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News
September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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Мультисекторная модель ограниченного соседства: сегрегация агентов и оптимизация характеристик среды

Математическое моделирование. 2021. Т. 33. № 11. С. 95–114.
Akopov A. S., Бекларян Л. А., Beklaryan A.

This article presents an approach to studying the effects of segregation using the developed multi-sector bounded-neighbourhood model. A model of the evolutionary dynamics of a community consisting of a local (natives) and external population (migrants) interacting in an artificial socio-economic system is proposed, in which the key sectors of the economy are highlighted: mining of raw materials (the primary sector, which attracts mainly migrants), the manufacturing sector (the secondary sector, which attracts mainly indigenous people), and the sphere of low-tech and high-tech services (the tertiary and quaternary sectors of the economy, which attract migrants and indigenous people, respectively). Formation of jobs in these sectors of the economy is carried out centrally using the previously proposed fuzzy clustering algorithm. Simulation experiments were carried out and the effects of segregation were investigated due to the desire of agents to search for the most preferable jobs in a bounded-neighbourhood under various scenario conditions. Using the proposed genetic algorithm, an important optimization problem was solved to maximize the GDP growth rate and minimize the level of population segregation.

Research target: Mathematics Computer Science
Language: Russian
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Keywords: генетический алгоритмgenetic algorithmbounded-neighbourhood modelsagent-based modelling of migration and socioeconomic processesmodels of tolerant threshold behavioursegregation effectsagent clusteringмодели ограниченного соседстваагентное моделирование миграционных и социально-экономических процессовмодели толерантного порогового поведенияэффекты сегрегациикластеризация агентов
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