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The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm.

This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm.

Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments.

The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents.

This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.

In recent years, with the increase in computing power, artificial intelligence can gradually be regarded as intelligent agents and interact with humans, this interactive network has become increasingly complex. Therefore, it is necessary to model and analyze this complex interactive network. This paper aims to model and demonstrate the evolution of crowd intelligence using visual complex networks.

This paper uses the complex network to model and observe the collaborative evolution behavior and self-organizing system of crowd intelligence.

The authors use the complex network to construct the cooperative behavior and self-organizing system in crowd intelligence. Determine the evolution mode of the node by constructing the interactive relationship between nodes and observe the global evolution state through the force layout.

The simulation results show that the state evolution map can effectively simulate the distribution, interaction and evolution of crowd intelligence through force layout and the intelligent agents’ link mode the authors proposed.

Based on the complex network, this paper constructs the interactive behavior and organization system in crowd intelligence and visualizes the evolution process.

Artificial intelligence is gradually penetrating into human society. In the network era, the interaction between human and artificial intelligence, even between artificial intelligence, becomes more and more complex. Therefore, it is necessary to describe and intervene the evolution of crowd intelligence network dynamically. This paper aims to detect the abnormal agents at the early stage of intelligent evolution.

In this paper, differential evolution (DE) and K-means clustering are used to detect the crowd intelligence with abnormal evolutionary trend.

This study abstracts the evolution process of crowd intelligence into the solution process of DE and use K-means clustering to identify individuals who are not conducive to evolution in the early stage of intelligent evolution.

Experiments show that the method we proposed are able to find out individual intelligence without evolutionary trend as early as possible, even in the complex crowd intelligent interactive environment of practical application. As a result, it can avoid the waste of time and computing resources.

In this paper, DE and K-means clustering are combined to analyze the evolution of crowd intelligent interaction.

Evolution can be easily observed in nature world, and this phenomenon is a research hotspot no matter in natural science or social science. In crowd science and technology, evolutionary phenomenon exists also among many agents in crowd network systems. This kind of phenomenon is named as crowd co-evolutionary, which cannot be easily studied by most existing methods for its nonlinearity. This paper aims to proposes a novel simulation framework for co-evolution to discover improvements and behaviors of intelligent agents in crowd network systems.

This paper introduces a novel simulation framework for crowd co-evolutions. There are three roles and one scene in the crowd. The scene represented by a band-right to a ringless diagram. The three roles are unit, advisor and monitor. Units find path in the scene. Advisors give advice to units. Monitors supervise units’ behavior in the scene. Building a network among these three kinds member, influencing individual relationships through information exchange, and finally enable the individual to find the optimal path in the scene.

Through this simulation framework, one can record the behavior of an individual in a group, the reasons for the individual's behavior and the changes in the relationships of others in the group that cause the individual to do so. The speed at which an individual finds the optimal path can reflect the advantages and disadvantages of the relationship change function.

The framework provides a new way to study the evolution of inter-individual relationships in crowd networks. This framework takes the first-person perspective of members of the crowd-sourced network as the starting point. Through this framework, the user can design relationship evolution methods and mathematical models for the members of different roles, so as to verify that the level of public intelligence of the crowd network is actually the essence of the rationality of the membership relationship.

Intelligence level of a crowd network is defined as the expected reward of the network when completing the latest tasks (e.g. last N tasks). The purpose of this paper is to improve the intelligence level of a crowd network by optimizing the profession distribution of the crowd network.

Based on the concept of information entropy, this paper introduces the concept of business entropy and puts forward several factors affecting business entropy to analyze the relationship between the intelligence level and the profession distribution of the crowd network. This paper introduced Profession Distribution Deviation and Subject Interaction Pattern as the two factors which affect business entropy. By quantifying and combining the two factors, a Multi-Factor Business Entropy Quantitative (MFBEQ) model is proposed to calculate the business entropy of a crowd network. Finally, the differential evolution model and k-means clustering are applied to crowd intelligence network, and the species distribution of intelligent subjects is found, so as to achieve quantitative analysis of business entropy.

By establishing the MFBEQ model, this paper found that when the profession distribution of a crowd network is deviate less to the expected distribution, the intelligence level of a crowd network will be higher. Moreover, when subjects within the crowd network interact with each other more actively, the intelligence level of a crowd network becomes higher.

This paper aims to build the MFBEQ model according to factors that are related to business entropy and then uses the model to evaluate the intelligence level of a number of crowd networks.

This paper aims to obtain optimal specialization mode and level for complex network or system structures. In the e-commerce system, this paper studies the changes of each transaction subject in the process of ecological structure based on the income level of each transaction subject.

This paper aims to research the change of transaction efficiency evolution process of intermediaries. With the improvement of transaction efficiency, intermediaries interact with other transaction subjects at given modes in e-commerce systems. This paper analyzes the relationship between the factors of production and trade and explains the quantitative relationship between them in the form of mathematical modeling. An evolution simulation framework is established to elaborate the simulation process and method of crowd network in e-commerce ecosystem and then sets up the simulation experiment.

During simulation processes, the changes of data are observed and analyzed to obtain the optimal evolution paths and specialization modes. Furthermore, this paper provides solid supports for the research of the quantitative analysis of ecological structure evolutions.

Evolution simulation of ecological structure is first proposed in the topic of crowd network. It is with the aid of the concept of ecology, the theory and method, simulation of complex network structure and system structure. This paper analyses and researches the evolution process of optimal specialization modes and intelligent level of crowd networks with transaction efficiency changing. The ecological structure optimal evolution paths can be obtained by trend of simulations.

The synthetic application and interaction of/between the internet, Internet of Things, cloud computing, big data, Industry 4.0 and other new patterns and new technologies shall breed future Web-based industrial operation system and social operation management patterns, manifesting as a crowd cyber eco-system composed of multiple interconnected intelligent agents (enterprises, individuals and governmental agencies) and its dynamic behaviors. This paper aims to explore the basic principles and laws of such a system and its behavior.

The authors propose the concepts of crowd science and engineering (CSE) and expound its main content, thus forming a research framework of theories and methodologies of crowd science.

CSE is expected to substantially promote the formation and development of crowd science and thus lay a foundation for the advancement of Web-based industrial operation system and social operation management patterns.

This paper is the first one to propose the concepts of CSE, which lights the beacon for the future research in this area.

The motivation to participate in crowdsourcing (CS) platforms is an emerging challenge. Although researchers and practitioners have focused on crowd motivation in the past, the results obtained through such practices have not been satisfactory. Researchers have left unexplored research areas related to CS pillars, such as the evolution of the crowd’s primary motivations, seekers applying effective policies and incentives, platform design challenges and addressing task complexity using the synchronicity of the crowd. Researchers are now more inclined to address these issues by focusing on sustaining the crowd’s motivation; however, sustaining the crowd’s motivation has many challenges.

To fill this gap, this study conducted a systematic literature review (SLR) to investigate and map the challenges and factors affecting sustained motivation during CS with the overcoming implications. Studies that satisfied the inclusion criteria were published between 2010 and 2021.

Important sustainable factors are extracted using the grounded theory that has sustained participation and the factors' cohesion leads to the identification of challenges that the pillars of CS face. Crowds being the most vital part of CS contests face the challenge of engagement. The results reported the factors that affect the crowd’s primary and post-intentions, perceived value of incentives and social and communal interaction. Seekers face the challenge of knowledge and understanding; the results identify the reason behind the crowd’s demotivation and the impact of theories and factors on the crowd's psychological needs which helped in sustaining participation. Similarly, the platforms face the challenge of being successful and demanding, the results identify the latest technologies, designs and features that seekers proclaim and need the platforms designer's attention. The identified task challenges are completion and achievement; the authors have identified the impact of trait of task and solving mechanisms that have sustained participation.

The study identifies, explores and summarizes the challenges on CS pillars researchers are facing now to sustain contributions by keeping participants motivated during online campaigns. Similarly, the study highlights the implication to overcome the challenges by identifying and prioritizing the areas concerning sustainability through the adoption of innovative methods or policies that can guarantee sustained participation.

This study aims to review the literature on sharing economy logistics and crowd logistics to answer the three following questions: How is the literature on sharing economy logistics structured? What are the main trends in sharing economy logistics and crowd logistics? What are the future research options?

Bibliometric analysis is used to evaluate 85 articles published over the past 12 years; it identifies the top academic journals, authors and research topics contributing to the field.

The sharing economy logistics and crowd logistics literature is structured around several disciplines and highlights that some are more scientifically advanced than others in their subject definitions, designs, modelling and innovative solutions. The main trends are organized around three clusters: Cluster 1 refers to the optimal allocation of costs, prices, distribution and supplier relationships; Cluster 2 corresponds to business related crowdsourcing and international industry practices; and Cluster 3 includes the impact of transport on last-mile delivery, crowd shipping and the environment.

The study is based on data from peer-reviewed scientific journals and conferences. A broader overview could include other data sources such as books, book chapters, working papers, etc.

Future research directions are discussed in the context of the evolution from crowd logistics to crowd intelligence, and the complexities of crowd logistics such as understanding how the social crowd can be integrated into the logistics process. Our results are part of the crowd science and engineering concept and provide some answers about crowd cyber-system questions regarding crowd intelligence in logistic sector.

The ubiquity of digitally intermediated interactions is changing the ways in which social interaction creates the cognitive and institutional underpinnings of new markets. Logics that define markets used to be localized, but they now emerge from crowds that span – and persist – across time and space. This article builds a theory of how crowds emerge and evolve in a way that influences the emergence of shared logics and helps explain why some markets are viable while others are not. What is revealed is that a crowd has a hidden niche structure that determines the fate of a new market.