A systematic analysis and relaxation of assumptions incorporated in the standard hidden-action model

The proposed project aims at providing a systematic analysis of (simplifying) assumptions incorporated in standard agency models with respect to the extent to which these assumptions limit the derived contracts’ applicability in situations where these assumptions do not hold. The focus will be put on problems investigated within the hidden action framework. The assumptions under investigation are particularly concerned with information and the distribution thereof, and the behavior of the involved individuals. 

We plan to investigate how efficient contracts derived from standard agency models with hidden action perform in situations where there is, e.g., additional information asymmetry with respect to the agents’ characteristics (like, e.g., the agent’s reaction function, the agent’s utility function, etc.). This additional information asymmetry stems from relaxing assumptions regarding the availability of information (on the agent’s characteristics) for the principal. Please note that this leads to scenarios which are, per se, not investigated in standard hidden action models. We also aim at testing whether there exist alternative conceptualizations of contracts that, as compared to mechanisms derived from standard agency models, perform more efficiently when the incorporated simplifying assumptions do not hold. The alternative conceptualizations of contracts are planned to be derived based on meta-models.

Schlagworte: simulation, agency theory, agentization, agent-based simulation, incentive systems, management, agent-based modeling, agent-based simulation
Kurztitel: SARAH OeNB
Zeitraum: 01.11.2018 - 31.10.2021


Projekttyp Forschungsförderung (auf Antrag oder Ausschreibung)
Förderungstyp §26
  • Grundlagenforschung
  • 101015 - Operations Research
  • 102009 - Computersimulation
  • 502052 - Betriebswirtschaftslehre
  • 502006 - Controlling
  • 502021 - Mikroökonomie
  • 502044 - Unternehmensführung
  • Selbstorganisierende Systeme
  • Humans in the Digital Age
Genderrelevanz Genderrelevanz nicht ausgewählt
  • Science to Science (Qualitätsindikator: I)
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