49,587 research outputs found
A fast no-rejection algorithm for the Category Game
The Category Game is a multi-agent model that accounts for the emergence of
shared categorization patterns in a population of interacting individuals. In
the framework of the model, linguistic categories appear as long lived
consensus states that are constantly reshaped and re-negotiated by the
communicating individuals. It is therefore crucial to investigate the long time
behavior to gain a clear understanding of the dynamics. However, it turns out
that the evolution of the emerging category system is so slow, already for
small populations, that such an analysis has remained so far impossible. Here,
we introduce a fast no-rejection algorithm for the Category Game that
disentangles the physical simulation time from the CPU time, thus opening the
way for thorough analysis of the model. We verify that the new algorithm is
equivalent to the old one in terms of the emerging phenomenology and we
quantify the CPU performances of the two algorithms, pointing out the neat
advantages offered by the no-rejection one. This technical advance has already
opened the way to new investigations of the model, thus helping to shed light
on the fundamental issue of categorization.Comment: 17 pages, 7 figure
Sharp transition towards shared vocabularies in multi-agent systems
What processes can explain how very large populations are able to converge on
the use of a particular word or grammatical construction without global
coordination? Answering this question helps to understand why new language
constructs usually propagate along an S-shaped curve with a rather sudden
transition towards global agreement. It also helps to analyze and design new
technologies that support or orchestrate self-organizing communication systems,
such as recent social tagging systems for the web. The article introduces and
studies a microscopic model of communicating autonomous agents performing
language games without any central control. We show that the system undergoes a
disorder/order transition, going trough a sharp symmetry breaking process to
reach a shared set of conventions. Before the transition, the system builds up
non-trivial scale-invariant correlations, for instance in the distribution of
competing synonyms, which display a Zipf-like law. These correlations make the
system ready for the transition towards shared conventions, which, observed on
the time-scale of collective behaviors, becomes sharper and sharper with system
size. This surprising result not only explains why human language can scale up
to very large populations but also suggests ways to optimize artificial
semiotic dynamics.Comment: 12 pages, 4 figure
In-depth analysis of the Naming Game dynamics: the homogeneous mixing case
Language emergence and evolution has recently gained growing attention
through multi-agent models and mathematical frameworks to study their behavior.
Here we investigate further the Naming Game, a model able to account for the
emergence of a shared vocabulary of form-meaning associations through
social/cultural learning. Due to the simplicity of both the structure of the
agents and their interaction rules, the dynamics of this model can be analyzed
in great detail using numerical simulations and analytical arguments. This
paper first reviews some existing results and then presents a new overall
understanding.Comment: 30 pages, 19 figures (few in reduced definition). In press in IJMP
Impaired decisional impulsivity in pathological videogamers
Abstract
Background
Pathological gaming is an emerging and poorly understood problem. Impulsivity is commonly impaired in disorders of behavioural and substance addiction, hence we sought to systematically investigate the different subtypes of decisional and motor impulsivity in a well-defined pathological gaming cohort.
Methods
Fifty-two pathological gaming subjects and age-, gender- and IQ-matched healthy volunteers were tested on decisional impulsivity (Information Sampling Task testing reflection impulsivity and delay discounting questionnaire testing impulsive choice), and motor impulsivity (Stop Signal Task testing motor response inhibition, and the premature responding task). We used stringent diagnostic criteria highlighting functional impairment.
Results
In the Information Sampling Task, pathological gaming participants sampled less evidence prior to making a decision and scored fewer points compared with healthy volunteers. Gaming severity was also negatively correlated with evidence gathered and positively correlated with sampling error and points acquired. In the delay discounting task, pathological gamers made more impulsive choices, preferring smaller immediate over larger delayed rewards. Pathological gamers made more premature responses related to comorbid nicotine use. Greater number of hours played also correlated with a Motivational Index. Greater frequency of role playing games was associated with impaired motor response inhibition and strategy games with faster Go reaction time.
Conclusions
We show that pathological gaming is associated with impaired decisional impulsivity with negative consequences in task performance. Decisional impulsivity may be a potential target in therapeutic management
Consequence of reputation in an open-ended Naming Game
We study a modified version of the Naming Game, a recently introduced model
which describes how shared vocabulary can emerge spontaneously in a population
without any central control. In particular, we introduce a new mechanism that
allows a continuous interchange with the external inventory of words. A novel
playing strategy, influenced by the hierarchical structure that individuals'
reputation defines in the community, is implemented. We analyze how these
features influence the convergence times, the cognitive efforts of the agents
and the scaling behavior in memory and time.Comment: 6 pages, 6 figure
Role of feedback and broadcasting in the naming game
The naming game (NG) describes the agreement dynamics of a population of
agents that interact locally in a pairwise fashion, and in recent years
statistical physics tools and techniques have greatly contributed to shed light
on its rich phenomenology. Here we investigate in details the role played by
the way in which the two agents update their states after an interaction. We
show that slightly modifying the NG rules in terms of which agent performs the
update in given circumstances (i.e. after a success) can either alter
dramatically the overall dynamics or leave it qualitatively unchanged. We
understand analytically the first case by casting the model in the broader
framework of a generalized NG. As for the second case, on the other hand, we
note that the modified rule reproducing the main features of the usual NG
corresponds in fact to a simplification of it consisting in the elimination of
feedback between the agents. This allows us to introduce and study a very
natural broadcasting scheme on networks that can be potentially relevant for
different applications, such as the design and implementation of autonomous
sensor networks, as pointed out in the recent literature.Comment: 7 pages, 6 figure
Guess the score, fostering collective intelligence in the class
This paper proposes the use of serious games as a tool to enhance collective intelligence of undergraduate and graduate students. The development of social skills of individuals in a group is related to the performance of the collective intelligence of the group manifested through the shared and collaborative development of intellectual tasks [1]. Guess the Score GS, is a serious game implemented by means of an online tool, created to foster the development, collaboration and engagement of students. It's has been designed with the intention of facilitating the development of individual’s social skills in a group in order to promote education of collective intelligence. This paper concludes that the design of learning activities using serious games as a support tool in education, generate awareness about of utilities of gaming in the collective learning environment and the fostering of collective intelligence education.Postprint (published version
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