Extended decision field theory with social-learning for long-term decision-making processes in social networks
Extended decision field theory with social-learning for long-term decision-making processes in social networks
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DOI：
10.1016/j.ins.2019.10.025
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
Seunghan Lee;Y. Son
中科院分区：
文献类型：
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作者：
Seunghan Lee;Y. Son
对社交网络中的人类行为进行建模和分析在在线商务、营销和金融等领域是必不可少的。然而，由于个体之间的决策结构不同，建立一个广义的人类行为决策框架是具有挑战性的。因此，我们提出了一个新的决策框架，决策场理论与学习（DFT-L），它结合了DFT模型和DeGroot模型。我们研究了三个因素影响偏好的演变：以前的经验，目前的评价，和邻居的喜好。在权值独立同分布（IID）条件下，得到了该框架下社交网络的均衡状态的显式表达式.这有助于识别有限的预期偏好值和协方差矩阵。使用模拟和真实的网络进行仿真分析，以验证DFT-L框架，并证明其效率与原来的DFT相比。我们的发现证实了DFT-L内的扩散过程在随机网络中传播最快，在环格网络中传播最慢。我们还表明，人与人之间的互动影响代理的决策DFT-L和强化嵌入式社会的特点，这有助于分析不规则的行为，如信息级联在社交网络。
Modeling and analysis of human behaviors in social networks are essential in fields such as online business, marketing, and finance. However, the establishment of a generalized decision-making framework for human behavior is challenging due to different decision structures among individuals. Thus, we propose a new decision-making framework, Decision Field Theory with Learning (DFT-L), which combines the DFT model and the DeGroot model. We investigated three factors influencing preference evolution: previous experiences, current evaluations, and neighbors’ preferences. The equilibrium status of social networks within this framework is obtained as an explicit formula under the independent and identically distributed (IID) conditions on weight values. This facilitates the identification of limiting expected preference values and covariance matrices. A simulation analysis using simulated and real networks is performed to validate the DFT-L framework and to demonstrate its efficiency compared with the original DFT. Our finding confirms that the diffusion process within DFT-L propagates fastest in the random network and slowest in the ring-lattice network. We also show that interactions among people affect the agent's decision within DFT-L and intensify embedded society characteristics, which helps to analyze irregular behaviors such as information cascades in social networks.