Mixed strategy approach destabilizes cooperation in finite populations with clustering coefficient

Evolutionary game theory, encompassing discrete, continuous, and mixed strategies, is pivotal for understanding cooperation dynamics. Discrete strategies involve deterministic actions with a fixed probability of one, whereas continuous strategies employ intermediate probabilities to convey the extent of cooperation and emphasize expected payoffs. Mixed strategies, though akin to continuous ones, calculate immediate payoffs based on the action chosen at a given moment within intermediate probabilities. Although previous research has highlighted the distinct impacts of these strategic approaches on fostering cooperation, the reasons behind the differing levels of cooperation among these approaches have remained somewhat unclear. This study explores how these strategic approaches influence cooperation in the context of the prisoner's dilemma game, particularly in networked populations with varying clustering coefficients. Our research goes beyond existing studies by revealing that the differences in cooperation levels between these strategic approaches are not confined to finite populations; they also depend on the clustering coefficients of these populations. In populations with nonzero clustering coefficients, we observed varying degrees of stable cooperation for each strategic approach across multiple simulations, with mixed strategies showing the most variability, followed by continuous and discrete strategies. However, this variability in cooperation evolution decreased in populations with a clustering coefficient of zero, narrowing the differences in cooperation levels among the strategies. These findings suggest that in more realistic settings, the robustness of cooperation systems may be compromised, as the evolution of cooperation through mixed and continuous strategies introduces a degree of unpredictability

Enhancing social cohesion with cooperative bots in societies of greedy, mobile individuals

Addressing collective issues in social development requires a high level of social cohesion, characterized by cooperation and close social connections. However, social cohesion is challenged by selfish, greedy individuals. With the advancement of artificial intelligence (AI), the dynamics of human-machine hybrid interactions introduce new complexities in fostering social cohesion. This study explores the impact of simple bots on social cohesion from the perspective of human-machine hybrid populations within network. By investigating collective self-organizing movement during migration, results indicate that cooperative bots can promote cooperation, facilitate individual aggregation, and thereby enhance social cohesion. The random exploration movement of bots can break the frozen state of greedy population, help to separate defectors in cooperative clusters, and promote the establishment of cooperative clusters. However, the presence of defective bots can weaken social cohesion, underscoring the importance of carefully designing bot behavior. Our research reveals the potential of bots in guiding social self-organization and provides insights for enhancing social cohesion in the era of human-machine interaction within social networks

Does Collaboration Always Enhance Work Efficiency? Investigating Collective IS Use from a Process Perspective

Previous studies have focused mainly on individual IS use, while empirical evidence on collective IS use remains limited. Collective IS use involves interdependent instances of individual IS use within a common work process to fulfill collaborative work. This paper investigates the impact of collective IS use on collaboration performance, what form of collective IS use is efficient, and how to improve work efficiency. Drawing on coordination theory and taking a process perspective, we conceptualize two forms of collective IS use: asynchronous use and synchronous use. Objective data from a high-tech company reveals that asynchronous use improves work efficiency in terms of the time to complete a workflow, while synchronous use prolongs the time resulting in lower work efficiency. We further investigate the moderating role of worker repetitiveness, manager involvement, and task routineness. This study contributes to understanding collective IS use and offers guidance for optimizing collaboration process design

Impact of Committed Minorities: Unveiling Critical Mass of Cooperation in the Iterated Prisoner's Dilemma Game

The critical mass effect is a prevailing topic in the study of complex systems. Recent research has shown that a minority of zealots can effectively drive widespread cooperation in social dilemma games. However, achieving a critical mass of cooperation in the prisoner's dilemma requires stricter conditions. The underlying mechanism behind this effect remains unclear, particularly in the context of repeated interactions. This paper aims to investigate the influence of a committed minority on cooperation in the Iterated Prisoner's Dilemma game, a widely studied model of repeated interactions between individuals facing a social dilemma. In contrast to previous findings, we identify tipping points for both well-mixed and structured populations. Our findings demonstrate that a committed minority of unconditional cooperators can induce full cooperation under weak imitation conditions. Conversely, a committed minority of conditional cooperators, who often employ Tit-for-Tat or extortion strategies, can promote widespread cooperation under strong imitation conditions. These results hold true across various network topologies and imitation rules, suggesting that critical mass effects may be a universal principle in social dilemma games. Additionally, we discover that an excessive density of committed conditional cooperators can hinder cooperation in structured populations. This research advances our understanding of the role of committed minorities in shaping social behavior and provides valuable insights into cooperation dynamics.Comment: 12 pages, 15 figure

A STUDY OF ENHANCING RESEARCH COMPETENCE AMONG YOUNG SPORTS TEACHERS THROUGH SWOT ANALYSIS: A CASE OF GUANGDONG TECHNOLOGY COLLEGE

The research focuses on investigating techniques for boosting the research capacities of young physical education teachers using the SWOT analysis approach, literature review method, and mathematical statistics method. By examining internal strengths, weaknesses, opportunities, and threats, the following solutions are suggested for improving the research capacities of young physical education teachers, using Guangdong Technology College as an example and taking the actual conditions into account: At the college level, enhancing top-level planning, providing institutional and financial support, and promoting international exchange and learning. At the department level, establishing research groups, organizing periodic research experience sharing activities, advocating the concept of "mentoring, sharing, and mutual success," and facilitating the organic development of multi-generational research teams. At the individual teacher level, focusing on personal development, cultivating a professional image, and strengthening the ability to convert between "theory and practice" in research outcomes. The project, which aims to improve the young physical education instructors' research skills, acts as a guide for the college's research decisions, ultimately increasing the college's competitiveness

Research Progress and Trend Analysis of "Artificial Intelligence +" Education -- Visual Analysis Based on CiteSpace Knowledge Graph

With the rapid development of artificial intelligence (AI) technology, its application in the field of education has gradually gained popularity. Based on the CiteSpace visual knowledge graph analysis method, this paper makes a systematic visual analysis of the core literatures on artificial intelligence collected by CNKI from 2018 to 2023 in terms of the number of publications, authors, institutions, keyword co-occurrence, clustering and emergence. The analysis results show that in the past six years, the research in the field of "artificial intelligence + education" is in a prosperous period, and the current research hotspots are mainly focused on the application scope of artificial intelligence in the field of education, the impact of artificial intelligence on education and the development direction of artificial intelligence in the field of education in the future. The practical focus of "artificial intelligence + education" mainly focuses on labor education and the cultivation of students' labor literacy

Optimizating Drilling Operating Parameters With Real-Time Surveillance and Mitigation System of Downhole Vibration in Deep Wells

Torsional vibration is the main bottleneck which leads to low efficiency of rock breaking. According to the characteristics of torsional dysfunctions performance, the paper defines three main types of stick-slip, and analyzed the relationship between ROP and energy from bit. Based on Newton’s equations of motion, established frequency domain, single degree of freedom, damped and forced drill string torsional vibration prediction model, with more accuracy for downhole drill string mechanical state description. On this basis, semi-analytical transfer matrix method is adopted to establish drill string response relationship between internal force wave and surface parameters changes in the condition of vibration, which greatly reduce the number of discrete elements and the associated computing time, enabling rapid screening of a large number of design alternatives on a PC. In addition, the response parameters for drillstring stick-slip are integrated into an index (Vibration Strength Estimate, VSE), which is used to quantitatively evaluate downhole stick-slip severity. A three-week pilot test has been conducted in deep wells in Yumen Oilfield in China, with 25% increase of the average ROP and 40% enhancement of the average bit footage compared with offset wells. Validation of field application shows that the downhole torsional vibration evaluation technology is an effective method for further excavate the potential of ROP and reduce drilling cost

Simple bots breed social punishment in humans

Costly punishment has been suggested as a key mechanism for stabilizing cooperation in one-shot games. However, recent studies have revealed that the effectiveness of costly punishment can be diminished by second-order free riders (i.e., cooperators who never punish defectors) and antisocial punishers (i.e., defectors who punish cooperators). In a two-stage prisoner's dilemma game, players not only need to choose between cooperation and defection in the first stage, but also need to decide whether to punish their opponent in the second stage. Here, we extend the theory of punishment in one-shot games by introducing simple bots, who consistently choose prosocial punishment and do not change their actions over time. We find that this simple extension of the game allows prosocial punishment to dominate in well-mixed and networked populations, and that the minimum fraction of bots required for the dominance of prosocial punishment monotonically increases with increasing dilemma strength. Furthermore, if humans possess a learning bias toward a "copy the majority" rule or if bots are present at higher degree nodes in scale-free networks, the fully dominance of prosocial punishment is still possible at a high dilemma strength. These results indicate that introducing bots can be a significant factor in establishing prosocial punishment. We therefore, provide a novel explanation for the evolution of prosocial punishment, and note that the contrasting results that emerge from the introduction of different types of bots also imply that the design of the bots matters.Comment: 12 pages, 4 figure

A Route Analysis Study of Artificial Intelligence Enabling Education—Visualization Analysis Based on Citespace Literature Keywords

With the advent of the intelligent era, artificial intelligence technology promotes the innovation and development of the education field. Based on the data in Web of Science database and CNKI database, the article uses Citespace software to construct keyword co-occurrence network mapping, keyword clustering mapping, keyword emergence map, etc. to visualize and analyze the data, and sort out the domestic and international research hotspots, themes, and trends of educational artificial intelligence. It is found that the current research on AI in education in China can be categorized into three major themes: research on empowering education based on technological support, AI for educational change, and research on the application and impact of AI in education; while the research results on AI in education abroad mainly focus on two major themes: case studies on the application of machine learning, and research on the way of focusing on people's teaching/learning based on technological support. The article presents and analyzes the hotspots, themes and trends of domestic and foreign educational artificial intelligence research visually, with a view to understanding the current status of domestic and foreign educational artificial intelligence research, grasping the current research hotspots, themes and trends, finding the differences and connections between domestic and foreign research, and thus putting forward useful references to the development of educational artificial intelligence in China

Cooperative bots exhibit nuanced effects on cooperation across strategic frameworks

The positive impact of cooperative bots on cooperation within evolutionary game theory is well documented; however, existing studies have predominantly used discrete strategic frameworks, focusing on deterministic actions with a fixed probability of one. This paper extends the investigation to continuous and mixed strategic approaches. Continuous strategies employ intermediate probabilities to convey varying degrees of cooperation and focus on expected payoffs. In contrast, mixed strategies calculate immediate payoffs from actions chosen at a given moment within these probabilities. Using the prisoner\u27s dilemma game, this study examines the effects of cooperative bots on human cooperation within hybrid populations of human players and simple bots, across both well-mixed and structured populations. Our findings reveal that cooperative bots significantly enhance cooperation in both population types across these strategic approaches under weak imitation scenarios, where players are less concerned with material gains. However, under strong imitation scenarios, while cooperative bots do not alter the defective equilibrium in well-mixed populations, they have varied impacts in structured populations across these strategic approaches. Specifically, they disrupt cooperation under discrete and continuous strategies but facilitate it under mixed strategies. These results highlight the nuanced effects of cooperative bots within different strategic frameworks and underscore the need for careful deployment, as their effectiveness is highly sensitive to how humans update their actions and their chosen strategic approach