This paper aims to optimize and evaluating the performance of the crowd networks through analyzing their information sharing patterns. That is, in a crowd network, the qualities of accomplishing tasks are highly dependent on the effective information sharing among intelligent subjects within the networks. Hence, proposing an adaptive information-sharing approach can help improve the performance of crowd networks on accomplishing tasks that are assigned to them.

This paper first introduces the factors that affect effectiveness of information-sharing pattern: the network topology, the resources owned by intelligent subjects and the degree of information demand. By analyzing the correlation between these factors and the performance of crowd networks, an Adaptive Information Sharing Approach for Crowd Networks (AISCN approach) is proposed. By referring to information needed for accomplishing the historical tasks that are assigned to a crowd network, the AISCN approach can explore the optimized information-sharing pattern based on the predefined composite objective function. The authors implement their approach on two crowd networks including bee colony and supply chain, to prove the effectiveness of the approach.

The shared information among intelligent subjects affects the efficiency of task completion in the crowd network. The factors that can be used to describe the effectiveness of information-sharing patterns include the network topology, the resources owned by intelligent subjects and the degree of information demand. The AISCN approach used heuristic algorithm to solve a composite objective function which takes all these factors into consideration, so that the optimized information-sharing pattern can be obtained.

This paper introduces a set of factors that can be used to describe the correlation between information-sharing pattern and performance of crowd network. By quantifying such correlation based on these factors, this paper proposes an adaptive information-sharing approach which can explore the optimized information-sharing pattern for a variety of crowd networks. As the approach is a data-driven approach that explores the information-sharing pattern based on the network’s performance on historical tasks and network’s characteristics, it is adaptive to the dynamic change (change of incoming tasks, change of network characteristics) of the target crowd network. To ensure the commonality of the information-sharing approach, the proposed approach is not designed for a specific optimization algorithm. In this way, during the implementation of the proposed approach, heuristic algorithms can be chosen according to the complexity of the target crowd network.

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.