Cooperative Behavior Research Articles

Hierarchical Reward Model of Deep Reinforcement Learning for Enhancing Cooperative Behavior in Automated Driving

In recent years, studies on practical application of automated driving have been conducted extensively. Most of the research assumes the existing road infrastructure and aims to replace human driving. There have also been studies that use reinforcement learning to optimize car control from a zero-based perspective in an environment without lanes, one of the existing types of road. In those studies, search and behavior acquisition using reinforcement learning has resulted in efficient driving control in an unknown environment. However, the throughput has not been high, while the crash rate has. To address this issue, this study proposes a hierarchical reward model that uses both individual and common rewards for reinforcement learning in order to achieve efficient driving control in a road, we assume environments of one-way, lane-less, automobile-only. Automated driving control is trained using a hierarchical reward model and evaluated through physical simulations. The results show that a reduction in crash rate and an improvement in throughput is attained by increasing the number of behaviors in which faster cars actively overtake slower ones.

Social trust and the stringency of public policies against the COVID-19 pandemic

ABSTRACT One may wonder why countries have used different public policies to cope with the Covid-19 pandemic. We argue that social trust could be the missing link in explaining why these public policies vary in stringency. Based on the logic of game theory, we draw the hypothesis that more social trust in a society means that less stringent policies are needed to abate the COVID-19 pandemic. Empirical results at both country and individual levels confirm this hypothesis. The implications for public policy stemming from these findings suggest the necessity of addressing initiatives like Team Europe’s 2020 package launched by the European External Action Service (EEAS) to combat Covid-19 in non-EU nations. The more social trust in a society, the higher the likelihood of informal cooperation. Thus, less stringent policies are needed to abate the COVID-19 pandemic. In perspective, policymakers are likely to encounter fewer compliance issues and more cooperative behavior in countries with high levels of trust when confronting future crises.

An evolutionary model of personality traits related to cooperative behavior using a large language model

This study aims to demonstrate that Large Language Models (LLMs) can empower research on the evolution of human behavior, based on evolutionary game theory, by using an evolutionary model positing that instructing LLMs with high-level psychological and cognitive character descriptions enables the simulation of human behavior choices in game-theoretical scenarios. As a first step towards this objective, this paper proposes an evolutionary model of personality traits related to cooperative behavior using a large language model. In the model, linguistic descriptions of personality traits related to cooperative behavior are used as genes. The deterministic strategies extracted from LLM that make behavioral decisions based on these personality traits are used as behavioral traits. The population is evolved according to selection based on average payoff and mutation of genes by asking LLM to slightly modify the parent gene toward cooperative or selfish. Through experiments and analyses, we clarify that such a model can indeed exhibit evolution of cooperative behavior based on the diverse and higher-order representation of personality traits. We also observed repeated intrusion of cooperative and selfish personality traits through changes in the expression of personality traits. The words that emerged in the evolved genes reflected the behavioral tendencies of their associated personalities in terms of semantics, thereby influencing individual behavior and, consequently, the evolutionary dynamics.

Impact of Teacher Classroom Behavior on Academic Stress and School Adjustment of Students

Present study was conducted to find the impact of teacher classroom behavior on academic stress and school adjustment of students at secondary school level in Sargodha division. Sample of 372 students (205 male students and 167 female students) participated in the study and this sample size was determined using Yamane's (1967) formula suggestions. Researcher developed three questionnaire including Teacher’s Classroom Behavior Scale (TCBS), Academic Stress Scale (ASS) and School Adjustment Scale (SAS) were administered for the collection of data. Validation of the instruments was completed through 10 experts in the field of social sciences whereas reliability of the tool was done through Cronbach’s Alpha methods. Different descriptive as well as inferential statistical tools were used for the analysis of data collected through questionnaires. Pearson product-moment correlation, linear and multiple regression, and t-test as inferential statistics whereas Mean and SD were used as descriptive statistics. Results of the study demonstrated that there is a negative association between teacher classroom behavior and academic stress and a positive association between teacher classroom behavior and school adjustment of students. Moreover, a significant impact of teachers’ classroom behavior on the academic stress and school adjustment of students was observed. These results show that good teachers’ behavior removes academic stress and improves the students’ adjustment. Therefore, the study recommends that teachers should keep cooperative behavior with students for better results.

Overcoming Resource Competition Among Co-Ethnics: Elites, Endorsements, and Multiracial Support for Urban Distributive Policies

When can we expect multiple racial groups to find common ground in the face of potentially unequal distributive urban policies? While we understand a great deal about the role of elites in inter-ethnic coalition building, we know less about the conditions under which cooperative behavior among their co-ethnic voters is more likely. Research has found that multiracial coalitions are critical to the political incorporation of racial/ethnic minority group interests at the local level but conflict between minority groups persists due to both real and perceived competition for resources. In this paper, we argue that elite co-ethnic endorsements can increase co-ethnic voters' support for urban distributive policies that disproportionately benefit outgroups over one’s own ingroup. We test our theory using a survey experiment from a representative sample of more than 1800 Los Angeles County voters. We find that respondents are less likely to support policy proposals that exclusively target benefits toward ethnic outgroups compared to when their ethnic ingroup exclusively benefits from an identical proposal. But we also find that the presence of co-ethnic endorsements can increase support for proposals that benefit an ethnic outgroup. We find this effect among Black, Latino, and Asian Americans in our sample.

Platoon-aware cooperative lane-changing strategy for connected automated vehicles in mixed traffic flow

The platooning management technology for Connected Automated Vehicles (CAVs) can potentially increase the efficiency of the traffic system. However, the randomness in the spatial distribution of CAVs poses a new challenge for CAVs’ platooning strategy in mixed traffic flow. To effectively utilize the advantages of platooning technology, this paper proposes a platoon-aware cooperative lane-changing (PCLC) strategy inspired by platoon formation. This strategy focuses on the formation of CAV platoons in mixed traffic flow and restructures the spatial distribution of CAVs on the road segment. First, the cooperative lane-changing behavior of CAVs is modeled based on vehicle-to-vehicle communication, and a gap suitable for lane-changing is created by controlling the acceleration of cooperative CAVs to complete the lane-changing process smoothly. Meanwhile, considering the maximum platoon size, a decision-making framework of cooperative lane-changing for CAVs is designed. Then, considering the change in spatial distribution of CAVs (i.e., platoon intensity) caused by PCLC, the impact of PCLC on traffic capacity is analyzed from theoretical and numerical verification perspectives. Finally, simulation experiments are conducted to investigate the impact of PCLC on lane-changing indicators, traffic flow stability, and traffic efficiency. The simulation experiments show that compared with the discretionary lane-changing (DLC) operation, the PCLC strategy may reduce the lane-changing rate and raise the average platoon size on road segments, increasing road capacity. In addition, the vehicles on the road segment have modest swings in average speed and acceleration, proving the safety and stability of the proposed strategy. Particularly, scenarios with moderate to heavy traffic flow and fairly balanced CAVs penetration rates favor the effectiveness of this strategy. With 50% CAVs and moderate traffic density, the lane capacity increases by 450 veh/h and improves by 10.135%. These findings support the ability of PCLC strategy to reduce traffic congestion and effectively explore traffic management strategies for mixed traffic flow.

Variance, norms and cooperative behavior in public goods games.

This study examines the relationship between the variance of others' contributions, social norms (empirical and normative expectations), and cooperative behavior using a classic linear public goods game. The following results are observed. First, the variance of a participant's group members' contributions had a negative impact on their contributions, empirical expectations, and normative expectations. Second, deviations from the mean, whether negative or positive, were deemed less socially appropriate. Third, while there was a strong relationship between variance, social norms, and cooperative behavior, the mediating effect of social norms was found to be insignificant. Finally, there were some notable findings regarding behavior type. Although free riders and cooperators exhibited distinct behavioral patterns, their normative expectations were similar. Free riders expected others to cooperate, but their empirical expectations were significantly lower than cooperators' expectations, which were aligned with their actual contributions. These findings contribute to research on the relationship between distribution heterogeneity, social norms and cooperative behavior. Furthermore, these findings provide valuable insights into management practices.

Co-evolutionary dynamics in optimal multi-agent game with environment feedback

At present, game-based analysis and control problems with environmental feedback significantly contribute to the study of collective cooperation, and pose great challenges for the analysis of multi-agent game systems in complex real world. We consider co-evolutionary dynamics based on an extended multi-agent game with environmental feedback, which is modeled by optional public goods games (OPGGs). Firstly, the co-evolutionary dynamics of OPGGs with environment feedback can be modeled as two parts: the dynamics of OPGGs and synergy coefficient, where the former can represent the group game behavior of three strategies, and the latter can be seen as the evolutionary behavior in changing environments. Subsequently, some possible fixed points will emerge with respect to three types of boundaries for these co-evolutionary dynamics, and the existence and stability for the corresponding boundary fixed points are determined and analyzed. Meanwhile, the results are further extended to interior equilibrium point. It is observed that by combining voluntary participation and environment feedback, cooperation behavior can be still feasible, and the evolving system can oscillatingly converge to the persistent cooperation under some certain threshold conditions. Finally, an example is illustrated to validate the proposed approach. To sum up, environmental feedback mechanism provides a novel perspective to investigate the collective cooperation in a changing world.

The effect of positive inter-group contact on cooperation: the moderating role of individualism.

The effect of intergroup contact on cooperation is well documented, but little is known about the cultural moderators of this relationship. Contributing to the literature, we examined whether cultural orientation moderates the effect of positive intergroup contact on cooperation and places emphasis on individualism. By creating a gamecooperation situation by the trust game paradigm, 322 Taiwanese youth were involved in the study and completed the positive intergroup contact scale, individual-collectivism scale, and social distance scale. (1) positive intergroup contact effectively promotes cooperative behavior; (2) Taiwanese youth who have closer social distance with mainland youth demonstrate higher levels of cooperative behavior after group interactions than larger social distance; and (3) individualism has a significant moderating role in the relationship between positive inter-group contact and cooperation. The effect of positive inter-group contact on cooperation became stronger in the less individualistic group. The effect of social distance on cooperation became stronger in the less individualistic group.Cultural orientation represented by individualism is proved to be one moderato of the intergroup contact-cooperation relationship.

Age of correlated information-optimal dynamic policy scheduling for sustainable Green IoT devices: A multi-agent deep reinforcement learning approach

The rapid progress in communication technologies and the widespread adoption of connected devices have given rise to various information-centric Internet-of-Things (IoT) systems, typically necessitating timely updates of information. Green IoT (G-IoT) strives to enhance the environment by reducing the power consumption of billions of devices engaged in extensive data exchange, addressing the substantial energy demand in the process. The Age of Correlated Information (AoCI) measures the freshness of information shared among two or more devices that contribute to the same decision-making process. Optimizing AoCI using Deep Reinforcement Learning (DRL) in IoT reduces energy consumption, optimizes resource utilization, and promotes environmentally conscious communication, contributing to the development of a sustainable G-IoT system. This paper focuses on scheduling the transmission of status update packets among interconnected G-IoT devices to minimize the application-specific long-term average AoCI. The problem is modeled as an NP-hard episodic Markov Decision Process (MDP), highlighting its computational complexity. To handle correlations and the curse of dimensionality, a multi-agent deep reinforcement learning algorithm, specifically the Multi-Agent Deep Deterministic Policy Grading (MADDPG) algorithm, is developed. The training progress displays episode rewards, with an environment designed to penalize multiple agents transmitting simultaneously or none at all, promoting cooperative behavior and minimizing the average age of correlated information. We provide comprehensive simulation results, including reward convergence, the learning process of actors and critics, and the resulting average AoCI with the number of episodes, demonstrating the effectiveness of the MADDPG algorithm.

Game-theoretic learning for the coordination of drone teams in autonomous cooperative inspection

Without the need for an on-board pilot, drones are designed to accomplish dull, dangerous and dirty missions. However, if a mission exhibits a large operative area and/or several objectives, it may entail poor performance when executed by a single drone. Drone teams may overcome this issue by acting as mobile sensor networks for proximal sensing. In such networks, cooperative autonomy is a key enabling behaviour for achieving resilient and cost-efficient systems. This work implements cooperative autonomous behaviour in the form of a dynamic and decentralized mission planner for a multi-drone inspection mission. The proposed design exploits multi-agent task allocation, distributed route planning and game theory for the assignment of inspection tasks and for the processing of optimal routes in reasonable time frames and with limited communication. In detail, it applies the learning-in-games framework for the coordination within the inspection team, by studying some ad-hoc variants of best response and of log linear learning. Moreover, this work presents some numerical results of model-in-the-loop tests for a comparison between the learning-in-games approaches.

Dynamic behavior of enzyme kinetics cooperative chemical reactions

This article uses computational mathematics to investigate the dynamics of cooperative occurrences in chemical reactions inside living organisms. We study the dynamics of complex systems using mathematical models based on ordinary differential equations, paying special attention to chemical equilibrium and reaction speed. Explanations of cooperation, non-cooperation, and positive cooperation are presented in our study. We analyze the stabilities of equilibrium points by a systematic analysis that makes use of the Jacobian matrix and the threshold parameter R0. We next extend our investigation to evaluate global stability and the probability of the model. Variations in k3 have a notable effect on substrate concentration probabilities, indicating that it plays an important role in reaction kinetics. Reducing k3 highlights the substrate's critical contribution to the system by extending its presence in the concentration. We find that different results were obtained for cooperative behavior: higher reaction rates at different binding sites are correlated with positive cooperativity, while slower reactions are induced by negative cooperativity. The Adams–Bashforth method is used to show numerical and graphical solutions with the help of MATLAB. Tables and graphs are used to further explain the effects of the parameters. This study underlines how well ordinary differential equations may represent the complicated system dynamics found in chemical reactions. It also provides elusive insights into cooperative occurrences, which develops our understanding of the phenomenon and serves as a foundation for future research.

Global-and-Local Attention-Based Reinforcement Learning for Cooperative Behaviour Control of Multiple UAVs

Global-and-Local Attention-Based Reinforcement Learning for Cooperative Behaviour Control of Multiple UAVs

Association of Temperament and Character Traits with Suicide Probability, Suicide Attempts, and Perceived Stress Level in Patients with Bipolar Disorder.

Bipolar disorder (BD) is a high-suicide-risk mental disorder. The purpose of this study was to identify the relationship between temperament and character traits with suicide probability, suicide attempts, and perceived stress level in patients with BD. A total of 39 euthymic patients with bipolar disorder who had a history of suicide attempts and 39 euthymic patients without a history of suicide attempts were included in this study. The sociodemographic and clinical data form, Hamilton Depression Rating Scale (HDRS), Young Mania Rating Scale (YMRS), Structured Clinical Interview for DSM-5-Clinician Version (SCID-5/CV), Temperament and Character Inventory (TCI), Perceived Stress Scale (PSS), and Suicide Probability Scale were used to obtain the data. HDRS, PSS, and SPS scores of the group comprised of patients who attempted suicide were higher than the other group. There was no significant difference between the group of patients who had attempted suicide and the other group in terms of temperament characteristics. In the group of patients who had attempted suicide, self-directedness (SD) and cooperativeness (CO) scores were lower, and the self-transcendence (ST) score was higher than the other group. HA and ST were positively and SD negatively associated with SPS scores. In the regression analysis for suicide risk, the factors most associated with suicide risk were high HDRS and low CO score. Low SD in BD and high ST with CO may be associated with suicide attempts. Alongside low SD, high HA and ST may be associated with suicidal ideation. Treating residual depressive symptoms can reduce the risk of suicide.

ПРИКЛАДНА ЛІНГВІСТИКА АБСТРАКЦІЇ У ВИКЛАДАННІ МЕХАНІКИ ТЕКСТИЛЬНИХ ВИРОБНИЦТВ

The purpose for this article aims to regulatory increased and an abstraction approach in philosophy ontology descriptive mechanics tests teaching descriptive mechanics of the textile industry from processing facilities owned by public universities was initiated of applied linguistics. The textile industry has led the world into and is leading the movement toward global manufacturing; there was a flurry of philosophy ontology abstraction in teaching descriptive mechanics of textiles in Egypt. Methodology solving the problems identified in philosophy ontology abstraction in teaching descriptive mechanics of textiles, these tests required the development of new descriptive mechanics education, Rephrasing the detailed description of the curriculum for teaching textile mechanics, stripping it of any excess content, and teaching it easily so assessment tools and the use of systems thinking and dialogue with the community, industry, and regulators. Results: The abstract teaching method of these tests allowed the development of new descriptive mechanics teaching evaluation tools, the use of systems thinking, and dialogue with society, industry, and regulatory authorities. A case study of the beneficial use of these tools is presented as an example of how a philosophical abstraction science approach to teaching meta-dynamics in the textile industry can improve productivity and production resulting from cooperative behavior of stakeholders. Conclusions: Through this study, which this period of time needs of applied linguistics, with the increase in regulatory controls and the beginning of the abstract approach in ontological philosophy and descriptive mechanics was developed tests for teaching descriptive mechanics for the textile industry from processing facilities owned by public universities. Recommendations: there is a need to generalize and expand in reformulating the curricula and teaching methods according to this study.

The effects of contractual and relational governance on public‐private partnership sustainability

AbstractThe study investigates how contractual and relational governance influence partner behaviors and promote sustainability performance of infrastructure public‐private partnerships (PPPs). Survey data from 217 Chinese PPP projects were analyzed using partial least squares structural equation modeling. This study found that both contractual and relational governance have positive effects on PPP sustainability, with contractual governance having a stronger impact. Contractual governance complements relational governance in promoting cooperative behavior, but relational governance does not amplify the effect of contractual governance on opportunistic behavior. The findings highlight the role of contracts in curbing opportunism and promoting collaboration via multiple functions of control, coordination, and adaption, while also revealing the limitations of relational approaches in the context of government‐business relationships and performance ambiguity. This study contributes to the PPP governance literature by uncovering the individual and interactive effects of contractual and relational governance on the economic, social, and environmental aspects of PPP sustainability.

Confusion cannot explain cooperative behavior in public goods games

Some scholars find that behavioral variation in the public goods game is explained by variations in participants' understanding of how to maximize payoff and that confusion leads to cooperation. Their findings lead them to question the common assumption in behavioral economics experiments that choices reflect motivations. We conduct two experiments, in which we minimize confusion by providing participants with increased training. We also introduce a question that specifically assesses participants' understanding of payoff maximization choices. Our experimental results show that the distribution of behavior types is significantly different when participants play with computers versus humans. A significant increase in contributions is also observed when participants play with humans compared to when they play with computers. Moreover, social norms may be the main motive for contributions when playing with computers. Our findings suggest that social preferences, rather than confusion, play a crucial role in determining contributions in public goods games when playing with humans. We therefore argue that the assumption in behavioral economics experiments that choices reveal motivations is indeed valid.

Channel selection strategy: based on buy online and pick up in-store cooperation

PurposeThis paper investigates the buy online and pick up in-store cooperation (BOPSC) of online and offline retailers. Specifically, this study solves the following questions: (1) What is the impact of BOPSC on their optimal price and sales volume of products? (2) When should an online retailer and an offline retailer conduct the BOPSC strategy with each other?Design/methodology/approachThe paper first establishes two game models to explore the equilibriums of online and offline retailers in non-BOPSC and BOPSC. Then the condition for online and offline retailers to implement BOPSC strategy are determined. Furthermore, the applicability of the BOPSC strategy is enhanced by incorporating numerical analysis.FindingsThe study’s findings reveal that BOPSC strategy will not always beneficial to online and offline retailers, which depends on the total cost of online shopping and the product valuation of consumers. BOPSC strategy leads to the increase of prices and online orders, and the demand of offline retailer is eroded. Moreover, BOPS cooperation between different retailers is easier to achieve than omni-channel integration strategy. When the convenience difference between offline shopping and BOPSC pick-up is moderate, the effectiveness of BOPSC strategy can be improved.Originality/valueThis study has the following two main contributions: Firstly, the authors investigate the effects of BOPSC strategy on the prices of online and offline retailers. The study results show that the BOPSC strategy alleviates price competition and promotes a win–win situation between online retailers and offline retailers. Secondly, this paper mainly studies the cooperative behavior between online and offline retailers and reveals the optimal conditions for online and offline retailers to adopt BOPSC strategy. It can help small- and medium-sized online and offline retailers to choose suitable products for BOPSC strategy, so as to achieve the purpose of increasing profit.

Online Planning for Autonomous Mobile Robots with Different Objectives in Warehouse Commissioning Task

Recently, multi-agent systems have become widespread as essential technologies for various practical problems. An essential problem in multi-agent systems is collaborative automating picking and delivery operations in warehouses. The warehouse commissioning task involves finding specified items in a warehouse and moving them to a specified location using robots. This task is defined as a spatial task-allocation problem (SPATAP) based on a Markov decision process (MDP). It is considered a decentralized multi-agent system rather than a system that manages and optimizes agents in a central manner. Existing research on SPATAP involving modeling the environment as a MDP and applying Monte Carlo tree searches has shown that this approach is efficient. However, there has not been sufficient research into scenarios in which all agents are provided a common plan despite the fact that their actions are decided independently. Thus, previous studies have not considered cooperative robot behaviors with different goals, and the problem where each robot has different goals has not been studied extensively. In terms of the cooperative element, the item exchange approach has not been considered effectively in previous studies. Therefore, in this paper, we focus on the problem of each robot being assigned a different task to optimize the percentage of picking and delivering items in time in social situations . We propose an action-planning method based on the Monte Carlo tree search and an item-exchange method between agents. We also generate a simulator to evaluate the proposed methods. The results of simulations demonstrate that the achievement rate is improved in small- and medium-sized warehouses. However, the achievement rate did not improve in large warehouses because the average distance from the depot to the items increased.

Slow magnetic relaxation and proton conduction in a highly hydrogen-bonded dinuclear holmium(III) complex

Multifunctional magnetic-electrical materials are highly interesting, such as proton-conductive lanthanide single-ion magnets (SIMs). Herein, we reported the synthesis, crystal structure, magnetic and electrical properties of a bifunctional binuclear holmium(III) complex, [Ho2(H2dobdc)(H2O)14]·(H2dobdc)2·8H2O (Ho2, H4dobdc = 2,5-dioxodo-1,4-benzenedicarboxylate), which was constructed by a hydroxy‑functionalizd dicarboxybenzene and holmium(III) ions. Interestingly, single-crystal X-ray diffraction analysis reveals a highly hydrogen-bonded supramolecular structure and highly oxygenated site were formed within the complex. Magnetic measurements indicate the complex exhibits filed-induced slow magnetic relaxation property. Additionally, the impedance spectroscopy measurements show the complex also exhibits humidity- and temperature-dependent proton conduction behaviour via the Grotthus mechanism. These results support a proton-conductive holmium(III) single-ion magnet of Ho2. This work not only provides the first bifunctional HoIII complexes showing slow magnetic relaxation and proton-conducting behaviour but also a supramolecular way for advanced magnetic-electrical systems linking slow magnetic relaxation with unique proton conducting behaviour exploiting a possible cooperative behaviour.