Algorithms for Economics and Politics & Economics and Politics of Algorithms

Prof. Dr. Florian Herold

Kursbeschreibung

This course (lecture + tutorial) offers an introduction to key concepts of algorithmic design and computations complexity in the context of economics and politics: algorithmic learning and decision making, mechanism- and market design, networks, simulations, public key cryptography, digital signatures, and electronic voting.

Topics (tentative):

• Introduction to Algorithms and Computational Complexity

• The Gale-Shapley Algorithm and Matching Markets

• Basics of Network Theory and the Page-Rank Algorithm

• Decision Making under Uncertainty, Bayes Rule, and Learning

• Micro-targeting in Political Campaigns

• Algorithmic Amplification, Attention, Newsfeed, Social Signals

• Big Data, Data Scoring, Risk Predictions, Insurance, and Inequality

• Basic Ideas of Mechanism Design, Auctions, and Algorithmic Game Theory

• Hash Functions, Public Key Cryptography, Digital Signatures, Blockchain, Electronic Voting

• Basics of Network Theory and the Page-Rank Algorithm

• Decision Making under Uncertainty, Bayes Rule, and Learning

• Micro-targeting in Political Campaigns

• Algorithmic Amplification, Attention, Newsfeed, Social Signals

• Big Data, Data Scoring, Risk Predictions, Insurance, and Inequality

• Basic Ideas of Mechanism Design, Auctions, and Algorithmic Game Theory

• Hash Functions, Public Key Cryptography, Digital Signatures, Blockchain, Electronic Voting

Veranstaltungstermine

Vorlesung

Prof. Dr. Florian Herold

• Montag, 10.15- 11.45 Uhr in F21/03.81

Übung (Stephan Eitel, M.Sc.)

• Donnerstag, 14.00 - 16.00 Uhr in F21/03.02

Literatur

Dasgupta, S., C. Papadimitriou, and U. Vazirani (2006), Algorithms, Mc Graw Hill Higher Education
Roughgarden, Tim (2016),Twenty Lectures on Algorithmic Game Theory, Cambridge Univeristy Press
Moore, Christopher and Stephan Mertens (2017), The Nature of Computation, Oxford Univeristy Press