Towards Detecting Group Identities in Complex Artificial Societies

Corrado Grappiolo, Georgios N. Yannakakis

Research output: Conference Article in Proceeding or Book/Report chapterBook chapterResearchpeer-review


This paper presents a framework for modelling group struc- tures and dynamics in both artificial societies and human-populated vir- tual environments such as computer games. The group modelling (GM) framework proposed focuses on the detection of existing, pre-defined group structures and is composed of a reinforcement learning method that infers collaboration values from the society’s local interactions and a clustering algorithm that detects group identities based on the learned collaboration values. An empirical evaluation of the framework in the social ultimatum bargain game shows that the GM method proposed is robust independently of the size of the society and the locality of the interactions.
Original languageEnglish
Title of host publicationFrom Animals to Animats 12 : 12th International Conference on Simulation of Adaptive Behavior, SAB 2012, Odense, Denmark, August 27-30, 2012. Proceedings
Publication date2012
ISBN (Print)978-3-642-33092-6
Publication statusPublished - 2012
SeriesLecture Notes in Computer Science


  • Group Identity Detection, Reinforcement Learning, Hierarchical Clustering, Artificial Societies, Complex Adaptive Systems, Complexity.


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