How to identify an emergent property in a social system is a challenging topic. Social system modeling is an effective way to understand and explain the emergence phenomena. A reversed process of Role-Based Collaboration (RBC) is proposed for emergence studies. The Environments–Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is the fundamental model of RBC. A well extended E-CARGO (EE-CARGO) model is proposed to modeling a social system with emergent properties. The system is abstracted into agent-role level, roles are modeled as a tree structure and role emergence is identified and analyzed at multiple scales. Then we derive that the masses are primary, and the individual agent is secondary to understanding the emergence forming. A case study is accomplished to verify the effectiveness of proposed approach (i.e., the reversed RBC process and EE-CARGO model). The results are revealed that the role emergence fits a cubic curve estimation by time and at a moderate scale, the role emergence happens easily. Role performances at a small scale affect super-role emergence at a large scale statistically, and vice versa. The proposed approach can be applied to investigate emergence in a variety of areas, such as social evolution.

Considered the cooperation of the container truck and quayside container crane in the container terminal, this paper constructs the model of the quay cranes operation and trucks scheduling problem in the container terminal. And the hybrid intelligence swarm algorithm combined the particle swarm optimization algorithm(PSO) with artificial fish swarm algorithm (AFSA) was proposed. The hybrid algorithm (PSO-AFSA) adopt the particle swarm optimization algorithm to produce diverse original paths, optimization of the choice nodes set of the problem, use AFSA's preying and chasing behavior improved the ability of PSO to avoid being premature. the proposed algorithm has more effectiveness, quick convergence and feasibility in solving the problem. The results of stimulation show that the scheduling operation efficiency of container terminal is improved and optimized.