Search result: Catalogue data in Spring Semester 2019
|Environmental Sciences Master|
|Major in Environmental Systems and Policy|
|Modeling and Statistical Analysis|
|701-1522-00L||Multi-Criteria Decision Analysis||W||3 credits||2G||J. Lienert|
|Abstract||This introduction to "Multi-Criteria Decision Analysis" (MCDA) combines prescriptive Decision Theory (MAVT, MAUT) with practical application and computer-based decision support systems. Aspects of descriptive Decision Theory (psychology) are introduced. Participants apply the theory to an environmental decision problem (group work).|
|Objective||The main objective is to learn the theory of "Multi-Attribute Value Theory" (MAVT) and "Multi-Attribute Utility Theory" (MAUT) and apply it step-by-step using an environmental decision problem. The participants learn how to structure complex decision problems and break them down into manageable parts. An important aim is to integrate the goals and preferences of different decision makers. The participants will practice how to elicit subjective (personal) preferences from decision makers with structured interviews. They should have an understanding of people's limitations to decision-making, based on insights from descriptive Decision Theory. They will use formal computer-based tools to integrate "objective / scientific" data with "subjective / personal" preferences to find consensus solutions that are acceptable to different decision makers.|
Multi-Criteria Decision Analysis is an umbrella term for a set of methods to structure, formalize, and analyze complex decision problems involving multiple objectives (aims, criteria), many different alternatives (options, choices), and different actors which may have conflicting preferences. Uncertainty (e.g., of the future or of environmental data) adds to the complexity of environmental decisions. MCDA helps to make decision problems more transparent and guides decision makers into making rational choices. Today, MCDA-methods are being applied in many complex decision situations. This class is designed for participants interested in transdisciplinary approaches that help to better understand real-world decision problems and that contribute to finding sustainable solutions. The course focuses on "Multi-Attribute Value Theory" (MAVT) and "Multi-Attribute Utility Theory" (MAUT). It also gives a short introduction to behavioral Decision Theory, the psychological field of decision-making.
The course consists of a combination of lectures, exercises in the class, exercises in small groups, reading, and one mandatory exam. Some exercises are computer assisted, applying MCDA software. The participants will choose an environmental case study to work on in small groups throughout the semester. Additional reading from the textbook Eisenführ et al. (2010) is required.
There will be one written examination at the end of the course that covers the important theory (50 % of final grade). The group work consists of two written reports (50 %).
|Lecture notes||No script (see below)|
|Literature||The course is based on: Eisenführ, Franz; Weber, Martin; and Langer, Thomas (2010) Rational Decision Making. 1st edition, 447 p., Springer Verlag, ISBN 978-3-642-02850-2.|
Additional reading material will be recommended during the course. Lecture slides will be made available for download.
|Prerequisites / Notice||The course requires some understanding of (basic) mathematics. The "formal" parts are not too complicated and we will guide students through the mathematical applications and use of software.|
|701-1674-00L||Geospatial Data Management and Analysis |
Number of participants limited to 25.
Prerequisites: 701-0951-00L "GIS - Introduction into Geoinformation Science" in autum semester or comparable preparatory training.
|W||5 credits||4G||M. A. M. Niederhuber, T. Crowther|
|Abstract||Problems encountered in forest and landscape management often have a spatial dimension. Methods of geoinformation sciences provide support in identifying creative solutions. Students will learn to a) understand, search for, and manage different forms of geospatial data; b) conceptualize, implement, and combine spatial analysis methods; and c) interpret the results.|
|Objective||Understand, search for, and manage various types of geospatial data; Carry out conceptual data modelling for a spatial problem and translate it into a tangible form within a GIS software; Conceptualize spatial problems and design a workflow that transitions from "data processing" through "advanced spatial analysis" to "presentation of results"; Implement such a workflow in standard GIS software, verify and validate the procedures, then present the final results.|
|Prerequisites / Notice||Knowledge and skills equal those of the course "GIST - Einführung in die räumliche Informationswissenschaften und Technologien"|
|363-1076-00L||Diffusion of Clean Technologies||W||3 credits||2G||B. Girod, C. Knöri|
|Abstract||How can the diffusion of clean technologies be accelerated by policy or business strategies? |
Participants learn to apply analytic tools to evaluate environmental and business potentials of clean technologies. Exercises that evaluate a selected clean technology deepen the theoretical knowledge gained. Students are trained to evaluate, explain and present a clean technology.
|Objective||Students are able to ...|
1) apply the theoretical concepts introduced to a specific clean technology case
2) determine key drivers and barriers (economic, environmental, technological, regulatory) for diffusion of clean technologies
3) quantitatively model key characteristics or dynamics of selected clean technologies
4) convincingly present a selected clean technology to a business or policy audience
|Content||Analytical tools to assess the environmental performance of clean technologies (e.g. Life Cycle-Assessment); economic view on the diffusion of clean technologies; evolutionary perspective (e.g. technological learning); decision process of adopters (e.g. status-quo bias of consumers, rebound effect); relevant environmental policies (e.g. standards, labels, carbon pricing); modelling approaches for diffusion of clean technologies (e.g. agent based modelling); techniques for convincing presentations (e.g. TED style presentation).|
|Lecture notes||Slides and exercises will be available on electronic platform.|
|Literature||Relevant literature will be available on electronic platform.|
|752-2110-00L||Multivariate Statistical Analysis||W||3 credits||2V||C. Hartmann, A. Bearth|
|Abstract||The course starts by introducing some basic statistical concepts and methods, e.g. data exploration, the idea behind significance testing, and the use of the statistical software SPSS. Based on these fundaments, the following analyses are discussed: regression analysis, factor analysis and variance analysis.|
|Objective||Students will learn to use multivariate analysis methods and to interpret their results, by means of theory and practice.|
|Content||This course provides an introduction into the theories and practice of multivariate analysis methods that are used in the fields of food sensory science, consumer behavior and environmental sciences. The course starts by introducing some basic statistical concepts and methods, e.g. data exploration, the idea behind significance testing, and the use of the statistical software SPSS. Based on these fundaments, the following analyses are discussed: regression analysis, factor analysis and variance analysis. During the course, theoretical lectures alternate with practical sessions in which data are analyzed and their results are interpreted using SPSS.|
21.02 Introduction to the course and basic concepts of multivariate statistics (Hartmann) in Room HG D5.2
28.02 Data handling and exploration + SPSS Introduction (Hartmann)
07.03 Exercise 1a+b (Hartmann)
14.03 Basic Statistical Tests (Bearth)
21.03 Exercise 2: Basic Statistical Tests (Bearth)
28.03 Regression analysis (Hartmann)
04.04 Exercise 3: Regression analysis (Hartmann)
11.04 Variance Analysis (Bearth)
18.04 Exercise 4: Variance Analysis (Hartmann)
02.05 Reliability Analysis (Bearth)
09.05 Principle Component Analysis (Bearth)
16.05 Exercise 5: PCA and Reliability Analysis (Hartmann)
23.05 EXAM (Room will be announced)
|Literature||Field, A. (2013). Discovering Statistics Using SPSS (all Editions). Sage Publications. ISBN: 1-4462-4918-2|
|Prerequisites / Notice||This course will be given in English.|
|860-0022-00L||Complexity and Global Systems Science |
Number of participants limited to 64.
Prerequisites: solid mathematical skills.
Particularly suitable for students of D-ITET, D-MAVT and ISTP
|W||3 credits||2V||D. Helbing, N. Antulov-Fantulin|
|Abstract||This course discusses complex techno-socio-economic systems, their counter-intuitive behaviors, and how their theoretical understanding empowers us to solve some long-standing problems that are currently bothering the world.|
|Objective||Participants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop models for open problems, to analyze them, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to think scientifically about complex dynamical systems.|
|Content||This course starts with a discussion of the typical and often counter-intuitive features of complex dynamical systems such as self-organization, emergence, (sudden) phase transitions at "tipping points", multi-stability, systemic instability, deterministic chaos, and turbulence. It then discusses phenomena in networked systems such as feedback, side and cascade effects, and the problem of radical uncertainty. The course progresses by demonstrating the relevance of these properties for understanding societal and, at times, global-scale problems such as traffic jams, crowd disasters, breakdowns of cooperation, crime, conflict, social unrests, political revolutions, bubbles and crashes in financial markets, epidemic spreading, and/or "tragedies of the commons" such as environmental exploitation, overfishing, or climate change. Based on this understanding, the course points to possible ways of mitigating techno-socio-economic-environmental problems, and what data science may contribute to their solution.|
|Prerequisites / Notice||Mathematical skills can be helpful|
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