260 research outputs found
Non-equilibrium phase transition in negotiation dynamics
We introduce a model of negotiation dynamics whose aim is that of mimicking
the mechanisms leading to opinion and convention formation in a population of
individuals. The negotiation process, as opposed to ``herding-like'' or
``bounded confidence'' driven processes, is based on a microscopic dynamics
where memory and feedback play a central role. Our model displays a
non-equilibrium phase transition from an absorbing state in which all agents
reach a consensus to an active stationary state characterized either by
polarization or fragmentation in clusters of agents with different opinions. We
show the exystence of at least two different universality classes, one for the
case with two possible opinions and one for the case with an unlimited number
of opinions. The phase transition is studied analytically and numerically for
various topologies of the agents' interaction network. In both cases the
universality classes do not seem to depend on the specific interaction
topology, the only relevant feature being the total number of different
opinions ever present in the system.Comment: 4 pages, 4 figure
Modeling the emergence of a new language: Naming Game with hybridization
In recent times, the research field of language dynamics has focused on the
investigation of language evolution, dividing the work in three evolutive
steps, according to the level of complexity: lexicon, categories and grammar.
The Naming Game is a simple model capable of accounting for the emergence of a
lexicon, intended as the set of words through which objects are named. We
introduce a stochastic modification of the Naming Game model with the aim of
characterizing the emergence of a new language as the result of the interaction
of agents. We fix the initial phase by splitting the population in two sets
speaking either language A or B. Whenever the result of the interaction of two
individuals results in an agent able to speak both A and B, we introduce a
finite probability that this state turns into a new idiom C, so to mimic a sort
of hybridization process. We study the system in the space of parameters
defining the interaction, and show that the proposed model displays a rich
variety of behaviours, despite the simple mean field topology of interactions.Comment: 12 pages, 10 figures, presented at IWSOS 2013 Palma de Mallorca, the
final publication will be available at LNCS http://www.springer.com/lnc
Microscopic activity patterns in the Naming Game
The models of statistical physics used to study collective phenomena in some
interdisciplinary contexts, such as social dynamics and opinion spreading, do
not consider the effects of the memory on individual decision processes. On the
contrary, in the Naming Game, a recently proposed model of Language formation,
each agent chooses a particular state, or opinion, by means of a memory-based
negotiation process, during which a variable number of states is collected and
kept in memory. In this perspective, the statistical features of the number of
states collected by the agents becomes a relevant quantity to understand the
dynamics of the model, and the influence of topological properties on
memory-based models. By means of a master equation approach, we analyze the
internal agent dynamics of Naming Game in populations embedded on networks,
finding that it strongly depends on very general topological properties of the
system (e.g. average and fluctuations of the degree). However, the influence of
topological properties on the microscopic individual dynamics is a general
phenomenon that should characterize all those social interactions that can be
modeled by memory-based negotiation processes.Comment: submitted to J. Phys.
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On the relation between Transversal and Longitudinal Scaling in Cities
Given that a group of cities follows a scaling law connecting urban population with socio-economic or infrastructural metrics (transversal scaling), should we expect that each city would follow the same behavior over time (longitudinal scaling)? This assumption has important policy implications, although rigorous empirical tests have been so far hindered by the lack of suitable data. Here, we advance the debate by looking into the temporal evolution of the scaling laws for 5507 municipalities in Brazil. We focus on the relationship between population size and two urban variables, GDP and water network length, analyzing the time evolution of the system of cities as well as their individual trajectory. We find that longitudinal (individual) scaling exponents are city-specific, but they are distributed around an average value that approaches to the transversal scaling exponent when the data are decomposed to eliminate external factors, and when we only consider cities with a sufficiently large growth rate. Such results give support to the idea that the longitudinal dynamics is a micro-scaling version of the transversal dynamics of the entire urban system. Finally, we propose a mathematical framework that connects the microscopic level to global behavior, and, in all analyzed cases, we find good agreement between theoretical prediction and empirical evidence
Bio-linguistic transition and Baldwin effect in an evolutionary naming-game model
We examine an evolutionary naming-game model where communicating agents are
equipped with an evolutionarily selected learning ability. Such a coupling of
biological and linguistic ingredients results in an abrupt transition: upon a
small change of a model control parameter a poorly communicating group of
linguistically unskilled agents transforms into almost perfectly communicating
group with large learning abilities. When learning ability is kept fixed, the
transition appears to be continuous. Genetic imprinting of the learning
abilities proceeds via Baldwin effect: initially unskilled communicating agents
learn a language and that creates a niche in which there is an evolutionary
pressure for the increase of learning ability.Our model suggests that when
linguistic (or cultural) processes became intensive enough, a transition took
place where both linguistic performance and biological endowment of our species
experienced an abrupt change that perhaps triggered the rapid expansion of
human civilization.Comment: 7 pages, minor changes, accepted in Int.J.Mod.Phys.C, proceedings of
Max Born Symp. Wroclaw (Poland), Sept. 2007. Java applet is available at
http://spin.amu.edu.pl/~lipowski/biolin.html or
http://www.amu.edu.pl/~lipowski/biolin.htm
Heterogeneous pair approximation for voter models on networks
For models whose evolution takes place on a network it is often necessary to
augment the mean-field approach by considering explicitly the degree dependence
of average quantities (heterogeneous mean-field). Here we introduce the degree
dependence in the pair approximation (heterogeneous pair approximation) for
analyzing voter models on uncorrelated networks. This approach gives an
essentially exact description of the dynamics, correcting some inaccurate
results of previous approaches. The heterogeneous pair approximation introduced
here can be applied in full generality to many other processes on complex
networks.Comment: 6 pages, 6 figures, published versio
Nonequilibrium phase transition in the coevolution of networks and opinions
Models of the convergence of opinion in social systems have been the subject
of a considerable amount of recent attention in the physics literature. These
models divide into two classes, those in which individuals form their beliefs
based on the opinions of their neighbors in a social network of personal
acquaintances, and those in which, conversely, network connections form between
individuals of similar beliefs. While both of these processes can give rise to
realistic levels of agreement between acquaintances, practical experience
suggests that opinion formation in the real world is not a result of one
process or the other, but a combination of the two. Here we present a simple
model of this combination, with a single parameter controlling the balance of
the two processes. We find that the model undergoes a continuous phase
transition as this parameter is varied, from a regime in which opinions are
arbitrarily diverse to one in which most individuals hold the same opinion. We
characterize the static and dynamical properties of this transition
Multiple primary malignancies of the liver and the colon: a complex diagnostic and decisional process with a final unanswered question
We herein present the case of a 78-year-old man with an incidental finding of a solid hepatic mass without symptoms and only a laparotomic cholecystectomy for acute cholecystitis in the past surgical history. A colonoscopy, a magnetic resonance imaging scan, a positron emission tomography scan, and a computed tomography scan completed the preoperative workup: a neoplastic lesion 4.3 × 3 cm in size was diagnosed at segments IV and V, associated with a neoplastic involvement of the splenic flexure without signs of colonic occlusion. After colonic resection, a frozen section on a granulomatous-like tissue at gastric border suggested a diagnosis of an adenocarcinoma of bilio-pancreatic type, changing the surgical strategy to include gastric resection and hepatic pedicle node dissection. The discussion turns around the idea that a final diagnosis of colon cancer with regional nodal involvement (pT3N1) and metastatic gallbladder cancer with multiple peritoneal seedings cannot be excluded
Voter models on weighted networks
We study the dynamics of the voter and Moran processes running on top of
complex network substrates where each edge has a weight depending on the degree
of the nodes it connects. For each elementary dynamical step the first node is
chosen at random and the second is selected with probability proportional to
the weight of the connecting edge. We present a heterogeneous mean-field
approach allowing to identify conservation laws and to calculate exit
probabilities along with consensus times. In the specific case when the weight
is given by the product of nodes' degree raised to a power theta, we derive a
rich phase-diagram, with the consensus time exhibiting various scaling laws
depending on theta and on the exponent of the degree distribution gamma.
Numerical simulations give very good agreement for small values of |theta|. An
additional analytical treatment (heterogeneous pair approximation) improves the
agreement with numerics, but the theoretical understanding of the behavior in
the limit of large |theta| remains an open challenge.Comment: 21 double-spaced pages, 6 figure
Critical behavior in a cross-situational lexicon learning scenario
The associationist account for early word-learning is based on the
co-occurrence between objects and words. Here we examine the performance of a
simple associative learning algorithm for acquiring the referents of words in a
cross-situational scenario affected by noise produced by out-of-context words.
We find a critical value of the noise parameter above which learning
is impossible. We use finite-size scaling to show that the sharpness of the
transition persists across a region of order about ,
where is the number of learning trials, as well as to obtain the
learning error (scaling function) in the critical region. In addition, we show
that the distribution of durations of periods when the learning error is zero
is a power law with exponent -3/2 at the critical point
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