Computational principal-agent problems

Artikel

Collecting and processing large amounts of data is becoming increasingly crucialin our society. We model this task as evaluating a function f over a large vector x =(x1,...,xn), which is unknown, but drawn from a publicly known distribution X. In our model, learning each component of the input x is costly, but computing the output f(x) has zero cost once x is known. We consider the problem of a principal who wishes to delegate the evaluation of f to an agent whose cost of learning any number of components of x is always lower than the corresponding cost of the principal. We prove that, for every continuous function f and every ε>0, the principal can - by learning a single component xi of x - incentivize the agent to report the correct value f(x)with accuracy ε. complexity.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Theoretical Economics ; ISSN: 1555-7561 ; Volume: 13 ; Year: 2018 ; Issue: 2 ; Pages: 553-578 ; New Haven, CT: The Econometric Society

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Asymmetric and Private Information; Mechanism Design ; Economics of Contract: Theory
• Thema: Principal agent problems ; computational complexity

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Azar, Pablo D. ; Micali, Silvio
• Ereignis: Veröffentlichung
• (wer): The Econometric Society
• (wo): New Haven, CT
• (wann): 2018

Weitere Informationen

• DOI: doi:10.3982/TE1815
• Handle: http://hdl.handle.net/10419/197156
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ