876-0201-00L Technology and Policy Analysis
|Semester||Autumn Semester 2022|
|Lecturers||T. Schmidt, E. Ash, F. M. Egli, R. Garrett, M. Leese, A. Rom, B. Steffen|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|876-0201-00 G||Technology and Policy Analysis||75s hrs||T. Schmidt, E. Ash, F. M. Egli, R. Garrett, M. Leese, 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.|
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.|
|Performance assessment information (valid until the course unit is held again)|
|Performance assessment as a semester course|
|ECTS credits||8 credits|
|Examiners||T. Schmidt, E. Ash, F. M. Egli, R. Garrett, M. Leese, A. Rom, B. Steffen|
|Type||graded semester performance|
|Language of examination||English|
|Repetition||Repetition possible without re-enrolling for the course unit.|
|No public learning materials available.|
|Only public learning materials are listed.|
|No information on groups available.|
|Priority||Registration for the course unit is only possible for the primary target group|
|Primary target group||CAS ETH Technol. & Public Policy: Impact Analysis (876000)
MAS ETH in Technology and Public Policy (877000)
|CAS in Technology and Public Policy: Impact Analysis||Module||O|
|MAS in Technology and Public Policy||Impact Analysis||O|