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: 22 June 2017
Related research program
Postdoctoral research at IIASA
Schepaschenko D ORCID: https://orcid.org/0000-0002-7814-4990, See L, Lesiv M, Bastin J-F, Mollicone D, Tsendbazar N-E, Bastin L, McCallum I, et al. (2019). Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery. Surveys in Geophysics DOI:10.1007/s10712-019-09533-z. (In Press)
Folberth C ORCID: https://orcid.org/0000-0002-6738-5238, Baklanov A ORCID: https://orcid.org/0000-0003-1599-3618, Balkovic J ORCID: https://orcid.org/0000-0003-2955-4931, Skalsky R ORCID: https://orcid.org/0000-0002-0983-6897, Khabarov N ORCID: https://orcid.org/0000-0001-5372-4668, & Obersteiner M ORCID: https://orcid.org/0000-0001-6981-2769 (2019). Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning. Agricultural and Forest Meteorology 264: 1-15. DOI:10.1016/j.agrformet.2018.09.021.
Baklanov A ORCID: https://orcid.org/0000-0003-1599-3618, Khachay M, & Pasynkov M (2019). Fully Convolutional Neural Networks for Mapping Oil Palm Plantations in Kalimantan. In: Learning and Intelligent Optimization. Eds. Battiti, R., Brunato, M., Kotsireas, I. & Pardalos, P., pp. 427-432 Cham, Switzerland: Springer. ISBN 978-3-030-05347-510.1007/978-3-030-05348-2_35.
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