Dual Process Theory of Decision Making: A Social Network Analysis

• Main Authors: Liu, Chia-Hung, 劉家宏
• Other Authors: Liang, Ting-Peng
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/z23349

碩士 === 國立政治大學 === 資訊管理學系 === 106 === Human behavior and decisions are deeply influenced by our thinking mechanisms. In recent years, scholars have investigated the decision making mechanism under different circumstances. A few theories have been proposed. One of which is the dual systems theory of the brain that divides human decision process into two subsystems: one is faster and intuitive while the other is slower and reasoning. System 1 is more automatic and heuristics-based, while system 2 is more deliberate and logical. A number of prior studies have revealed that these two systems coexist and are employed in different decision tasks. Cognitive theories called the dual process theories are also developed based on the dual systems model of the brain. A large volume of papers in business and decision sciences have been published based on the dual process theories. However, most of them are behavioral in nature that derives interpretations from questionnaire survey or experimental data. Recent development in cognitive neural science has allowed us to further examine how different brain areas are activated through the use of special instruments such as functional magnetic reasoning Imaging (fMRI) to better understand this dual systems model. Many research results under different contexts have been reported, but different experimental settings and the complexity of the human brains often result in inconsistent observations that are hard to see the complete picture. The purpose of this study is to conduct a meta-analysis on existing studies that adopted the dual systems theory to develop a better understanding of how these two subsystem works. We collected experimental results of published literatures and aggregated their findings with the social network analysis, which is a data mining technique used for finding relationships among objects. The circuits of both subsystems are derived and evaluated. The result allows us to better understand the collaboration of brain areas in these two systems.