Lynn Kaack: Catalogue data in Autumn Semester 2021 |
| Name | Ms Lynn Kaack |
| Address | Dep. Geistes-,Sozial-u.Staatswiss. Haldeneggsteig 4 ETH Zürich IFW D29.2, Frau Benita 8092 Zürich SWITZERLAND |
| Department | Humanities, Social and Political Sciences |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 876-0201-00L | Technology and Policy Analysis  Only for CAS in Technology and Public Policy: Impact Analysis | 8 credits | 5G | T. Schmidt, E. Ash, R. Garrett, I. Günther, L. Kaack, A. Rom, B. Steffen | |
| Abstract | Technologies substantially affect the way we live and how our societies function. Technological change, i.e. the innovation and diffusion of new technologies, is a fundamental driver of economic growth but can also have detrimental side effects. This module introduces methods to assess technology-related policy alternatives and to analyse how policies affect technological changes and society. | ||||
| Learning objective | Introduction: Participants understand (1) what ex ante and ex post policy impact analysis is, (2) in what forms and with what methods they can be undertaken, (3) why they are important for evidence-based policy-making. Analysis of Policy and Technology Options: Participants understand (1) how to perform policy analyses related to technology; (2) a policy problem and the rationale for policy intervention; (3) how to select appropriate impact categories and methods to address a policy problem through policy analysis; (4) how to assess policy alternatives, using various ex ante policy analysis methods; (5) and how to communicate the results of the analysis. Evaluation of Policy Outcomes: Participants understand (1) when and why policy outcomes can be evaluated based on observational or experimental methods, (2) basic methods for evaluating policy outcomes (e.g. causal inference methods and field experiments), (3) how to apply concepts and methods of policy outcome evaluation to specific cases of interest. Big Data Approaches to Policy Analysis: Participants understand (1) why "big data" techniques for making policy-relevant assessments and predictions are useful, and under what conditions, (2) key techniques in this area, such as procuring big datasets; pre-processing and dimension reduction of massive datasets for tractable computation; machine learning for predicting outcomes; interpreting machine learning model predictions to understand what is going on inside the black box; data visualization including interactive web apps. | ||||
| Literature | Course materials can be found on Moodle. | ||||