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. 

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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

Nielson SN, Fath B ORCID: https://orcid.org/0000-0001-9440-6842, Bastianoni S, Marques JC, Müller F, Patten BC, Ulanowicz RE, Jørgensen SE, et al. (2020). A New Ecology: Systems Perspective, Second Edition. Elsevier. ISBN 978-0-444-63757-4 DOI:10.1016/C2015-0-01948-7.

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.

Feichtinger G, Grass D, & Kort PM (2019). Optimal scientific production over the life cycle. Journal of Economic Dynamics and Control 108: e103752. DOI:10.1016/j.jedc.2019.103752.

Haller-Bull V & Rovenskaya E (2019). Optimizing functional groups in ecosystem models: Case study of the Great Barrier Reef. Ecological Modelling 411: e108806. DOI:10.1016/j.ecolmodel.2019.108806.

Zhang X, Zhang Y, & Fath B ORCID: https://orcid.org/0000-0001-9440-6842 (2019). Analysis of anthropogenic nitrogen and its influencing factors in Beijing. Journal of Cleaner Production: e118780. DOI:10.1016/j.jclepro.2019.118780. (In Press)

Zhang N, Bai Z, Winiwarter W ORCID: https://orcid.org/0000-0001-7131-1496, Ledgard S, Luo J, Liu J, Guo Y, & Ma L (2019). Reducing Ammonia Emissions from Dairy Cattle Production via Cost-Effective Manure Management Techniques in China. Environmental Science & Technology 53 (20): 11840-11848. DOI:10.1021/acs.est.9b04284.

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.

Leutner M, Matzhold C, Bellach L, Deischinger C, Harreiter J, Thurner S, Klimek P, & Kautzky-Willer A (2019). Diagnosis of osteoporosis in statin-treated patients is dose-dependent. Annals of the Rheumatic Diseases DOI:10.1136/annrheumdis-2019-215714. (In Press)

Hochrainer-Stigler S, Colon C, Boza G, Brännström Å, Linnerooth-Bayer J, Pflug G ORCID: https://orcid.org/0000-0001-8215-3550, Poledna S, Rovenskaya E, et al. (2019). Measuring, modeling, and managing systemic risk: the missing aspect of human agency. Journal of Risk Research: 1-17. DOI:10.1080/13669877.2019.1646312. (In Press)

Heine D, Semmler W, Mazzucato M, Braga JO, Gevorkyan A, Hayde EK, & Radpour S (2019). Financing Low-Carbon Transitions Through Carbon Pricing and Green Bonds. World Bank Policy Research Working Paper No. 8891

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