus tend to be stable, and will no longer change with the increase of the coordination cost budget. The cost required by each department is basically consistent with the response trend of the cost required to coordinate the overall situation to the pre-set lower limit of group consensus.Originality/valueThe proposed approach can succeed in identifying DMs' information, and mine the DMs' information and help make a relatively more scientific decision.

Social incentives foster cooperation through guilt aversion: An effect that diminishes with primary psychopathic traits.

The association between primary psychopathic traits and non-cooperative behaviors is well-identified. There is a lack of studies on how to motivate cooperative behaviors in individuals with primary psychopathic traits. This study investigated the effects of monetary incentives and social incentives on promoting cooperation in healthy adults with varying primary psychopathic traits. Participants played a one-shot public goods game (PGG) with other anonymous players in three different contexts: a social incentives context where participants' decisions would be judged by others, a monetary incentives context where participants' decisions would result in winning or losing money depending on their contributions, and a control condition where no additional incentives were implemented. We found that, compared to the control condition, both monetary and social incentives significantly improved participants' contributions to the public project-an indicator of cooperative behavior. However, the association between higher primary psychopathic traits and less cooperation was only observed in the context of social incentives. Computational modeling further revealed that this effect can be explained by the diminishing guilt aversion when participants deliberately violated their inferred expectations of themselves from others' perspectives. This study found that social incentives can encourage cooperative behaviors in non-clinical psychopathy, and identified the mental processes navigating this effect.

Applicability of the Future State Maximization Paradigm to Agent-Based Modeling: A Case Study on the Emergence of Socially Sub-Optimal Mobility Behavior

Novel developments in artificial intelligence excel in regard to the abilities of rule-based agent-based models (ABMs), but are still limited in their representation of bounded rationality. The future state maximization (FSX) paradigm presents a promising methodology for describing the intelligent behavior of agents. FSX agents explore their future state space using “walkers” as virtual entities probing for a maximization of possible states. Recent studies have demonstrated the applicability of FSX to modeling the cooperative behavior of individuals. Applied to ABMs, the FSX principle should also represent non-cooperative behavior: for example, in microscopic traffic modeling, there is a need to model agents that do not fully adhere to the traffic rules. To examine non-cooperative behavior arising from FSX, we developed a road section model populated by agent-cars endowed with an augmented FSX decision making algorithm. Simulation experiments were conducted in four scenarios modeling various traffic settings. A sensitivity analysis showed that cooperation among the agents was the result of a balance between exploration and exploitation. We showed that our model reproduced several patterns observed in rule-based traffic models. We also demonstrated that agents acting according to FSX can stop cooperating. We concluded that FSX can be useful for studying irrational behavior in certain traffic settings, and that it is suitable for ABMs in general.

A model of a two-level hierarchical system with non-cooperative behavior of lower-level elements

One of the possible generalizations for the case of many persons of the classical hierarchical Germeier game is investigated. It is assumed that lower-level players tend to Nash equilibrium situations. The solution of the corresponding problem for games without feedback is given. An example is constructed demonstrating the fundamental difficulties encountered in the study of a game with feedback. A meaningful example is considered in which the proposed constructions adequately describe reality.

Novel consensus-reaching model in the social network environment for large-group emergency decision-making: an approach to managing non-cooperative behaviors

Novel consensus-reaching model in the social network environment for large-group emergency decision-making: an approach to managing non-cooperative behaviors