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
Folberth C, Baklanov A, Balkovic J, Skalsky R, Khabarov N, & Obersteiner M (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, 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.
Folberth C, Baklanov A, Balkovic J, Skalsky R, Khabarov N, & Obersteiner M (2018). Supplementary Datasets S1 and S2 for the paper “Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning”.
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