Behavioral Perspectives on Risk Prone Behavior: Why Do People Take Risks?
Bibliographic record
Abstract
Utilizing the principles and concepts of behavioral economics and operant psychology, researchers in both fields initiated the creation of the optimal foraging theory. This theory describes foraging behaviors mostly within animals other than humans. However, within recent empirical studies, optimal foraging theory has been modified to explain risky choices and decision-making processes within the context of risk-sensitive foraging theory for both animals and humans alike. Although most individuals belonging to the homo sapiensspecies would not like to admit that their behavior is very animalistic in nature, there is a great deal of veracity behind this idea, ranging from explaining gambling behavior to addictive behaviors to even homicide. Risk prone behavior describes behavior elicited for the potential gain of rewards under certain conditions, usually competitive in nature. The purpose of the current paper is to shed some light on this topic and how it relates to the most primitive of behaviors exhibited by human beings.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".