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August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
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The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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Mapping issues and envisaging futures: An evolutionary scenario approach

Technological Forecasting and Social Change. 2012. Vol. 79. No. 3. P. 509–529.
Saritas O., Nugroho Y.

In parallel with the increasing complexity and uncertainty of social, technological, economic, environmental, political and value systems (STEEPV), there is a need for a systemic approach in Foresight. Recognizing this need, the paper begins with the introduction of the Systemic Foresight Methodology (SFM) is introduced briefly as a conceptual framework to understand and appreciate the complexity of systems and interdependencies and interrelationships between their elements. Conducting Foresight systemically involves a set of ‘systemic’ thought experiments, which is about how systems (e.g. human and social systems, industrial/sectoral systems, and innovation systems) are understood, modelled and intervened for a successful change programme. A methodological approach is proposed with the use of network analysis to show an application of systemic thinking in Foresight through the visualisation of interrelationships and interdependencies between trends, issues and actors, and their interpretation to explain the evolution of systems. Network analysis is a powerful approach as it is able to analyse both the whole system of relations and parts of the system at the same time and hence it reveals the otherwise hidden structural properties of the systems. Our earlier work has attempted to incorporate network analysis in Foresight, which helped to reveal structural linkages of trends and identify emerging important trends in the future. Following from this work, in this paper we combine systemic Foresight, network analysis and scenario methods to propose an ‘Evolutionary Scenario Approach,’ which explains the ways in which the future may unfold based on the mapping of the gradual change and the dynamics of aspects or variables that characterise a series of circumstances in a period of time. Thus, not only are evolutionary scenarios capable of giving a snapshot of a particular future, but also explaining the emerging transformation pathways of events and situations from the present into the future as systemic narratives.

Language: English
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Keywords: scanningnetwork analysisScenariosSystemic ForesightBig picture surveyEvolutionary scenarios
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