Sage Journals: Discover world-class research

Abstract

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.

Keywords

swarming self-organization participative game aggressor defender stalker

Get full access to this article

View all access options for this article.

References

Anderson, C. (2003). Linking micro- to macro-level behavior in the Aggressor-Defender-Stalker Game . In C. Anderson , & T. Balch (Eds.), Proceedings of the Second International Workshop on the Mathematics and Algorithms of Social Insects (pp. 9-16 ). Atlanta, GA: Georgia Institute of Technology.

Anderson, C. (in press). Creation of desirable complexity: Strategies for designing self-organized systems. In D. Braha , A. Minai , & Y. Bar-Yam (Eds.), Complex engineering systems (New England Complex Systems Institute Series on Complexity). New York: Perseus Books Group .

Bonabeau, E. (2002a). Agent-based modeling: methods and techniques for simulating human systems . Proceedings of the National Academy of Sciences, 99, 7280-7287 .

Bonabeau, E. (2002b). Predicting the unpredictable . Harvard Business Review, 3, 109-116 .

Bonabeau, E. , & Meyer, C. (2001). Swarm intelligence. A whole new way to think about business . Harvard Business Review, 5, 107-114 .

Bonabeau, E. , Funes, P. , & Orme, B. (2003a). Exploratory Design Of Swarms . In C. Anderson & T. Balch (Eds.), Proceedings of the Second International Workshop on the Mathematics and Algorithms of Social Insects (pp. 17-24 ). Atlanta, GA: Georgia Institute of Technology.

Bonabeau, E. , Hunt, C. W. , & Gaudiano P. (2003b.) Agent-based modeling and designing novel decentralized command and control systems paradigms . In Modeling and Simulation and Network-Centric Application Topics for the Eight International Command and Control Research and Technology Symposium. Washington, DC: National Defense University.

Casti, J. (1994). Complexification. New York: Harper Collins .

Coveney, P. , & Highfield, R. (1996). Frontiers of complexity. London: Faber and Faber .

10.

Franks, N. R. , Gomez, N. , Goss, S. , & Deneubourg J. L. (1991). The blind leading the blind in army ant raid patterns: Testing a model of self-organization (Hymenoptera: Formicidae) . Journal of Insect Behavior, 38, 583-607 .

11.

Funes, P. , Orme, B. , & Bonabeau, E. (2003). Evolving emergent group behaviors for simple humans agents . In P. Dittrich & J. T. Kim (Eds.) Proceedings of the Seven European Conference on Artificial Life(ECAL 2003) (pp. 76-89 ), 14-17 September, 2003.

12.

Lewin, R. (1995). Complexity. Life on the edge. London: Phoenix .

13.

Palmer, D. W. , Kirschenbaum, M. , Murton, J. , Vaidyanathan, R. , & Quinn, R. D. (2003a). Development of collective control architecture for small quadruped robots based on human swarming behavior . In C. Anderson & T. Balch (Eds.), Proceedings of the Second International Workshop on the Mathematics and Algorithms of Social Insects (pp. 123-130 ). Atlanta, GA: Georgia Institute of Technology.

14.

Palmer, D. , Kirschenbaum, M. , Murton, J. P. , Kovacina, M. A. , Steinberg, D. H. , Calabrese, S. N. , Zajac, K. M. , Hantak, C. M. , & Schatz J. E. (2003b). Using a collection of humans as an execution testbed for swarm algorithms . In Proceedings of the IEEE Swarm Intelligence Symposium (pp. 58-64 ).

15.

Ünsal, C. (1993). Self-organization in large populations of mobile robots. M.Sc. thesis, Virginia State University.

16.

Uspensky, J. V. (1937). An introduction to mathematical probability. New York: McGraw-Hill .

17.

Wilson, E. O. (1975). Sociobiology. Cambridge, MA: Harvard University Press .

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

Abstract

Keywords

Get full access to this article

References