Risk and emotion: measuring the effect of emotions and other visceral factors on decision making under risk
Bibliographic record
Abstract
The science of modelling choice preferences has evolved into an interdisciplinary field contributing to several branches of microeconomics and mathematical psychology. As theories in decision science and related fields mature, descriptive theories have emerged to explain systematic violations of rationality through cognitive mechanisms underlying the thought processes that guide human behaviour. Cognitive limitations are not, however, solely responsible for systematic deviations from rationality and there is a growing body of literature exploring the effect of visceral factors as the more dominant drivers. This study builds on the existing literature by investigating the impact of anger, sadness, happiness, anxiety, hunger, energy, tiredness and stress on three distinct elements that define risk preference: utility, decision weights and loss aversion. By decomposing the impact of visceral factors on risk preference, I am able to provide evidence supporting the proposition that a portion of the variability in individual choice preferences can be explained by interacting visceral states. My findings suggest that visceral factors have the strongest effect on loss aversion, which is a major factor in how people code and evaluate financial outcomes. Anger, sadness, happiness, anxiety, energy and tiredness each affect five or more of the model parameters, while hunger and stress are significant only in their interaction with other visceral factors. I also provide evidence to show that the generalized approaches to characterizing visceral factors and risk preference are too broad to be descriptively meaningful. The results of this study show that emotions and other drive states effect the way people process and interpret information, which is crucial in informing decision-makers of the sources and consequences of irrational behaviour. These findings will be of immediate interest to wealth management specialists, public relations advisers as well as to engineers in designing socially intelligent machines capable of interacting more effectively with humans.
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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".