6 research outputs found
Granular discharge and clogging for tilted hoppers
We measure the flux of spherical glass beads through a hole as a systematic
function of both tilt angle and hole diameter, for two different size beads.
The discharge increases with hole diameter in accord with the Beverloo relation
for both horizontal and vertical holes, but in the latter case with a larger
small-hole cutoff. For large holes the flux decreases linearly in cosine of the
tilt angle, vanishing smoothly somewhat below the angle of repose. For small
holes it vanishes abruptly at a smaller angle. The conditions for zero flux are
discussed in the context of a {\it clogging phase diagram} of flow state vs
tilt angle and ratio of hole to grain size
A bounded confidence approach to understanding user participation in peer production systems
Commons-based peer production does seem to rest upon a paradox. Although
users produce all contents, at the same time participation is commonly on a
voluntary basis, and largely incentivized by achievement of project's goals.
This means that users have to coordinate their actions and goals, in order to
keep themselves from leaving. While this situation is easily explainable for
small groups of highly committed, like-minded individuals, little is known
about large-scale, heterogeneous projects, such as Wikipedia.
In this contribution we present a model of peer production in a large online
community. The model features a dynamic population of bounded confidence users,
and an endogenous process of user departure. Using global sensitivity analysis,
we identify the most important parameters affecting the lifespan of user
participation. We find that the model presents two distinct regimes, and that
the shift between them is governed by the bounded confidence parameter. For low
values of this parameter, users depart almost immediately. For high values,
however, the model produces a bimodal distribution of user lifespan. These
results suggest that user participation to online communities could be
explained in terms of group consensus, and provide a novel connection between
models of opinion dynamics and commons-based peer production.Comment: 17 pages, 5 figures, accepted to SocInfo201
Crises and collective socio-economic phenomena: simple models and challenges
Financial and economic history is strewn with bubbles and crashes, booms and
busts, crises and upheavals of all sorts. Understanding the origin of these
events is arguably one of the most important problems in economic theory. In
this paper, we review recent efforts to include heterogeneities and
interactions in models of decision. We argue that the Random Field Ising model
(RFIM) indeed provides a unifying framework to account for many collective
socio-economic phenomena that lead to sudden ruptures and crises. We discuss
different models that can capture potentially destabilising self-referential
feedback loops, induced either by herding, i.e. reference to peers, or
trending, i.e. reference to the past, and account for some of the phenomenology
missing in the standard models. We discuss some empirically testable
predictions of these models, for example robust signatures of RFIM-like herding
effects, or the logarithmic decay of spatial correlations of voting patterns.
One of the most striking result, inspired by statistical physics methods, is
that Adam Smith's invisible hand can badly fail at solving simple coordination
problems. We also insist on the issue of time-scales, that can be extremely
long in some cases, and prevent socially optimal equilibria to be reached. As a
theoretical challenge, the study of so-called "detailed-balance" violating
decision rules is needed to decide whether conclusions based on current models
(that all assume detailed-balance) are indeed robust and generic.Comment: Review paper accepted for a special issue of J Stat Phys; several
minor improvements along reviewers' comment
The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance
INTRODUCTION
Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic.
RATIONALE
We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs).
RESULTS
Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants.
CONCLUSION
Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century
Herding interactions as an opportunity to prevent extreme events in financial markets
A characteristic feature of complex systems in general is a tight coupling
between their constituent parts. In complex socio-economic systems this kind of
behavior leads to self-organization, which may be both desirable (e.g. social
cooperation) and undesirable (e.g. mass panic, financial "bubbles" or
"crashes"). Abundance of the empirical data as well as general insights into
the trading behavior enables the creation of simple agent-based models
reproducing sophisticated statistical features of the financial markets. In
this contribution we consider a possibility to prevent self-organized extreme
events in artificial financial market setup built upon a simple agent-based
herding model. We show that introduction of agents with predefined
fundamentalist trading behavior helps to significantly reduce the probability
of the extreme price fluctuations events. We also test random trading control
strategy, which was previously found to be promising, and find that its impact
on the market is rather ambiguous. Though some of the results indicate that it
might actually stabilize financial fluctuations.Comment: 11 pages, 5 figure