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Bounded Rationality, Social Learning and Collective Behavior: Decisional Analysis in a Nested World

机译:有限的理性,社会学习和集体行为:嵌套世界中的决策分析

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People are usually brought together in a social network to make synergetic decisions. This decision making process often involves information acquisition and social learning, which are essential to overcome individuals'bounded rationality. The performance of a society thus depends on the collective behavior of individuals. Besides information attributes, organizational properties often influenced such a decision process. In this article, we introduce a paradigm - nested world - that treats social network as a symbolic system. Based on this paradigm, we developed a research model to investigate how information attributes, social parameters, and their interactions influenced the performance of a social network. This research model was subsequently converted to a computational model for analysis and validation. Our findings suggested that informativeness, network density, social influence, and their interactions had significant influence on the performance of whole society. Besides these findigns, many interesting phenomenon were also observed, including significant social learning curve, U-shape decision speed, threshold of network density, and inter changeability between network density and social influence.
机译:人们通常在社交网络中汇集在一起​​,以使协调决定。该决策过程往往涉及信息获取和社会学习,这对于克服个人的合理性至关重要。因此,社会的表现取决于个人的集体行为。除了信息属性,组织属性通常会影响此类决策过程。在本文中,我们介绍了一个范式嵌套的世界 - 将社交网络视为象征系统。基于此范例,我们开发了一种研究模型,用于调查信息属性,社会参数及其互动的方式影响社交网络的表现。随后将该研究模型转换为用于分析和验证的计算模型。我们的研究结果表明,信息性,网络密度,社会影响力及其互动对整个社会的表现产生了重大影响。除了这些发现之外,还观察到许多有趣的现象,包括显着的社会学习曲线,U形决定速度,网络密度的阈值,以及网络密度与社会影响之间的间差异性。

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