Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/300052 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
CESifo Working Paper No. 11124
Verlag: 
CESifo GmbH, Munich
Zusammenfassung: 
Algorithms play an increasingly important role in economic situations. These situations are often strategic, where the artificial intelligence may or may not be cooperative. We study the deter-minants and forms of algorithmic cooperation in the infinitely repeated prisoner's dilemma. We run a sequence of computational experiments, accompanied by additional repeated prisoner's dilemma games played by humans in the lab. We find that the same factors that increase human cooperation largely also determine the cooperation rates of algorithms. However, algorithms tend to play different strategies than humans. Algorithms cooperate less than humans when cooperation is very risky or not incentive-compatible.
Schlagwörter: 
artificial intelligence
cooperation
large language models
Q-learning
repeated prisoner's dilemma
JEL: 
C72
C73
C92
D83
Dokumentart: 
Working Paper
Erscheint in der Sammlung:

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.