1664. Data-Driven Decision Making in a Social Network Environment
Contributed abstract in session MA-33: Social Networks and Decisions, stream Decision Analysis.
Monday, 8:30-10:00Room: Maurice Keyworth 1.31
Authors (first author is the speaker)
| 1. | Yu-wang Chen
|
| Alliance Manchester Business School, The University of Manchester | |
| 2. | Tao Wen
|
| Alliance Manchester Business School, The University of Manchester | |
| 3. | Ting Wu
|
| Department of Operations, Technology, Events and Hospitality Management, Manchester Metropolitan University |
Abstract
Data-driven decision-making is widely recognised as fundamental to enhancing decision-making process and outcomes in business and management. On one hand, it enables decision-makers to respond more efficiently to uncertain and rapidly changing environments, improving both the decision-making process and outcomes. On the other hand, social network interactions have become an integral part of daily life, adding complexity to the analysis of decision-making behaviours. In a social network environment, individuals often have diverse prior beliefs and communicate with their social connections to make informed decisions.
In this study, we aim to develop a holistic framework for analysing decision-making in a social network. This framework explores how individual decision-makers implicitly form their initial beliefs within the paradigm of multiple criteria decision-making, interact with others, and exchange beliefs, leading to dynamic belief updating. Ultimately, individuals make decisions, shaping group opinion dynamics and decision behaviours within the social network. The framework is expected to provide valuable insights into analysing decision-making in social networks and offer guidance for future research and real-world applications that integrate decision sciences with social network analysis.
Keywords
- Decision Analysis
- Social Networks
- Analytics and Data Science
Status: accepted
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