922 research outputs found
Optimal shepherding and transport of a flock
We investigate how a shepherd should move in order to effectively herd and
guide a flock of agents towards a target. Using a detailed agent-based model
(ABM) for the members of the flock, we pose and solve an optimization problem
for the shepherd that has to simultaneously work to keep the flock cohesive
while coercing it towards a prescribed project. We find that three distinct
strategies emerge as potential solutions as a function of just two parameters:
the ratio of herd size to shepherd repulsion length and the ratio of herd speed
to shepherd speed. We term these as: (i) mustering, in which the shepherd
circles the herd to ensure compactness, (ii) droving, in which the shepherd
chases the herd in a desired direction, and (iii) driving, a hitherto
unreported strategy where the flock surrounds a shepherd that drives it from
within. A minimal dynamical model for the size, shape and position of the herd
captures the effective behavior of the ABM, and further allows us to
characterize the different herding strategies in terms of the behavior of the
shepherd that librates (mustering), oscillates (droving) or moves steadily
(driving). All together, our study yields a simple and intuitive classification
of herding strategies that ought to be of general interest in the context of
controlling the collective behavior of active matter.Comment: A couple paragraphs removed for brevit
A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality
Microtask crowdsourcing is increasingly critical to the creation of extremely
large datasets. As a result, crowd workers spend weeks or months repeating the
exact same tasks, making it necessary to understand their behavior over these
long periods of time. We utilize three large, longitudinal datasets of nine
million annotations collected from Amazon Mechanical Turk to examine claims
that workers fatigue or satisfice over these long periods, producing lower
quality work. We find that, contrary to these claims, workers are extremely
stable in their quality over the entire period. To understand whether workers
set their quality based on the task's requirements for acceptance, we then
perform an experiment where we vary the required quality for a large
crowdsourcing task. Workers did not adjust their quality based on the
acceptance threshold: workers who were above the threshold continued working at
their usual quality level, and workers below the threshold self-selected
themselves out of the task. Capitalizing on this consistency, we demonstrate
that it is possible to predict workers' long-term quality using just a glimpse
of their quality on the first five tasks.Comment: 10 pages, 11 figures, accepted CSCW 201
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