Daten exportieren

 

Forschungsprojekt ::
Crazy Fruits - A Casino-Based Analysis of Economic Decicion-Making in Unusual Situations

Projektbeschreibung

We use slot machine games to study economic decision-making in complex high-uncertainty environments. Despite monetary stakes, roughly 30 percent of (non-trivially playing) study participants adopt a non-Bayesian strategy tied to the game's entropy. This playful-exploratory approach leads to suboptimal game switching behavior compared to an optimal choice in the Bayesian multi-armed bandit model. We explain this by a model of expected-utility-maximizing agents choosing between a rational Bayesian and a curiosity-driven perspective.
A set of treatments capture and disentangle this deliberate rationality-curiosity trade-off: By changing key game features, such as the stakes, we increase the opportunity cost of curious behavior. By offering additional, albeit payoff-neutral, choices, we allow players to explore within the payoff-maximizing strategy.
Comparing in-game behavior to the predictions, we find that a significant share of players choose to incorporate elements of competitiveness into their approach to the game and seem to acquire inherently valuable but potentially costly non-Bayesian beliefs about the nature of the game.
Our new approach contributes to a better understanding of how economic agents deal with unusual, high-uncertainty environments---as seen in the context of trading apps that explicitely combine gambling and trading elements.

Angaben zum Forschungsprojekt

Beginn des Projekts:2021
Projektstatus:laufend
Projektleitung:Hartinger, Katharina
Beteiligte Personen:Patt, Dr. Alexander
Lehrstuhl/Institution:
Finanzierung des Projekts:Aus Lehrstuhletat (intern)
Schlagwörter:Gambling, uncertainty, decision-making, exploration
Themengebiete:Q Wirtschaftswissenschaften > QB Wirtschaftswissenschaften - Allgemeines, Methoden, Wirtschaftspädagogik
Projekttyp:Grundlagenforschung
Projekt-ID:3210
Eingestellt am: 07. Sep 2022 07:50
Letzte Änderung: 20. Jul 2023 03:35
URL zu dieser Anzeige: https://fordoc.ku.de/id/eprint/3210/
Analytics