The pandemic is being used as a pretext to push unproven artificial-intelligence tools into workplaces and schools.

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Kai-Cheng Yang, Francesco Pierri, Pik-Mai Hui, David Axelrod, Christopher Torres-Lugo, John Bryden, Filippo Menczer

The global spread of the novel coronavirus is affected by the spread of related misinformation — the so-called COVID-19 Infodemic — that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook. We characterize cross-platform similarities and differences in popular sources, diffusion patterns, influencers, coordination, and automation. Comparing the two platforms, we find divergence among the prevalence of popular low-credibility sources and suspicious videos. A minority of accounts and pages exert a strong influence on each platform. These misinformation “superspreaders” are often associated with the low-credibility sources and tend to be verified by the platforms. On both platforms, there is evidence of coordinated sharing of Infodemic content. The overt nature of this manipulation points to the need for societal-level rather than in-house mitigation strategies. However, we highlight limits imposed by inconsistent data-access policies on our capability to study harmful manipulations of information ecosystems.

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Matthew Koehler, David M Slater, Garry Jacyna and James R Thompson

Journal of Artificial Societies and Social Simulation 24 (2) 9

As a result of the COVID-19 worldwide pandemic, the United States instituted various non-pharmaceutical interventions (NPIs) in an effort to slow the spread of the disease. Although necessary for public safety, these NPIs can also have deleterious effects on the economy of a nation. State and federal leaders need tools that provide insight into which combination of NPIs will have the greatest impact on slowing the disease and at what point in time it is reasonably safe to start lifting these restrictions to everyday life. In the present work, we outline a modeling process that incorporates the parameters of the disease, the effects of NPIs, and the characteristics of individual communities to offer insight into when and to what degree certain NPIs should be instituted or lifted based on the progression of a given outbreak of COVID-19. We apply the model to the 24 county-equivalents of Maryland and illustrate that different NPI strategies can be employed in different parts of the state. Our objective is to outline a modeling process that combines the critical disease factors and factors relevant to decision-makers who must balance the health of the population with the health of the economy.

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W. Brian Arthur

Standard economic theory uses mathematics as its main means of understanding,
and this brings clarity of reasoning and logical power. But there is a
drawback: algebraic mathematics restricts economic modeling to what can be
expressed only in quantitative nouns, and this forces theory to leave out
matters to do with process, formation, adjustment, creation and nonequilibrium.
For these we need a different means of understanding, one that allows verbs as
well as nouns. Algorithmic expression is such a means. It allows verbs
(processes) as well as nouns (objects and quantities). It allows fuller
description in economics, and can include heterogeneity of agents, actions as
well as objects, and realistic models of behavior in ill-defined situations.
The world that algorithms reveal is action-based as well as object-based,
organic, possibly ever-changing, and not fully knowable. But it is strangely
and wonderfully alive.

Read the full article at: arxiv.org