Title
Collective learning for the emergence of social norms in networked multiagent systems.
Abstract
Social norms such as social rules and conventions play a pivotal role in sustaining system order by regulating and controlling individual behaviors toward a global consensus in large-scale distributed systems. Systematic studies of efficient mechanisms that can facilitate the emergence of social norms enable us to build and design robust distributed systems, such as electronic institutions and norm-governed sensor networks. This paper studies the emergence of social norms via learning from repeated local interactions in networked multiagent systems. A collective learning framework, which imitates the opinion aggregation process in human decision making, is proposed to study the impact of agent local collective behaviors on the emergence of social norms in a number of different situations. In the framework, each agent interacts repeatedly with all of its neighbors. At each step, an agent first takes a best-response action toward each of its neighbors and then combines all of these actions into a final action using ensemble learning methods. Extensive experiments are carried out to evaluate the framework with respect to different network topologies, learning strategies, numbers of actions, influences of nonlearning agents, and so on. Experimental results reveal some significant insights into the manipulation and control of norm emergence in networked multiagent systems achieved through local collective behaviors.
Year
DOI
Venue
2014
10.1109/TCYB.2014.2306919
IEEE T. Cybernetics
Keywords
Field
DocType
norms,emergence,social,systems
Collaborative learning,Agent-based social simulation,Norm (social),Multi-agent system,Network topology,Artificial intelligence,Wireless sensor network,Ensemble learning,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
44
12
2168-2267
Citations 
PageRank 
References 
7
0.44
0
Authors
3
Name
Order
Citations
PageRank
Chao Yu19112.97
Minjie Zhang225530.01
Fenghui Ren315320.05