University of Oulu

An extended model of decision field theory integrated with AHP structure for complex decision making problems

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Author: Shao, Lan1
Organizations: 1University of Oulu, Faculty of Information Technology and Electrical Engineering, Department of Information Processing Science, Information Processing Science
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.9 MB)
Pages: 78
Persistent link:
Language: English
Published: Oulu : L. Shao, 2015
Publish Date: 2015-05-26
Thesis type: Master's thesis
Tutor: Markkula, Jouni
Reviewer: Markkula, Jouni
Kelanti, Markus
Decision Field Theory (DFT) provides an approach to explain the deliberation process of decision making under dynamic environment. However, the performance of original DFT theory is imperfect when the dynamic environment is getting complex. This research is aimed to build an extended model of DFT with good explanation and prediction abilities under complex dynamic environment. The dynamic structure of Analytic Hierarchy Process (AHP) was used in order to improve the flexibility and adaptability of extended model. In this study, the systematic literature review (SLR) was conducted to explore the previous research in dynamic decision making field. The review protocol, regarding to review questions, review purpose and review method was developed during planning phase. After performing several steps of selection, 62 primary studies were selected. According to the results of analysis, limited number of primary studies are related to the practical application of DFT in specific context. Therefore, it is necessary to extend the DFT model. In practice, class attending behavior of students was selected, as one example of complex dynamic making problems, to evaluate the extended model. In order to collect relevant data of decision making, three rounds of web survey were conducted. The students from University of Oulu are the respondents of the web survey. In conclusion, the analysis results of data proved that proposed model is able to explain and predict the dynamic behavior of decision making well. This research opens space for future research about model studying and building.
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