15,405 research outputs found
Finite-time influence systems and the Wisdom of Crowd effect
Recent contributions have studied how an influence system may affect the
wisdom of crowd phenomenon. In the so-called naive learning setting, a crowd of
individuals holds opinions that are statistically independent estimates of an
unknown parameter; the crowd is wise when the average opinion converges to the
true parameter in the limit of infinitely many individuals. Unfortunately, even
starting from wise initial opinions, a crowd subject to certain influence
systems may lose its wisdom. It is of great interest to characterize when an
influence system preserves the crowd wisdom effect. In this paper we introduce
and characterize numerous wisdom preservation properties of the basic
French-DeGroot influence system model. Instead of requiring complete
convergence to consensus as in the previous naive learning model by Golub and
Jackson, we study finite-time executions of the French-DeGroot influence
process and establish in this novel context the notion of prominent families
(as a group of individuals with outsize influence). Surprisingly, finite-time
wisdom preservation of the influence system is strictly distinct from its
infinite-time version. We provide a comprehensive treatment of various
finite-time wisdom preservation notions, counterexamples to meaningful
conjectures, and a complete characterization of equal-neighbor influence
systems
Empirical Methodology for Crowdsourcing Ground Truth
The process of gathering ground truth data through human annotation is a
major bottleneck in the use of information extraction methods for populating
the Semantic Web. Crowdsourcing-based approaches are gaining popularity in the
attempt to solve the issues related to volume of data and lack of annotators.
Typically these practices use inter-annotator agreement as a measure of
quality. However, in many domains, such as event detection, there is ambiguity
in the data, as well as a multitude of perspectives of the information
examples. We present an empirically derived methodology for efficiently
gathering of ground truth data in a diverse set of use cases covering a variety
of domains and annotation tasks. Central to our approach is the use of
CrowdTruth metrics that capture inter-annotator disagreement. We show that
measuring disagreement is essential for acquiring a high quality ground truth.
We achieve this by comparing the quality of the data aggregated with CrowdTruth
metrics with majority vote, over a set of diverse crowdsourcing tasks: Medical
Relation Extraction, Twitter Event Identification, News Event Extraction and
Sound Interpretation. We also show that an increased number of crowd workers
leads to growth and stabilization in the quality of annotations, going against
the usual practice of employing a small number of annotators.Comment: in publication at the Semantic Web Journa
Gradient descent for sparse rank-one matrix completion for crowd-sourced aggregation of sparsely interacting workers
We consider worker skill estimation for the singlecoin
Dawid-Skene crowdsourcing model. In
practice skill-estimation is challenging because
worker assignments are sparse and irregular due
to the arbitrary, and uncontrolled availability of
workers. We formulate skill estimation as a
rank-one correlation-matrix completion problem,
where the observed components correspond to
observed label correlation between workers. We
show that the correlation matrix can be successfully
recovered and skills identifiable if and only
if the sampling matrix (observed components) is
irreducible and aperiodic. We then propose an
efficient gradient descent scheme and show that
skill estimates converges to the desired global optima
for such sampling matrices. Our proof is
original and the results are surprising in light of
the fact that even the weighted rank-one matrix
factorization problem is NP hard in general. Next
we derive sample complexity bounds for the noisy
case in terms of spectral properties of the signless
Laplacian of the sampling matrix. Our proposed
scheme achieves state-of-art performance on a
number of real-world datasets.Published versio
Challenges in Complex Systems Science
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda
In crowdfunding we trust : a trust-building model in lending crowdfunding
Trust critically affects the perceived probability of receiving expected returns on investment. Crowdfunding differs in many ways from traditional forms of investing. We have to ask what builds trust in this particular context. Based on literature regarding the formation of initial trust, we developed a model to explain which factors lead to crowdfunders’ trust in a crowdfunding project. We tested it on data collected from actual investors in a real project on a crowdlending platform. Our results show that trust in the crowdfunding platform and the information quality are more important factors of project trust than trust in the creator
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