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Applying Deep Learning Models to Consumer Choice Theory in Western Economics: A Case Study on Consumer Preference Prediction

In: Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024)

Author

Listed:
  • Sicheng Geng

    (Harbin University of Commerce, Finance School)

  • Fangfei Liu

    (Harbin University of Commerce, School of Economics)

  • Meilin Gong

    (Harbin University of Commerce, College of Public Finance and Administration)

  • Tianhai Wu

    (Harbin University of Commerce, Finance School)

  • Yufei Li

    (Harbin University of Commerce, College of Public Finance and Administration)

Abstract

This paper explores the application of Multilayer Perceptron (MLP) models in predicting consumer preferences within the framework of Western economics’ consumer choice theory. Leveraging a dataset encompassing a wide array of consumer choices across different product categories, this study employs an MLP to simulate and forecast consumer behavior in response to varying prices and product characteristics. This research contributes to the growing body of literature at the intersection of artificial intelligence and economics, offering a novel approach to understanding and predicting consumer behavior.

Suggested Citation

  • Sicheng Geng & Fangfei Liu & Meilin Gong & Tianhai Wu & Yufei Li, 2024. "Applying Deep Learning Models to Consumer Choice Theory in Western Economics: A Case Study on Consumer Preference Prediction," Advances in Economics, Business and Management Research, in: Qiujing Wu & Songsong Liu & Guoliang Wang & Jia Li (ed.), Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024), pages 436-446, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-598-0_44
    DOI: 10.2991/978-94-6463-598-0_44
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