342,030 research outputs found
Social pressure in opinion dynamics
Motivated by privacy and security concerns in online social networks, we study the role of social pressure in opinion dynamics. These are dynamics, introduced in economics and sociology literature, that model the formation of opinions in a social network. We enrich one of the most classical opinion dynamics, by introducing the pressure, increasing with time, to reach an agreement.
We prove that for clique social networks, the dynamics always converges to consensus (no matter the level of noise) if the social pressure is high enough. Moreover, we provide (tight) bounds on the speed of convergence; these bounds are polynomial in the number of nodes in the network provided that the pressure grows sufficiently fast. We finally look beyond cliques: we characterize the graphs for which consensus is guaranteed, and make some considerations on the computational complexity of checking whether a graph satisfies such a condition
Stochastic Opinion Dynamics under Social Pressure in Arbitrary Networks
Social pressure is a key factor affecting the evolution of opinions on
networks in many types of settings, pushing people to conform to their
neighbors' opinions. To study this, the interacting Polya urn model was
introduced by Jadbabaie et al., in which each agent has two kinds of opinion:
inherent beliefs, which are hidden from the other agents and fixed; and
declared opinions, which are randomly sampled at each step from a distribution
which depends on the agent's inherent belief and her neighbors' past declared
opinions (the social pressure component), and which is then communicated to
their neighbors. Each agent also has a bias parameter denoting her level of
resistance to social pressure. At every step, the agents simultaneously update
their declared opinions according to their neighbors' aggregate past declared
opinions, their inherent beliefs, and their bias parameters. We study the
asymptotic behavior of this opinion dynamics model and show that agents'
declaration probabilities converge almost surely in the limit using Lyapunov
theory and stochastic approximation techniques. We also derive necessary and
sufficient conditions for the agents to approach consensus on their declared
opinions. Our work provides further insight into the difficulty of inferring
the inherent beliefs of agents when they are under social pressure
Social Pressure in Opinion Games
Motivated by privacy and security concerns in online social networks, we study the role of social pressure in opinion games. These are games, important in economics and sociology, that model the formation of opinions in a social network. We enrich the definition of (noisy) best-response dynamics for opinion games by introducing the pressure, increasing with time, to reach an agreement. We prove that for clique social networks, the dynamics always converges to consensus (no matter the level of noise) if the social pressure is high enough. Moreover, we provide (tight) bounds on the speed of convergence; these bounds are polynomial in the number of players provided that the pressure grows sufficiently fast. We finally look beyond cliques: we characterize the graphs for which consensus is guaranteed, and make some considerations on the computational complexity of checking whether a graph satisfies such a condition
The role of homophily in the emergence of opinion controversies
Understanding the emergence of strong controversial issues in modern
societies is a key issue in opinion studies. A commonly diffused idea is the
fact that the increasing of homophily in social networks, due to the modern
ICT, can be a driving force for opinion polariation. In this paper we address
the problem with a modelling approach following three basic steps. We first
introduce a network morphogenesis model to reconstruct network structures where
homophily can be tuned with a parameter. We show that as homophily increases
the emergence of marked topological community structures in the networks
raises. Secondly, we perform an opinion dynamics process on homophily dependent
networks and we show that, contrary to the common idea, homophily helps
consensus formation. Finally, we introduce a tunable external media pressure
and we show that, actually, the combination of homophily and media makes the
media effect less effective and leads to strongly polarized opinion clusters.Comment: 24 pages, 10 figure
Expressed and Private Opinion Dynamics on Influence Networks with Asynchronous Updating
© 2020 IEEE. In this paper, an asynchronous discrete-time opinion dynamics model on a social influence network is considered. At each time instant, a single individual activates and updates two state variables simultaneously. The individual's new private opinion is a weighted average of her current private opinion, the expressed opinions of her neighbors, and a constant prejudice. Meanwhile, the individual's new expressed opinion is equal to her current private opinion, altered due to a pressure to conform to the public opinion as perceived by the individual, being the average expressed opinion among her neighbors. We analyze the system for social networks which are rooted, and show that if no individual holds a prejudice, then a mild assumption on the activation sequence of the individuals guarantees convergence. In particular, the expressed and private opinions of all individuals converge to the same value exponentially fast, with two lower bounds on convergence speeds based on two different assumptions on the network topology. Simulations are provided to illustrate the result, and provide support to the conjecture that the system dynamics may converge even if individuals hold an existing prejudice
Estimating True Beliefs from Declared Opinions
Social networks often exert social pressure, causing individuals to adapt
their expressed opinions to conform to their peers. An agent in such systems
can be modeled as having a (true and unchanging) inherent belief but broadcasts
a declared opinion at each time step based on her inherent belief and the past
declared opinions of her neighbors. An important question in this setting is
parameter estimation: how to disentangle the effects of social pressure to
estimate inherent beliefs from declared opinions. To address this, Jadbabaie et
al. formulated the interacting P\'olya urn model of opinion dynamics under
social pressure and studied it on complete-graph social networks using an
aggregate estimator, and found that their estimator converges to the inherent
beliefs unless majority pressure pushes the network to consensus. In this work,
we study this model on arbitrary networks, providing an estimator which
converges to the inherent beliefs even in consensus situations. Finally, we
bound the convergence rate of our estimator in both consensus and non-consensus
scenarios; to get the bound for consensus scenarios (which converge slower than
non-consensus) we additionally found how quickly the system converges to
consensus
Socio-Physical Approach to Consensus Building and the Occurrence of Opinion Divisions Based on External Efficacy
The proliferation of public networks has enabled instantaneous and
interactive communication that transcends temporal and spatial constraints. The
vast amount of textual data on the Web has facilitated the study of
quantitative analysis of public opinion, which could not be visualized before.
In this paper, we propose a new theory of opinion dynamics. This theory is
designed to explain consensus building and opinion splitting in opinion
exchanges on social media such as Twitter. With the spread of public networks,
immediate and interactive communication that transcends temporal and spatial
constraints has become possible, and research is underway to quantitatively
analyze the distribution of public opinion, which has not been visualized until
now, using vast amounts of text data. In this paper, we propose a model based
on the Like Bounded Confidence Model, which represents opinions as continuous
quantities. However, the Bounded Confidence mModel assumes that people with
different opinions move without regard to their opinions, rather than ignoring
them. Furthermore, our theory modeled the phenomenon in such a way that it can
incorporate and represent the effects of external external pressure and
dependence on surrounding conditions. This paper is a revised version of a
paper submitted in December 2018(Opinion Dynamics Theory for Analysis of
Consensus Formation and Division of Opinion on the Internet).Comment: Revised Paper:Opinion Dynamics Theory for Analysis of Consensus
Formation and Division of Opinion on the Internet(2018
Culture Outsmarts Nature in the Evolution of Cooperation
A one dimensional cellular automata model describes the evolutionary dynamics of cooperation when grouping by cooperators provides protection against predation. It is used to compare the dynamics of evolution of cooperation in three settings. G: only vertical transmission of information is allowed, as an analogy of genetic evolution with heredity; H: only horizontal information transfer is simulated, through diffusion of the majority\'s opinion, as an analogy of opinion dynamics or social learning; and C: analogy of cultural evolution, where information is transmitted both horizontally (H) and vertically (V) so that learned behavior can be transmitted to offspring. The results show that the prevalence of cooperative behavior depends on the costs and benefits of cooperation so that: a- cooperation becomes the dominant behavior, even in the presence of free-riders (i.e., non-cooperative obtaining benefits from the cooperation of others), under all scenarios, if the benefits of cooperation compensate for its cost; b- G is more susceptible to selection pressure than H achieving a closer adaptation to the fitness landscape; c- evolution of cooperative behavior in H is less sensitive to the cost of cooperation than in G; d- C achieves higher levels of cooperation than the other alternatives at low costs, whereas H does it at high costs. The results suggest that a synergy between H and V is elicited that makes the evolution of cooperation much more likely under cultural evolution than under the hereditary kind where only V is present.Social Simulation, Interactions, Group Size, Selfish Heard, Cultural Evolution, Biological Evolution
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