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Navigating the hype wave: a generalized economic-mathematical model for managing socio-economic processes in the neural-network economy

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  • Wanting Zhang
  • Dyatlov Sergey Alekseevich
  • Minakov Vladimir Fedorovich
  • Selishcheva Tamara Alekseevna
  • Ehsan Elahi

Abstract

The main objective of this study is to establish a comprehensive model for analysing how socio-economic processes are shaped by hype. Utilizing market indicators, the research exposes unusual patterns in stock prices, supply, and demand, highlighting the impact of hype. It elucidates the generation of hype cycles through two primary mechanisms: the dynamics of sales volumes and information dissemination, and the emergence of counteracting trends. This interaction precipitates inflation and subsequent bursts of market bubbles. A significant outcome of this study is the development of an economic-mathematical model that integrates material and financial resources, information flows, and cognitive behaviours of individuals across diverse markets, including social media. The empirical evidence demonstrates that a 1% increase in hype-driven investor sentiment leads to a 3% surge in asset prices. Moreover, it shows that socio-economic adjustments, such as a 10% increase in social media sentiment or a 5% change in regulatory policies, can significantly influence market indicators by up to 15%. These findings highlight the critical interplay between hype, decision-making, and socio-economic factors in market behaviour. This model serves as a vital tool for policymakers and market participants to understand and counteract the destabilizing effects of hype on markets.

Suggested Citation

  • Wanting Zhang & Dyatlov Sergey Alekseevich & Minakov Vladimir Fedorovich & Selishcheva Tamara Alekseevna & Ehsan Elahi, 2025. "Navigating the hype wave: a generalized economic-mathematical model for managing socio-economic processes in the neural-network economy," Applied Economics, Taylor & Francis Journals, vol. 57(33), pages 4916-4937, July.
  • Handle: RePEc:taf:applec:v:57:y:2025:i:33:p:4916-4937
    DOI: 10.1080/00036846.2024.2364104
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