Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2003-06-20
Physics
Condensed Matter
Disordered Systems and Neural Networks
Contribution to the Workshop on Collectives and the Design of Complex Systems, Stanford University, August 2003 52 pages 9 fig
Scientific paper
We discuss a crowd-based theory for describing the collective behavior in a generic multi-agent population which is competing for a limited resource. These systems -- whose binary versions we refer to as B-A-R (Binary Agent Resource) collectives -- have a dynamical evolution which is determined by the aggregate action of the heterogeneous, adaptive agent population. Accounting for the strong correlations between agents' strategies, yields an accurate description of the system's dynamics in terms of a 'Crowd-Anticrowd' theory. This theory can incorporate the effects of an underlying network within the population. Most importantly, its applicability is not just limited to the El Farol Problem and the Minority Game. Indeed, the Crowd-Anticrowd theory offers a powerful approach to tackling the dynamical behavior of a wide class of agent-based Complex Systems, across a range of disciplines. With this in mind, the present working paper is written for a general multi-disciplinary audience within the Complex Systems community.
Hui Pak Ming
Johnson Neil F.
No associations
LandOfFree
Crowd-Anticrowd Theory of Collective Dynamics in Competitive, Multi-Agent Populations and Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Crowd-Anticrowd Theory of Collective Dynamics in Competitive, Multi-Agent Populations and Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Crowd-Anticrowd Theory of Collective Dynamics in Competitive, Multi-Agent Populations and Networks will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-243366