Crowd-Anticrowd Theory of Collective Dynamics in Competitive, Multi-Agent Populations and Networks

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dc.creatorJohnson, Neil F.
dc.creatorHui, Pak Ming
dc.date2003-06-20
dc.date.accessioned2026-07-25T15:13:28Z
dc.descriptionWe 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.
dc.descriptionContribution to the Workshop on Collectives and the Design of Complex Systems, Stanford University, August 2003 52 pages 9 figures
dc.identifierhttps://arxiv.org/abs/cond-mat/0306516
dc.identifierhttp://arxiv.org/abs/cond-mat/0306516
dc.identifier.urihttps://dspace.dare.co.zw/handle/123456789/32084
dc.subjectDisordered Systems and Neural Networks
dc.titleCrowd-Anticrowd Theory of Collective Dynamics in Competitive, Multi-Agent Populations and Networks
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