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Learning And Predicting Individual Preferences In Multicriteria Decision Making With Neural Networks Vs. Utility Functions

Dat‐Dao Nguyen

Journal of Business and Economics Research2016https://doi.org/10.19030/jber.v15i1.9854article
ABDC C
Weight
0.34

What the paper says

This paper reports an empirical investigation into the performance of neural network technique vs. traditional utility theory-based method in capturing and predicting individual preference in multi-criteria decision making. As a universal function approximator, a neural network can assess individual utility function without imposing strong assumptions on functional form and behavior of the underlying data. Results of this study show that in all cases, the predictive ability of neural network technique was comparable to the multi-attribute utility theory-based models.

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https://doi.org/https://doi.org/10.19030/jber.v15i1.9854

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@article{dat‐dao2016,
  title        = {{Learning And Predicting Individual Preferences In Multicriteria Decision Making With Neural Networks Vs. Utility Functions}},
  author       = {Dat‐Dao Nguyen},
  journal      = {Journal of Business and Economics Research},
  year         = {2016},
  doi          = {https://doi.org/https://doi.org/10.19030/jber.v15i1.9854},
}

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Learning And Predicting Individual Preferences In Multicriteria Decision Making With Neural Networks Vs. Utility Functions

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Evidence weight

0.34

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.