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News
September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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Does Incomplete Information Reduce Manipulability?

NRU Higher School of Economics , 2016. No. 152/EC/2016.
Veselova Y. A.
We consider the problem of individual manipulation under incomplete information, i.e. the whole preference profile is not known to voters. Instead, voters know the result of an opinion poll (the outcome of a poll information function π, e.g. a list of scores or a set of winners). In this case, a voter has an incentive to misrepresent his preferences (π-manipulate) if he knows that he will not become worse off and there is a chance of becoming better off. We consider six social choice rules and eight types of poll information functions differing in their informativeness. To compare manipulability, first we calculate the probability that there is a voter which has an incentive to π-manipulate and show that this measure is not illustrative in the case of incomplete information. Then we suggest considering two other measures: the probability of a successful manipulation and an aggregate stimulus of voters to manipulate which demonstrate more intuitive behaviour. We provide results of computational experiments and analytical proofs of some of the observed effects.
Priority areas: economics IT and mathematics
Language: English
Full text
Keywords: манипулированиеincomplete informationmanipulability of voting rulesопроснеполная информацияmanipulationтеория коллективного выбораsocial choice theoryopinion pollmanipulability indexиндекс манипулируемости
Publication based on the results of:
Development and research of new mathematical models in socio-economic and political spheres (2016)
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