Artem Baklanov

Artem Baklanov applies control theory, game theory, and machine learning to informed environmental decision making.

Environmental decision making needs to be informed in many ways, also depending on the scale:

  • At the scale of a global economy, it is important to develop tools for a trade-off analysis in Integrated Assessment Models (IAMs);
  • At the scale of countries, one has to take into account strategic interactions when designing institutions to deal with environment as “common good”;
  • At the level of citizens, an emerging area of citizen science (including crowd-sourced science, civic science, volunteer monitoring) calls for new robust methods to deal with the results of the crowdsourcing campaigns, in particular, methods for vote aggregation and data pre-processing.

Namely, nonlinearities in IAMs escalate the issue of consistency between sort-term actions and long-term targets. Artem Baklanov applies the attainable set approach to circumscribe possible short–term actions that are consistent with a specified long–term target, as well as to reveal which long–term targets are still attainable depending on a chosen short–term policy.

To analyze global socioeconomic problems, it is helpful to study models formulated as repeated games and use the concept of the strategic equilibrium to describe a rational outcome of multi-agent interactions. Artem Baklanov focuses on strategies with restricted memory representing bounded rationality and explores how a small change in the complexity of strategies, which can be interpreted as a change in the ‘boundedness’ of rationality, influences some important properties of the Nash equilibrium.

Public participation in scientific research is a new global trend helping to improve existing monitoring tools. To improve the quality of data collected at crowdsourcing campaigns, Artem Baklanov uses vote aggregation procedures based on state-of-the-art machine learning algorithms and performs data pre-processing using computer vision algorithms to exclude ambiguous and low-quality images from visual inspection by volunteers. 

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Funding: IIASA Postdoctoral Program

Nationality: Russian

Program: Advanced Systems Analysis Program

Dates: August 2014 – August 2016

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Last edited: 14 December 2016


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Rekabsaz N, Lupu M, Baklanov A, Hanbury A, Duer A, & Anderson L (2017). Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models. ArXiv (Submitted)

Subkhankulova D, Baklanov A, & McCollum D (2017). Demand Side Management: A Case for Disruptive Behaviour. In: Advanced Computational Methods for Knowledge Engineering. Eds. Le, Nguyen-Thinh, van Do, Tien, Nguyen, Ngoc Thanh & Thi, Hoai An Le, Advances in Intelligent Systems and Computing, 629 . pp. 47-59 Cham, Switzerland: Springer International Publishing AG. ISBN 978-3-319-61911-8 DOI:10.1007/978-3-319-61911-8_5.

Baklanov A (2016). Nash equilibria in reactive strategies. In: International conference in memory of Academician Arkady Kryazhimskiy, 3-8 October 2016, Ekaterinburg, Russia.

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