# Suchergebnis: Katalogdaten im Herbstsemester 2018

Umweltnaturwissenschaften Master | ||||||

Vertiefung in Umweltsysteme und Politikanalyse | ||||||

Modellierung und statistische Datenanalyse | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
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701-1453-00L | Ecological Assessment and Evaluation | W | 3 KP | 3G | F. Knaus | |

Kurzbeschreibung | The course provides methods and tools for ecological evaluations dealing with nature conservation or landscape planning. It covers census methods, ecological criteria, indicators, indices and critically appraises objectivity and accuracy of the available methods, tools and procedures. Birds and plants are used as main example guiding through different case studies. | |||||

Lernziel | Students will be able to: 1) critically consider biological data books and local, regional, and national inventories; 2) evaluate the validity of ecological criteria used in decision making processes; 3) critically appraise the handling of ecological data and criteria used in the process of evaluation 4) perform an ecological evaluation project from the field survey up to the descision making and planning. | |||||

Skript | Powerpoint slides are available on the webpage. Additional documents are handed out as copies. | |||||

Literatur | Basic literature and references are listed on the webpage. | |||||

Voraussetzungen / Besonderes | The course structure changes between lecture parts, seminars and discussions. The didactic atmosphere is intended as working group. Prerequisites for attending this course are skills and knowledge equivalent to those taught in the following ETH courses: - Pflanzen- und Vegetationsökologie - Systematische Botanik - Raum- und Regionalentwicklung - Naturschutz und Naturschutzbiologie | |||||

701-1541-00L | Multivariate MethodsStudierenden der Umweltnaturwissenschaften mit der Vertiefung Umweltsysteme und Politikanalyse wird sehr empfohlen entweder die Lehrveranstaltung 701-1541-00 im Herbstsemester ODER 752-2110-00 im Frühjahrssemester zu belegen. | W | 3 KP | 2V + 1U | R. Hansmann | |

Kurzbeschreibung | Die Veranstaltung behandelt multivariate statistische Methoden wie lineare Regression, Varianzanalyse, Clusteranalyse, Faktorenanalyse und logistische Regression. | |||||

Lernziel | Erlernen (1) von Grundlagen und Anwendungsbedingungen unterschiedlicher multivariater Methoden, (2) der Schätzung, Spezifikation und Diagnostik von Modellen, (3) der Anwendung der Methoden mittels geeigneter Software anhand von Datensätzen im PC-Labor. | |||||

Inhalt | Die Veranstaltung beginnt mit einer Einführung in multivariate Methoden wie Varianzanalyse und multiple lineare Regression, bei denen eine metrische abhängige Variable durch mehrere unabhängige Variablen "erklärt" wird. Es folgen die zwei strukturierenden Verfahren Clusteranalyse und Faktorenanalyse. Im letzten Teil werden Verfahren zur Untersuchung von Zusammenhängen mit dichotomen oder polytomen abhängigen Variablen (z.B. die Wahl von Verkehrsmitteln) vorgestellt. | |||||

Literatur | Wird zu Veranstaltungsbeginn bekannt gegeben. | |||||

101-0491-00L | Agent Based Modeling in Transportation | W | 6 KP | 4G | T. J. P. Dubernet, M. Balac | |

Kurzbeschreibung | This lectures provides a round tour of agent based models for transportation policy analysis. First, it introduces statistical methods to combine heterogeneous data sources in a usable representation of the population. Then, agent based models are described in details, and applied in a case study. | |||||

Lernziel | At the end of the course, the students should: - be aware of the various data sources available for mobility behavior analysis - be able to combine those data sources in a coherent representation of the transportation demand - understand what agent based models are, when they are useful, and when they are not - have working knowledge of the MATSim software, and be able to independently evaluate a transportation problem using it | |||||

Inhalt | This lecture provides a complete introduction to agent based models for transportation policy analysis. Two important topics are covered: 1) Combination of heterogeneous data sources to produce a representation of the transport system At the center of agent based models and other transport analyses is the synthetic population, a statistically realistic representation of the population and their transport needs. This part will present the most common types of data sources and statistical methods to generate such a population. 2) Use of Agent-Based methods to evaluate transport policies The second part will introduce the agent based paradigm in details, including tradeoffs compared to state-of-practice methods. An important part of the grade will come from a policy analysis to carry with the MATSim open-source software, which is developed at ETH Zurich and TU Berlin and gets used more and more by practitioners, notably the Swiss rail operator SBB. | |||||

Literatur | Agent-based modeling in general Helbing, D (2012) Social Self-Organization, Understanding Complex Systems, Springer, Berlin. Heppenstall, A., A. T. Crooks, L. M. See and M. Batty (2012) Agent-Based Models of Geographical Systems, Springer, Dordrecht. MATSim Horni, A., K. Nagel and K.W. Axhausen (eds.) (2016) The Multi-Agent Transport Simulation MATSim, Ubiquity, London (http://www.matsim.org/the-book) Additional relevant readings, mostly scientific articles, will be recommended throughout the course. | |||||

Voraussetzungen / Besonderes | There are no strict preconditions in terms of which lectures the students should have previously attended. However, knowledge of basic statistical theory is expected, and experience with high-level programming languages (Java, R, Python...) is useful. | |||||

363-0541-00L | Systems Dynamics and Complexity | W | 3 KP | 3G | F. Schweitzer, G. Casiraghi, V. Nanumyan | |

Kurzbeschreibung | 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 | |||||

Lernziel | 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 | |||||

Inhalt | 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. | |||||

Skript | 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 | |||||

Voraussetzungen / Besonderes | 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. | |||||

860-0002-00L | Quantitative Policy Analysis and Modeling | O | 6 KP | 4G | A. Patt, S. Hanger-Kopp, S. Pfenninger, T. Schmidt | |

Kurzbeschreibung | The lectures will introduce students to the principles of quantitative policy analysis, namely the methods to predict and evaluate the social, economic, and environmental effects of alternative strategies to achieve public objectives. A series of graded assignments will give students an opportunity for students to apply those methods to a set of case studies | |||||

Lernziel | The objectives of this course are to develop the following key skills necessary for policy analysts: - Identifying the critical quantitative factors that are of importance to policy makers in a range of decision-making situations. - Developing conceptual models of the types of processes and relationships governing these quantitative factors, including stock-flow dynamics, feedback loops, optimization, sources and effects of uncertainty, and agent coordination problems. - Develop and program numerical models to simulate the processes and relationships, in order to identify policy problems and the effects of policy interventions. - Communicate the findings from these simulations and associated analysis in a manner that makes transparent their theoretical foundation, the level and sources of uncertainty, and ultimately their applicability to the policy problem. The course will proceed through a series of policy analysis and modeling exercises, involving real-world or hypothetical problems. The specific examples around which work will be done will concern the environment, energy, health, and natural hazards management. |

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