Name
Abstract | ||
|---|---|---|
Modeling complex human social interactions is an important part in agent-based social simulation research. For example, results of interactions (negotiations) for scheduling joint social activities could influence the future plans of the involved individuals, which has a great impact on the researches such as activity-based travel demand analysis and agent-based epidemic models. To describe these interactions is a rather difficult task than it may seem, in particular when the system has a very large scale (millions of individuals). Current research efforts ignore or simplify the negotiation/coordination part of the social interactions in order to reduce complexity, either by using fixed and predefined human daily schedules as input or by constraining the joint social activities (interaction purposes) into several specific types (e.g. eating out). In this paper, we describe an agent-based approach to model large-scale complex social interactions, by which individuals can discuss the duration and location of the coming social activities and make decisions about their attendance. We conducted a simulation experiment including nearly 20 million agents with complex social interactions on the basis of dynamic generation of friendship networks to realize this approach, and the simulation results comply with some social interaction phenomena. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2769458.2773790 | SIGSIM-PADS |
Field | DocType | Citations |
Social relation,Data science,Social network,Friendship,Simulation,Computer science,Social simulation,Social learning,Social dynamics,Social heuristics,Negotiation,Distributed computing | Conference | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingxin Zhang | 1 | 1 | 1.03 |
| Alexander Verbraeck | 2 | 483 | 76.95 |
| Rongqing Meng | 3 | 12 | 2.38 |
| Xiaogang Qiu | 4 | 149 | 20.35 |