ASA Program aims to discover, develop, and deploy new, more effective, and efficient ways of infusing systems science into policy and decision making for sustainable development.
Despite numerous success stories, many policies and decisions that currently aim to deal with global change are not sufficiently informed by cutting-edge science. Among the major barriers that prevent the effective input of science into policy are perceived shortfalls in agility, realism, and relevance of the current generation of methods and models from the standpoint of end users. To address these barriers, ASA strives to advance agile, realistic, and relevant systems analytical tools and methods, and facilitate a shared understanding of the capabilities and limits of these tools and methods with end users. Consequently, ASA’s efforts span the full range, from advancing research methods and tools of systems analysis to innovating at the interface between policy- and decision making, as well as with society at large.
ASA Program’s major objectives are:
- To innovate approaches and tools to analyze increasingly systemic, social-ecological risks and support decisions aimed at enhancing resilience and facilitating sustainability transitions and transformations.
- To further the capacity of agile, on-demand systems analysis underpinned by a suite of modeling frameworks of appropriate complexity.
- To mobilize multiple sources of data and the power of data science to diagnose and identify solutions to reduce vulnerabilities and risks.
- To advance feasible and effective ways of engagement with policymakers, the private sector, and citizens.
- To enhance trust and shared understanding of systems analysis methods and tools, in particular, through open science.
Projects
Staff
News
04 October 2024
Countries under fiscal pressure from recent disaster events
30 September 2024
Transforming climate action through data
26 September 2024
In Memory of Yuri Ermoliev
Events
Odeon Theater, Taborstraße 10, 1020 Vienna
End to begin: Interactive stage production addressing the state and future of our planet
University of Applied Arts, Vienna
IIASA colleagues presenting at the 5th Circular Strategies Symposium
Focus
17 October 2024
AI at IIASA for earth monitoring in the era of transition
As the TED AI conference unfolds in Vienna, Ian McCallum, IIASA Novel Data Ecosystems for Sustainability Research Group Leader, brings a fresh perspective on the pivotal role of artificial intelligence in Earth monitoring. He explores how AI can revolutionize our understanding of environmental changes and support sustainable practices in an era marked by significant transitions.
17 October 2024
Navigating multi-hazard risks: building resilience in a systemic risk landscape
IIASA researchers Robert Sakic Trogrlic and Stefan Hochrainer-Stigler explore the growing complexity of natural hazards and their interconnected impacts on communities. Their research offers insights into the challenges faced by communities worldwide and underscores the importance of building more resilient systems in an interconnected, increasingly hazard-prone world.
Publications
Feichtinger, G. & Wrzaczek, S. (2024). The optimal momentum of population growth and decline. Theoretical Population Biology 155 51-66. 10.1016/j.tpb.2023.12.002. Barthelme, P., Darbyshire, E., Spracklen, D.V., & Watmough, G. (2024). Detecting Vietnam War bomb craters in declassified historical KH-9 satellite imagery. Science of Remote Sensing 10 e100143. 10.1016/j.srs.2024.100143. Santoro, M., Cartus, O., Quegan, S., Kay, H., Lucas, R.M., Araza, A., Herold, M., Labrière, N., Chave, J., Rosenqvist, Å., Tadono, T., Kobayashi, K., Kellndorfer, J., Avitabile, V., Brown, H., Carreiras, J., Campbell, M.J., Cavlovic, J., Bispo, P.d.C., Gilani, H., Khan, M.L., Kumar, A., Lewis, S.L., Liang, J., Mitchard, E.T.A., Pacheco-Pascagaza, A.M., Phillips, O.L., Ryan, C.M., Saikia, P., Shchepashchenko, D. , Sukhdeo, H., Verbeeck, H., Vieilledent, G., Wijaya, A., Willcock, S., & Seifert, F.M. (2024). Design and performance of the Climate Change Initiative Biomass global retrieval algorithm. Science of Remote Sensing 10 e100169. 10.1016/j.srs.2024.100169. Liu, J., Xu, X., Qi, Y., Lin, N., Bian, J., Wang, S., Zhang, K., Zhu, Y., Liu, R., & Zou, C. (2024). A Copula-based spatiotemporal probabilistic model for heavy metal pollution incidents in drinking water sources. Ecotoxicology and Environmental Safety 286 e117110. 10.1016/j.ecoenv.2024.117110.