Centers and peripheries: Network roles in language change

Fagyal, Zsuzsanna; Swarup, Samarth; Escobar, Anna María; Gasser, Les; Lakkaraju, Kiran

doi:10.1016/j.lingua.2010.02.001

Cited by 83 publications

(101 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fagyal et al investigate the role of network structure in language change, and further validate the assumption of the degree of a location being an important indicator of the influence of an agent at that location [11]. They also conclude that peripheral agents play an important role in keeping norms stable, demonstrating that network features other than degree deserve investigation.…”

Section: Influence Propagationmentioning

confidence: 85%

“…A wide variety of research has shown that these networks display rich structure that significantly influences the dynamics of agent interactions and the flow of information (e.g. [8,11,36,41,45]). The structure of a network can mean that some individual locations are significantly more important than others, by virtue of being able to influence large proportions of a population, controlling the flow of information through a network, or connecting disparate communities of individuals (as with the vital hub nodes in scale-free networks [30]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning agent influence in MAS with complex social networks

Franks

Griffiths

Anand³

2013

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

In complex open Multi-Agent Systems (MAS), where there is no centralised control and individuals have equal authority, ensuring cooperative and coordinated behaviour is challenging. Norms and conventions are useful means of supporting cooperation in an emergent decentralised manner, however it takes time for effective norms and conventions to emerge. Identifying influential individuals enables the targeted seeding of desirable norms and conventions, which can reduce the establishment time and increase efficacy. Existing research is limited with respect to considering (i) how to identify influential agents, (ii) the extent to which network location imbues influence on an agent, and (iii) the extent to which different network structures affect influence.In this paper, we propose a methodology for learning a model for predicting the network value of an agent, in terms of the extent to which it can influence the rest of the population. Applying our methodology, we show that exploiting knowledge of the network structure can significantly increase the ability of individuals to influence which convention emerges. We evaluate our methodology in the context of two agent-interaction models, namely, the language coordination domain used by Salazar et al. [40] and a coordination game of the form used by Sen and Airiau [42] with heterogeneous agent learning mechanisms, and on a variety of synthetic and real-world networks. We further show that (i) the models resulting from our methodology are effective in predicting influential network locations, (ii) there are very few locations that can be classified as influential in typical networks, (iii) four single metrics are robustly indicative of influence across a range of network structures, and (iv) our methodology learns which single metric or combined measure is the best predictor of influence in a given network.

show abstract

Section: Influence Propagationmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Learning agent influence in MAS with complex social networks

Franks

Griffiths

Anand³

2013

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

show abstract

“…Indeed, Bloomfield realised that the amount of dialect differentiation caused by diffusion depends on the communication density (Nerbonne, 2010, p. 3822). Fagyal et al (2010) used a series of simulations to show that innovations spread following an S-shaped curve and stabilise as norms only when the network is: (1) socially heterogeneous (scale-free), with tight-knit communities (high clustering) that keep their members in close reach (small diameter), and (2) group members imitate the language use of those whom all perceive as the most popular (ibid. p. 17).…”

Section: Combining Approach Esmentioning

confidence: 99%

Social Structure and Language Structure: the New Nomothetic Approach

Roberts

Winters

2012

Psychology of Language and Communication

View full text Add to dashboard Cite

Recent studies have taken advantage of newly available, large-scale, cross-linguistic data and new statistical techniques to look at the relationship between language structure and social structure. These 'nomothetic' approaches contrast with more traditional approaches and a tension is observed between proponents of each method. We review some nomothetic studies and point out some challenges that must be overcome. However, we argue that nomothetic approaches can contribute to our understanding of the links between social structure and language structure if they address these challenges and are taken as part of a body of mutually supporting evidence. Nomothetic studies are a powerful tool for generating hypotheses that can go on to be corroborated and tested with experimental and theoretical approaches. These studies are highlighting the effect of interaction on language.

show abstract

“…This might be realistic if we consider that, for example, a popular speaker (i.e., one with many neighbors) is given more weight by her interlocutors, for example as in [25]. Alternatively, speakers might divide their attention between all of their interlocutors.…”

Section: B Asymmetry Depends On Speakers Degreementioning

confidence: 99%

Fast fixation with a generic network structure

2012

View full text Add to dashboard Cite

We investigate the dynamics of a broad class of stochastic copying processes on a network that includes examples from population genetics (spatially-structured Wright-Fisher models), ecology (Hubbell-type models), linguistics (the utterance selection model) and opinion dynamics (the voter model) as special cases. These models all have absorbing states of fixation where all the nodes are in the same state. Earlier studies of these models showed that the mean time when this occurs can be made to grow as different powers of the network size by varying the the degree distribution of the network. Here we demonstrate that this effect can also arise if one varies the asymmetry of the copying dynamics whilst holding the degree distribution constant. In particular, we show that the mean time to fixation can be accelerated even on homogeneous networks when certain nodes are very much more likely to be copied from than copied to. We further show that there is a complex interplay between degree distribution and asymmetry when they may co-vary; and that the results are robust to correlations in the network or the initial condition.

show abstract

Centers and peripheries: Network roles in language change

Cited by 83 publications

References 61 publications

Learning agent influence in MAS with complex social networks

Learning agent influence in MAS with complex social networks

Social Structure and Language Structure: the New Nomothetic Approach

Fast fixation with a generic network structure

Contact Info

Product

Resources

About