Overview

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

CONTACT DETAILS

Elena Rovenskaya

Program Director

Advanced Systems Analysis

T +43(0) 2236 807 608

PUBLICATIONS

Li Z, Wang Z, Liu X, Fath B, Liu X, Xu Y, Hutjes R, & Kroeze C (2019). Causal relationship in the interaction between land cover change and underlying surface climate in the grassland ecosystems in China. Science of the Total Environment 647: 1080-1087. DOI:10.1016/j.scitotenv.2018.07.401.

Li J, Zhang Y, Liu N, Fath B, & Hao Y (2018). Flow analysis of the carbon metabolic processes in Beijing using carbon imbalance and external dependence indices. Journal of Cleaner Production 201: 295-307. DOI:10.1016/j.jclepro.2018.07.306.

Auad Guillermo, Blythe Jonathan, Coffman Kim, & Fath Brian (2018). A dynamic management framework for socio-ecological system stewardship: A case study for the United States Bureau of Ocean Energy Management. Journal of Environmental Management 225: 32-45. DOI:10.1016/j.jenvman.2018.07.078.

Fiscus DA & Fath B (2018). Foundations for Sustainability: A Coherent Framework of Life-Environment Relations. Academic Press. ISBN 9780128114605

Naqvi A & Stockhammer E (2018). Directed Technological Change in a Post-Keynesian Ecological Macromodel. Ecological Economics 154: 168-188. DOI:10.1016/j.ecolecon.2018.07.008.

Ruzzenenti F (2018). The Prism of Elasticity in Rebound Effect Modelling: An Insight from the Freight Transport Sector. Sustainability 10 (8): p. 2874. DOI:10.3390/su10082874.

Watanabe C, Naveed K, Tou Y, & Neittaanmäki P (2018). Measuring GDP in the digital economy: Increasing dependence on uncaptured GDP. Technological Forecasting and Social Change DOI:10.1016/j.techfore.2018.07.053. (In Press)

Wildemeersch Matthias, Ronald Chan Wai Hong, Rovenskaya Elena, & Quek Tony QS (2018). Characterization and Control of Conservative and Non-conservative Network Dynamics. IFAC Journal of Systems and Control 5: 22-29. DOI:10.1016/j.ifacsc.2018.07.002.

Eker S, Reese G, & Obersteiner M (2018). Meat or not? - A model-based analysis of the global diet change dynamics. In: 36th International Conference of the System Dynamics Society, 6-10 August 2018, Reykjavík, Iceland.

Fischer-Kowalski M, Rovenskaya E, Krausmann F, Pallua I, & Mc Neill JR (2018). Energy transitions and social revolutions. Technological Forecasting and Social Change DOI:10.1016/j.techfore.2018.08.010. (In Press)

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