Experimentation and Learning in Repeated Cooperation

Rudolf Vetschera
Additional contact information
Rudolf Vetschera: University of Vienna

Computational and Mathematical Organization Theory, 2003, vol. 9, issue 1, No 3, 37-60

Abstract: Abstract We study an agency model, in which the principal has only incomplete information about the agent's preferences, in a dynamic setting. Through repeated interaction with the agent, the principal learns about the agent's preferences and can thus adjust the inventive system. In a dynamic computational model, we compare different learning strategies of the principal when facing different types of agents. The results indicate that better learning of preferences can improve the situation of both parties, but the learning process is rather sensitive to random disturbances.

Keywords: agency theory; learning; preference model; simulation (search for similar items in EconPapers)
Date: 2003
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1023/B:CMOT.0000012308.48001.d9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:comaot:v:9:y:2003:i:1:d:10.1023_b:cmot.0000012308.48001.d9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10588

DOI: 10.1023/B:CMOT.0000012308.48001.d9

Access Statistics for this article

Computational and Mathematical Organization Theory is currently edited by Terrill Frantz and Kathleen Carley

More articles in Computational and Mathematical Organization Theory from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().