368 research outputs found
Analytic Methods for Optimizing Realtime Crowdsourcing
Realtime crowdsourcing research has demonstrated that it is possible to
recruit paid crowds within seconds by managing a small, fast-reacting worker
pool. Realtime crowds enable crowd-powered systems that respond at interactive
speeds: for example, cameras, robots and instant opinion polls. So far, these
techniques have mainly been proof-of-concept prototypes: research has not yet
attempted to understand how they might work at large scale or optimize their
cost/performance trade-offs. In this paper, we use queueing theory to analyze
the retainer model for realtime crowdsourcing, in particular its expected wait
time and cost to requesters. We provide an algorithm that allows requesters to
minimize their cost subject to performance requirements. We then propose and
analyze three techniques to improve performance: push notifications, shared
retainer pools, and precruitment, which involves recalling retainer workers
before a task actually arrives. An experimental validation finds that
precruited workers begin a task 500 milliseconds after it is posted, delivering
results below the one-second cognitive threshold for an end-user to stay in
flow.Comment: Presented at Collective Intelligence conference, 201
The Value-of-Information in Matching with Queues
We consider the problem of \emph{optimal matching with queues} in dynamic
systems and investigate the value-of-information. In such systems, the
operators match tasks and resources stored in queues, with the objective of
maximizing the system utility of the matching reward profile, minus the average
matching cost. This problem appears in many practical systems and the main
challenges are the no-underflow constraints, and the lack of matching-reward
information and system dynamics statistics. We develop two online matching
algorithms: Learning-aided Reward optimAl Matching () and
Dual- () to effectively resolve both challenges.
Both algorithms are equipped with a learning module for estimating the
matching-reward information, while incorporates an additional
module for learning the system dynamics. We show that both algorithms achieve
an close-to-optimal utility performance for any
, while achieves a faster convergence speed and a
better delay compared to , i.e., delay and convergence under
compared to delay and convergence under
( and are maximum estimation errors for
reward and system dynamics). Our results reveal that information of different
system components can play very different roles in algorithm performance and
provide a systematic way for designing joint learning-control algorithms for
dynamic systems
Adaptive Matching for Expert Systems with Uncertain Task Types
A matching in a two-sided market often incurs an externality: a matched
resource may become unavailable to the other side of the market, at least for a
while. This is especially an issue in online platforms involving human experts
as the expert resources are often scarce. The efficient utilization of experts
in these platforms is made challenging by the fact that the information
available about the parties involved is usually limited.
To address this challenge, we develop a model of a task-expert matching
system where a task is matched to an expert using not only the prior
information about the task but also the feedback obtained from the past
matches. In our model the tasks arrive online while the experts are fixed and
constrained by a finite service capacity. For this model, we characterize the
maximum task resolution throughput a platform can achieve. We show that the
natural greedy approaches where each expert is assigned a task most suitable to
her skill is suboptimal, as it does not internalize the above externality. We
develop a throughput optimal backpressure algorithm which does so by accounting
for the `congestion' among different task types. Finally, we validate our model
and confirm our theoretical findings with data-driven simulations via logs of
Math.StackExchange, a StackOverflow forum dedicated to mathematics.Comment: A part of it presented at Allerton Conference 2017, 18 page
MAP/PH/1 queueing model with working vacation and crowdsourcing
Crowdsourcing has been used in different domains such as healthcare, computer science, environmental sciences, business and marketing. However, only recently, queueing models useful in the context of crowdsourcing have been studied. These studies involve queueing models of the type M/M/c, MAP/PH/1, and MAP/PH/c. In this paper we introduce vacation and working vacation in the context of MAP/PH/1 with crowdsourcing and highlight the qualitative aspects of the model through illustrative examples
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