Responding to Uncertainties in COVID-19 with Real Options: The Perspective of Governments
Bibliographic record
Abstract
COVID-19 has affected people, businesses and governments worldwide, causing widespread uncertainty in the business world. Here we look at COVID-19 uncertainty using the tool of real options. We focus on the perspective of governments, particularly the Finnish Government, which in its decisions has endeavoured to keep many of its options open in this situation. We describe the real options relevant here, how uncertainty impacts them and also how selected cognitive biases may influence the decisions through the use of such real options. The real options discussed are the option to delay, the time-to-build option, the option to alter scale and the option to switch. The cognitive biases relevant here are the status quo bias and the confirmation bias. The study extends the research on real options by scrutinising a highly topical case. The study also offers guidance to governments on how to respond to the COVID-19 crisis. Moreover, the study provides suggestions on how to evaluate governmental decisions in the COVID-19 crisis. This study addresses a highly topical phenomenon, the COVID-19 crisis, in order to shed light on how real options can be used as a tool to analyse such a crisis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".