Algorithmic Economics

The lecture deals with issues at the intersection of computer science, artificial intelligence and economics. We will discuss key issues from game theory (cooperative and non-cooperative), social choice theory, mechanism design and social network analysis. The lecture will focus on algorithms and solutions that are practically relevant.

The lecture deals with issues at the intersection of computer science, artificial intelligence and economics.

We consider systems with multiple independent participants who potentially have different goals. They may cooperate, but there may

also be conflicts of interest between them. We will discuss methods of analyzing such systems and of designing algorithms for them. These

algorithms, in addition to having low computational complexity, must satisfy other desiderata, such as fairness (to the participants of the

system), stability (participants do not want to leave the system) or strategy-proofness (participants do not have incentives to play against

the system).

Examples of specific models and issues discussed in the lecture include:

1. Social network analysis (centrality measures, PageRank).

2. Algorithms for barter markets (based on the example of the kidney exchange in the United States).

3. Mechanisms for conducting auctions (used in Internet marketing, among other things).

4. Algorithms for finding fair allocation of goods, algorithms for finding stable matchings and algorithms for fair election.

5. Algorithms for finding equilibria based on concepts from game theory (and their applications in security systems).

6. Solution concepts from coalition game theory (used, among others, in the SHAP library explaining the outputs of ML

We will discuss key issues from game theory (cooperative and non- cooperative), social choice theory, mechanism design and social network analysis. The lecture will focus on algorithms and solutions that are practically relevant.

Algorithmic game theory, N. Nisan, T. Roughgarden, É. Tardos, V. Vazirani

Handbook of computational social choice, Felix Brandt, Vincent Conitzer, Ulle Endriss, Jérôme Lang, Ariel D. Procaccia

Multiagent systems: algorithmic, game-theoretic, and Logical Foundations, Yoav Shoham, Kevin Leyton-Brown

Network Analysis: Methodological Foundations, Urlik Brandes, Thomas Erlebach

Knowledge:

1. Has fundamental knowledge in the key areas of research at the interface of artificial intelligence and economics: game theory, mechanism design, social choice, social networks.

Competences:

1. Knows limitations of his/her knowledge, is willing to constantly upgrade and update his/her knowledge and raise qualifications within the field of computer science and related scientific areas and disciplines (K_K01)

2. Knows how to precisely formulate questions in order to deepen own understanding of the studied subject (in particular in contacts with non-computer scientists) or to find gaps in own reasoning about the subject (K_K02)

3. Is able to formulate opinions about fundamental topics in computer sciences (K_K06).

4. Understands the need of systematically updating one's own knowledge by reading scientific and popular scientific journals (K_K08).

Final grade is based on points scored in a written exam. Same rules apply in the retake session.