Knightian and Keynesian Uncertainty: Eclipsed or Resurgent?

Uncertainty, as defined by Knight and Keynes in the 1920s, is now an essential key to understanding large-scale unpredictable events, such as 2020's pandemic. The inability of predictive models to anticipate the scope and impact of this unprecedented crisis has highlighted the relevance of a century-old theory that has long been marginalized in favor of predictive mathematical formalism. Beyond the economic field, this study offers an extensive and in-depth bibliometric analysis of literature to assess whether Knightian and Keynesian uncertainty remains overshadowed or whether, on the contrary, it is attracting renewed interest considering contemporary context. Using a corpus of 455 multidisciplinary articles from Scopus database from 1977 to 2024, this research uses R-Biblioshiny and VOSviewer software to provide a detailed overview of publication dynamics, key players and collaborative interactions. Through performance and scientific mapping analysis, the results reveal that the subprime crisis and the Covid shock acted as catalysts for a renewed awareness of the risk/uncertainty dichotomy, while highlighting the existence of two paradigms. The first, emerging and with a strong academic impact, tends to reintegrate this uncertainty into operational modelling frameworks, while the second, derived from heterodox economics, remains attached to its irreducible conception.

Authors

Institutions

Publication Details

Journal
International Journal of Research in Economics and Finance
Published
2025-10-12
DOI
https://doi.org/10.71420/ijref.v2i9.166
Primary Topic
Economic Theory and Policy
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Knightian and Keynesian Uncertainty: Eclipsed or Resurgent?

Benyounes Rahouti
International Journal of Research in Economics and Finance
Economic Theory and Policy
article

Knightian and Keynesian Uncertainty: Eclipsed or Resurgent?

Benyounes Rahouti
article en

Abstract

Uncertainty, as defined by Knight and Keynes in the 1920s, is now an essential key to understanding large-scale unpredictable events, such as 2020's pandemic. The inability of predictive models to anticipate the scope and impact of this unprecedented crisis has highlighted the relevance of a century-old theory that has long been marginalized in favor of predictive mathematical formalism. Beyond the economic field, this study offers an extensive and in-depth bibliometric analysis of literature to assess whether Knightian and Keynesian uncertainty remains overshadowed or whether, on the contrary, it is attracting renewed interest considering contemporary context. Using a corpus of 455 multidisciplinary articles from Scopus database from 1977 to 2024, this research uses R-Biblioshiny and VOSviewer software to provide a detailed overview of publication dynamics, key players and collaborative interactions. Through performance and scientific mapping analysis, the results reveal that the subprime crisis and the Covid shock acted as catalysts for a renewed awareness of the risk/uncertainty dichotomy, while highlighting the existence of two paradigms. The first, emerging and with a strong academic impact, tends to reintegrate this uncertainty into operational modelling frameworks, while the second, derived from heterodox economics, remains attached to its irreducible conception.

International Journal of Research in Economics and FinanceVol. 2(9)
Mohamed I University (MA), Premier University (BD)
Openalex Percentile: Top 54%
Economic Theory and Policy
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.