Linking Micro- to Macro-level Behavior in the Aggressor-Defender-Stalker Game

In many multiagent systems, small changes in individual-level rules may lead to very large changes at the group-level. This phenomenon is striking in the “aggressor-defender game,” a simple participative game in which each participant randomly selects two others from the group (A and B). In the aggressor game, everyone tries to position themselves so that A is always between themselves and B. In the defender game everyone tries to position themselves between A and B. Despite these exceedingly simple rules and the seemingly small difference between them, the two games exhibit very different dynamics. The aggressor game produces a highly dynamic group that rapidly expands over time whereas the defender game quickly collapses to a tight knot. I analyze these games and provide some insight as to how these two group level behaviors arise, thereby linking the micro- and macro-levels. I also introduce and analyze a new, related and simpler game, the stalker game, in which each participant selects and pursues a single participant, and which also produces a collapsing group. It is suggested that such a geometrical analysis may be applicable for other multiagent systems such as insect societies and collective robotics.

[1]  Carl W. Hunt,et al.  Agent-Based Modeling for Testing and Designing Novel Decentralized Command and Control System Paradigms , 2003 .

[2]  Carl Anderson Creation of desirable complexity: strategies for designing selforganized systems , 2006 .

[3]  Eric Bonabeau,et al.  Predicting the unpredictable. , 2002, Harvard business review.

[4]  J. Uspensky Introduction to mathematical probability , 1938 .

[5]  AndersonCarl Linking Micro- to Macro-level Behavior in the Aggressor-Defender-Stalker Game , 2004 .

[6]  J. Urry Complexity , 2006, Interpreting Art.

[7]  Daniel W. Palmer,et al.  Using a collection of humans as an execution testbed for swarm algorithms , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[8]  E Bonabeau,et al.  Swarm Intelligence: A Whole New Way to Think about Business , 2001 .

[9]  J. Deneubourg,et al.  The blind leading the blind in army ant raid patterns: Testing a model of self-organization (Hymenoptera: Formicidae) , 1991, Journal of Insect Behavior.

[10]  Roger D. Quinn,et al.  Development of Collective Control Architectures for Small Quadruped Robots Based on Human Swarming Behavior , 2004 .

[11]  Peter V. Coveney,et al.  Frontiers of Complexity: The Search for Order in a Chaotic World, Peter Coveney and Roger Highfield. 1995. Random House, Inc., New York, NY. 480 pages. ISBN: 0-449-90832-1. $27.50 , 1996 .

[12]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[13]  M. Midgley Sociobiology. , 1984, Journal of medical ethics.

[14]  John S. Bay,et al.  Spatial self-organization in large populations of mobile robots , 1994, Proceedings of 1994 9th IEEE International Symposium on Intelligent Control.