35,498 research outputs found
The Network Analysis of Urban Streets: A Primal Approach
The network metaphor in the analysis of urban and territorial cases has a
long tradition especially in transportation/land-use planning and economic
geography. More recently, urban design has brought its contribution by means of
the "space syntax" methodology. All these approaches, though under different
terms like accessibility, proximity, integration,connectivity, cost or effort,
focus on the idea that some places (or streets) are more important than others
because they are more central. The study of centrality in complex
systems,however, originated in other scientific areas, namely in structural
sociology, well before its use in urban studies; moreover, as a structural
property of the system, centrality has never been extensively investigated
metrically in geographic networks as it has been topologically in a wide range
of other relational networks like social, biological or technological. After
two previous works on some structural properties of the dual and primal graph
representations of urban street networks (Porta et al. cond-mat/0411241;
Crucitti et al. physics/0504163), in this paper we provide an in-depth
investigation of centrality in the primal approach as compared to the dual one,
with a special focus on potentials for urban design.Comment: 19 page, 4 figures. Paper related to the paper "The Network Analysis
of Urban Streets: A Dual Approach" cond-mat/041124
Low-temperature behaviour of social and economic networks
Real-world social and economic networks typically display a number of
particular topological properties, such as a giant connected component, a broad
degree distribution, the small-world property and the presence of communities
of densely interconnected nodes. Several models, including ensembles of
networks also known in social science as Exponential Random Graphs, have been
proposed with the aim of reproducing each of these properties in isolation.
Here we define a generalized ensemble of graphs by introducing the concept of
graph temperature, controlling the degree of topological optimization of a
network. We consider the temperature-dependent version of both existing and
novel models and show that all the aforementioned topological properties can be
simultaneously understood as the natural outcomes of an optimized,
low-temperature topology. We also show that seemingly different graph models,
as well as techniques used to extract information from real networks, are all
found to be particular low-temperature cases of the same generalized formalism.
One such technique allows us to extend our approach to real weighted networks.
Our results suggest that a low graph temperature might be an ubiquitous
property of real socio-economic networks, placing conditions on the diffusion
of information across these systems
Signs of universality in the structure of culture
Understanding the dynamics of opinions, preferences and of culture as whole
requires more use of empirical data than has been done so far. It is clear that
an important role in driving this dynamics is played by social influence, which
is the essential ingredient of many quantitative models. Such models require
that all traits are fixed when specifying the "initial cultural state".
Typically, this initial state is randomly generated, from a uniform
distribution over the set of possible combinations of traits. However, recent
work has shown that the outcome of social influence dynamics strongly depends
on the nature of the initial state. If the latter is sampled from empirical
data instead of being generated in a uniformly random way, a higher level of
cultural diversity is found after long-term dynamics, for the same level of
propensity towards collective behavior in the short-term. Moreover, if the
initial state is randomized by shuffling the empirical traits among people, the
level of long-term cultural diversity is in-between those obtained for the
empirical and uniformly random counterparts. The current study repeats the
analysis for multiple empirical data sets, showing that the results are
remarkably similar, although the matrix of correlations between cultural
variables clearly differs across data sets. This points towards robust
structural properties inherent in empirical cultural states, possibly due to
universal laws governing the dynamics of culture in the real world. The results
also suggest that this dynamics might be characterized by criticality and
involve mechanisms beyond social influence.Comment: 16 pages, 7 figures; the same results as in version 3, but a shorter
Introduction, Discussion and Conclusio
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
The self-organization of combinatoriality and phonotactics in vocalization systems
This paper shows how a society of agents can self-organize a shared vocalization system that is
discrete, combinatorial and has a form of primitive phonotactics, starting from holistic inarticulate
vocalizations. The originality of the system is that: (1) it does not include any explicit pressure for
communication; (2) agents do not possess capabilities of coordinated interactions, in particular they
do not play language games; (3) agents possess no specific linguistic capacities; and (4) initially
there exists no convention that agents can use. As a consequence, the system shows how a primitive
speech code may bootstrap in the absence of a communication system between agents, i.e. before the
appearance of language
From Holistic to Discrete Speech Sounds: The Blind Snow-Flake Maker Hypothesis
Sound is a medium used by humans to carry information.
The existence of this kind of
medium is a pre-requisite for language. It is organized
into a code, called speech, which
provides a repertoire of forms that is shared in each
language community. This code is necessary to support the linguistic
interactions that allow humans to communicate.
How then may a speech code be formed prior to the
existence of linguistic interactions?
Moreover, the human speech code is characterized by several
properties: speech is digital and compositional (vocalizations
are made of units re-used systematically in other syllables);
phoneme inventories have precise regularities as well as
great diversity in human languages; all the speakers of a
language community categorize sounds in the same manner,
but each language has its own system of categorization,
possibly very different from every other.
How can a speech code with these properties form?
These are the questions we will approach in the paper. We will
study them using the method of the artificial. We will
build a society of artificial agents, and study what mechanisms
may provide answers. This will not prove directly what mechanisms
were used for humans, but rather give ideas about what kind
of mechanism may have been used. This allows us to shape the
search space of possible answers, in particular by showing
what is sufficient and what is not necessary.
The mechanism we present is based on a low-level model of
sensory-motor interactions. We show that the integration of certain very
simple and non language-specific neural devices
allows a population of agents to build a speech code that
has the properties mentioned above. The originality is
that it pre-supposes neither a functional pressure for
communication, nor the ability to have coordinated
social interactions (they do not play language or imitation
games). It relies on the self-organizing properties of a generic
coupling between perception and production both
within agents, and on the interactions between agents
Social encounter networks : characterizing Great Britain
A major goal of infectious disease epidemiology is to understand and predict the spread of infections within human populations, with the intention of better informing decisions regarding control and intervention. However, the development of fully mechanistic models of transmission requires a quantitative understanding of social interactions and collective properties of social networks. We performed a cross-sectional study of the social contacts on given days for more than 5000 respondents in England, Scotland and Wales, through postal and online survey methods. The survey was designed to elicit detailed and previously unreported measures of the immediate social network of participants relevant to infection spread. Here, we describe individual-level contact patterns, focusing on the range of heterogeneity observed and discuss the correlations between contact patterns and other socio-demographic factors. We find that the distribution of the number of contacts approximates a power-law distribution, but postulate that total contact time (which has a shorter-tailed distribution) is more epidemiologically relevant. We observe that children, public-sector and healthcare workers have the highest number of total contact hours and are therefore most likely to catch and transmit infectious disease. Our study also quantifies the transitive connections made between an individual's contacts (or clustering); this is a key structural characteristic of social networks with important implications for disease transmission and control efficacy. Respondents' networks exhibit high levels of clustering, which varies across social settings and increases with duration, frequency of contact and distance from home. Finally, we discuss the implications of these findings for the transmission and control of pathogens spread through close contact
Tangled Nature: A model of emergent structure and temporal mode among co-evolving agents
Understanding systems level behaviour of many interacting agents is
challenging in various ways, here we'll focus on the how the interaction
between components can lead to hierarchical structures with different types of
dynamics, or causations, at different levels. We use the Tangled Nature model
to discuss the co-evolutionary aspects connecting the microscopic level of the
individual to the macroscopic systems level. At the microscopic level the
individual agent may undergo evolutionary changes due to mutations of
strategies. The micro-dynamics always run at a constant rate. Nevertheless, the
system's level dynamics exhibit a completely different type of intermittent
abrupt dynamics where major upheavals keep throwing the system between
meta-stable configurations. These dramatic transitions are described by a
log-Poisson time statistics. The long time effect is a collectively adapted of
the ecological network. We discuss the ecological and macroevolutionary
consequences of the adaptive dynamics and briefly describe work using the
Tangled Nature framework to analyse problems in economics, sociology,
innovation and sustainabilityComment: Invited contribution to Focus on Complexity in European Journal of
Physics. 25 page, 1 figur
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