Quantitative analysis on L.-K. Hua's input-output model

In his celebrating input-output model, Loo-Keng Hua's revealed a profound and original economic phenomena, which indicated that eventually there existed at least one product used up. This was called the 'collapse' theorem. Mu-Fa Chen gave a Markov chain proof for the 'collapse' theorem. In this paper, we found a new and equivalent quantity description of the collapse, from which we gave the quantity estimate for the upper bound of the collapse time. The method was to use the strong ergodicity for the Markov chain. The absorbing Markov chain was introduced to describe the input-output model with consumption, and the continuous-time evolution equation was also presented to settle the real production process.

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Publication Details

Journal
Discrete and Continuous Dynamical Systems - S
Published
2025-01-01
DOI
https://doi.org/10.3934/dcdss.2025093
Primary Topic
Advanced Sensor and Control Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Quantitative analysis on L.-K. Hua's input-output model

Yong-Hua Mao, Qian Du, Cen-Yu Liao
Discrete and Continuous Dynamical Systems - S
Advanced Sensor and Control Systems
article

Quantitative analysis on L.-K. Hua's input-output model

Yong-Hua Mao, Qian Du, Cen-Yu Liao
article en

Abstract

In his celebrating input-output model, Loo-Keng Hua's revealed a profound and original economic phenomena, which indicated that eventually there existed at least one product used up. This was called the 'collapse' theorem. Mu-Fa Chen gave a Markov chain proof for the 'collapse' theorem. In this paper, we found a new and equivalent quantity description of the collapse, from which we gave the quantity estimate for the upper bound of the collapse time. The method was to use the strong ergodicity for the Markov chain. The absorbing Markov chain was introduced to describe the input-output model with consumption, and the continuous-time evolution equation was also presented to settle the real production process.

Discrete and Continuous Dynamical Systems - SVol. 18(10)
Beijing Normal University (CN)
Strong, National Natural Science Foundation of China
Openalex Percentile: Top 85%
Advanced Sensor and Control Systems
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