Seminar: MA-Seminar: Economic Policy and Artificial Intelligence (M.WIWI-VWL.0026 & M.WIWI.VWL-0036) - Details

Seminar: MA-Seminar: Economic Policy and Artificial Intelligence (M.WIWI-VWL.0026 & M.WIWI.VWL-0036) - Details

Close message box
You are not logged into Stud.IP.

General information

Course name Seminar: MA-Seminar: Economic Policy and Artificial Intelligence (M.WIWI-VWL.0026 & M.WIWI.VWL-0036)
Subtitle
Course number 801534
Semester WiSe 2018/19
Current number of participants 4
expected number of participants 12
Home institute Wirtschaftspolitik und Mittelstandsforschung
Courses type Seminar in category Teaching
First date Wednesday, 20.03.2019 (all-day)
Type/Form
Learning organisation To sign up for the seminar, write an email including preferred topic(s) to lukas.meub@wiwi.uni-goettingen.de.

Preliminary discussion by arrangement. Location: Oec 2.156.
Performance record Seminar paper of max. 12 pages, presentation and discussion in class.
ECTS points 6

Rooms and times

No room preference
Wednesday, 20.03.2019 (all-day)
(Raum 1.162, Gebaeude Oeconomicum: Pl. d. Göttinger Sieben 3)
Wednesday, 27.03.2019 - Thursday, 28.03.2019 08:00 - 18:00

Fields of study

Comment/Description

This seminar addresses new challenges and opportunities for economic policy in the advent
of effective machine learning techniques and artificial intelligence. We discuss how these
transformations might alter economists" understanding of human-decision processes and
respective optimal policy design. Topics encompass various fields of interests that are either
already influenced by AI or expected to be substantially affected in the future.
Students are expected to develop their own research question expanding on the introductory
literature. All research questions should relate to aspects of digital transformation and economic
policy. Given the overall exploratory approach as well as the variety and interdisciplinarity
of topics, students are encouraged to analyze the bigger picture and hypothesize about likely
developments in economic policy.
Topics can be assigned twice. The deadline for handing in the seminar papers is one week
before the blocked seminar. The deadline to enroll/withdraw in FlexNow is six weeks before the blocked seminar.



Literatur:
List of Topics

1. Prediction Policy Problems

* Kleinberg, Jon, Jens Ludwig, Sendhil Mullainathan, and Ziad Obermeyer. 2015. Prediction Policy Problems. American Economic Review, 105 (5): 491-95.
* Athey, Susan. 2017. Beyond prediction: Using big data for policy problems. Science, 355(6324): 483-485
* Athey, Susan. 2018. The Impact of Machine Learning on Economics. http://www.nber.org/chapters/c14009.pdf

2. Labor Markets

* Acemoglu, Daron and Pascual Restrepo. 2018. Artificial Intelligence, Automation and Work. https://www.nber.org/chapters/c14027.pdf
* Acemoglu, Daron and Pascual Restrepo (2016) "The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment," forthcoming. American Economic Review.
* Frey, Carl Benedict and Michael A. Osborne. 2017. The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114: 254-280.

3. Financial Markets

* Philippon, T., 2016. The FinTech opportunity. Unpublished working paper. NBER.
* Royal Academy of Engineering. 2015. Connecting data - driving productivity and innovation. www.raeng.org.uk/connectingdata.
* Hendershott, T., C. M. Jones, and A. J. Menkveld. 2011. Does algorithmic trading improve liquidity? The Journal of Finance, 66: 1-33.
* Bank of International Settlements, 2017. FinTech credit. Market structure, business models and financial stability implications.

4. Criminal Justice System

* Kleinberg, Jon, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan. 2018. Human Decisions and Machine Predictions. The Quarterly Journal of Economics, Volume 133, Issue 1, Pages 237-293
* Brayne, Sarah. 2017. Big Data Surveillance: The Case of Policing. American Sociological Review, 82(5): 977-1008.
* Berk, R. 2012. Criminal Justice Forecasts of Risk: A Machine Learning Approach. Springer Verlag.

5. Healthcare System

* Conor, Farrington. 2016. Big data meets human health. Science, 353(6296): 227.
* Obermeyer, Z. and E. J. Emanuel. 2016. Predicting the future — big data, machine learning, and clinical medicine. The New England journal of medicine, 375: 1216-1219.

6. Business Sector

* Brynjolfsson, E. and K. McElheran. 2016. The rapid adoption of data-driven decision-making. American Economic Review 106, 133-139.
* Corea, F. 2017. Artificial Intelligence and Exponential Technologies: Business Models Evolution and New Investment Opportunities.
* Li,Y., W. Jiang, L. Yang, and T. Wu. 2018. On neural networks and learning systems for business computing. Neurocomputing 275: 1150-1159.

Admission settings

The course is part of admission "Anmeldung mit Passwort: MA-Seminar: Economic Policy and Artificial Intelligence (M.WIWI-VWL.0026 & M.WIWI.VWL-0036)".
The following rules apply for the admission:
  • Password required.