Status, cognitive overload, and incomplete information in advice-seeking networks: An agent-based model

Advice-seeking typically occurs across organizational boundaries through informal connections. By using Stochastic Actor-Oriented Models (SAOM), previous research has tried to identify the micro-level mechanisms behind these informal connections. Unfortunately, these models assume perfect network in...

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Bibliographic Details
Published in:Social networks 2024-01, Vol.76, p.150-159
Main Authors: Renzini, Francesco, Bianchi, Federico, Squazzoni, Flaminio
Format: Article
Language:English
Subjects:
Advice-seeking
Agent-based modeling
Cognitive overload
Network formation
Status
Stochastic actor-oriented models
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Summary:Advice-seeking typically occurs across organizational boundaries through informal connections. By using Stochastic Actor-Oriented Models (SAOM), previous research has tried to identify the micro-level mechanisms behind these informal connections. Unfortunately, these models assume perfect network information, require agents to perform too cognitively demanding decisions, and do not account for threshold-based critical events, such as simultaneous tie changes. In the context of knowledge-intensive organizations, the shortage of high-skilled professionals could determine complex network effects given that many less-skilled professionals would seek advice from a few easily overloaded, selective high-skilled, who are also sensitive to status demotion. To capture these context-specific organizational features, we have elaborated on SAOM with an agent-based model that assumes local information, status-based tie selection, and simultaneous re-direction of multiple ties. By fitting our simulated networks to Lazega’s advice network used in previous research, we reproduced the same set of macro-level network metrics with a parsimonious model based on more empirically plausible assumptions than previous research. Our findings show the advantage of exploring multiple generative paths of network formation with different models. •Preferences for ties with high-status advisors drive advice network formation.•We present a model that considers local information and status-related tie selection.•We fit Lazega advice network metrics as in previous research with simpler assumptions.
ISSN:0378-8733
DOI:10.1016/j.socnet.2023.09.001