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Locally noisy autonomous agents improve global human coordination in network experiments.

ABSTRACT: Coordination in groups faces a sub-optimization problem and theory suggests that some randomness may help to achieve global optima. Here we performed experiments involving a networked colour coordination game in which groups of humans interacted with autonomous software agents (known as bots). Subjects (n?=?4,000) were embedded in networks (n?=?230) of 20 nodes, to which we sometimes added 3 bots. The bots were programmed with varying levels of behavioural randomness and different geodesic locations. We show that bots acting with small levels of random noise and placed in central locations meaningfully improve the collective performance of human groups, accelerating the median solution time by 55.6%. This is especially the case when the coordination problem is hard. Behavioural randomness worked not only by making the task of humans to whom the bots were connected easier, but also by affecting the gameplay of the humans among themselves and hence creating further cascades of benefit in global coordination in these heterogeneous systems.

SUBMITTER: Shirado H

PROVIDER: S-EPMC5912653 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Locally noisy autonomous agents improve global human coordination in network experiments.

Shirado Hirokazu H Christakis Nicholas A NA

Nature 20170501 7654

Coordination in groups faces a sub-optimization problem and theory suggests that some randomness may help to achieve global optima. Here we performed experiments involving a networked colour coordination game in which groups of humans interacted with autonomous software agents (known as bots). Subjects (n = 4,000) were embedded in networks (n = 230) of 20 nodes, to which we sometimes added 3 bots. The bots were programmed with varying levels of behavioural randomness and different geodesic locat ...[more]

PMID: 28516927

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Project description:Neisseria gonorrhoeae is a Gram-negative diplococcus that is responsible for the sexually transmitted infection gonorrhea, a high-morbidity disease in the United States and worldwide. Over the past several years, N. gonorrhoeae strains resistant to antibiotics used to treat this infection have begun to emerge across the globe. Thus, new treatment strategies are needed to combat this organism. Here, we utilized N. gonorrhoeae transcriptomic data sets, including those obtained from natural infection of the human genital tract, to infer the first global gene coexpression network of this pathogen. Interrogation of this network revealed genes central to the network that are likely critical for gonococcal growth, metabolism, and virulence, including genes encoding hypothetical proteins expressed during mucosal infection. In addition, network analysis revealed overlap in the response of N. gonorrhoeae to incubation with neutrophils and exposure to hydrogen peroxide stress in vitro Network analysis also identified new targets of the gonococcal global regulatory protein Fur, while examination of the network neighborhood of genes allowed us to assign additional putative categories to several proteins. Collectively, the characterization of the first gene coexpression network for N. gonorrhoeae described here has revealed new regulatory pathways and new categories for proteins and has shown how processes important to gonococcal infection in both men and women are linked. This information fills a critical gap in our understanding of virulence strategies of this obligate human pathogen and will aid in the development of new treatment strategies for gonorrhea.IMPORTANCENeisseria gonorrhoeae is the causative agent of the sexually transmitted infection (STI) gonorrhea, a disease with high morbidity worldwide with an estimated 87 million cases annually. Current therapeutic and pharmacologic approaches to treat gonorrhea have been compromised by increased antibiotic resistance worldwide, including to the most recent FDA-approved antibiotic. New treatment strategies are urgently needed to combat this organism. In this study, we used network analysis to interrogate and define the coordination of pathways and processes in N. gonorrhoeae An analysis of the gonococcal network was also used to assign categories to genes and to expand our understanding of regulatory strategies. Network analysis provides important insights into pathogenic mechanisms of this organism that will guide the design of new strategies for disease treatment.

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Locally noisy autonomous agents improve global human coordination in network experiments.

Publications

Locally noisy autonomous agents improve global human coordination in network experiments.

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