Search result: Catalogue data in Autumn Semester 2017

Environmental Sciences Master Information
Major in Environmental Systems Policy
Modeling and Statistical Analysis
NumberTitleTypeECTSHoursLecturers
701-1453-00LEcological Assessment and Evaluation Information W3 credits3GF. Knaus
AbstractThe 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.
ObjectiveStudents 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.
Lecture notesPowerpoint slides are available on the webpage. Additional documents are handed out as copies.
LiteratureBasic literature and references are listed on the webpage.
Prerequisites / NoticeThe 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
701-1541-00LMultivariate Methods
One of the lectures 701-1541-00 (autumn semester) OR 752-2110-00 (spring semester) are highly recommended for students in Environmental Sciences with the Major Environmental systems and Policy.
W3 credits2V + 1UR. Hansmann
AbstractThe course teaches multivariate statistical methods such as linear regression, analysis of variance, cluster analysis, factor analysis and logistic regression.
ObjectiveUpon completion of this course, the student should have acquired:
(1) Knowledge on the foundations of several methods of multivariate data analysis, along with the conditions under which their use is appropriate
(2) Skill in the estimation, specification and diagnostics of the various models
(3) Hands-on experience with those methods through the use of appropriate software and actual data sets in the PC lab
ContentThe course will begin with an introduction to multivariate methods such as analysis of variance and multiple linear regression, where a metric dependent variable is "explained" by two or more independent variables. Then two methods for structuring complex data, cluster analysis and factor analysis will be covered. In the last part, procedures for the analysis of relationships involving dichotomous or polytomous dependent variables (e.g., the choice of a mode of transportation) will be discussed.
LiteratureWill be announced at the beginning of the course.
101-0491-00LAgent Based Modeling in TransportationW3 credits2GM. Balac, T. J. P. Dubernet
AbstractThe main topics of the lecture are:
1) Introduction to the agent-based paradigm and overview on existing agent-based models in transportation, including MATSim
2) Learn how to setup MATSim for policy analysis
3) Learn how to extend the software (includes Java programming)
4) Create, run and analyse a policy study
ObjectiveThe objective of this course is to make the students familiar with agent-based models and in particular with the software MATSim. They will learn the pros and cons of this type of approach versus traditional transport models and will learn to use the simulation. They will design a policy study and run simulations to evaluate the impacts of the proposed policies.
ContentThe main topics are:
1) Introduction to the agent-based paradigm and overview on existing agent-based models in transportation, including MATSim
2) Introduction of basic modeling concepts (activity-based approach, user equilibrium...)
3) Learn how to setup MATSim for policy analysis
4) Learn how to extend the software (includes Java programming)
5) Create, run and analyse a policy study
LiteratureAgent-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
(Link)

Additional relevant readings, mostly scientific articles, will be recommended throughout the course.
Prerequisites / NoticeThere are no strict preconditions in terms of which lectures the students should have previously attended. However, it is expected that the students have some experience with some high level programming language (i.e. C, C++, Fortran or Java). If this is not the case, attending the additional java exercises (101-0491-00U) is strongly encouraged.
363-0541-00LSystems Dynamics and ComplexityW3 credits3GF. Schweitzer, G. Casiraghi, V. Nanumyan
AbstractFinding 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
ObjectiveA 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
ContentWhy 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 notesThe 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 / NoticeSelf-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.
860-0002-00LQuantitative Policy Analysis and ModelingO6 credits4GA. Patt, T. Schmidt, E. Trutnevyte, O. van Vliet
AbstractThe 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
ObjectiveThe 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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