咨询网络中的状态、认知超载和不完全信息:一个基于代理的模型

IF 2.9 2区 社会学 Q1 ANTHROPOLOGY
Francesco Renzini, Federico Bianchi, Flaminio Squazzoni
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引用次数: 0

摘要

咨询通常通过非正式联系跨越组织边界进行。通过使用面向参与者的随机模型(SAOM),先前的研究试图识别这些非正式联系背后的微观机制。不幸的是,这些模型假设了完美的网络信息,要求代理执行认知要求太高的决策,并且没有考虑基于阈值的关键事件,例如同时的平局变化。在知识密集型组织的背景下,高技能专业人员的短缺可能决定复杂的网络效应,因为许多低技能专业人员会向少数容易过载、选择性高技能的人寻求建议,这些人也对地位降级敏感。为了捕捉这些特定于上下文的组织特征,我们用一个基于代理的模型详细介绍了SAOM,该模型假设本地信息、基于状态的关系选择和多个关系的同时重定向。通过将我们的模拟网络与先前研究中使用的Lazega的建议网络进行拟合,我们使用基于比先前研究更具实证合理性假设的简约模型再现了同一组宏观层面的网络指标。我们的发现显示了用不同的模型探索网络形成的多种生成路径的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.

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来源期刊
Social Networks
Social Networks Multiple-
CiteScore
5.90
自引率
12.90%
发文量
118
期刊介绍: Social Networks is an interdisciplinary and international quarterly. It provides a common forum for representatives of anthropology, sociology, history, social psychology, political science, human geography, biology, economics, communications science and other disciplines who share an interest in the study of the empirical structure of social relations and associations that may be expressed in network form. It publishes both theoretical and substantive papers. Critical reviews of major theoretical or methodological approaches using the notion of networks in the analysis of social behaviour are also included, as are reviews of recent books dealing with social networks and social structure.
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