Environmental decision making needs to be informed in many ways, also depending on the scale:
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.
Funding: IIASA Postdoctoral Program
Program: Advanced Systems Analysis Program
Dates: August 2014 – August 2016
Last edited: 14 December 2016
Related research program
Postdoctoral research at IIASA
Chentsov A, Baklanov A, & Savenkov I (2017). On Control Problem with Constraints of Asymptotic Character. In: 8th International Conference on Physics and Control (PhysCon 2017). International Physics and Control Society (IPACS).
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 DOI:arXiv:1702.01978v1.
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, pp. 47-59 Cham, Switzerland: Springer International Publishing AG. ISBN 978-3-319-61911-8 DOI:10.1007/978-3-319-61911-8_5.
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