11,897 research outputs found
Collective intelligence: aggregation of information from neighbors in a guessing game
Complex systems show the capacity to aggregate information and to display
coordinated activity. In the case of social systems the interaction of
different individuals leads to the emergence of norms, trends in political
positions, opinions, cultural traits, and even scientific progress. Examples of
collective behavior can be observed in activities like the Wikipedia and Linux,
where individuals aggregate their knowledge for the benefit of the community,
and citizen science, where the potential of collectives to solve complex
problems is exploited. Here, we conducted an online experiment to investigate
the performance of a collective when solving a guessing problem in which each
actor is endowed with partial information and placed as the nodes of an
interaction network. We measure the performance of the collective in terms of
the temporal evolution of the accuracy, finding no statistical difference in
the performance for two classes of networks, regular lattices and random
networks. We also determine that a Bayesian description captures the behavior
pattern the individuals follow in aggregating information from neighbors to
make decisions. In comparison with other simple decision models, the strategy
followed by the players reveals a suboptimal performance of the collective. Our
contribution provides the basis for the micro-macro connection between
individual based descriptions and collective phenomena.Comment: 9 pages, 9 figure
Deep Forward and Inverse Perceptual Models for Tracking and Prediction
We consider the problems of learning forward models that map state to
high-dimensional images and inverse models that map high-dimensional images to
state in robotics. Specifically, we present a perceptual model for generating
video frames from state with deep networks, and provide a framework for its use
in tracking and prediction tasks. We show that our proposed model greatly
outperforms standard deconvolutional methods and GANs for image generation,
producing clear, photo-realistic images. We also develop a convolutional neural
network model for state estimation and compare the result to an Extended Kalman
Filter to estimate robot trajectories. We validate all models on a real robotic
system.Comment: 8 pages, International Conference on Robotics and Automation (ICRA)
201
Learning to Embed Words in Context for Syntactic Tasks
We present models for embedding words in the context of surrounding words.
Such models, which we refer to as token embeddings, represent the
characteristics of a word that are specific to a given context, such as word
sense, syntactic category, and semantic role. We explore simple, efficient
token embedding models based on standard neural network architectures. We learn
token embeddings on a large amount of unannotated text and evaluate them as
features for part-of-speech taggers and dependency parsers trained on much
smaller amounts of annotated data. We find that predictors endowed with token
embeddings consistently outperform baseline predictors across a range of
context window and training set sizes.Comment: Accepted by ACL 2017 Repl4NLP worksho
Evolutionary establishment of moral and double moral standards through spatial interactions
Situations where individuals have to contribute to joint efforts or share
scarce resources are ubiquitous. Yet, without proper mechanisms to ensure
cooperation, the evolutionary pressure to maximize individual success tends to
create a tragedy of the commons (such as over-fishing or the destruction of our
environment). This contribution addresses a number of related puzzles of human
behavior with an evolutionary game theoretical approach as it has been
successfully used to explain the behavior of other biological species many
times, from bacteria to vertebrates. Our agent-based model distinguishes
individuals applying four different behavioral strategies: non-cooperative
individuals ("defectors"), cooperative individuals abstaining from punishment
efforts (called "cooperators" or "second-order free-riders"), cooperators who
punish non-cooperative behavior ("moralists"), and defectors, who punish other
defectors despite being non-cooperative themselves ("immoralists"). By
considering spatial interactions with neighboring individuals, our model
reveals several interesting effects: First, moralists can fully eliminate
cooperators. This spreading of punishing behavior requires a segregation of
behavioral strategies and solves the "second-order free-rider problem". Second,
the system behavior changes its character significantly even after very long
times ("who laughs last laughs best effect"). Third, the presence of a number
of defectors can largely accelerate the victory of moralists over non-punishing
cooperators. Forth, in order to succeed, moralists may profit from immoralists
in a way that appears like an "unholy collaboration". Our findings suggest that
the consideration of punishment strategies allows to understand the
establishment and spreading of "moral behavior" by means of game-theoretical
concepts. This demonstrates that quantitative biological modeling approaches
are powerful even in domains that have been addressed with non-mathematical
concepts so far. The complex dynamics of certain social behaviors becomes
understandable as result of an evolutionary competition between different
behavioral strategies.Comment: 15 pages, 5 figures; accepted for publication in PLoS Computational
Biology [supplementary material available at
http://www.soms.ethz.ch/research/secondorder-freeriders/ and
http://www.matjazperc.com/plos/moral.html
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