Jakub Steiner (CERGE-EI and the University of Zurich) 30.5.2024

V rámci Research Seminar Series in Economics máme tu čest, že nám ve čtvrtek 30.5.2024 od 12:45 do 14:15 v místnosti RB435 bude přednášet dr. Jakub Steiner (CERGE-EI and the University of Zurich) o „Constrained data-fitters.“

Registration is not required and anyone who would like to attend is warmly invited.

It is also possible to participate online via MS Teams. To get access, please, contact lubomir.cingl@vse.cz.

ABSTRACT: We explore approximate Bayesian updating and likelihood maximization within a machine learning-inspired framework. We demonstrate that, under certain cognitive constraints, simpler models yield the most effective constrained fit to the actual data-generating process. While more complex models could potentially offer a superior fit, agents facing cognitive constraints may lack the capability to assess this fit accurately.

BIO: Jakub Steiner is an economist. He uses mathematical models to study human decision-making and strategic interactions. He finds inspiration in fields such as neuroscience and information theory. In one of his articles, he explains how manipulating human attention influences decision-making. In another work, he studies habits – automated behaviour – which reduce the mental costs of decision-making within the theory of rational inattention. In the past, he has worked at Northwestern University in the USA and the University of Edinburgh. He currently works part-time at CTS and CERGE-EI in Prague and at the University of Zurich. He has published in journals such as the American Economic Review and Econometrica. He serves on the editorial board of the Journal of Economic Theory and, in the past, on the editorial boards of the American Economic Review and Review of Economic Studies. He was the principal investigator of an ERC consolidator grant on the topic of behavioral economics. He served on the ERC scientific panel for economics and related fields.

Jakub Steiner (CERGE-EI and the University of Zurich) 30.5.2024
  • Autor: RSSE Team
  • Vytvořeno:
  • Poslední aktualizace: