A systems approach to the world highlights structural similarities across diverse domains. With a certain level of analytical abstraction, these similarities allow a unified language for description and analysis of different systems. This universality offers opportunities to transfer concepts, methods, and insights across different disciplinary domains, fostering cross-domain innovation and sense-making.
ASA builds on IIASA's rich tradition in operations research, optimization, and optimal control—core methodologies that served as the backbone of systems analysis in the 20th century. In response to the new realities of the 21st century, the program has embraced a broader focus. ASA’s methodological toolbox extends to game theory, complexity science, data science, and machine learning, as well as ‘soft’ systems-analysis approaches.
Selected Research Highlights:
- Optimal Control Theory for Navigating Emerging Challenges
Optimal control theory enables the comprehensive derivation of dynamically optimal strategies in evolving environments with feedback loops. Due to technical limitations, models utilizing optimal control are often rather stylized, which forces researchers to focus only on the most essential processes and interdependencies relevant to a specified research question. This, in turn, enables the extraction of qualitative insights that remain valid across a range of parameter values. This approach is especially valuable for analyzing novel challenges that arise suddenly and unpredictably. COVID-19 was a prime example. ASA researchers provided early analyses into optimal response strategies, evaluating both pharmaceutical and non-pharmaceutical measures to balance economic impacts and public health outcomes (Grass et al., 2024, Caulkins et al., 2023, Freiberger et al., 2022, Caulkins et al., 2021, Caulkins et al., 2020). Other emergent topics where such qualitative insights are particularly valuable include the rapid advent of the digital economy (Feichtinger et al., 2024) and the exploration of a radical shift to net-negative carbon economy (Bednar et al., 2021).
- Exploring Risk-adjusted Optimization for Resource Management under Uncertain
The approach of stochastic optimization developed at IIASA (Ermoliev et al., 2000) enables the derivation of solutions that ensure a desired outcome with a specified probability while allowing a small chance of deviation, termed ‘risk-adjusted optimization’. Such probabilistic planning is inevitable in the world dominated by various uncertainties, arising from natural and human-made processes. Although the theory of stochastic optimization was coined decades ago, its adoption in decision-making for socio-environmental systems has been limited by various conceptual and technical challenges. ASA has developed a series of case studies to advance the practice of using risk-adjusted optimization to inform sustainable management of water (Gao et al., 2021), land (Leip et al., 2022), strategic and operational planning of energy resources and technologies (Ermoliev et al., 2023), robust operations of multipurpose reservoirs (Ermoliev et al., 2019), sustainable resource use and technological developments of steel production industry (Ren et al., 2018), exploring conditions for cost-effectiveness and environmental safety of robust carbon trading (Ermoliev et al., 2015; Ermolieva et al., 2014), and other natural resources amid growing uncertainty. These applications inspire further developments of stochastic optimization algorithms, uncertainty and vulnerability modelling approaches, nonsmooth (stochastic) optimization procedures to modelling and robust management of risks of all kinds, in particular, of extreme and systemic nature, in interdependent systems.
- Exploring Eco-Evolutionary and Societal Adaptation through Evolutionary Game Theory
Evolutionary Game Theory (EGT) enables modelling behavior strategies that evolve over time through learning, where players adopt strategies that mimick successful competitors. ASA has developed EGT models to deepen our understanding of eco-evolutionary adaptation to disturbances in nature (Joshi et al., 2023) and to explore cooperative societal responses to shocks (Choquette-Levy et al., 2024, Colon et al., 2020). Ongoing research explores the use of EGT to better understand successful strategies in the platform and digital ecosystem economy, in addition to the traditional focus on natural ecosystems and societal cooperation at large.
- Network-Based Approaches to Systemic Risk and Resilience
Viewing interconnected systems as networks of interacting agents enables the quantification of interdependencies and risks of cascading failures. Poledna et al., 2021 investigated the financial system as a multi-layer network of direct interbank exposures (default contagion) and indirect external exposures (overlapping portfolios), and estimated the mutual influence of different channels of contagion on the overall system’s stability, highlighting the crucial role of indirect effects. Zisopoulos et al., 2025 analyzed network properties of the global waste trade to highlight concentration of trade flows in this market. Ongoing research examines the relationship between a system's resilience to shocks and its network properties in both natural and human-made systems.
- System of Systems approaches in the Risk&Resilience space
The growing interconnectedness of global systems, alongside the impacts of climate change and other worldwide risks, has resulted in increased systemic risks within the complex systems that shape our world. These risks, with their far-reaching consequences and long-term sustainability challenges, require transformative strategies to address their effects across various system levels. However, there remains a gap in translating transformative change ideas into practical policy solutions for managing systemic risk. In this context, we take a system-of-systems perspective to develop analytical processes that help bridge the gap between methodologies rooted in natural sciences, mathematics and engineering, which offer well-established tools for systemic risk analysis focusing on event frequency and interconnections, and those grounded in social sciences, which emphasize human agency and governance processes. While traditional concepts like probability and utility remain important, they are increasingly insufficient on their own for understanding global systemic risks. Instead, there is a need to explore the critical elements of specific global systems, where numerous human agents interact within complex, dynamic networks. In such systems, even the most influential agents cannot achieve optimal outcomes without engaging in a co-evolutionary process with others, creating mutually beneficial opportunities within expanding networks.
Grass, D., Wrzaczek, S., Caulkins, J.P., Feichtinger, G., Hartl, R.F., Kort, P.M., Kuhn, M., Fürnkranz-Prskawetz, A., Sanchez-Romero, M., & Seidl, A. (2024). Riding the waves from epidemic to endemic: Viral mutations, immunological change and policy responses. Theoretical Population Biology 156 46-65. 10.1016/j.tpb.2024.02.002.
Caulkins, J.P., Grass, D., Feichtinger, G., Hartl, R.F., Kort, P.M., Kuhn, M., Fürnkranz-Prskawetz, A., Sanchez-Romero, M. , Seidl, A., & Wrzaczek, S. (2023). The hammer and the jab: Are COVID-19 lockdowns and vaccinations complements or substitutes? European Journal of Operational Research 311 (1) 233-250. 10.1016/j.ejor.2023.04.033.
Freiberger, M., Grass, D., Kuhn, M., Seidl, A., & Wrzaczek, S. (2022). Chasing up and locking down the virus: Optimal pandemic interventions within a network. Journal of Public Economic Theory 24 (5) 1182-1217. 10.1111/jpet.12604.
Caulkins, J.P., Grass, D., Feichtinger, G., Hartl, R.F., Kort, P.M., Prskawetz, A., Seidl, A., & Wrzaczek, S. (2021). The optimal lockdown intensity for COVID-19. Journal of Mathematical Economics 93 e102489. 10.1016/j.jmateco.2021.102489.
Feichtinger, G., Grass, D., Hartl, R.F., Kort, P.M., & Seidl, A. (2024). The digital economy and advertising diffusion models: Critical mass and the Stalling equilibrium. European Journal of Operational Research 10.1016/j.ejor.2024.05.043.
Ermoliev, Y.M., Ermolieva, T.Y., MacDonald, G.J., & Norkin, V.I. (2000). Stochastic optimization of insurance portfolios for managing exposure to catastrophic risks. Annals of Operations Research 99 (1) 207-225. 10.1023/A:1019244405392.
Gao, J., Xu, X., Cao, G.-Y., Ermoliev, Y., Ermolieva, T., & Rovenskaya, E. (2021). Strategic decision-support modeling for robust management of the food–energy–water nexus under uncertainty. Journal of Cleaner Production 292 e125995. 10.1016/j.jclepro.2021.125995.
Leip, D., Rovenskaya, E. , & Wildemeersch, M. (2022). Protecting Food Supply and Farmer Livelihoods in West Africa: Strategies for Risk Reduction. In: Systems Analysis for Reducing Footprints and Enhancing Resilience, 16-17 November, 2022, Vienna, Austria.
Ermoliev, Y., Komendantova, N. , & Ermolieva, T. (2023). Energy Production and Storage Investments and Operation Planning Involving Variable Renewable Energy Sources A Two-stage Stochastic Optimization Model with Rolling Time Horizon and Random Stopping Time. In: Modern Optimization Methods for Decision Making Under Risk and Uncertainty. Eds. Gaivoronski, A., Knopov, P., & Zaslavskyi, V., Taylor & Francis. ISBN 9781003260196 10.1201/9781003260196-13.
Ermoliev, Y., Ermolieva, T., Kahil, T. , Obersteiner, M. , Gorbachuk, V., & Knopov, P. (2019). Stochastic Optimization Models for Risk-Based Reservoir Management*. Cybernetics and Systems Analysis 55 (1) 55-64. 10.1007/s10559-019-00112-z.
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.
Ermoliev, Y., Ermolieva, T., Jonas, M. , Obersteiner, M. , Wagner, F. , & Winiwarter, W. (2015). Integrated model for robust emission trading under uncertainties: cost-effectiveness and environmental safety. Technological Forecasting and Social Change 98 234-244. 10.1016/j.techfore.2015.01.003.
Ermolieva, T., Ermoliev, Y., Jonas, M. , Obersteiner, M. , Wagner, F. , & Winiwarter, W. (2014). Uncertainty, cost-effectiveness and environmental safety of robust carbon trading: Integrated approach. Climatic Change 124 (3) 633-646. 10.1007/s10584-013-0824-2.
Joshi, J., Hofhansl, F. , Singh, S., Stocker, B., Brännström, Å., Franklin, O. , Blanco, C.C., Aleixo, I., Lapola, D.M., Prentice, I.C., & Dieckmann, U. (2023). Competition for light can drive adverse species-composition shifts in the Amazon Forest under elevated CO2. BioRxiv 10.1101/2023.07.03.547575. (Submitted)
Choquette-Levy, N., Wildemeersch, M. , Santos, F.P., Levin, S.A., Oppenheimer, M., & Weber, E.U. (2024). Prosocial preferences improve climate risk management in subsistence farming communities. Nature Sustainability 10.1038/s41893-024-01272-3.
Colon, C. , Brännström, Å., Rovenskaya, E. , & Dieckmann, U. (2020). Fragmentation of production amplifies systemic risks from extreme events in supply-chain networks. PLoS ONE 15 (12) e0244196. 10.1371/journal.pone.0244196.
Poledna, S., Martínez-Jaramillo, S., Caccioli, F., & Thurner, S. (2021). Quantification of systemic risk from overlapping portfolios in the financial system. Journal of Financial Stability 52 e100808. 10.1016/j.jfs.2020.100808.
Zisopoulos, F.K., Fath, B. , Tong, X., & de Jong, M. (2025). Network properties of the global waste trade. Environmental and Sustainability Indicators 25 e100550. 10.1016/j.indic.2024.100550.
Poledna, S., Miess, M.G., Hommes, C., & Rabitsch, K. (2022). Economic forecasting with an agent-based model. European Economic Review 151 e104306. 10.1016/j.euroecorev.2022.104306.
Hochrainer-Stigler, S. , Šakić Trogrlic, Robert , Reiter, K., Ward, P.J., de Ruiter, M.C., Duncan, M.J., Torresan, S., Ciurean, R., Mysiak, J., Stuparu, D., & Gottardo, S. (2023). Towards a framework for systemic multi-hazard and multi-risk assessment and management. iScience 26 (5) e106736. 10.1016/j.isci.2023.106736.
Hochrainer-Stigler, S. , Deubelli, T. , Parviainen, J., Cumiskey, L., Schweizer, P.-J., & Dieckmann, U. (2024). Managing systemic risk through transformative change: Combining systemic risk analysis with knowledge co-production. One Earth 7 (5) 771-781. 10.1016/j.oneear.2024.04.014.
