2005/05/24 by Sonic H. Y. Chan, Chan, Sonic H. Y., T. S. Lo +6
Mathematics · Medicine · Physics and Astronomy · Social Sciences · #COVID-19 epidemiological studies #Disordered Systems and Neural Networks (cond-mat.dis-nn) #Evolutionary Game Theory and Cooperation #FOS: Physical sciences #Mathematical and Theoretical Epidemiology and Ecology Models #cond-mat.dis-nn
paper · pdf · doi:10.48550/arxiv.cond-mat/0505575
17 pages, 4 figures. Paper to be presented in WEHIA 2005
arxiv created 2005/05/24 · openalex publication_date 2005/05/24 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We aim to study the effects of controlling the resource level in agent-based models. We study, both numerical and analytically, a Binary-Agent-Resource (B-A-R) model in which N agents are competing for resources described by a resource level 1/2 ≤ \cal L < 1, where \cal L = L/N with L being the maximum amount of resource per turn available to the agents. Each agent picks the momentarily best-performing strategy for decision with the performance of the strategy being a result of the cumulative collective decisions of the agents. The agents may or may not be networked for information sharing. Detailed numerical simulations reveal that the system exhibits well-defined plateaux regions in the success rate which are separated from each other by abrupt transitions. As L increases, the maximum success rate forms a well defined sequence of simple fractions. We analyze the features by studying the outcome time series, the dynamics of the strategies' performance ranking pattern and the dynamics in the history space. While the system tends to explore the whole history space due to its competitive nature, an increasing L has the effect of driving the system to a restricted portion of the history space. Thus the underlying cause of the observed features is an interesting self-organized phenomena in which the system, in response to the global resource level, effectively avoids particular patterns of history outcomes. We also compare results in networked population with those in non-networked population.