872 research outputs found
Network-based ranking in social systems: three challenges
Ranking algorithms are pervasive in our increasingly digitized societies,
with important real-world applications including recommender systems, search
engines, and influencer marketing practices. From a network science
perspective, network-based ranking algorithms solve fundamental problems
related to the identification of vital nodes for the stability and dynamics of
a complex system. Despite the ubiquitous and successful applications of these
algorithms, we argue that our understanding of their performance and their
applications to real-world problems face three fundamental challenges: (i)
Rankings might be biased by various factors; (2) their effectiveness might be
limited to specific problems; and (3) agents' decisions driven by rankings
might result in potentially vicious feedback mechanisms and unhealthy systemic
consequences. Methods rooted in network science and agent-based modeling can
help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure
A Topic Recommender for Journalists
The way in which people acquire information on events and form their own
opinion on them has changed dramatically with the advent of social media. For many
readers, the news gathered from online sources become an opportunity to share points
of view and information within micro-blogging platforms such as Twitter, mainly
aimed at satisfying their communication needs. Furthermore, the need to deepen the
aspects related to news stimulates a demand for additional information which is often
met through online encyclopedias, such as Wikipedia. This behaviour has also
influenced the way in which journalists write their articles, requiring a careful assessment
of what actually interests the readers. The goal of this paper is to present
a recommender system, What to Write and Why, capable of suggesting to a journalist,
for a given event, the aspects still uncovered in news articles on which the
readers focus their interest. The basic idea is to characterize an event according to
the echo it receives in online news sources and associate it with the corresponding
readers’ communicative and informative patterns, detected through the analysis of
Twitter and Wikipedia, respectively. Our methodology temporally aligns the results
of this analysis and recommends the concepts that emerge as topics of interest from
Twitter and Wikipedia, either not covered or poorly covered in the published news
articles
Human Computation and Convergence
Humans are the most effective integrators and producers of information,
directly and through the use of information-processing inventions. As these
inventions become increasingly sophisticated, the substantive role of humans in
processing information will tend toward capabilities that derive from our most
complex cognitive processes, e.g., abstraction, creativity, and applied world
knowledge. Through the advancement of human computation - methods that leverage
the respective strengths of humans and machines in distributed
information-processing systems - formerly discrete processes will combine
synergistically into increasingly integrated and complex information processing
systems. These new, collective systems will exhibit an unprecedented degree of
predictive accuracy in modeling physical and techno-social processes, and may
ultimately coalesce into a single unified predictive organism, with the
capacity to address societies most wicked problems and achieve planetary
homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added
references to page 1 and 3, and corrected typ
Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives
Agent-based modeling and simulation has evolved as a powerful tool for
modeling complex systems, offering insights into emergent behaviors and
interactions among diverse agents. Integrating large language models into
agent-based modeling and simulation presents a promising avenue for enhancing
simulation capabilities. This paper surveys the landscape of utilizing large
language models in agent-based modeling and simulation, examining their
challenges and promising future directions. In this survey, since this is an
interdisciplinary field, we first introduce the background of agent-based
modeling and simulation and large language model-empowered agents. We then
discuss the motivation for applying large language models to agent-based
simulation and systematically analyze the challenges in environment perception,
human alignment, action generation, and evaluation. Most importantly, we
provide a comprehensive overview of the recent works of large language
model-empowered agent-based modeling and simulation in multiple scenarios,
which can be divided into four domains: cyber, physical, social, and hybrid,
covering simulation of both real-world and virtual environments. Finally, since
this area is new and quickly evolving, we discuss the open problems and
promising future directions.Comment: 37 page
From Ranked Lists to Carousels: A Carousel Click Model
Carousel-based recommendation interfaces allow users to explore recommended
items in a structured, efficient, and visually-appealing way. This made them a
de-facto standard approach to recommending items to end users in many real-life
recommenders. In this work, we try to explain the efficiency of carousel
recommenders using a \emph{carousel click model}, a generative model of user
interaction with carousel-based recommender interfaces. We study this model
both analytically and empirically. Our analytical results show that the user
can examine more items in the carousel click model than in a single ranked
list, due to the structured way of browsing. These results are supported by a
series of experiments, where we integrate the carousel click model with a
recommender based on matrix factorization. We show that the combined
recommender performs well on held-out test data, and leads to higher engagement
with recommendations than a traditional single ranked list
Faith in the Algorithm, Part 1: Beyond the Turing Test
Since the Turing test was first proposed by Alan Turing in 1950, the primary
goal of artificial intelligence has been predicated on the ability for
computers to imitate human behavior. However, the majority of uses for the
computer can be said to fall outside the domain of human abilities and it is
exactly outside of this domain where computers have demonstrated their greatest
contribution to intelligence. Another goal for artificial intelligence is one
that is not predicated on human mimicry, but instead, on human amplification.
This article surveys various systems that contribute to the advancement of
human and social intelligence
Self-Organizing Teams in Online Work Settings
As the volume and complexity of distributed online work increases, the
collaboration among people who have never worked together in the past is
becoming increasingly necessary. Recent research has proposed algorithms to
maximize the performance of such teams by grouping workers according to a set
of predefined decision criteria. This approach micro-manages workers, who have
no say in the team formation process. Depriving users of control over who they
will work with stifles creativity, causes psychological discomfort and results
in less-than-optimal collaboration results. In this work, we propose an
alternative model, called Self-Organizing Teams (SOTs), which relies on the
crowd of online workers itself to organize into effective teams. Supported but
not guided by an algorithm, SOTs are a new human-centered computational
structure, which enables participants to control, correct and guide the output
of their collaboration as a collective. Experimental results, comparing SOTs to
two benchmarks that do not offer user agency over the collaboration, reveal
that participants in the SOTs condition produce results of higher quality and
report higher teamwork satisfaction. We also find that, similarly to machine
learning-based self-organization, human SOTs exhibit emergent collective
properties, including the presence of an objective function and the tendency to
form more distinct clusters of compatible teammates
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