ASA research ultimately aims to produce, practice, and prototype novel system-analytical approaches, methods and tools, which enable solving problems that cannot be addressed by existing tools, or which enable addressing problems more efficiently.

© Aprescindere | Dreamstime.com

© Aprescindere | Dreamstime.com

Referring to the methodological dimension of the IIASA’s mandate, the ASA Program’s overall mission is to advance systems analysis by substantiating the integration of systems methods and applied research on problems of global relevance and universal importance. Central to this mission is the exploratory development of mathematical methods and analytical techniques to investigate complex systems undergoing global change with a focus on an integrated, interdisciplinary approach. 

ASA’s research is organized around three mutually complementing and cross-fertilizing methodological research domains. 

Optimal behavior of systems

  • Focus on how decision making can be formalized in models, notably, under uncertainty and risks, and what consequences different decisions yield
  • Develop decision support tools and applications, which are traditionally based on the optimization of a utility describing decision-maker’s preferences
  • Employ and advance methods of the optimization theory, control theory, theory of dynamic systems, and other related fields
  • Applications to economic models, notably, long-term economic growth (also under environmental constraints) and resource management models

Interactions within systems

  • Focus on the role of indirect links and connectivity between individual systems within a larger networked system
  • Employ and advance methods of the graph theory, information theory, network analysis and other related fields are employed
  • Develop network-based modeling and assessment frameworks
  • Ecological and social applications
  • Some methodologies are also being transferred to other disciplinary areas, for example, to economics, energy policy, and resource management 

System transitions and resilience of systems

  • Focus on systems of systems, characterized by complex dynamics, decentralized decision-making, and significant uncertainties with the aim to study system’s resilience
  • Experiment with qualitative (e.g., soft systems mapping) and quantitative (e.g., agent-based modeling) methods and approaches to evaluate possible consequences of extreme shocks affecting the system under study and, based on that, system resilience
  • Develop novel methods of data analysis aiming to identify precursors of system flips and general patterns via learning from the past 

More info is available under the following links:

In accordance with its strategy, ASA is actively maintaining and expanding its network consisting of methodologists, applied scientists and decision-makers all over the world, and, based on it, develops international and interdisciplinary collaboration.

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Last edited: 08 September 2016


Elena Rovenskaya

Program Director

Advanced Systems Analysis

T +43(0) 2236 807 608


Eker S & Kwakkel JH (2018). Including robustness considerations in the search phase of Many-Objective Robust Decision Making. Environmental Modelling & Software 105: 201-216. DOI:10.1016/j.envsoft.2018.03.029.

Komendantova N & Stepanova A (2018). Impacts of Risk Perceptions on Foreign Direct Investment in Energy Generation and Transmission Projects in Russia. Energy Systems Research: 44-50. DOI:10.25729/esr.2018.01.0005.

Jonas M, Zebrowski P, & Jarnicka J (2018). The crux of reducing emissions in the long-term: The underestimated “now” versus the overestimated “then”. In: 19. Österreichischer Klimatag, 23 –25 April 2018, Salzburg, Austria.

Vilkkumaa E, Liesiö J, Salo A, & Ilmola-Sheppard L (2018). Scenario-based portfolio model for building robust and proactive strategies. European Journal of Operational Research 266 (1): 205-220. DOI:10.1016/j.ejor.2017.09.012.

Zhang Y, Wu Q, & Fath B (2018). Review of spatial analysis of urban carbon metabolism. Ecological Modelling 371: 18-24. DOI:10.1016/j.ecolmodel.2018.01.005.

Bun R, Nahorski Z, Horabik-Pyzel J, Danylo O, See L, Charkovska N, Topylko P, Halushchak M, et al. (2018). Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources. Mitigation and Adaptation Strategies for Global Change: 1-28. DOI:10.1007/s11027-018-9791-2.

Jonas M, Zebrowski P, & Jarnicka J (2018). Towards Handling Uncertainty in Prognostic Scenarios: Advanced Learning from the Past. IIASA Report. Vienna, Austria: Austrian Academy of Sciences

Orlov S, Rovenskaya E, Puaschunder J, & Semmler W (2018). Green bonds, transition to a low-carbon economy, and intergenerational fairness: Evidence from an extended DICE model. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-18-001

Ren M, Xu X, Ermolieva T, Cao G-Y, & Yermoliev Y (2018). The Optimal Technological Development Path to Reduce Pollution and Restructure Iron and Steel Industry for Sustainable Transition. International Journal of Science and Engineering Investigations 7 (73): 100-105.

Patten BC & Fath B (2018). Notes from an introductory course on Field Systems Ecology. Ecological Modelling 368: 33-40. DOI:10.1016/j.ecolmodel.2017.11.014.

International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313