Teaching1 Economics Students about Different Models for Dealing with Uncertainty and Risk Besides the Standard Capital Asset Pricing Model (CAPM) and the Subjective Expected Utility (SEU) Model
Bibliographic record
Abstract
There has been a great deal of uncertainty (doubt) and fear about how the COVID-19 corona virus would impact the world's economies in the future.This fear of the future would explain the manner in which individuals and countries have responded to the outbreaks by buying gold and/or other "hard" assets, which decision makers have great confidence in.In times of uncertainty (doubt), holding gold is a reliable and dependable way of combatting the likely impact of uncertain events on future events.The COVID-19 virus has generated a great deal of fear regarding the economic effects of the virus on the economy.Holding hard assets would allow the holder of such assets to feel safer and more secure about their ability to successfully deal with and/or wait out such events .We argue that undergraduate students would be better prepared for decision making in the real world after they graduate if the standard approach taken in microeconomic courses based on risk assessment alone was supplemented by alternative treatments that do not model decision making as taking place only under the assumption of additivity and linearity as is made in CAPM and SEU.It is important to teach students how to modify their probabilities to transform them into decision weights which (a) consider uncertainty, but (b) simplify to probabilities if the uncertainty should diminish substantially in the future.This is accomplished by using Tversky -Kahneman's Cumulative Prospect Theory and Keynes's Conventional Coefficient model from the A Treatise on Probability.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".