«YSSP Participants 2016 Biographical Sketches and Research Project Abstracts Table of Contents Advanced Systems Analysis Program (ASA) Air Quality and ...»
a new generation of scientists
YSSP Young Scientists Summer Program
YSSP Participants 2016
Biographical Sketches and
Research Project Abstracts
Table of Contents
Advanced Systems Analysis Program (ASA)
Air Quality and Greenhouse Gases Program (AIR)
Arctic Futures Initiative
Evolution and Ecology Program (EEP)
Energy Program (ENE)
Ecosystems Services and Management Program (ESM)
World Population Program (POP)
Risk and Resilience Program (RISK)
Transitions to New Technologies Program (TNT)
Advanced Systems Analysis Program (ASA) Program Director: Elena Rovenskaya Osama Ibrahim Supervisor: Matthias Wildemeersch Research Project: A systems tool for prescriptive policy analysis.
Labelled causal mapping, A problem structuring method for policy modelling, simulation and decision analysis Abstract: Elicitation and processing of the relevant information is the core of any policy decision-making process. Modelling is about making sense of the available information. Models are able to incorporate the contextual influences on policy making (e.g. political and economic environments, community sentiment etc). Using simulation and visualisation techniques on the outputs of these models can help policy makers to reduce uncertainties on the possible impacts of policies.
In an effort to enable adoption of the systems thinking approach to address the central problem of empirical political study, this research presents an operational approach for structuring policy problems and prescribing preferred policies. Systems analysis allows quantitative, empirical testing of models that exist in the study of public policy. The proposed approach supports a prescriptive analysis for policy that includes: the problem definition, ex-ante impact assessment and evaluation activities carried out at the policy formulation stage of the policymaking process. This research presents a new tool 1 for systemic modelling and simulation of policy decision situations. It aims to facilitate the cognitive activity of representing complex mental models using system dynamics simulation modelling. Using the ’labelled causal mapping’ method, a policy-oriented problem structuring method is introduced in this research, the tool bridges the gap between the user’s mental model and the explicit graphical representation, in order to enable knowledge representation and system analysis. We are in several iterations of the build-evaluate cycle, using multiple demonstration and use cases, from various policy areas and different EU policymaking levels for the tool evaluation.
Biographical Sketch: Osama Ibrahim is a 3rd year PhD candidate at the Department of Computer and Systems Sciences, Stockholm University, Sweden. His research interests includes: decision support systems, risk and decision analysis, policy modelling and simulation, mathematical and statistical analysis, and stochastic operations research. He received his B.Sc. in Computer Engineering, (honours) and MSc in Engineering Mathematics and Operations Research, from Military Technical College in Egypt in 2000 and 2008.
1 A web-based prototype for the tool is reached through the URL: http://dev1.egovlab.eu:4001/
Abstract: Unmatched supply and demand is experienced differently at every region and for different development levels. On East coast of Scotland, agricultural run-off limits the availability of water to farmers themselves, as well as to environment and to other users. It is projected that the need for irrigation will increase greatly in the future in comparison to current amounts. These projections for increased agricultural water demand and variability in future makes the regulation of catchment based water resources and pollution a pressing issue for decision makers.
Water quality trading schemes and markets, are similar to carbon emission trading, and they are becoming popular as an economic tool to control ambient water pollution at reduced cost. However such schemes are also inherently uncertain, in particular, due to uncertainties of nonpoint and point pollution sources.
Adequately addressing the risks and uncertainties in the design of water pricing pollution taxation (trading) scheme is essential to create non-stagnant markets and achieve water quality targets in a safe and costeffective manner, which is the aim of this research. In our research we aim to revise and to apply the IIASA stochastic decentralized water pollution taxation model (WAP) operating under inherent uncertainties to one of these problematic catchments in Scotland. The overall objective is to investigate pathways to equalize the marginal value of pollution permits to shift water from low value users to high value users and achieve higher fairness and social return (with improved economic return and ambient water quality) at catchment level.
Biographical Sketch: Nazli Koseoglu is a third-year PhD student at the School of Geoscience, the University of Edinburgh and Scotland’s Agricultural College. Her PhD is a part of the Scottish Government’s Hydro Nation Programme and looks to evaluate the economic value of water and its optimal allocation to its competing uses in Scotland (as well as a partial identification of key Scottish industries’ water footprint). Her research aims to inform the Hydro Nation policy aspiration. Prior to her PhD, she received a joint MSc in Environmental Studies from Autonomous University of Barcelona and Technical University of Hamburg..
Research Project: Financing climate stability Abstract: Global warming has become reality in temperature anomalies, extreme weather events, unprecedented hurricane seasons and up to 50 inches sea level rise predicted until the end of the century.
History has also been made in reaching an iconic agreement on global warming mitigation at the UN Paris climate change conference.
The project proposes an innovative climate change mitigation approach with bonds funded through taxation imposed on future generations. Shifting the ultimate costs of climate change aversion to later generations leverages climate stability into a Pareto improving strategy for mankind.
While intergenerational burden sharing on climate change appears as viable real-world relevant emergent risk prevention; we currently lack an analytical understanding of the impact of climate mitigation through bonds on economic growth, the coordinated implementation of climate change burden sharing bonds as well as the model’s long-term effects.
The planned project will introduce intergenerational burden sharing into contemporary growth models of the Nordhaus type with attention to public deficit spending and tax ethics. Empirical analyses will help revealing the model’s viability in order to derive suggestions for global governance policy makers to efficiently herald climate justice in the 21st century.
Biographical Sketch: Julia M. Puaschunder graduated from the University of Vienna (2003 Master of Philosophy/Psychology, 2010 Doctor of Natural Sciences), Vienna University of Economics and Business (2006 Doctor of Social and Economic Sciences, 2007 Master of Business Administration), and the Maxwell School (2008 Master of Public Administration). She currently studies Law and Economics at the University of Vienna and The New School Department of Economics.
Julia launched and administered research projects on four continents. She was invited to present her research at Harvard, Princeton, Columbia, Oxford and Cambridge as well as The Academic Council on the United Nations System. As a current Prize Fellow in the Inter-University Consortium of New York, she supports the Economics of Climate Change Project Speaker Series hosted at The Schwartz Center for Economic Policy Analysis. At The New School, her research investigates western world intergenerational equity constraints in the domains of environmental sustainability, overindebtedness, and demographic aging.
Abstract: Detecting instability and predicting systemic risk in financial systems is of crucial importance for promoting long-term safety and stability of systems. In contrast to the common approaches to systemic risk assessment based on recorded numbers and statistical data (hard information), we measure the attitude and opinions of the financial system stakeholders toward risk by analyzing textual data (soft information).
The study exploits annual reports and publications of the publicly traded corporation. It extracts the opinions with forward-looking attitudes as a novel information resource for systemic financial risk prediction. We use sentiment analysis techniques to make a quantified assessment of the sentiments expressed in the financial texts. Beside systemic risk detection, an essential event in the financial system is when corporations or institutes face bankruptcy or are delisted from stock markets. Considering the importance of these cases, we also explore identifying them by modeling observed change patterns in the quantified risk indicators.
For these purposes, we apply state-of-the-art approaches in sentiment analysis, namely deep learning. Deep learning is an effective Neural-Network based method that has recently shown superior improvement for the sentiment analysis tasks in different domains e.g. social opinion mining, product reviews. The effectiveness of the method in other domains promises an interesting potential to study it on the financial data.
Biographical Sketch: Navid Rekabsaz is a second-year PhD candidate in the Vienna University of Technology, with research interest on information retrieval, natural language processing, and text mining.
He graduated (2015) from the master program of Software Engineering and Internet Computing from the same university. His research focus is on domain-specific search based on semantic text processing by applying information retrieval, machine learning and specifically deep learning techniques. Currently, he works at the Vienna University of Technology as research assistant on two projects on professional search in the domains of patent and health data.
Abstract: Cocoa is one of the most important commodities in Indonesia, contributing a significant portion to the country’s agricultural exports. Among others, Aceh has been recognized as one of the most vulnerable cocoa-producing regions in Indonesia. This results in a fragile cocoa industry in the region, consisting of an ineffective supply chain, an unequal distribution of economic value, less environmentalfriendly production and distribution, and social frictions between producing/transporting sub-regions. To enhance the supply-chain, an Appropriate Technology has been introduced into the system, requiring a system modeling to properly discover interventions for sustaining the technology and expanding its effects all over the chain. This research aims at developing the model by investigating the existing cocoa supplychain in Aceh. The interventions are first established through a multidisciplinary approach by being separated based on four perspectives. Technically, Appropriate Technology is posited as intervening cocoa post-harvest processing. Based on an economic perspective, Appropriate Technology is posited as intervening cocoa value-chain. Next, the system is interpreted by observing the system boundary of new intersection(s) between Life-Cycle Assessments of cocoa supply-chain and Appropriate Technology. Then, the technology is posited to affect accessibilities within the social system as a key to establish social interactions between producing/transporting cities/regencies. Finnally, these multidisciplinary approaches are treated as an interdisciplinary one to holistically solve interconnected problems within the observed system. The expected result includes an effective supply-chain, an equal economic value redistribution and environmental-friendly production and distribution, as a means to establish a more harmonious society.
Biographical Sketch: Corinthias P.M. Sianipar (Morgan) is a third-year PhD candidate at the School of Business and Management, Bandung Institute of Technology (ITB), Indonesia. Besides, he is currently enrolled as a third-year EngD candidate at the Department of Industrial Administration, Tokyo University of Science, Japan. His research foci in Indonesia include local economic development, community empowerment and the design & engineering of Appropriate Technology, while he has corresponding research interests in Japan, including systems engineering and social engineering. In terms of organizational activities, he is an active member of the Triple-Helix Association (THA) and the International Society for Horticultural Science (ISHS).
Abstract: Traditionally, energy systems evolved to have a hierarchical structure with central control. This has been working well with large dispatchable power generators and central grids feeding our homes with electricity. However, such a set-up has been demonstrated to be unsustainable posing threats to human security in the form of climate change, environmental pollution, unstable energy prices and geopolitical conflict.
Renewable electricity generation has been recognised as a very promising solution to tackle today’s energy sustainability problem. However, increasing penetration of renewable energy resources raises concerns for grid stability due to their intermittent nature of electricity generation. Plug-in electric vehicles (PEVs) offer a natural source of flexibility to the grid since charging can be coordinated with the power output from intermittent energy resources. ‘Smart grid’ technologies can provide the framework for optimal component management through bi-directional flow of information and energy. However, the distributed nature of renewable energy resources and the many stakeholders involved in the system challenge the effectiveness of centralised control.
This work explores the possibility of decentralised control of PEV charging guided by autonomous agents (representing self-interested consumers). Agents’ performance is evaluated in terms of their ability in reaching the global system goal of balancing intermittent energy supply with consumer demand. The simulation is performed using agent-based modelling and a number of mathematical approaches are investigated in terms of optimal agent behaviour.