Search result: Catalogue data in Autumn Semester 2017
|Agroecosystem Sciences Master|
|Master Studies (Programme Regulations 2011)|
|Major in Food and Resource Use Economics|
|Methods in Food and Resource Use Economics|
|751-3801-00L||Experimental Design and Applied Statistics in Agroecosystem Science||W+||3 credits||2G||A. Hund, W. Eugster, C. Grieder, R. Kölliker|
|Abstract||In this course, different experimental designs will be discussed and various statistical tools will be applied to research questions in agroecosystem sciences. Both manipulative (field and laboratory) experiments and surveys are addressed and students work with a selection of basic techniques and methods to analyse data using a hands-on approach. Methods range from simple t-tests to multi-factoria|
|Objective||Students will know various statistical analyses and their application to science problems in their study area as well as a wide range of experimental design options used in environmental and agricultural sciences. They will practice to use statistical software packages (R), understand pros and cons of various designs and statistics, and be able to statistically evaluate their own results as well as those of published studies.|
|Content||The course program uses a learning-by-doing approach ("hands-on minds-on"). New topics are introduced in the lecture hall, but most of the work is done in the computer lab to allow for the different speeds of progress of the student while working with data and analyzing results. In addition to contact hours exercises must be finalized and handed in for grading. The credit points will be given based on successful assessments of selected exercises.|
The tentative schedule containst the following topics:
Introduction To Experimental Design and Applied Statistics
Introduction to 'R' / Revival of 'R' Skills
Designs of Field and Growth Chamber Experiments
Nonlinear Regression Fits
Multivariate Techniques: Principle Component Analysis, Canonical Correpondence Analysis (CCA), Cluster Analysis
ANOVA using linear and mixed effect models
Error Analysis, Error Propagation and Error Estimation
Introduction to autoregression and autocorrelations in temporal and spatial data and how to consider them in ANOVA-type analysis
This course does not provide the mathematical background that students are expected to bring along when signing up to this course. Alternatively, students can consider some aspects of this course as a first exposure to solutions in experimental design and applied statistics and then deepen their understanding in follow-up statistical courses.
|Lecture notes||Handouts will be available (in English)|
|Literature||A selection of suggested additional literature, especially for German speaking students will be presented in the introductory lecture.|
|Prerequisites / Notice||This course is based on the course Mathematik IV: Statistik, passed in the 2nd year and the Bachelor's course "Wissenschaftliche Datenauswertung und Datenpräsentation" (751-0441-00L)|
|363-0541-00L||Systems Dynamics and Complexity||W+||3 credits||3G||F. Schweitzer, G. Casiraghi, V. Nanumyan|
|Abstract||Finding solutions: what is complexity, problem solving cycle.|
Implementing solutions: project management, critical path method, quality control feedback loop.
Controlling solutions: Vensim software, feedback cycles, control parameters, instabilities, chaos, oscillations and cycles, supply and demand, production functions, investment and consumption
|Objective||A successful participant of the course is able to: |
- understand why most real problems are not simple, but require solution methods that go beyond algorithmic and mathematical approaches
- apply the problem solving cycle as a systematic approach to identify problems and their solutions
- calculate project schedules according to the critical path method
- setup and run systems dynamics models by means of the Vensim software
- identify feedback cycles and reasons for unintended systems behavior
- analyse the stability of nonlinear dynamical systems and apply this to macroeconomic dynamics
|Content||Why are problems not simple? Why do some systems behave in an unintended way? How can we model and control their dynamics? The course provides answers to these questions by using a broad range of methods encompassing systems oriented management, classical systems dynamics, nonlinear dynamics and macroeconomic modeling. |
The course is structured along three main tasks:
1. Finding solutions
2. Implementing solutions
3. Controlling solutions
PART 1 introduces complexity as a system immanent property that cannot be simplified. It introduces the problem solving cycle, used in systems oriented management, as an approach to structure problems and to find solutions.
PART 2 discusses selected problems of project management when implementing solutions. Methods for identifying the critical path of subtasks in a project and for calculating the allocation of resources are provided. The role of quality control as an additional feedback loop and the consequences of small changes are discussed.
PART 3, by far the largest part of the course, provides more insight into the dynamics of existing systems. Examples come from biology (population dynamics), management (inventory modeling, technology adoption, production systems) and economics (supply and demand, investment and consumption). For systems dynamics models, the software program VENSIM is used to evaluate the dynamics. For economic models analytical approaches, also used in nonlinear dynamics and control theory, are applied. These together provide a systematic understanding of the role of feedback loops and instabilities in the dynamics of systems. Emphasis is on oscillating phenomena, such as business cycles and other life cycles.
Weekly self-study tasks are used to apply the concepts introduced in the lectures and to come to grips with the software program VENSIM.
|Lecture notes||The lecture slides are provided as handouts - including notes and literature sources - to registered students only. All material is to be found on the Moodle platform. More details during the first lecture|
|Prerequisites / Notice||Self-study tasks (discussion exercises, Vensim exercises) are provided as home work. Weekly exercise sessions (45 min) are used to discuss selected solutions. Regular participation in the exercises is an efficient way to understand the concepts relevant for the final exam.|
|401-0647-00L||Introduction to Mathematical Optimization||W+||5 credits||2V + 1U||D. Adjiashvili|
|Abstract||Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering.|
|Objective||The goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering.|
|Content||Topics covered in this course include:|
- Linear programming (simplex method, duality theory, shadow prices, ...).
- Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...).
- Modelling with mathematical optimization: applications of mathematical programming in engineering.
|Literature||Information about relevant literature will be given in the lecture.|
|Prerequisites / Notice||This course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications.|
|751-0423-00L||Risk Analysis and Risk Management in Agriculture||W+||3 credits||2G||R. Finger|
|Abstract||Agricultural production is exposed to various risks which are important for decisions taken by farmers and other actors in the agri-food sector. Moreover, risk management is indispensable for all actors. This course introduces modern concepts on decision making under risk and recent developments in risk management. The focus of this course in on agriculture applications.|
|Objective||-to develop a better understanding of decision making under uncertainty and risk;|
-to gain experience in different approaches to analyze risky decisions;
-to develop an understanding for different sources of risk in agricultural production;
-to understand the crucial role of subjective perceptions and preferences for risk management decisions;
-to get an overview on risk management in the agricultural sector, with a particular focus on insurance solutions
|Content||- Quantification and measurement of risk|
- Risk preferences, expected utility theory and alternative models of risk behavior
- Concepts on the decision making under risk
- Production, investment and diversification decisions under risk
- Risk management in agriculture
|Lecture notes||Handouts will be distributed in the lecture and available on the moodle.|
|Prerequisites / Notice||knowledge of basic concepts of probability theory and microeconomics|
|751-1573-00L||Dynamic Simulation in Agricultural and Regional Economics||W||2 credits||2V||B. Kopainsky|
|Abstract||In this class, students learn the basics of system dynamics and its application to agricultural and regional economic questions. In the second half of the class, students develop their own simulation model, with which they evaluate potential interventions for improving the economic as well as the ecological sustainability of food systems.|
|Objective||- Students learn the basic theory and practice of dynamic simulation|
- Students can develop, analyze and extend a dynamic simulation model and interpret its results.
- By applying the developed simulation model, students gain insights into food system issues. They also learn to recognize the benefits and pitfalls of dynamic simulation, both from a theoretical and an applied perspective.
|Lecture notes||slides (will be provided during the class)|
|Literature||articles and papers (will be provided during the class)|
|751-1575-00L||Applied Optimization in Agricultural Economics|
Does not take place this semester.
|363-0305-00L||Empirical Methods in Management||W+||3 credits||2G||F. von Wangenheim|
|Abstract||Evidence-based management requires valid empirical research. In this course, students will learn the basics of research design, fundamentals of data collection and statistical methods to analyze the data acquired in social science research. Students are expected to apply their knowledge in class discussions and out-of-class assignments.|
|Objective||- Ability to formulate research questions and designing an appropriate study|
- Ability to collect and analyze data using a variety of methods
- Ability to critically assess the quality of empirical research in management
- Applied knowledge of empirical methods through out-of-class assignments
|Content||1) Introduction to empirical management research|
2) Research designs: exploratory, descriptive, experimental
3) Measurement and scaling
4) Data collection and sampling
5) Data analysis methods
6) Reporting and presenting empirical research
|Prerequisites / Notice||Assignments and projects: This course includes out-of-class assignments and projects to give students some hands-on experience in conducting empirical research in management. Projects will focus on one particular aspect of empirical research, like the formulation of a research question or the design of a study. Students will have at least one week to work on each assignment. Students are expected to work on these assignments individually. Duplicate answers will receive no credit and will be subject to a disciplinary review. Assignments will be graded and need to be turned-in on time. |
Class participation: Class participation is encouraged and can greatly improve students' learning in this class. In this spirit, students are expected to attend class regularly and come to class prepared.
|363-0585-00L||Intermediate Econometrics||W+||3 credits||2V||M. Kesina|
|Abstract||The idea of this course is to familiarize students with instrumental variables estimation of linear regression models and the estimation of models with limited dependent variables as well as of nonlinear regression models. While most of the material covered will pertain to cross-sectional data, we will also work on selected issues with panel data.|
|Objective||I will provide STATA programs and show the execution thereof. After having participated in this course, students will be able to carry out simple research projects and understand the basics of intermediate econometrics. In particular, they will be able to write simple programs in STATA and to qualify their own and others' regression output relating to problems covered.|
|Literature||Jeffrey M. Wooldridge: Introductory Econometrics; Jeffrey M. Wooldridge: Econometric Analysis of Cross Section and Panel Data; A. Colin Cameron and Pravin K. Trivedi. Microeconometrics: Methods and Applications.|
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