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Decision makers and socializers, social networks and the role of individuals as participants

机译：决策者和社交者，社交网络以及个人作为参与者的角色

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摘要

Models explaining and predicting human travel behavior have gone through many changes in the past few decades. As researchers attempt to explain more and predict with more accuracy, the inclusion of social interactions in modeling and simulation is being recognized as a necessity. Among these efforts, researchers have focused on issues such as the composition of social networks, and the constraints and influences that others have on spatial decisions. An important aspect that has been understudied however is the variability or heterogeneity of individuals both as social network members and as participants in these social networks. Understanding the role individuals play in decision-making in different social networks can further define our models to include more accurate representations of human behavior. This research explores the differences between social network composition, and the decision roles members play within different social networks specifically when deciding where to participate in activities. A survey was conducted in Santa Barbara, California on social network involvement, network attributes and decision-making roles within each network. Two separate latent class cluster analysis models were developed to classify social network involvement and roles. Results show that there are clearly different types of social involvement and roles within networks. Further data collection and analysis will be used to better understand how these decision-making roles manifest themselves in activity decision-making.
机译：在过去的几十年中，解释和预测人类出行行为的模型经历了许多变化。随着研究人员试图进行更多的解释和更准确的预测，将社交互动纳入建模和仿真已被认为是必要的。在这些努力中，研究人员将重点放在诸如社交网络的组成以及其他人对空间决策的约束和影响等问题上。然而，已经被研究的一个重要方面是作为社交网络成员和这些社交网络参与者的个人的变异性或异质性。了解个人在不同社交网络中的决策中所扮演的角色，可以进一步定义我们的模型，以包含人类行为的更准确表示。这项研究探讨了社交网络组成之间的差异，以及成员在决定参与活动的地点时，在不同社交网络中扮演的决策角色。在加利福尼亚州圣塔芭芭拉市进行了一项有关社交网络参与度，网络属性和每个网络内决策角色的调查。开发了两个单独的潜在类聚类分析模型来对社交网络的参与和角色进行分类。结果表明，网络内部显然存在不同类型的社会参与和角色。进一步的数据收集和分析将用于更好地理解这些决策角色如何在活动决策中表现出来。

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