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Marek Makowski
Guest Research Scholar
Sustainable Service Systems Research Group
Energy, Climate, and Environment Program
Guest Research Scholar
Integrated Assessment and Climate Change Research Group
Energy, Climate, and Environment Program
Contact
Biography
Marek Makowski, from the Systems Research Institute (SRI) of the Polish Academy of Sciences, joined IIASA in August 1987 to work on the development and application of mathematical programming software. He is currently a guest research scholar with the Energy, Climate, and Environment (ECE) Program, and continues to be a research scholar of the SRI.Makowski graduated in 1970 with an Engineering and a Master of Science degree in the field of automatic control and computer sciences from the Faculty of Electronics of the Warsaw University of Technology in Poland. He also studied mathematics at the Warsaw University. He received his PhD in 1976, from the Polish Academy of Sciences for his thesis on the optimization of environmental models. He was leader of the project on the development of decision support systems for various applications in Poland, and since 1976, he has been actively participating in joint projects with IIASA.
He joined the IIASA Computer Services Department in 1987 to provide support on mathematical programming. From September 1989 to August 1991, he was Acting Project Leader of the Methodology of Decision Analysis (MDA) Project, then Senior Research Scholar with MDA, and from 1996, worked with the Risk, Modeling, and Society Project. From 2006 to 2011 he was the leader of the Integrated Modeling Environment (IME) Project, and from 2012 to 2014, he was a senior research scholar with the former Advanced Systems Analysis (ASA) Program. From 2014, he was a guest research scholar with the former Energy Program, moving over to the new Energy, Climate, and Environment Program in 2021. He also led many long-term IIASA collaborative projects with Japan and Poland, as well as diverse work packages in several EU-funded projects.
Makowski's research interests focus on model-based support for solving complex problems, which incorporates three interlinked areas. First, integration of interdisciplinary knowledge and its representation by mathematical models. Second, the creation of knowledge by multiple-criteria model analysis (MCMA), as well as other analysis methods, in particular various simulation methods. Third, tailoring the modeling process to meet the needs of decision-making processes.
Thus his research interests cover a cluster of areas relevant to the adaptation (whenever possible) or development (when needed) of methodology, algorithms, and software for model-based decision-making support. This includes more specific topics in Operations Research (OR) such as: multiple-criteria problem analysis, large scale optimization, optimization of badly conditioned problems, the use of database management systems for complex models, decision analysis and support, user interfaces in decision support systems, and the effective treatment of uncertainty and risk. Specific activities focused on Structured Modeling Technology (SMT) developed to meet the institute's needs for tailored modeling support for problems characterized by complex relations, huge amounts of data, and the demand for integrated model analysis. SMT supports interdisciplinary teams working at dispersed locations on model specification, collection and verification of data, definition of various model instances, and applying diversified methods of model analysis. Therefore, the SMT methodology provides a consistent framework for the whole modeling process.
His current focus is on the further development and application of modular tools for Multiple-Criteria Analysis (MCA) and their applications to diverse energy models. Two types of MCA methods and tools are being developed. First, Multiple Criteria Analysis of Alternatives (MCAA) for MCA of discrete alternatives. Second, Multiple Criteria Model Analysis (MCMA) for MCA of models, especially for large models. Both MCA methods and software tools developed over decades and applied to diverse problems, are now extended to effectively support the analysis of newly developed models, in particular, focusing on China's energy technology pathways and variability of renewable energy sources. For effective multiple-criteria analysis of the latter two classes of models he re-developed the MCMA into a Python modular package called pyMCMA, which autonomously computes the Pareto-front of independently developed substantive models. The pyMCMA is publicly available on gitHub and applied in collaboration with former participants of the IIASA Young Scientists Summer Program (YSSP), who worked as part of the ECE Program.
Last update: 06 JUN 2024
Publications
McCollum, D.L., Krey, V. , Riahi, K. , Kolp, P., Makowski, M. , & Schreck, B. (2012). The IIASA Energy-Multi Criteria Analysis Tool (ENE-MCA). IIASA, Laxenburg, Austria
Ermolieva, T., Ermoliev, Y., Fischer, G., Jonas, M. , Makowski, M. , & Wagner, F. (2011). Carbon emission trading and carbon taxes under uncertainties. In: Greenhouse Gas Inventories: Dealing With Uncertainty. Eds. White, T, Jonas, M , Nahorski, Z, & Nilsson, S, Dordrecht: Springer. 10.1007/978-94-007-1670-4_16.
Borodina, O., Borodina, E., Ermolieva, T., Ermoliev, Y., Fischer, G., Makowski, M. , & van Velthuizen, H.T. (2010). Integrated modeling approach to the analysis of food security and sustainable rural developments: Ukrainian case study. IIASA Interim Report. IIASA, Laxenburg, Austria: IR-10-017
Ermolieva, T., Ermoliev, Y., Fischer, G., Jonas, M. , Makowski, M. , & Wagner, F. (2010). Carbon emission trading and carbon taxes under uncertainties. Climatic Change 103 (1) 277-289. 10.1007/s10584-010-9910-x.
Bolc, L., Makowski, M. , & Wierzbicki, A. (2010). Preface. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. v-vii . ISBN 978-364216566-5 10.1007/978-3-642-16567-2.