Artikel

Decision making in times of Knightian uncertainty: An info-gap perspective

The distinction of risk vs uncertainty as made by Knight has important implications for policy selection. Assuming the former when the latter is relevant can lead to wrong decisions. With the aid of a stylized model that describes a bank's decision on how to allocate loans, the authors discuss policy insights for decision making under Knightian uncertainty. They use the info-gap robust satisficing approach to derive a trade-off between confidence and performance (analogous to confidence intervals in the Bayesian approach but without assignment of probabilities). The authors show that this trade off can be interpreted as a cost of robustness. They show that the robustness analysis can lead to a reversal of policy preference from the putative optimum. The authors then compare this approach to the min-max method which is the other main non-probabilistic approach available in the literature. They also consider conceptual proxies for robustness and demonstrate their use in qualitative analysis of financial architecture and monetary policy.

Language
Englisch

Bibliographic citation
Journal: Economics: The Open-Access, Open-Assessment E-Journal ; ISSN: 1864-6042 ; Volume: 10 ; Year: 2016 ; Issue: 2016-23 ; Pages: 1-30 ; Kiel: Kiel Institute for the World Economy (IfW)

Classification
Wirtschaft
Mathematical Methods
Methodological Issues: General
Criteria for Decision-Making under Risk and Uncertainty
General Financial Markets: General (includes Measurement and Data)
Subject
uncertainty vs risk
confidence
robustness
satisficing
info-gap

Event
Geistige Schöpfung
(who)
Ben-Haim, Yakov
Demertzis, Maria
Event
Veröffentlichung
(who)
Kiel Institute for the World Economy (IfW)
(where)
Kiel
(when)
2016

DOI
doi:10.5018/economics-ejournal.ja.2016-23
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Ben-Haim, Yakov
  • Demertzis, Maria
  • Kiel Institute for the World Economy (IfW)

Time of origin

  • 2016

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