127,616 research outputs found
The Contagion Effects of Repeated Activation in Social Networks
Demonstrations, protests, riots, and shifts in public opinion respond to the
coordinating potential of communication networks. Digital technologies have
turned interpersonal networks into massive, pervasive structures that
constantly pulsate with information. Here, we propose a model that aims to
analyze the contagion dynamics that emerge in networks when repeated activation
is allowed, that is, when actors can engage recurrently in a collective effort.
We analyze how the structure of communication networks impacts on the ability
to coordinate actors, and we identify the conditions under which large-scale
coordination is more likely to emerge.Comment: Submitted for publicatio
Network Structure, Efficiency, and Performance in WikiProjects
The internet has enabled collaborations at a scale never before possible, but
the best practices for organizing such large collaborations are still not
clear. Wikipedia is a visible and successful example of such a collaboration
which might offer insight into what makes large-scale, decentralized
collaborations successful. We analyze the relationship between the structural
properties of WikiProject coeditor networks and the performance and efficiency
of those projects. We confirm the existence of an overall
performance-efficiency trade-off, while observing that some projects are higher
than others in both performance and efficiency, suggesting the existence
factors correlating positively with both. Namely, we find an association
between low-degree coeditor networks and both high performance and high
efficiency. We also confirm results seen in previous numerical and small-scale
lab studies: higher performance with less skewed node distributions, and higher
performance with shorter path lengths. We use agent-based models to explore
possible mechanisms for degree-dependent performance and efficiency. We present
a novel local-majority learning strategy designed to satisfy properties of
real-world collaborations. The local-majority strategy as well as a localized
conformity-based strategy both show degree-dependent performance and
efficiency, but in opposite directions, suggesting that these factors depend on
both network structure and learning strategy. Our results suggest possible
benefits to decentralized collaborations made of smaller, more tightly-knit
teams, and that these benefits may be modulated by the particular learning
strategies in use.Comment: 11 pages, 5 figures, to appear in ICWSM 201
Coordination of Decisions in a Spatial Agent Model
For a binary choice problem, the spatial coordination of decisions in an
agent community is investigated both analytically and by means of stochastic
computer simulations. The individual decisions are based on different local
information generated by the agents with a finite lifetime and disseminated in
the system with a finite velocity. We derive critical parameters for the
emergence of minorities and majorities of agents making opposite decisions and
investigate their spatial organization. We find that dependent on two essential
parameters describing the local impact and the spatial dissemination of
information, either a definite stable minority/majority relation
(single-attractor regime) or a broad range of possible values (multi-attractor
regime) occurs. In the latter case, the outcome of the decision process becomes
rather diverse and hard to predict, both with respect to the share of the
majority and their spatial distribution. We further investigate how a
dissemination of information on different time scales affects the outcome of
the decision process. We find that a more ``efficient'' information exchange
within a subpopulation provides a suitable way to stabilize their majority
status and to reduce ``diversity'' and uncertainty in the decision process.Comment: submitted for publication in Physica A (31 pages incl. 17 multi-part
figures
Extremism propagation in social networks with hubs
One aspect of opinion change that has been of academic interest is the impact of people with extreme opinions (extremists) on opinion dynamics. An agent-based model has been used to study the role of small-world social network topologies on general opinion change in the presence of extremists. It has been found that opinion convergence to a single extreme occurs only when the average number of network connections for each individual is extremely high. Here, we extend the model to examine the effect of positively skewed degree distributions, in addition to small-world structures, on the types of opinion convergence that occur in the presence of extremists. We also examine what happens when extremist opinions are located on the well-connected nodes (hubs) created by the positively skewed distribution. We find that a positively skewed network topology encourages opinion convergence on a single extreme under a wider range of conditions than topologies whose degree distributions were not skewed. The importance of social position for social influence is highlighted by the result that, when positive extremists are placed on hubs, all population convergence is to the positive extreme even when there are twice as many negative extremists. Thus, our results have shown the importance of considering a positively skewed degree distribution, and in particular network hubs and social position, when examining extremist transmission
Cooperation in the snowdrift game on directed small-world networks under self-questioning and noisy conditions
Cooperation in the evolutionary snowdrift game with a self-questioning
updating mechanism is studied on annealed and quenched small-world networks
with directed couplings. Around the payoff parameter value , we find a
size-invariant symmetrical cooperation effect. While generally suppressing
cooperation for payoffs, rewired networks facilitated cooperative
behavior for . Fair amounts of noise were found to break the observed
symmetry and further weaken cooperation at relatively large values of .
However, in the absence of noise, the self-questioning mechanism recovers
symmetrical behavior and elevates altruism even under large-reward conditions.
Our results suggest that an updating mechanism of this type is necessary to
stabilize cooperation in a spatially structured environment which is otherwise
detrimental to cooperative behavior, especially at high cost-to-benefit ratios.
Additionally, we employ component and local stability analyses to better
understand the nature of the manifested dynamics.Comment: 7 pages, 6 figures, 1 tabl
A model of riots dynamics: shocks, diffusion and thresholds
We introduce and analyze several variants of a system of differential
equations which model the dynamics of social outbursts, such as riots. The
systems involve the coupling of an explicit variable representing the intensity
of rioting activity and an underlying (implicit) field of social tension. Our
models include the effects of exogenous and endogenous factors as well as
various propagation mechanisms. From numerical and mathematical analysis of
these models we show that the assumptions made on how different locations
influence one another and how the tension in the system disperses play a major
role on the qualitative behavior of bursts of social unrest. Furthermore, we
analyze here various properties of these systems, such as the existence of
traveling wave solutions, and formulate some new open mathematical problems
which arise from our work
Instability and network effects in innovative markets
We consider a network of interacting agents and we model the process of
choice on the adoption of a given innovative product by means of
statistical-mechanics tools. The modelization allows us to focus on the effects
of direct interactions among agents in establishing the success or failure of
the product itself. Mimicking real systems, the whole population is divided
into two sub-communities called, respectively, Innovators and Followers, where
the former are assumed to display more influence power. We study in detail and
via numerical simulations on a random graph two different scenarios:
no-feedback interaction, where innovators are cohesive and not sensitively
affected by the remaining population, and feedback interaction, where the
influence of followers on innovators is non negligible. The outcomes are
markedly different: in the former case, which corresponds to the creation of a
niche in the market, Innovators are able to drive and polarize the whole
market. In the latter case the behavior of the market cannot be definitely
predicted and become unstable. In both cases we highlight the emergence of
collective phenomena and we show how the final outcome, in terms of the number
of buyers, is affected by the concentration of innovators and by the
interaction strengths among agents.Comment: 20 pages, 6 figures. 7th workshop on "Dynamic Models in Economics and
Finance" - MDEF2012 (COST Action IS1104), Urbino (2012
Simulating the Influence of Collaborative Networks on the Structure of Networks of Organizations, Employment Structure, and Organization Value
From the perspective of reindustrialization, it is important to understand
the evolution of the structure of the network of organizations employment
structure, and organization value. Understanding the potential influence of
collaborative networks (CNs) on these aspects may lead to the development of
appropriate economic policies. In this paper, we propose a theoretical approach
to analysis this potential influence, based on a model of dynamic networked
ecosystem of organizations encompassing collaboration relations among
organization, employment mobility, and organization value. A large number of
simulations has been performed to identify factors influencing the structure of
the network of organizations employment structure, and organization value. The
main findings are that 1) the higher the number of members of CNs, the better
the clustering and the shorter the average path length among organizations; 2)
the constitution of CNs does not affect neither the structure of the network of
organizations, nor the employment structure and the organization value.Comment: 10 pages, 1 figure, conference paper at the 14th IFIP Working
Conference on Virtual Enterprises, PRO-VE'13, http://www.pro-ve.org
Causal inference for social network data
We describe semiparametric estimation and inference for causal effects using
observational data from a single social network. Our asymptotic result is the
first to allow for dependence of each observation on a growing number of other
units as sample size increases. While previous methods have generally
implicitly focused on one of two possible sources of dependence among social
network observations, we allow for both dependence due to transmission of
information across network ties, and for dependence due to latent similarities
among nodes sharing ties. We describe estimation and inference for new causal
effects that are specifically of interest in social network settings, such as
interventions on network ties and network structure. Using our methods to
reanalyze the Framingham Heart Study data used in one of the most influential
and controversial causal analyses of social network data, we find that after
accounting for network structure there is no evidence for the causal effects
claimed in the original paper
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