463 research outputs found
Opinion formation models based on game theory
A way to simulate the basic interactions between two individuals with
different opinions, in the context of strategic game theory, is proposed.
Various games are considered, which produce different kinds of opinion
formation dynamics. First, by assuming that all individuals (players) are
equals, we obtain the bounded confidence model of continuous opinion dynamics
proposed by Deffuant et al. In such a model a tolerance threshold is defined,
such that individuals with difference in opinion larger than the threshold can
not interact. Then, we consider that the individuals have different
inclinations to change opinion and different abilities in convincing the
others. In this way, we obtain the so-called ``Stubborn individuals and
Orators'' (SO) model, a generalization of the Deffuant et al. model, in which
the threshold tolerance is different for every couple of individuals. We
explore, by numerical simulations, the dynamics of the SO model, and we propose
further generalizations that can be implemented.Comment: 18 pages, 4 figure
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
Role of social environment and social clustering in spread of opinions in co-evolving networks
Taking a pragmatic approach to the processes involved in the phenomena of
collective opinion formation, we investigate two specific modifications to the
co-evolving network voter model of opinion formation, studied by Holme and
Newman [1]. First, we replace the rewiring probability parameter by a
distribution of probability of accepting or rejecting opinions between
individuals, accounting for the asymmetric influences in relationships among
individuals in a social group. Second, we modify the rewiring step by a
path-length-based preference for rewiring that reinforces local clustering. We
have investigated the influences of these modifications on the outcomes of the
simulations of this model. We found that varying the shape of the distribution
of probability of accepting or rejecting opinions can lead to the emergence of
two qualitatively distinct final states, one having several isolated connected
components each in internal consensus leading to the existence of diverse set
of opinions and the other having one single dominant connected component with
each node within it having the same opinion. Furthermore, and more importantly,
we found that the initial clustering in network can also induce similar
transitions. Our investigation also brings forward that these transitions are
governed by a weak and complex dependence on system size. We found that the
networks in the final states of the model have rich structural properties
including the small world property for some parameter regimes. [1] P. Holme and
M. Newman, Phys. Rev. E 74, 056108 (2006)
Consensus formation on adaptive networks
The structure of a network can significantly influence the properties of the
dynamical processes which take place on them. While many studies have been
devoted to this influence, much less attention has been devoted to the
interplay and feedback mechanisms between dynamical processes and network
topology on adaptive networks. Adaptive rewiring of links can happen in real
life systems such as acquaintance networks where people are more likely to
maintain a social connection if their views and values are similar. In our
study, we consider different variants of a model for consensus formation. Our
investigations reveal that the adaptation of the network topology fosters
cluster formation by enhancing communication between agents of similar opinion,
though it also promotes the division of these clusters. The temporal behavior
is also strongly affected by adaptivity: while, on static networks, it is
influenced by percolation properties, on adaptive networks, both the early and
late time evolution of the system are determined by the rewiring process. The
investigation of a variant of the model reveals that the scenarios of
transitions between consensus and polarized states are more robust on adaptive
networks.Comment: 11 pages, 14 figure
Similarity solutions of Fokker-Planck equation with time-dependent coefficients
In this work, we consider the solvability of the Fokker-Planck equation with
both time-dependent drift and diffusion coefficients by means of the similarity
method. By the introduction of the similarity variable, the Fokker-Planck
equation is reduced to an ordinary differential equation. Adopting the natural
requirement that the probability current density vanishes at the boundary, the
resulted ordinary differential equation turns out to be integrable, and the
probability density function can be given in closed form. New examples of
exactly solvable Fokker-Planck equations are presented, and their properties
analyzed.Comment: 13 pages, 8 figures. Version to appear in Ann. Phys. Presentation
improved. Discussions and figures of easy examples remove
Volatility clustering and scaling for financial time series due to attractor bubbling
A microscopic model of financial markets is considered, consisting of many
interacting agents (spins) with global coupling and discrete-time thermal bath
dynamics, similar to random Ising systems. The interactions between agents
change randomly in time. In the thermodynamic limit the obtained time series of
price returns show chaotic bursts resulting from the emergence of attractor
bubbling or on-off intermittency, resembling the empirical financial time
series with volatility clustering. For a proper choice of the model parameters
the probability distributions of returns exhibit power-law tails with scaling
exponents close to the empirical ones.Comment: For related publications see http://www.helbing.or
USP15 targets ALK3/BMPR1A for deubiquitylation to enhance bone morphogenetic protein signalling
Protein kinase ALK3/BMPR1A mediates bone morphogenetic protein (BMP) signalling through phosphorylation and activation of SMADs 1/5/8. SMAD6, a transcriptional target of BMP, negatively regulates the BMP pathway by recruiting E3 ubiquitin ligases and targeting ALK3 for ubiquitin-mediated degradation. Here, we identify a deubiquitylating enzyme USP15 as an interactor of SMAD6 and ALK3. We show that USP15 enhances BMP-induced phosphorylation of SMAD1 by interacting with and deubiquitylating ALK3. RNAi-mediated depletion of USP15 increases ALK3 K48-linked polyubiquitylation, and reduces both BMP-induced SMAD1 phosphorylation and transcription of BMP target genes. We also show that loss of USP15 expression from mouse myoblast cells inhibits BMP-induced osteoblast differentiation. Furthermore, USP15 modulates BMP-induced phosphorylation of SMAD1 and transcription during Xenopus embryogenesis
Irreversible Opinion Spreading on Scale-Free Networks
We study the dynamical and critical behavior of a model for irreversible
opinion spreading on Barab\'asi-Albert (BA) scale-free networks by performing
extensive Monte Carlo simulations. The opinion spreading within an
inhomogeneous society is investigated by means of the magnetic Eden model, a
nonequilibrium kinetic model for the growth of binary mixtures in contact with
a thermal bath. The deposition dynamics, which is studied as a function of the
degree of the occupied sites, shows evidence for the leading role played by
hubs in the growth process. Systems of finite size grow either ordered or
disordered, depending on the temperature. By means of standard finite-size
scaling procedures, the effective order-disorder phase transitions are found to
persist in the thermodynamic limit. This critical behavior, however, is absent
in related equilibrium spin systems such as the Ising model on BA scale-free
networks, which in the thermodynamic limit only displays a ferromagnetic phase.
The dependence of these results on the degree exponent is also discussed for
the case of uncorrelated scale-free networks.Comment: 9 pages, 10 figures; added results and discussion on uncorrelated
scale-free networks; added references. To appear in PR
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