Overview

ASA’s innovations in methodology and exploratory applications to case studies respond to IIASA’s Strategic Plan 2011-2020, which, in particular, emphasizes the need for “innovation and exploration ... to cope with rapid changes and new crises and opportunities”, suggesting that “A new infusion of advanced systems analysis models and techniques in the exploratory and innovative research projects … will help IIASA to achieve international recognition as the leader in systems analysis and integrated assessments on a global scale.”

ASA’s mission

ASA’s mission is to develop, test, and make available new quantitative and qualitative methods from areas including mathematics, statistics, operations research, and management science for addressing problems arising in the policy analysis of complex socio-environmental systems. 

Thereby, ASA’s activities advance IIASA’s ability to conduct research to improve human and societal well-being, as well as environmental quality by allowing for solving problems that cannot be addressed by existing tools and by enabling solving problems more efficiently.

Why are new methods needed? 

We live in a VUCA world: 

  • V stands for Volatility
  • U stands for Uncertainty
  • C stands for Complexity, and
  • A stands for Ambiguity 

The term “VUCA” was coined by U.S. Army analysts to describe the changing geo-political landscape in the late 1980s - early 1990s. It then spread to the business world, where it is currently popular for describing a challenging environment, in which corporate decision makers must develop strategies that will bring success in spite of the challenges.

The VUCA challenge is equally relevant to policy making, national and international. 

What should advanced systems analysis be capable of?

In our view, systems analysis would move to the next level of relevance to real-life policy in a VUCA-world, if it could advance in several major directions, including finding effective and efficient ways to benefit from the multiplicity of models, to take maximal advantage of big and small data, to account for uncertainty, interconnectednes, non-linearity, and heterogeneity of agents, to recognize the multi-objective nature of decision making, to add more realism to model assumptions on agents’ behavior, and to contribute to the actual policy design. 

ASA’s research

The above listed challeneges are relevant to a vast array of particular instances of socio-environmental systems. Specifically, we consider (this list is certainly not exhaustive)

  • Ecosystems and natural resource management, food-water-energy systems, and climate;
  • Economic systems linked with population, technology and the environment;
  • Global and regional trade networks, networks of virtual (embodied) water and emissions, supply chains, multi-sector and multi-regional production;
  • Systems supporting national well-being

To be able to develop and advance the above listed capabilities, we employ

  • Optimization theory, including stochastic optimization and control theory, game theory, multi-objective analysis;  
  • Complexity science and network theory;  
  • Dynamic systems and simulations, including agent-based modeling (ABM);
  • Data analysis and machine learning;
  • Qualitative systems modeling, scenario planning, and foresight techniques;
  • Participatory modeling and stakeholders dialogue.

Some selected highlights in the current research cycle (2016-2020) are

  • Advancement of the methodology of stochastic optimization to address problems of catastrophic risk management including the applications to insurance;
  • Exploration of the usefulness of network analysis, including approaches based on information theory, to study the resilience of natural systems (ecosystems) and man-made systems (virtual water and emission flows, trade etc.);
  • Development of network-based policies to mitigate financial systemic risk (including the ‘systemic risk tax’);
  • Pioneering the creation and application of large-scale simulators of national economies representing realistic behavior rules of agents (economic ABMs);
  • Exercising stakeholder engagement processes to support sense-making and consensus-building by policy makers on issues of strategic importance for their countries/regions. 

Print this page

Last edited: 07 February 2019

CONTACT DETAILS

Elena Rovenskaya

Acting Deputy Director General/CEO

Directorate - DDG For Science

Program Director

Advanced Systems Analysis

Acting Program Director

Evolution and Ecology

T +43(0) 2236 807 608

PUBLICATIONS

Barbosa LF, Nascimento A, Mathias MH, & de Carvalho JA (2019). Machine learning methods applied to drilling rate of penetration prediction and optimization - A review. Journal of Petroleum Science and Engineering 183: e106332. DOI:10.1016/j.petrol.2019.106332.

Wildemeersch M, Franklin O ORCID: https://orcid.org/0000-0002-0376-4140, Seidl R, Rogelj J ORCID: https://orcid.org/0000-0003-2056-9061, Moorthy I, & Thurner S (2019). Modelling the multi-scaled nature of pest outbreaks. Ecological Modelling 409: e108745. DOI:10.1016/j.ecolmodel.2019.108745.

Tou Y, Watanabe C, Moriya K, Naveed N, Vurpillat V, & Neittaanmäki P (2019). The transformation of R&D into neo open innovation- a new concept in R&D endeavor triggered by amazon. Technology in Society 58: e101141. DOI:10.1016/j.techsoc.2019.05.005.

Hunt J ORCID: https://orcid.org/0000-0002-1840-7277, Byers E ORCID: https://orcid.org/0000-0003-0349-5742, Balogun A-L, Leal Filho W, Viviani Colling A, Nascimento A, & Wada Y ORCID: https://orcid.org/0000-0003-4770-2539 (2019). Using the jet stream for sustainable airship and balloon transportation of cargo and hydrogen. Energy Conversion and Management: X: e100016. DOI:10.1016/j.ecmx.2019.100016. (In Press)

Eker S ORCID: https://orcid.org/0000-0003-2264-132X, Reese G, & Obersteiner M ORCID: https://orcid.org/0000-0001-6981-2769 (2019). Modelling the drivers of a widespread shift to sustainable diets. Nature Sustainability DOI:10.1038/s41893-019-0331-1.

Eker S ORCID: https://orcid.org/0000-0003-2264-132X, Rovenskaya E, Langan S ORCID: https://orcid.org/0000-0003-0742-3658, & Obersteiner M ORCID: https://orcid.org/0000-0001-6981-2769 (2019). Model validation: A bibliometric analysis of the literature. Environmental Modelling & Software 117: 43-54. DOI:10.1016/j.envsoft.2019.03.009.

Strelkovskii N ORCID: https://orcid.org/0000-0001-6862-1768, Ilmola-Sheppard L, Komendantova N ORCID: https://orcid.org/0000-0003-2568-6179, Martusevich A, & Rovenskaya E (2019). Navigating through deep waters of uncertainty: Systems analysis approach to strategic planning of water resources and water infrastructure under high uncertainties and conflicting interests. IIASA Research Report. Laxenburg, Austria: RR-19-004

Watanabe C & Tou Y (2019). Transformative direction of R&D– lessons from Amazon's endeavor. Technovation DOI:10.1016/j.technovation.2019.05.007. (In Press)

Komendantova N ORCID: https://orcid.org/0000-0003-2568-6179, Maraschdeh L, Al Salaymeh A, Bohm S, Ekenberg L ORCID: https://orcid.org/0000-0002-0665-1889, Krüger C, Zejli D, Mtimet N, et al. (2019). Energy Policy at Crossroad: potentials for sustainable energy transition in the Middle East and North African region. IIASA Working Paper. Laxenburg, Austria: WP-19-004

Lalith M, Gill A, Poledna S, Hori M, Hikaru I, Tomoyuki N, Koyo T, & Ichimura T (2019). Distributed Memory Parallel Implementation of Agent-Based Economic Models. In: Lecture Notes in Computer Science. pp. 419-433 Faro, Portugal: Springer. 10.1007/978-3-030-22741-8_30.

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