Back to the Future: Economic Self-Organisation and Maximum Entropy Prediction

Barde, Sylvain

doi:10.1007/s10614-014-9422-2

Cited by 30 publications

(20 citation statements)

References 26 publications

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“…Scholars have analyzed the mechanism of the Internet industry on economic growth from a theoretical and empirical perspective and measured the influence of the Internet industry on economic growth. e literature [4] empirically analyzed the pulling and supporting effects of the Internet industry on the national economy. e literature [5] conducted the cointegration test and the Granger causality test on the relationship between the information industry and economic growth and established a corresponding error correction model.…”

Section: Related Workmentioning

confidence: 99%

Economic Market Fluctuation Model Based on Internet of Things Technology

Zhai

2022

Scientific Programming

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In order to explore the impact of the Internet of Things technology on economic market fluctuations and the analysis effect of the Internet of Things technology on economic market fluctuations, this paper uses the Internet of Things algorithm to improve the economic fluctuation model. Moreover, this paper uses the Internet of Things algorithm to locate economic transactions and performs data processing to optimize the intelligent network system to improve the operating effect of the economic system. In addition, this paper improves the sensor node algorithm and proposes to use the weighted value of network node density to balance the positioning problem caused by the unbalanced distribution of network nodes in the detection area. Finally, this paper analyzes the market economy volatility model through the Internet of Things technology, combined with simulation experiments to explore the application of the Internet of Things technology in the economic market volatility model. Through experimental research, it can be known that economic market fluctuation models based on Internet of Things technology can play an important role in market economic analysis.

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Section: Related Workmentioning

confidence: 99%

Economic Market Fluctuation Model Based on Internet of Things Technology

Zhai

2022

Scientific Programming

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“…The main aim of the paper is to apply the image processing interpretation of the Maximum Entropy (MaxEnt) method to the Kirman (1993) model and the Abrams and Strogatz (20003) voter model as implemented by Stauffer et al (2007). This follows the initial work in Barde 2012 which showed that the Schelling (1969) model of segregation can be predicted with the methodology. The discussant does point out some of the major issues that are associated with the methodology, many of which I agree with.…”

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confidence: 63%

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Barde¹

2012

Revue De l'OFCE

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“…Moreover, it concluded that one of the indicators of the economic basis has the greatest contribution to the rural financial ecological environment, followed by the financial status, the degree of social security, the legal environment, and the social integrity culture. The literature [7] believed that the rural financial ecological environment has a strong correlation with financial efficiency, and the social cultural environment and social credit environment that constitute the rural financial ecological environment have a significant impact on financial efficiency. Moreover, its research found that the rural financial ecological environment in our country is very fragile, which leads to low efficiency of rural finance.…”

Section: Related Workmentioning

confidence: 99%

“…The rotation methods mainly include orthogonal rotation and oblique rotation, which makes the factor easier to interpret. (7) The algorithm determines the factor model and obtains the comprehensive score. The general expression is:…”

Section: Related Workmentioning

confidence: 99%

Evaluation of rural financial ecological environment based on machine learning and improved neural network

Wen

Zhang

2021

Neural Comput & Applic

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In order to improve the effect of rural financial ecological environment evaluation, this paper combines machine learning and improved neural network algorithms to construct a rural financial ecological environment evaluation system. First, this paper optimizes the input layer structure and its initial weight random assignment. The input layer structure is processed by factor analysis, and the initial weight random assignment is optimized by particle swarm optimization. Secondly, this paper constructs a rural financial ecological environment evaluation model based on factor analysis method and PSO-SOM to avoid the defects of traditional SOM algorithm used in financial ecological environment evaluation research. Finally, this paper constructs a system framework based on actual needs and designs experiments to verify the performance of the evaluation system constructed in this paper. The research results show that the system constructed in this paper meets the basic needs of rural financial ecological environment evaluation.

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Back to the Future: Economic Self-Organisation and Maximum Entropy Prediction

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 30 publications

References 26 publications

Economic Market Fluctuation Model Based on Internet of Things Technology

Economic Market Fluctuation Model Based on Internet of Things Technology

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Evaluation of rural financial ecological environment based on machine learning and improved neural network

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