Artem Baklanov

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. 

Last edited: 22 June 2017