Abstract
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 interchangeability between network density and social influence.
Recommended Citation
Hu, Hao; Lai, Siu-king; and Jia, Yanli, "Bounded Rationality, Social Learning and Collective Behavior: Decisional Analysis in a Nested World" (2011). ICIS 2011 Proceedings. 3.
https://aisel.aisnet.org/icis2011/proceedings/economicvalueIS/3
Bounded Rationality, Social Learning and Collective Behavior: Decisional Analysis in a Nested World
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 interchangeability between network density and social influence.