Suchergebnis: Katalogdaten im Frühjahrssemester 2021

Umweltnaturwissenschaften Master Information
Vertiefung in Umweltsysteme und Politikanalyse
Modellierung und statistische Analyse
701-1252-00LClimate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation Belegung eingeschränkt - Details anzeigen
Number of participants limited to 50.

Waiting list until 05.03.2021.
W3 KP2V + 1UD. N. Bresch, R. Knutti
KurzbeschreibungThe course introduces the concepts of predictability, probability, uncertainty and probabilistic risk modelling and their application to climate modeling and the economics of climate adaptation.
LernzielStudents will acquire knowledge in uncertainty and risk quantification (probabilistic modelling) and an understanding of the economics of climate adaptation. They will become able to construct their own uncertainty and risk assessment models (in Python), hence basic understanding of scientific programming forms a prerequisite of the course.
InhaltThe first part of the course covers methods to quantify uncertainty in detecting and attributing human influence on climate change and to generate probabilistic climate change projections on global to regional scales. Model evaluation, calibration and structural error are discussed. In the second part, quantification of risks associated with local climate impacts and the economics of different baskets of climate adaptation options are assessed – leading to informed decisions to optimally allocate resources. Such pre-emptive risk management allows evaluating a mix of prevention, preparation, response, recovery, and (financial) risk transfer actions, resulting in an optimal balance of public and private contributions to risk management, aiming at a more resilient society.
The course provides an introduction to the following themes:
1) basics of probabilistic modelling and quantification of uncertainty from global climate change to local impacts of extreme events
2) methods to optimize and constrain model parameters using observations
3) risk management from identification (perception) and understanding (assessment, modelling) to actions (prevention, preparation, response, recovery, risk transfer)
4) basics of economic evaluation, economic decision making in the presence of climate risks and pre-emptive risk management to optimally allocate resources
SkriptPowerpoint slides will be made available.
LiteraturMany papers for in-depth study will be referred to during the lecture.
Voraussetzungen / BesonderesHands-on experience with probabilistic climate models and risk models will be acquired in the tutorials; hence good understanding of scientific programming forms a prerequisite of the course, in Python (teaching language, object oriented) or similar. Basic understanding of the climate system, e.g. as covered in the course 'Klimasysteme' is required, as well as beginner level in statistical and time series analysis.

Examination: graded tutorials during the semester (benotete Semesterleistung)
701-1522-00LMulti-Criteria Decision Analysis Belegung eingeschränkt - Details anzeigen
Number of participants limited to 25.
W3 KP2GJ. Lienert
KurzbeschreibungThis 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).
LernzielThe 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 will learn to include uncertainty into decision models and test assumptions with sensitivity analyses. Participants 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, and reading. 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. They will summarize this work in three graded reports. Additional reading from the textbook Eisenführ et al. (2010) is required.

The group work consists of three written reports to be delivered at fixed dates during the semester with following grading: Report 1: 20%, Report 2: 40%, Report 3: 40%.
SkriptNo script (see below)
LiteraturThe 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.
Voraussetzungen / BesonderesThe 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.

The course is limited to 25 participants (first come, first served).
701-1674-00LSpatial Analysis, Modelling and Optimisation Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 25

Voraussetzung: Teilnahme an der Lehrveranstaltung 701-0951-00L "GIST - Einführung in die räumlichen Informationswissenschaften und -technologien" oder eine gleichwertige Vorbildung.
W5 KP4GM. A. M. Niederhuber, V. Griess
KurzbeschreibungProblems encountered in forest- and landscape management often have a spatial dimension. Methods and technics of geoinformation sciences GIS and/or optimization give support to identify good solutions. Students learn to conceptualize, implement and combine I) spatial analysis & modeling of geodata and, II) optimization techniques, based on theoretical inputs and practical work on small projects.
LernzielUnderstand, search for, and manage various types of geospatial data; Carry out conceptual data modelling for a spatial and/or optimisation problem and translate it into a tangible form within a GIS software; Conceptualize spatial and/or optimisation 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 and/or optimisation software, verify and validate the procedures, then present the final results.
Voraussetzungen / BesonderesKnowledge and skills equal those of the course "GIST - Einführung in die räumliche Informationswissenschaften und Technologien"
752-2110-00LMultivariate Statistical Analysis Belegung eingeschränkt - Details anzeigen W3 KP2VC. Hartmann, A. Bearth
KurzbeschreibungEs wird in die Logik des Signifikanztests, in die Datenexploration und in die Anwendung des Statistikprogramms SPSS eingeführt. Die folgende Analysemethoden werden behandelt: Regressionsanalyse, Faktorenanalyse und Varianzanalyse. Theoretische Vorlesungen werden abgewechselt mit Übungen am Computer, wobei die Daten mit Hilfe des SPSS analysiert und die Ergebnisse interpretiert werden.
LernzielStudierenden lernen multivariate Analysemethoden anzuwenden und die Ergebnisse zu interpretieren, durch Theorie und Übung.
InhaltIn der Lehrveranstaltung werden die theoretischen und auswertungstechnischen Grundlagen der multivariaten Analysemethoden vermittelt, die in den Bereichen Lebensmittelsensorik, Verbraucherverhalten und Umweltwissenschaften verbreitet eingesetzt werden. Damit die Studierenden über die erforderlichen Grundlagen verfügen, werden sie zu Beginn der Veranstaltung in die Logik des Signifikanztests, in die Datenexploration und in die Anwendung des Statistikprogramms SPSS eingeführt. Die folgende Analysemethoden werden behandelt: die Regressionsanalyse, Faktorenanalyse und die Varianzanalyse. Theoretische Vorlesungen werden abgewechselt mit Übungen am Computer, wobei die Daten mit Hilfe des SPSS analysiert und die Ergebnisse interpretiert werden.
LiteraturField, A. (2013). Discovering Statistics Using SPSS (4th edition). Sage Publications. ISBN: 1-4462-4918-2 (and any other edition)
Voraussetzungen / BesonderesDieser Kurs wird auf English gehalten.
Dieser Kurs wird online stattfinden.
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