41,165 research outputs found
Homogeneous symmetrical threshold model with nonconformity: independence vs. anticonformity
We study two variants of the modified Watts threshold model with a noise
(with nonconformity, in the terminology of social psychology) on a complete
graph. Within the first version, a noise is introduced via so-called
independence, whereas in the second version anticonformity plays the role of a
noise, which destroys the order. The modified Watts threshold model, studied
here, is homogeneous and posses an up-down symmetry, which makes it similar to
other binary opinion models with a single-flip dynamics, such as the
majority-vote and the q-voter models. Because within the majority-vote model
with independence only continuous phase transitions are observed, whereas
within the q-voter model with independence also discontinuous phase transitions
are possible, we ask the question about the factor, which could be responsible
for discontinuity of the order parameter. We investigate the model via the
mean-field approach, which gives the exact result in the case of a complete
graph, as well as via Monte Carlo simulations. Additionally, we provide a
heuristic reasoning, which explains observed phenomena. We show that indeed, if
the threshold r = 0.5, which corresponds to the majority-vote model, an
order-disorder transition is continuous. Moreover, results obtained for both
versions of the model (one with independence and the second one with
anticonformity) give the same results, only rescaled by the factor of 2.
However, for r > 0.5 the jump of the order parameter and the hysteresis is
observed for the model with independence, and both versions of the model give
qualitatively different results.Comment: 12 pages, 4 figures, accepted to Complexit
Consensus Emerging from the Bottom-up: the Role of Cognitive Variables in Opinion Dynamics
The study of opinions e.g., their formation and change, and their effects
on our society by means of theoretical and numerical models has been one of
the main goals of sociophysics until now, but it is one of the defining topics
addressed by social psychology and complexity science. Despite the flourishing
of different models and theories, several key questions still remain
unanswered. The aim of this paper is to provide a cognitively grounded
computational model of opinions in which they are described as mental
representations and defined in terms of distinctive mental features. We also
define how these representations change dynamically through different
processes, describing the interplay between mental and social dynamics of
opinions. We present two versions of the model, one with discrete opinions
(voter model-like), and one with continuous ones (Deffuant-like). By means of
numerical simulations, we compare the behaviour of our cognitive model with the
classical sociophysical models, and we identify interesting differences in the
dynamics of consensus for each of the models considered.Comment: 14 pages, 8 figure
Dynamic Monopolies in Colored Tori
The {\em information diffusion} has been modeled as the spread of an
information within a group through a process of social influence, where the
diffusion is driven by the so called {\em influential network}. Such a process,
which has been intensively studied under the name of {\em viral marketing}, has
the goal to select an initial good set of individuals that will promote a new
idea (or message) by spreading the "rumor" within the entire social network
through the word-of-mouth. Several studies used the {\em linear threshold
model} where the group is represented by a graph, nodes have two possible
states (active, non-active), and the threshold triggering the adoption
(activation) of a new idea to a node is given by the number of the active
neighbors.
The problem of detecting in a graph the presence of the minimal number of
nodes that will be able to activate the entire network is called {\em target
set selection} (TSS). In this paper we extend TSS by allowing nodes to have
more than two colors. The multicolored version of the TSS can be described as
follows: let be a torus where every node is assigned a color from a finite
set of colors. At each local time step, each node can recolor itself, depending
on the local configurations, with the color held by the majority of its
neighbors. We study the initial distributions of colors leading the system to a
monochromatic configuration of color , focusing on the minimum number of
initial -colored nodes. We conclude the paper by providing the time
complexity to achieve the monochromatic configuration
Using synchronous Boolean networks to model several phenomena of collective behavior
In this paper, we propose an approach for modeling and analysis of a number
of phenomena of collective behavior. By collectives we mean multi-agent systems
that transition from one state to another at discrete moments of time. The
behavior of a member of a collective (agent) is called conforming if the
opinion of this agent at current time moment conforms to the opinion of some
other agents at the previous time moment. We presume that at each moment of
time every agent makes a decision by choosing from the set {0,1} (where
1-decision corresponds to action and 0-decision corresponds to inaction). In
our approach we model collective behavior with synchronous Boolean networks. We
presume that in a network there can be agents that act at every moment of time.
Such agents are called instigators. Also there can be agents that never act.
Such agents are called loyalists. Agents that are neither instigators nor
loyalists are called simple agents. We study two combinatorial problems. The
first problem is to find a disposition of instigators that in several time
moments transforms a network from a state where a majority of simple agents are
inactive to a state with a majority of active agents. The second problem is to
find a disposition of loyalists that returns the network to a state with a
majority of inactive agents. Similar problems are studied for networks in which
simple agents demonstrate the contrary to conforming behavior that we call
anticonforming. We obtained several theoretical results regarding the behavior
of collectives of agents with conforming or anticonforming behavior. In
computational experiments we solved the described problems for randomly
generated networks with several hundred vertices. We reduced corresponding
combinatorial problems to the Boolean satisfiability problem (SAT) and used
modern SAT solvers to solve the instances obtained
From Knowledge, Knowability and the Search for Objective Randomness to a New Vision of Complexity
Herein we consider various concepts of entropy as measures of the complexity
of phenomena and in so doing encounter a fundamental problem in physics that
affects how we understand the nature of reality. In essence the difficulty has
to do with our understanding of randomness, irreversibility and
unpredictability using physical theory, and these in turn undermine our
certainty regarding what we can and what we cannot know about complex phenomena
in general. The sources of complexity examined herein appear to be channels for
the amplification of naturally occurring randomness in the physical world. Our
analysis suggests that when the conditions for the renormalization group apply,
this spontaneous randomness, which is not a reflection of our limited
knowledge, but a genuine property of nature, does not realize the conventional
thermodynamic state, and a new condition, intermediate between the dynamic and
the thermodynamic state, emerges. We argue that with this vision of complexity,
life, which with ordinary statistical mechanics seems to be foreign to physics,
becomes a natural consequence of dynamical processes.Comment: Phylosophica
Searching for superspreaders of information in real-world social media
A number of predictors have been suggested to detect the most influential
spreaders of information in online social media across various domains such as
Twitter or Facebook. In particular, degree, PageRank, k-core and other
centralities have been adopted to rank the spreading capability of users in
information dissemination media. So far, validation of the proposed predictors
has been done by simulating the spreading dynamics rather than following real
information flow in social networks. Consequently, only model-dependent
contradictory results have been achieved so far for the best predictor. Here,
we address this issue directly. We search for influential spreaders by
following the real spreading dynamics in a wide range of networks. We find that
the widely-used degree and PageRank fail in ranking users' influence. We find
that the best spreaders are consistently located in the k-core across
dissimilar social platforms such as Twitter, Facebook, Livejournal and
scientific publishing in the American Physical Society. Furthermore, when the
complete global network structure is unavailable, we find that the sum of the
nearest neighbors' degree is a reliable local proxy for user's influence. Our
analysis provides practical instructions for optimal design of strategies for
"viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure
The Development of Social Simulation as Reflected in the First Ten Years of JASSS: a Citation and Co-Citation Analysis
Social simulation is often described as a multidisciplinary and fast-moving field. This can make it difficult to obtain an overview of the field both for contributing researchers and for outsiders who are interested in social simulation. The Journal for Artificial Societies and Social Simulation (JASSS) completing its tenth year provides a good opportunity to take stock of what happened over this time period. First, we use citation analysis to identify the most influential publications and to verify characteristics of social simulation such as its multidisciplinary nature. Then, we perform a co-citation analysis to visualize the intellectual structure of social simulation and its development. Overall, the analysis shows social simulation both in its early stage and during its first steps towards becoming a more differentiated discipline.Citation Analysis, Co-Citation Analysis, Lines of Research, Multidisciplinary, Science Studies, Social Simulation
Mixing Dyadic and Deliberative Opinion Dynamics in an Agent-Based Model of Group Decision-Making
International audienceIn this article, we propose an agent-based model of opinion diffusion and voting where influence among individuals and deliberation in a group are mixed. The model is inspired from social modeling, as it describes an iterative process of collective decision-making that repeats a series of interindividual influences and collective deliberation steps, and studies the evolution of opinions and decisions in a group. It also aims at founding a comprehensive model to describe collective decision-making as a combination of two different paradigms: argumentation theory and ABM-influence models, which are not obvious to combine as a formal link between them is required. In our model, we find that deliberation, through the exchange of arguments, reduces the variance of opinions and the proportion of extremists in a population as long as not too much deliberation takes place in the decision processes. Additionally, if we define the correct collective decisions in the system in terms of the arguments that should be accepted, allowing for more deliberation favors convergence towards the correct decisions
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