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  • Зачем (и как) измерять экономическое поведение домашних хозяйств: о проекте НИУ ВШЭ «Экономическое поведение домашних хозяйств»
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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Зачем (и как) измерять экономическое поведение домашних хозяйств: о проекте НИУ ВШЭ «Экономическое поведение домашних хозяйств»

Вопросы экономики. 2026. № 5. С. 5–16.
Kuzminov Y., Ovcharova L., Radaev V.

This article introduces the special issue of the journal “Voprosy Ekonomiki”, which focuses on household economic behavior. The issue is based on the “Economic Behavior of Households” project, which has been implemented at the Higher School of Economics since 2023. The project consists of two components: a field component, which involves collecting sociological data, and an analytical component, which analyzes the collected data, systematizes it, generalizes it, and compares it with other data sources. The authors of the article, who are the scientific supervisors of the project, emphasize the relevance and significance of this project in terms of combining economic and sociological aspects. They highlight the state of development of economic and sociological thought, which often remains disconnected from each other. This makes it difficult to view macroeconomic processes from the perspective of consumption. However, in a situation where households demand for goods is not clearly defined or predictable, macroeconomists need to have a better understanding of this demand to formulate appropriate programs and policies. The article also emphasizes the importance of collecting microdata on the economic behavior of households, including those with high incomes, to address questions about the future behavior of households if the situation were to change. To make it easier for readers to understand the articles in this special issue, the authors have provided detailed explanations of the data collection methodology, fieldwork specifics, and data composition. Additionally, the project’s microdata are available to researchers, experts, and business associations starting from March 2026.

Research target: Sociology (including Demography and Anthropology Economics and Management
Language: Russian
Text on another site
Keywords: потреблениедомохозяйстваэкономическое поведениеconsumptionhouseholdsmicrodataмикроданныеmodels of economic behaviour
Publication based on the results of:
Consumption and economic behavior of households in Russia, 2025-2027 (2027)
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