With the rise of service computing, cloud computing, and IoT, service ecosystems are becoming increasingly complex. The intricate interactions among intelligent agents make abnormal emergence analysis challenging, as traditional causal methods focus on individual trajectories. Large language models offer new possibilities for Agent-Based Modeling (ABM) through Chain-of-Thought (CoT) reasoning to reveal agent intentions. However, existing approaches remain limited to microscopic and static analysis. This paper introduces a framework: Emergence Analysis based on Multi-Agent Intention (EAMI), which enables dynamic and interpretable emergence analysis. EAMI first employs a dual-perspective thought track mechanism, where an Inspector Agent and an Analysis Agent extract agent intentions under bounded and perfect rationality. Then, k-means clustering identifies phase transition points in group intentions, followed by a Intention Temporal Emergence diagram for dynamic analysis. The experiments validate EAMI in complex online-to-offline (O2O) service system and the Stanford AI Town experiment, with ablation studies confirming its effectiveness, generalizability, and efficiency. This framework provides a novel paradigm for abnormal emergence and causal analysis in service ecosystems. The code is available at https://anonymous.4open.science/r/EAMI-B085.

As the social environment is growing more complex and collaboration is deepening, factors affecting the healthy development of service ecosystem are constantly changing and diverse, making its governance a crucial research issue. Applying the scenario analysis method and conducting scenario rehearsals by constructing an experimental system before managers make decisions, losses caused by wrong decisions can be largely avoided. However, it relies on predefined rules to construct scenarios and faces challenges such as limited information, a large number of influencing factors, and the difficulty of measuring social elements. These challenges limit the quality and efficiency of generating social and uncertain scenarios for the service ecosystem. Therefore, we propose a scenario generator design method, which adaptively coordinates three Large Language Model (LLM) empowered agents that autonomously optimize experimental schemes to construct an experimental system and generate high quality scenarios. Specifically, the Environment Agent (EA) generates social environment including extremes, the Social Agent (SA) generates social collaboration structure, and the Planner Agent (PA) couples task-role relationships and plans task solutions. These agents work in coordination, with the PA adjusting the experimental scheme in real time by perceiving the states of each agent and these generating scenarios. Experiments on the ProgrammableWeb dataset illustrate our method generates more accurate scenarios more efficiently, and innovatively provides an effective way for service ecosystem governance related experimental system construction.

The increasing prevalence of smart services in daily life drives the rapid emergence of service ecosystems across various domains such as E-commerce, cloud manufacturing, and crowdsourcing. Evaluating the utility of these ecosystems is challenging due to complex characteristics like diverse crowd intelligence, cascading service network effects, and the interplay of individual interests. To address these challenges, this study introduces an innovative utility evaluation model that integrates individual and systemic factors with multidimensional metrics, reconciling micro and macro perspectives. This model effectively addresses potential conflicts between individual and systemic benefits, supporting the continuous learning and evolution of agents within service ecosystems. It employs value entropy to precisely model and interpret complex nonlinear emergence phenomena. The model's universal framework offers high customizability and broad applicability, facilitating specific adaptations across different service ecosystem scenarios. Additionally, a visual multi-agent system simulation tool has been developed to adjust agent attributes and cooperative topologies, allowing for the observation of system responses. Empirical validation confirms the model's accuracy and provides a mechanistic explanation for emergence phenomena in human societies. The findings demonstrate the model's high coordination and effectiveness in managing both linear and nonlinear characteristics, presenting a powerful and flexible tool for utility evaluation in service ecosystems. Our code is available at https://github.com/yxn9191/value_entropy.

With the increasing prevalence of smart services in our daily lives, service ecosystems are rapidly emerging across various domains, including E-commerce, cloud manufacturing, and crowdsourcing, among others. However, evaluating the utility of service ecosystems presents several challenges due to their complex characteristics, including the diversity in crowd intelligence, the cascading effects of service networks, and the interest game among individuals. Current evaluation methods are limited in providing a comprehensive system-wide view, highlighting various shortcomings. To address these challenges, this article introduces a multidimensional and integrated model for evaluating service utility, comprising three key components: (1) The individual utility model is built to facilitate the transition from specific scenarios to general evaluations. (2) Value entropy is introduced to assess the impact of nonlinear emergence on system utility. (3) The system utility model is used to enable the integration of both linear and nonlinear factors within the system. Furthermore, a crowdsourcing service platform is taken as an example to verify the effectiveness of the proposed model. The results show that the proposed model is effective in providing new means and ideas for evaluating and analyzing service ecosystems.

Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber–physical–social systems (CPSS). In consideration of the cost, legal, and institutional constraints on the study of CPSS in real world, computational experiments have emerged as a new method for quantitative analysis of CPSS. However, with the increase of application scenarios, how to map complex and diverse domain models to artificial society models has become a key challenge to hinder the wide use of computational experiments. In this article, the docking framework between the domain model and the artificial society model was proposed in this article, and the model docking specification is given from three aspects: the agent model, the environmental model, and the rules model. In addition, the effectiveness of the framework was verified by two classic cases: artificial stock market and epidemic prevention and control. The result showed that the proposed model docking framework can provide technical support for the multidisciplinary applications of computational experiments and significantly reduce the difficulty of using the method.

Given the complex nature of cyber-physical-social systems (CPSSs), understanding their mechanism is essential for analyzing and controlling their actions while minimizing potential harm. However, studying CPSS in the real world is costly and constrained by legal and institutional factors. Computational experiments have emerged as a new method for quantitative analysis, and this article proposes a method of using computational experiments for analyzing CPSS, which consists of model docking, experiment design, and experiment analysis. The cloud manufacturing service ecosystem (CMSE) is used as a typical case study to verify the effectiveness of the proposed method by simulating different operation strategies. The results show that the computational experiments method is effective in providing new means and ideas for analyzing CPSS.

With the development of ICT (information and communications technology) and service economy, service ecosystem is emerging in a lot of fields, including E-commerce, O2O(Online To Offline) life service, healthcare service, cloud manufacturing, and so on. As a complex socio-technical system, the evolution of service ecosystem is the joint result of the interaction of the three heterogeneous networks, including social network, service network and value network. Under such circumstances, the traditional SOA (Service Oriented Architecture)-based analysis model is powerless. As a result, how to analyze the laws behind the evolution of service ecosystem is still a serious challenge in the field. This paper proposes a value oriented analysis framework (VOA) of service ecosystem, which can use value as a clue to describe the interaction of the three heterogeneous networks. In addition, a computational experiment system is established to verify the effectiveness of the VOA framework, which stimulates the effect of different intervention strategies on service ecosystem. The result shows that our analysis framework can provide new means and ideas for the analysis of service ecosystem.

Computational experiments have emerged as a new method for quantitative analysis of complex social systems. It has been applied to many interdisciplinary research fields, such as economics, finance, and epidemiology. Though the representation form of computational experiments is relatively flexible, the real system is more complex. Therefore, it is important to seek a balance between the flexibility of computational modeling and the credibility of conclusion. This article proposes a customized design framework for computational experiment models, so as to meet the diverse application demands of computational experiments in different fields. Finally, this article outlines some typical applications of computational experiments to provide a roadmap for its rapid development and widespread application.

As a new form of manufacturing in the industrial Internet era, cloud manufacturing service ecosystem (CMSE) can meet complex customization needs through a dynamic collaborative network between cloud manufacturing services. The source of cloud manufacturing service is social, and such sociality aggravates the uncertainty and dynamics of CMSE. This poses new challenges to the analysis of CMSE's evolution. The existing model social learning evolution (SLE) model only analyzes manufacturing service ecosystem from the perspective of individuals and lacks research on the organizational structure among individuals. In this article, we propose the improved model (SLE2) from a system perspective, which reconstructed the three layers of the SLE model: the individual layer describes the learning and evolution characteristics of service agents; the organization layer describes the competition and cooperation among service agents; and the social layer describes the value-driven social network operation mode. Finally, the article verifies that the SLE2 model is effective through computational experimental results.