Stochastic Actor-Oriented Models for Network Dynamics

Snijders, Tom A. B.

doi:10.1146/annurev-statistics-060116-054035

Cited by 168 publications

(117 citation statements)

References 80 publications

(77 reference statements)

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“…There are multiple options to estimate undirected networks in RSiena; we chose "option 3", which assumes a two-step process of unilateral proposal by one actor and acceptance or rejection by the other actor for tie creation; tie deletion is a unilateral decision. Details on RSiena for undirected networks are found in Snijders and Pickup (2017).…”

Section: A222 Model Estimationmentioning

confidence: 99%

Change we can believe in: Comparing longitudinal network models on consistency, interpretability and predictive power

et al. 2018

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While several models for analysing longitudinal network data have been proposed, their main differences, especially regarding the treatment of time, have not been discussed extensively in the literature. However, differences in treatment of time strongly impact the conclusions that can be drawn from data. In this article we compare auto-regressive network models using the example of TERGMs -an extensions of ERGMs -and process-based models using SAOMs as an example. We conclude that the basic TERGM, in contrast to the ERGM, has no consistent micro-level interpretation, and thus only allows interpretation on the level of the network. Further, parameters in the TERGM are strongly dependent on the interval length between two time-points. Neither limitations is true for processbased network models such as the SAOM. Finally, both compared models perform poorly in out-of-sample prediction compared to trivial predictive models.

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Section: A222 Model Estimationmentioning

confidence: 99%

Change we can believe in: Comparing longitudinal network models on consistency, interpretability and predictive power

et al. 2018

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“…We then manipulated the level of agents' resources and neediness in idealized simulation scenarios. Following Flache and Stark (2009), our ABM was based on a stochastic actor-oriented model (SAOM) for multiplex network dynamics (Snijders, 1996(Snijders, , 2017Snijders et al, 2013). This allowed us to model tie formation, maintenance, and disruption by considering the complex interdependencies between agent preferences for partners' attributes (e.g., collaboration ties with high-resource nodes) and endogenous structural processes (e.g., a tendency to reciprocate trust ties).…”

Section: The Modelmentioning

confidence: 99%

“…In this study, we examined the effect of these counterbalancing social forces on the formation of mutual support expectations between professionals who have unequal resources and compete for resourceful collaboration partners. To do so, we used a stochastic actor-oriented model for multiplex network dynamics (Snijders, 1996(Snijders, , 2017Snijders, Lomi, & Torló, 2013) as an agent-based model (Macy & Flache, 2009;Bianchi & Squazzoni, 2015; see also Snijders & Steglich, 2015;Stadtfeld, 2018;Stadtfeld, Takács, & Vörös, 2019). This permitted us to simulate the evolution of a multiplex network of collaboration, trust and expectations of support, while controlling various factors and manipulating relevant parameters to generate new theoretical hypotheses about the conditions under which socioeconomic exchanges can be expected to give rise to solidarity.…”

Section: Introductionmentioning

confidence: 99%

Solidarity in collaboration networks when everyone competes for the strongest partner: a stochastic actor-based simulation model

Bianchi

Flache

Squazzoni

2020

The Journal of Mathematical Sociology

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This article examines the emergence of solidarity from interactions between professionals competing for collaboration. Research on multiplex collaboration networks has shown that economic exchange can elicit solidarity when mediated by trust but did not consider the effect of competition. To fill this gap, we built an agent-based model that simulates the evolution of a multiplex network of collaboration, trust, and support expectations. Simulations show that while resource heterogeneity is key for collaboration, competition for attractive collaboration partners penalizes low-resource professionals, who are less connected and highly segregated. Heterogeneous resource distribution can trigger segregation because of preferential selection of resourceful peers and reciprocity. Interestingly, we also found that low-resource professionals can reduce their marginalization by building in-group mutual support expectations. ARTICLE HISTORY

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“…These models are very useful in finding parameter vectors for fully defined functions in which it is possible to infer the value of any single parameter from the values of all others and the data. For example, these models are used in the Siena algorithm (Snijders, 2017a) to find probability functions underlying network dynamics.…”

Section: Parameter Estimationmentioning

confidence: 99%

Simulating Hidden Dynamics. Introducing Agent-Based Models as a tool for linkage analysis

Wettstein¹

2019

Preprint

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Linkage analyses use data from panel surveys and content analyses to assess media effects under field conditions and are able to close the gap between experimental and survey-based media effects research. Results from current studies and simulations indicate, however, that these studies systematically under-estimate real media effects as they aggregate measurement errors and reduce the complexity of media content. In response to these issues, we propose a new method for linkage analysis which applies agent-based simulations to directly assess short-term media effects using empirical data as guideposts.Results from an example study modeling opinion dynamics in the run-up of a Swiss referendum show that this method outperforms traditional regression-based linkage analyses in detail and explanatory power. In spite of the time-consuming modeling and computation process, this approach is a promising tool to study individual media effects under field conditions.

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Stochastic Actor-Oriented Models for Network Dynamics

Cited by 168 publications

References 80 publications

Change we can believe in: Comparing longitudinal network models on consistency, interpretability and predictive power

Change we can believe in: Comparing longitudinal network models on consistency, interpretability and predictive power

Solidarity in collaboration networks when everyone competes for the strongest partner: a stochastic actor-based simulation model

Simulating Hidden Dynamics. Introducing Agent-Based Models as a tool for linkage analysis

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