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Tieju Ma joined IIASA as a Research Scholar in January 2005. Before joining IIASA, he was a postdoctoral researcher at the School of Knowledge Science, Japan Advanced Institute of Science and Technology. He received his bachelor's degree in industrial engineering in 1998 from Dalian University of Technology, China and from the same university, he received his master's degree in systems engineering in 2000. In October 2003, he received his PhD in knowledge science from the Japan Advanced Institute of Science and Technology.
Dr. Ma is currently a professor at the School of Business, East China University of Science and Technology, Shanghai, China. His research interests include modeling on technology (especially energy technology) transitions considering uncertainty, technological learning, and heterogeneous agents.
Last update: 16-JUL-2013
Fang C & Ma T (2020). Stylized agent-based modeling on linking emission trading systems and its implications for China's practice. Energy Economics 92: e104916. DOI:10.1016/j.eneco.2020.104916.
Liang S, Yu Y, Kharrazi A , Fath B , Feng C, Daigger GT, Chen S, Ma T, et al. (2020). Network resilience of phosphorus cycling in China has shifted by natural flows, fertilizer use and dietary transitions between 1600 and 2012. Nature Food 1 (6): 365-375. DOI:10.1038/s43016-020-0098-6.
Xu Z, Fang C, & Ma T (2020). Analysis of China's olefin industry using a system optimization model considering technological learning and energy consumption reduction. Energy 191: e116462. DOI:10.1016/j.energy.2019.116462.
Ren H, Zhou W, Makowski M , Yan H, Yu Y, & Ma T (2019). Incorporation of life cycle emissions and carbon price uncertainty into the supply chain network management of PVC production. Annals of Operations Research DOI:10.1007/s10479-019-03365-1. (In Press)
Fang C & Ma T (2019). Technology adoption with carbon emission trading mechanism: modeling with heterogeneous agents and uncertain carbon price. Annals of Operations Research DOI:10.1007/s10479-019-03297-w. (In Press)
Zhang Y, Ma T, & Guo F (2018). A multi-regional energy transport and structure model for China’s electricity system. Energy 161: 907-919. DOI:10.1016/j.energy.2018.07.133.
Zhang Y, Yu Y, & Ma T (2018). System optimization of long-distance energy transportation in China using ultra-high-voltage power transmission. Journal of Renewable and Sustainable Energy 10 (4): e045503. DOI:10.1063/1.5013177.
Zhang S , Ren H, Zhou W, Yu Y, Ma T, & Chen C (2018). Assessing air pollution abatement co-benefits of energy efficiency improvement in cement industry: A city level analysis. Journal of Cleaner Production 185: 761-771. DOI:10.1016/j.jclepro.2018.02.293.
Shen F & Ma T (2018). A methodology to position nations’ efforts in a technology domain with a patent network analysis: case of the electric vehicle domain. Technology Analysis & Strategic Management 30 (9): 1084-1104. DOI:10.1080/09537325.2018.1442571.
Fang C & Ma T (2018). Technology Adoption Optimization with Heterogeneous Agents and Carbon Emission Trading Mechanism. In: Integrated Uncertainty in Knowledge Modelling and Decision Making. Eds. Huynh, V.N., Inuiguchi, M., Tran, D. & Denoeux, T., pp. 238-249 Cham, Switzerland: Springer. ISBN 978-3-319-75429-110.1007/978-3-319-75429-1_20.
Yu Y, Zhou L, Zhou W, Ren H, Kharrazi A , Ma T, & Zhu B (2017). Decoupling environmental pressure from economic growth on city level: The Case Study of Chongqing in China. Ecological Indicators 75: 27-35. DOI:10.1016/j.ecolind.2016.12.027.
Chen H & Ma T (2017). Optimizing systematic technology adoption with heterogeneous agents. European Journal of Operational Research 257 (1): 287-296. DOI:10.1016/j.ejor.2016.07.007.
Shchiptsova A, Zhao J, Grubler A , Kryazhimskiy A, & Ma T (2016). Assessing historical realibility of the agent-based model of the global energy system. Journal of Systems Science and Systems Engineering 25 (3): 326-350. DOI:10.1007/s11518-016-5303-7.
Zhao J & Ma T (2016). Optimizing the initial setting of complex adaptive systems-optimizing the layout of initial AFVs stations for maximizing the diffusion of AFVs. Complexity 21 (1): 275-290. DOI:10.1002/cplx.21742.
Zhao J & Ma T (2016). Optimizing layouts of initial AFV refueling stations targeting different drivers, and experiments with agent-based simulations. European Journal of Operational Research 249 (2): 706-716. DOI:10.1016/j.ejor.2015.08.065.
Zhang Y, Chen H, & Ma T (2016). System optimization model of adoption of a new infrastructure with multi-resource and multi-demand sites. Journal of Systems Science and Systems Engineering 25 (1): 62-76. DOI:10.1007/s11518-016-5302-8.
Zhao J & Ma T (2016). Optimizing layouts of initial refueling stations for alternative-fuel vehicles and experiments with agent-based simulations. Simulation 92 (3): 251-266. DOI:10.1177/0037549716629726.
Yu Y, Ren H, Kharrazi A , Ma T, & Zhu B (2015). Exploring socioeconomic drivers of environmental pressure on the city level: The case study of Chongqing in China. Ecological Economics 118: 123-131. DOI:10.1016/j.ecolecon.2015.07.019.
Ma T (2015). Adoption of an emerging infrastructure with uncertain technological learning and spatial reconfiguration. European Journal of Operational Research 243 (3): 995-1003. DOI:10.1016/j.ejor.2014.12.026.
Ma T (2015). Comparing Countries’/Areas’ Productive Knowledge with International Trade Data. In: KSS'2014. Proceedings of the 15th International Symposium on Knowledge and Systems Science. Eds. Wang, S., Nakamori, Z. & Huynh, V.N., pp. 111-116 Japan: JAIST Press. ISBN 978-4-903092-39-3
Chen H & Ma T (2014). Technology adoption with limited foresight and uncertain technological learning. European Journal of Operational Research 239 (1): 266-275. DOI:10.1016/j.ejor.2014.03.031.
Ma T, Zhao J, Xiang S, Zhu Y, & Liu P (2014). An agent-based training system for optimizing the layout of AFVs' initial filling stations. Journal of Artificial Societies and Social Simulation 17 (4): p. 6.
Ma T, Zhu Y, Liu P, & Chi C (2014). A simulation method to generate commute trips-for agent-based modeling on co-diffusion of alternative fuel vehicles and their filling stations. Simulation : Transactions of the Society for Modeling and Simulation International 90 (5): 560-569. DOI:10.1177/0037549714530780.
Chi C, Ma T, & Zhu B (2012). Towards a low-carbon economy: Coping with technological bifurcations with a carbon tax. Energy Economics 34 (3): 2081-2088. DOI:10.1016/j.eneco.2012.02.011.
Yan J, Ma T, & Nakamori Y (2011). Exploring the triple helix of academia-industry-government for supporting roadmapping in academia. International Journal of Management and Decision Making 11 (3): 249-267. DOI:10.1504/IJMDM.2011.040702.
Zhou W, Zhu B, Li Q, Ma T, Hu S, & Griffy-Brown C (2010). CO2 emissions and mitigation potential in China's ammonia industry. Energy Policy 38 (7): 3701-3709. DOI:10.1016/j.enpol.2010.02.048.
Nie K, Lin S, Ma T, & Nakamori Y (2010). Connecting informal networks to management of tacit knowledge. Journal of Systems Science and Systems Engineering 19 (2): 237-253. DOI:10.1007/s11518-010-5130-1.
Ma T & Nakamori Y (2009). Modeling technological change in energy systems -- From optimization to agent-based modeling. Energy 34 (7): 873-879. DOI:10.1016/j.energy.2009.03.005.
Ma T, Grubler A , & Nakamori Y (2009). Modeling technology adoptions for sustainable development under increasing returns, uncertainty, and heterogeneous agents. European Journal of Operational Research 195 (1): 296-306. DOI:10.1016/j.ejor.2008.01.036.
Ma T (2009). Can we Move to a Low Carbon Economy with Little Costs? A View from the Perspective of Uncertain Endogenous Technological Change. Conference presentation: CSM'2009: 22nd Workshop on Methodologies and Tools for Complex System Modeling, 31 August - 2 September 2009, IIASA, Laxenburg, Austria
Ma T & Nakamori Y (2008). Modeling uncertainties of technological learning with stochastic optimization. In: Proceedings, 9th International Symposium on Knowledge and Systems Sciences, 11-12 December.
Zhang L, Nie G, Ma T, Liu F, & Shi Y (2008). An intelligent process-oriented knowledge management system between human, process and knowledge. In: Proceedings, 9th International Symposium on Knowledge and Systems Sciences, 11-12 December.
Ma T, Grubler A , Nakicenovic N , & Arthur WB (2008). Technologies as agents of change: A simulation model of the evolving complexity of the global energy system. IIASA Interim Report. IIASA, Laxenburg, Austria: IR-08-021
Ma T & Nakamori Y (2008). Coping with uncertainties in endogenous technological change models. Workshop presentaion, IFIP WG 7.6 Workshop on Modelling and decision support for network-based services, 1-3 September 2008, Warsaw, Poland
Ma T & Grubler A (2008). The evolution of technological complexity: An agent-based simulation model of the global energy system. In: Modeling Environment-Improving Technological Innovations under Uncertainty. Eds. Golub, A.A. & Markandya, A., pp. 205-244 London, UK: Routledge. ISBN 978-0-415-46376-8
Ma T, Wierzbicki A, & Nakamori Y (2007). Establish a creative environment for roadmapping in academy—From the perspective of i-system methodology. Journal of Systems Science and Systems Engineering 16 (4): 469-488. DOI:10.1007/s11518-007-5047-5.
Ma T, Wierzbicki AP, & Nakamori Y (2007). Establish a creative environment for roadmapping in academy - From the perspective of i-system methodology. Journal of Systems Science and Systems Engineering 16 (4): 469-488. DOI:10.1007/s11518-007-5047-5.
Nie K, Ma T, & Nakamori Y (2007). Building a taxonomy for understanding knowledge management. Electronic Journal of Knowledge Management 5 (4): 453-466.
Ma T & Nakamori Y (2007). Agent-based simulation for Kansei Engineering: Testing a fuzzy linear quantification method in an artificial world. Journal of Systems Science and Systems Engineering 16 (3): 308-322. DOI:10.1007/s11518-007-5055-5.
Ma T & Nakamori Y (2007). Agent-based modeling and simulation on network externality and knowledge strategy. International Journal of Knowledge and Systems Sciences 4 (1): 25-30.
Ma T, Yan J, Nakamori Y, & Wierzbicki AP (2007). Creativity Support for Roadmapping. In: Creative Environments: Issues of Creativity Support for the Knowledge Civilization Age. Eds. Wierzbicki, A. & Nakamori, Y., pp. 155-189 Springer. ISBN 978-3-540-71466-810.1007/978-3-540-71562-7_7.
Ma T, Yan Y, & Nakamori Y (2007). Roadmapping and i-systems. In: Computational Science - ICCS 2007. Eds. Shi, Y., Aldaba, G.D. van, Dongara, J. & Sloot, P.M.A., Germany pp.1-8 (2007): Springer Berlin/Heidelberg. ISBN 978-3-540-72589-310.1007/978-3-540-72590-9_1.
Ma T (2006). An Agent-Based Model of Endogenous Technological Change -- An Extension to the Grubler-Gritsevskyi Model. IIASA Interim Report. IIASA, Laxenburg, Austria: IR-06-044
Ma T, Liu S, & Nakamori Y (2006). Roadmapping as a way of knowledge management for supporting scientific research in academia. Systems Research and Behavioral Science 23 (6): 743-755. DOI:10.1002/sres.708.
Ma T, Nakamori Y, & Huang W (2006). An agent-based approach for predictions based on multi-dimensional complex data. Information Sciences 176 (9): 1156-1174. DOI:10.1016/j.ins.2005.07.011.
Ma T (2006). Modeling endogenous uncertain technological change with heterogeneous agents. In: Proceedings of the 3rd International Symposium on Systems & Human Science: Complex Systems Approaches for Safety, Security and Reliability (SSR 2006), 6-8 March 2006.
Ma T (2005). Modeling endogenous technological change with heterogeneous agents. In: Proceedings of the 1st World Congress of the International Federation for Systems Research, 14-17 November 2005.
Ma T (2005). Modeling technology transitions under increasing returns, uncertainty, and heterogeneous agents. In: Proceedings of the 6th International Symposium on Knowledge and Systems Sciences, 29-31 August 2005.
Ma T & Nakamori Y (2005). Roadmapping and i-systems for supporting scientific research. International Journal of Knowledge and Systems Sciences 2 (1): 66-72.
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