Relevant bibliographies by topics / Stochastic-actor oriented model

Academic literature on the topic 'Stochastic-actor oriented model'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Stochastic-actor oriented model"

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Lospinoso, Josh, and Tom AB Snijders. "Goodness of fit for stochastic actor-oriented models." Methodological Innovations 12, no. 3 (September 2019): 205979911988428. http://dx.doi.org/10.1177/2059799119884282.

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We propose a Mahalanobis distance–based Monte Carlo goodness of fit testing procedure for the family of stochastic actor-oriented models for social network evolution. A modified model distance estimator is proposed to help the researcher identify model extensions that will remediate poor fit. A limited simulation study is provided to establish baseline legitimacy for the Mahalanobis distance–based Monte Carlo test and modified model distance estimator. A forward model selection workflow is proposed, and this procedure is demonstrated on a real data set.
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INDLEKOFER, NATALIE, and ULRIK BRANDES. "Relative importance of effects in stochastic actor-oriented models." Network Science 1, no. 3 (December 2013): 278–304. http://dx.doi.org/10.1017/nws.2013.21.

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AbstractA measure of relative importance of network effects in the stochastic actor-oriented model (SAOM) is proposed. The SAOM is a parametric model for statistical inference in longitudinal social networks. The complexity of the model makes the interpretation of inferred results difficult. So far, the focus is on significance tests while the relative importance of effects is usually ignored. Indeed, there is no established measure to determine the relative importance of an effect in a SAOM. We introduce such a measure based on the influence effects have on decisions of individual actors in the network. We demonstrate its utility on empirical data by analyzing an evolving friendship network of university freshmen.
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Niezink, Nynke M. D., Tom A. B. Snijders, and Marijtje A. J. van Duijn. "No Longer Discrete: Modeling the Dynamics of Social Networks and Continuous Behavior." Sociological Methodology 49, no. 1 (May 9, 2019): 295–340. http://dx.doi.org/10.1177/0081175019842263.

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The dynamics of individual behavior are related to the dynamics of the social structures in which individuals are embedded. This implies that in order to study social mechanisms such as social selection or peer influence, we need to model the evolution of social networks and the attributes of network actors as interdependent processes. The stochastic actor-oriented model is a statistical approach to study network-attribute coevolution based on longitudinal data. In its standard specification, the coevolving actor attributes are assumed to be measured on an ordinal categorical scale. Continuous variables first need to be discretized to fit into such a modeling framework. This article presents an extension of the stochastic actor-oriented model that does away with this restriction by using a stochastic differential equation to model the evolution of a continuous attribute. We propose a measure for explained variance and give an interpretation of parameter sizes. The proposed method is illustrated by a study of the relationship between friendship, alcohol consumption, and self-esteem among adolescents.
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Kalish, Yuval. "Stochastic Actor-Oriented Models for the Co-Evolution of Networks and Behavior: An Introduction and Tutorial." Organizational Research Methods 23, no. 3 (January 23, 2019): 511–34. http://dx.doi.org/10.1177/1094428118825300.

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Stochastic actor-oriented (SAO) models are a family of models for network dynamics that enable researchers to test multiple, often competing explanations for network change and estimate the extent and relative power of various influences on network evolution. SAO models for the co-evolution of network ties and actor behavior, the most comprehensive category of SAO models, examine how networks and actor attributes—their behavior, performance, or attitudes—influence each other over time. While these models have been widely used in the social sciences, and particularly in educational settings, their use in organizational scholarship has been extremely limited. This paper provides a layperson introduction to SAO models for the co-evolution of networks and behavior and the types of research questions they can address. The models and their underpinnings are explained in nonmathematical terms, and theoretical explanations are supported by a concrete, detailed example that includes step-by-step model building and hypothesis testing, alongside an R script.
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Westra, Daan, Federica Angeli, Martin Carree, and Dirk Ruwaard. "Does Price Competition Drive Cooperation in Health Care? A Stochastic Actor Oriented Model Analysis." Academy of Management Proceedings 2016, no. 1 (January 2016): 12839. http://dx.doi.org/10.5465/ambpp.2016.12839abstract.

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Leifeld, Philip, and Skyler J. Cranmer. "A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model." Network Science 7, no. 1 (March 2019): 20–51. http://dx.doi.org/10.1017/nws.2018.26.

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AbstractThe temporal exponential random graph model (TERGM) and the stochastic actor-oriented model (SAOM, e.g., SIENA) are popular models for longitudinal network analysis. We compare these models theoretically, via simulation, and through a real-data example in order to assess their relative strengths and weaknesses. Though we do not aim to make a general claim about either being superior to the other across all specifications, we highlight several theoretical differences the analyst might consider and find that with some specifications, the two models behave very similarly, while each model out-predicts the other one the more the specific assumptions of the respective model are met.
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Leifeld, Philip, and Skyler J. Cranmer. "A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model – Corrigendum." Network Science 10, no. 1 (March 2022): 111. http://dx.doi.org/10.1017/nws.2022.11.

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Block, Per, Christoph Stadtfeld, and Tom A. B. Snijders. "Forms of Dependence: Comparing SAOMs and ERGMs From Basic Principles." Sociological Methods & Research 48, no. 1 (November 17, 2016): 202–39. http://dx.doi.org/10.1177/0049124116672680.

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Two approaches for the statistical analysis of social network generation are widely used; the tie-oriented exponential random graph model (ERGM) and the stochastic actor-oriented model (SAOM) or Siena model. While the choice for either model by empirical researchers often seems arbitrary, there are important differences between these models that current literature tends to miss. First, the ERGM is defined on the graph level, while the SAOM is defined on the transition level. This allows the SAOM to model asymmetric or one-sided tie transition dependence. Second, network statistics in the ERGM are defined globally but are nested in actors in the SAOM. Consequently, dependence assumptions in the SAOM are generally stronger than in the ERGM. Resulting from both, meso- and macro-level properties of networks that can be represented by either model differ substantively and analyzing the same network employing ERGMs and SAOMs can lead to distinct results. Guidelines for theoretically founded model choice are suggested.
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Kang, Ahrom, Joongsan Oh, and Chulsoon Park. "Evolution of the Korean Automotive Industry Supply Network: An Exploratory Study Based on SAOM (Stochastic Actor-Oriented Model)*." Korean Production and Operations Management Society 33, no. 4 (December 30, 2022): 699–721. http://dx.doi.org/10.32956/kopoms.2022.33.4.699.

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Most studies on the supply network structure have focused on revealing the “stationary’ network structure based on cross-sectional data. However, when the supply network is defined as a complex adaptive system that is a dynamic process, it is particularly important to explore its structure and evolution. Therefore, this study empirically analyzed how and through which micro-mechanisms the traditional vertical structure of the Korean automotive industry supply network is evolving into a horizontal structure. The results confirmed that new firms were continuously entering the Korean automotive industry supply network, and transaction relationships that were not found in the traditional supply chain structure were increasing. Additionally, among the existing suppliers, high-performing suppliers continued to form new transaction relationships while maintaining existing relationships. The significance of this study is as follows. SAOM was applied to the supply network of the Korean automotive industry for the first time, and the existing vertical and linear relationship studies were expanded from a network perspective. Furthermore, unlike the existing supply network-related studies, which were limited to an analysis at a specific point in time, the dynamic evolution of the supply network was identified.
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Xing, Lizhi, and Wen Chen. "Structural Characteristics and Evolutionary Drivers of Global Virtual Water Trade Networks: A Stochastic Actor-Oriented Model for 2000–2015." International Journal of Environmental Research and Public Health 20, no. 4 (February 12, 2023): 3234. http://dx.doi.org/10.3390/ijerph20043234.

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The globalization of trade has caused tremendous pressure on water resources globally, and a virtual water trade provides a new perspective on global freshwater sharing and water sustainability. No study has yet explored the structural characteristics and drivers of the evolution of global virtual water trade networks from a network structure evolution perspective. This paper aims to fill this critical gap by developing a research framework to explore how endogenous network structures and external factors have influenced the evolution of virtual water trade networks. We constructed virtual water trade networks for 62 countries worldwide from 2000 to 2015 and used an innovative combination of multi-regional input–output data and stochastic actor-oriented models for analytical purposes. Our results support the theoretical hypothesis of ecologically unequal exchange and trade drivers, arguing that virtual water flows from less developed countries to developed countries under global free trade and that unequal trade patterns lead to excessive consumption of virtual water in less developed countries. The results partially support the theoretical content of water endowment and traditional gravity models, finding that trade networks are expanding to farther and larger markets, confirming that national water scarcity levels do not impact the evolution of virtual water trade networks. Finally, we point out that meritocratic links, path dependence, reciprocity, and transmissive links have extreme explanatory power for the evolutionary development of virtual water networks.
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Dissertations / Theses on the topic "Stochastic-actor oriented model"

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AMATI, VIVIANA. "New statistics for the parameters estimation of the stochastic actor-oriented model for network change." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19389.

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The Stochastic actor-oriented model (SAO) is a statistical model for longitudinal network data. The most often used procedure for the estimation of the parameter of the SAO model is the Method of Moments (MoM), which estimates the parameters using one observed statistic for each estimated parameter. A new set of statistics is defined taking into account the different ways of creating and deleting ties to which a certain effect can contribute. This definition leads to having more than one statistic for a single parameter, i.e. to an over-determined system of equations. Thus, the ordinary MoM cannot be applied. A suitable method then is the Generalized Method of Moments (GMM), an estimation technique mainly used in econometrics, and potentially more efficient than the MoM. Like the regular MoM, the GMM is based on the differences between the expected values of the statistics and their sample counterparts, but the GMM involves the minimization of a quadratic function of these differences rather than setting all differences to 0. This means that an extra problem arises: the determination of a matrix of weights reflecting the different importance and correlations of the statistics involved. An optimization-simulation algorithm is used, following the approach suggested by Gelman (1995) and based on the Newton-Raphson algorithm, to compare the estimators deriving from the MoM and the GMM. Simulation results suggest that the new set of statistics performs better when network observations are close. In fact, in this context the standard errors of the GMM estimators are lower than those of the MoM.
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Hagala, Jennifer A. "HOW FAR WILL YOU GO WHEN THERE IS AN EMBARGO?A STOCHASTIC ACTOR-ORIENTED MODEL OF THE EFFECTS OF ARMS EMBARGOS ON ILLICIT WEAPONS TRADE." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/538.

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The international community routinely implements embargoes in an effort to restrict the flow of small arms and light weapons into politically unstable regions. The effectiveness of sanctions fall into question when reports indicate that weapons continue to pour into embargoed territories. Using stochastic actor-based modeling, the current study investigates how shipment patterns change over time, and how trade patterns evolve in the presence of endogenous influences, such as embargoes, while controlling for corruption levels and national wealth (e.g., gross domestic product). The analysis here indicated that embargoes did have an effect in diverting illicit weapons trade through indirect ties. This was seen highest during the embargo implementation period and post embargo implementation period. The results stand to improve our understanding of this complex illegal global market and the role national control of corruption and gross domestic product play in the enforceability of these sanctions. In the final analysis what was discovered was that embargoes do effect change in the illegal arms trade network. This effect is seen in the form of indirect ties to end user countries. This suggests that improvements to policies and regulation on transshipment points need to be highly scrutinized.
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Indlekofer, Natalie [Verfasser]. "Methods for Diagnosis and Interpretation of Stochastic Actor-oriented Models for Dynamic Networks / Natalie Indlekofer." Konstanz : Bibliothek der Universität Konstanz, 2014. http://d-nb.info/1049892860/34.

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Block, Per. "A situational understanding of friendship networks." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:4b954504-4bd2-4cef-a949-8e102d05939d.

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The structure of social networks, and people's position within these networks, are important predictors of many individual and group-level outcomes. One type of social networks that is regularly studied are the mutually interdependent relations of friends. This thesis focusses on friendship networks between adolescents in the context of schools. Arguably the most important and consistently found regularities in adolescent friendship networks are i) the tendency of friendships to be mutual, called reciprocity; ii) their tendency to cluster in groups, known as transitivity; and iii) the tendency of friendships to be present between those that are similar to one another, called homophily. Various social theories originating in different disciplines have theoretically proposed and empirically found micro-mechanisms that explain the regular occurrence of these substructures in friendship networks. This thesis introduces a framework of how the relation between these different networks tendencies can be understood. I propose that each of the three network evolution mechanisms can be connected to a type of social situation in which friends interact to form and maintain their friendships. Social situations that are dyadic and only involve two persons are connected to reciprocal friendships. Group-based social situations, on the other hand, are related to transitivity and homophily, where the groups are either defined socially or through common characteristics. Starting from this proposition, I suggest that when two adolescents share one forum for interaction with one another, i.e. they regularly meet within one of the social situations, meeting in additional other situations does not increase the likelihood of a friendship tie existing as much as could be expected from the sum of the effect of meeting in either situation. Consequently, I expect a negative interaction between the different network mechanisms. After a series of empirical analyses that support the outlined reasoning, I use the developed perspective to investigate how the micro-mechanisms contribute differentially to the creation of newly formed friendships and to the maintenance of already existing friendships. Finally, I show how a situational understanding of friendship can be used to differentiate which friendships are most important for social influence and for peer pressure.
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Fisher, David Newton. "Social networks and individual behaviour variation in wild crickets." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/21128.

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Individuals engage in competitive and cooperative interactions with conspecifics. Furthermore, within any population of interacting individuals there are typically consistent differences among-individuals in behavioural traits. Understanding the importance of both these types of individual-specific behaviours allows us to understand why populations are structured as they are, why individuals show apparently limited behavioural flexibility, and how these elements link to population-level properties. I used extensive video camera monitoring of a population of wild field crickets (Gryllus campestris) to study the interactions and behaviours of uniquely identified individuals. I studied the shyness, activity and exploration of individuals of this population across contexts: from young to old and between captivity and the wild. This allowed me to confirm that individuals were relatively consistent across their adult lifetimes for all three traits, but only consistent between captivity and the wild for activity and exploration. I then found that high activity levels were positively related to high mating rates and short lifespans. Crucially, lifetime mating success was not related to activity level, indicating that the trade-off between lifespan and mating success was sufficient to allow variation in activity level to persist across generations. I also found that cricket social network structure is stable across generations despite the complete turnover of individuals every year. This social network structure influences sexual selection, with some male crickets heavily involved in networks of both pre- and post-copulatory competition, yet males are unable to use pre-copulatory competition to avoid post-copulatory competition. Additionally, positive assortment by mating rate between males and females may reduce the fitness of males with high mating rates, as they face stronger sperm competition. Finally, I used actor-based models to determine the factors predicting cricket social network structure and to test and reject the social-niche hypothesis for the maintenance of among-individual variation in behaviour. I also demonstrated that little else is needed in a stochastically changing network aside from positive assortment by mating rate to simulate a population with a similar skew in mating success to the one observed in the real cricket population. These results give insights into the importance of trade-offs and stochasticity in maintaining the extensive variation in the natural world.
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Shappell, Heather M. "Methods for longitudinal complex network analysis in neuroscience." Thesis, 2017. https://hdl.handle.net/2144/27343.

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The study of complex brain networks, where the brain can be viewed as a system with various interacting regions that produce complex behaviors, has grown tremendously over the past decade. With both an increase in longitudinal study designs, as well as an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for dynamic brain network analysis are needed. We first propose a paradigm for longitudinal brain network analysis over patient cohorts where we adapt the Stochastic Actor Oriented Model (SAOM) framework and model a subject's network over time as observations of a continuous time Markov chain. Network dynamics are represented as being driven by various factors, both endogenous (i.e., network effects) and exogenous, where the latter include mechanisms and relationships conjectured in the literature. We outline an application to the resting-state fMRI network setting, where we draw conclusions at the subject level and then perform a meta-analysis on the model output. As an extension of the models, we next propose an approach based on Hidden Markov Models to incorporate and estimate type I and type II error (i.e., of edge status) in our observed networks. Our model consists of two components: 1) the latent model, which assumes that the true networks evolve according to a Markov process as they did in the original SAOM framework; and 2) the measurement model, which describes the conditional distribution of the observed networks given the true networks. An expectation-maximization algorithm is developed for estimation. Lastly, we focus on the study of percolation - the sudden emergence of a giant connected component in a network. This has become an active area of research, with relevance in clinical neuroscience, and it is of interest to distinguish between different percolation regimes in practice. We propose a method for estimating a percolation model from a given sequence of observed networks with single edge transitions. We outline a Hidden Markov Model approach and EM algorithm for the estimation of the birth and death rates for the edges, as well as the type I and type II error rates.
2018-07-25T00:00:00Z
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Books on the topic "Stochastic-actor oriented model"

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Snijders, Tom A. B., and Mark Pickup. Stochastic Actor Oriented Models for Network Dynamics. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.10.

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Stochastic Actor Oriented Models for Network Dynamics are used for the statistical analysis of longitudinal network data collected as a panel. The probability model defines an unobserved stochastic process of tie changes, where social actors add new ties or drop existing ties in response to the current network structure; the panel observations are snapshots of the resulting changing network. The statistical analysis is based on computer simulations of this process, which provides a great deal of flexibility in representing data constraints and dependence structures. In this Chapter we begin by defining the basic model. We then explicate a new model for nondirected ties, including several options for the specification of how pairs of actors coordinate tie changes. Next, we describe coevolution models. These can be used to model the dynamics of several interdependent sets of variables, such as the analysis of panel data on a network and the behavior of the actors in the network, or panel data on two or more networks. We finish by discussing the differences between Stochastic Actor Oriented Models and some other longitudinal network models. A major distinguishing feature is the treatment of time, which allows straightforward application of the model to panel data with different time lags between waves. We provide a variety of applications in political science throughout.
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Desmarais, Bruce A., and Skyler J. Cranmer. Statistical Inference in Political Networks Research. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.8.

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Researchers interested in statistically modeling network data have a well-established and quickly growing set of approaches from which to choose. Several of these methods have been regularly applied in research on political networks, while others have yet to permeate the field. This chapter reviews the most prominent methods of inferential network analysis for both cross-sectionally and longitudinally observed networks, including (temporal) exponential random graph models, latent space models, the quadratic assignment procedure, and stochastic actor oriented models. For each method, the chapter summarizes its analytic form, identifies prominent published applications in political science, and discusses computational considerations. It concludes with a set of guidelines for selecting a method for a given application.
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Book chapters on the topic "Stochastic-actor oriented model"

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Arata, Yoshiyuki, Abhijit Chakraborty, Yoshi Fujiwara, Hiroyasu Inoue, Hazem Krichene, and Masaaki Terai. "Shock Propagation Through Customer-Supplier Relationships: An Application of the Stochastic Actor-Oriented Model." In Studies in Computational Intelligence, 1100–1110. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72150-7_89.

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Mohrenberg, Steffen. "Studying Policy Diffusion with Stochastic Actor-Oriented Models." In Networked Governance, 163–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50386-8_10.

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Windzio, Michael. "Snijders (1996): Stochastic Actor-oriented Models for Network Change." In Schlüsselwerke der Netzwerkforschung, 515–18. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-21742-6_121.

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"STOCHASTIC ACTOR-ORIENTED MODELS FOR NETWORK CHANGE." In Evolution of Social Networks, 193–216. Routledge, 2013. http://dx.doi.org/10.4324/9780203059500-13.

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Asal, Victor, Brian J. Phillips, and R. Karl Rethemeyer. "Longitudinal Modeling of Insurgent Alliances." In Insurgent Terrorism, 155–88. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780197607015.003.0008.

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This chapter uses stochastic actor-oriented models to analyze the factors that shape the alliances that organizations form and keep with other insurgents. Given the assumption that insurgents will behave differently given different global environments, analyses are conducted for theoretically relevant periods: the pre-September 11, 2001 (9/11) attack period (1998–2001), the post-9/11 time period (2002–2007), and the post-surge period (2008–2012), as the United States removed most of its troops from Iraq. Organizations are more likely to build ties with organizations in the same country and with those that are allies of their allies. In the first two time periods, organizations that are involved in terrorist attacks are more likely to make more ties. There are also several factors that drive alliance formation and persistence during both periods after 9/11. For example, older organizations make fewer alliances. Overall, alliance formation is strongly affected by both temporal and organization-specific attributes.
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Angelini, Pier Paolo. "The Role of Proximity in Inter-Organizational Network Evolution." In Relational Methodologies and Epistemology in Economics and Management Sciences, 232–56. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9770-6.ch008.

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This chapter presents and discusses some methodological issues in the application of stochastic actor-oriented modeling for longitudinal network analysis. By following a forward-selection procedure, three models will be defined and run on four observations of the collaboration network subsidized by the European Union Framework Programmes in the Aerospace sector, covering a 20-years time span (1994-2013). Specifically, the influence exerted by five dimensions of inter-organizational proximity (geographical, organizational, social, institutional and technological) on the longitudinal evolution of the network is analyzed. Results show that organizational proximity is the most important driver for the longitudinal evolution of the network. Further, this form of proximity is constant in time, analogously to the geographical one which, on its side, only moderately affects network's evolution. Network proximity plays a weak but positive influence, while the institutional and technological dimensions do not affect the evolution of the network. Anyway, when proximity is evaluated on single institutional and technological types, different roles are detected. Organizations' patenting activity, introduced as a control variable, does not play a significant role on network's evolution.
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