Risk and emotion: measuring the effect of emotions and other visceral factors on decision making under risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>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.</p>
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it