65,154 research outputs found
Learning from Neighbors about a Changing State
Agents learn about a changing state using private signals and past actions of
neighbors in a network. We characterize equilibrium learning and social
influence in this setting. We then examine when agents can aggregate
information well, responding quickly to recent changes. A key sufficient
condition for good aggregation is that each individual's neighbors have
sufficiently different types of private information. In contrast, when signals
are homogeneous, aggregation is suboptimal on any network. We also examine
behavioral versions of the model, and show that achieving good aggregation
requires a sophisticated understanding of correlations in neighbors' actions.
The model provides a Bayesian foundation for a tractable learning dynamic in
networks, closely related to the DeGroot model, and offers new tools for
counterfactual and welfare analyses.Comment: minor revision tweaking exposition relative to v5 - which added new
Section 3.2.2, new Theorem 2, new Section 7.1, many local revision
Asymptotic Learning on Bayesian Social Networks
Understanding information exchange and aggregation on networks is a central
problem in theoretical economics, probability and statistics. We study a
standard model of economic agents on the nodes of a social network graph who
learn a binary "state of the world" S, from initial signals, by repeatedly
observing each other's best guesses.
Asymptotic learning is said to occur on a family of graphs G_n = (V_n, E_n),
with |V_n| tending to infinity, if with probability tending to 1 as n tends to
infinity all agents in G_n eventually estimate S correctly. We identify
sufficient conditions for asymptotic learning and contruct examples where
learning does not occur when the conditions do not hold.Comment: 28 pages, 2 figures. arXiv admin note: substantial text overlap with
arXiv:1105.476
Strategic Learning and the Topology of Social Networks
We consider a group of strategic agents who must each repeatedly take one of
two possible actions. They learn which of the two actions is preferable from
initial private signals, and by observing the actions of their neighbors in a
social network.
We show that the question of whether or not the agents learn efficiently
depends on the topology of the social network. In particular, we identify a
geometric "egalitarianism" condition on the social network that guarantees
learning in infinite networks, or learning with high probability in large
finite networks, in any equilibrium. We also give examples of non-egalitarian
networks with equilibria in which learning fails.Comment: 30 pages, one figur
A Data-Driven Approach for Tag Refinement and Localization in Web Videos
Tagging of visual content is becoming more and more widespread as web-based
services and social networks have popularized tagging functionalities among
their users. These user-generated tags are used to ease browsing and
exploration of media collections, e.g. using tag clouds, or to retrieve
multimedia content. However, not all media are equally tagged by users. Using
the current systems is easy to tag a single photo, and even tagging a part of a
photo, like a face, has become common in sites like Flickr and Facebook. On the
other hand, tagging a video sequence is more complicated and time consuming, so
that users just tag the overall content of a video. In this paper we present a
method for automatic video annotation that increases the number of tags
originally provided by users, and localizes them temporally, associating tags
to keyframes. Our approach exploits collective knowledge embedded in
user-generated tags and web sources, and visual similarity of keyframes and
images uploaded to social sites like YouTube and Flickr, as well as web sources
like Google and Bing. Given a keyframe, our method is able to select on the fly
from these visual sources the training exemplars that should be the most
relevant for this test sample, and proceeds to transfer labels across similar
images. Compared to existing video tagging approaches that require training
classifiers for each tag, our system has few parameters, is easy to implement
and can deal with an open vocabulary scenario. We demonstrate the approach on
tag refinement and localization on DUT-WEBV, a large dataset of web videos, and
show state-of-the-art results.Comment: Preprint submitted to Computer Vision and Image Understanding (CVIU
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