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
Postdoc Coordinator & YSSP Administrative Assistant Post Doc - Capacity Development and Academic Training Unit
Related research program
Postdoctoral research at IIASA
Baklanov, A. (2021). Reactive Strategies: An Inch of Memory, a Mile of Equilibria. Games 12 (2), e42. 10.3390/g12020042.
Bednar, J., Obersteiner, M. , Baklanov, A. , Thomson, M., Wagner, F. , Geden, O., Allen, M., & Hall, J.W. (2021). Operationalizing the net-negative carbon economy. Nature 10.1038/s41586-021-03723-9. (In Press)
Rovenskaya, E. , Aghababaei Samani, K., Baklanov, A. , Ermolieva, T., Folberth, C. , Fritz, S., Hadi, H., Javalera Rincón, V. , Krasovskii, A. , Laurien, F. , Poblete Cazenave, M., Schinko, T. , Smilovic, M. , & Zebrowski, P. (2019). Artificial Intelligence and Machine Learning for Systems Analysis of the 21st Century. IIASA Working Paper. Laxenburg, Austria: WP-19-010
International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313