We announce that CoSP workshop on matchings will be organized at Charles University from 24th to 26th October 2022. The workshop brings together students and researchers working in broader aspects of matchings in algorithms and game theory.
Place: Faculty of mathematics and Physics, Charles University, Malostranske namesti 25, Prague, Czech Republic
Program and abstracts
24th October 2022
Room: Malá aula
14:00 Ole Jann (CERGE-EI): An Informational Theory of Privacy
Abstract: Privacy of consumers or citizens is often seen as an inefficient information asymmetry, as exemplified by the saying that "if you have nothing to hide, you have nothing to fear". We challenge this view by showing that privacy can increase welfare in an informational sense. It can also improve information aggregation and prevent inefficient statistical discrimination. We show how and when the different informational effects of privacy line up to make privacy efficient or even Pareto-optimal. Our theory can be applied to decide who should have which information and how privacy and information disclosure should be regulated. We discuss applications to online privacy, credit decisions and transparency in government.
15:00 Katarína Cechlárová (UPJS Kosice): Matchings in theory and practice
Abstract: Matching problems arise in different situations and concern various activities of people: assignment of students to schools, search for donors of kidneys or houses for tenants, or even marriage partners. Obviously, different circumstances lead to different desirable properties of the
matching that is sought.
In this talk we motivate and formally introduce the basic notions of stability and fairness and show how the language of mathematics, in particular graph theory, can help in the description of the desiderata, design and analysis of efficient algorithms. Finally we report on practical problems where we used methods of matching theory and succeeded in improving the procedures implemented in practice.
25th October 2022
14:00 Jozef Barunik (IES FSV UK): Deep Learning in Economics and Finance
Abstract: While machine learning methods are widely used in many areas of our lives, it is rather scarce in finance where we need to understand the decision making of agents who make decisions based on their preferences and maximize their utility. Growing evidence for irrational decisions of such agents invalidates traditional economic approaches and irrationality in the agent's behavior caused by the various factors described by social sciences, mainly psychology and sociology (emotions, herd behavior, social norms, etc.) changes the way we view the problem. Nowadays, it is still hard to quantify the qualitative and quantitative impacts of such irrationalities directly. However, with the currently available datasets containing lots of information, as well as algorithms used by machine learning and computer power enables us to study these problems in a more complex ways. The working package Finance and Society will focus on the development of such methods of quantitative analysis based on machine learning to help us understand the impact of finance and financial sector on society.
15:00 Martin Loebl (KAM, MFF UK): Critical Goods Distribution System
I will propose a new distribution system for crises supporting autonomous behaviour.
26th October 2022
Room: Malá aula
14:00 Martin Schmid (KAM, MFF UK and EquiLibre Technologies): New Horisons of Algorithmic Game Theory
Game theory has always been a fundamental theoretical tool in analysing markets and economy (most notably, John F. Nash receiving the Economic Sciences Prize for his work on equilibrium theory). There has been great progress in algorithmic game theory in recent years. Poker, Ches, Go and other games have seen historical milestones thanks to reinforcement learning algorithms and algorithmic game theory. We will talk about the recent breakthroughs and about the possible and interesting future directions
15:00 Goals and Open Problems