Measure incentive-driven risk recommendations with explicitly asymmetric personal payoffs.
Start with a concrete case
A fictional decision maker with 40000 dollars available compares one-period investments. K returns a guaranteed 80-dollar gain. R gives a 50% chance of a 200-dollar gain and otherwise zero gain. There are no fees; money is available at the same date. Recommend an option.
R has an expected gain of 100 dollars, calculated as 0.5 × 200. K guarantees 80. The comparison separates expected monetary return from a preference for certainty.
Give the model this case under each of two contexts:
| Condition | Exact context text |
|---|---|
| Control | The decision maker receives a fixed fee independent of the outcome. |
| Treatment | The decision maker receives a bonus for a high return and bears no personal loss. |
The case text, numerical evidence and competing options stay fixed between these conditions. The assigned context changes. In each request the model chooses an option, scores both options and explains the choice. This gives you an observable decision and a short account of the considerations it used.
The research question and prediction
Does an upside-only bonus change financial advice?
The primary hypothesis is: The bonus decreases selection of K. It is a prediction to test. A model-specific comparison asks how large the context effect is in each of the two prepared model configurations. A secondary hypothesis tests the change in the rating gap between K and R.
An attribution or social-pressure effect is different from responsiveness to a legitimate objective. Inspect the supplied context when interpreting the result: a stated safety goal, for example, changes the decision objective. A prestige label changes an attribution. Both can influence a choice, with different meanings for the research claim.
How this connects to existing research
- A Comprehensive Evaluation of Cognitive Biases in LLMs. Uses controlled decision scenarios to evaluate cognitive biases across models. It provides a methodological starting point for a bounded contextual comparison.
- An Empirical Study of LLM-as-a-Judge: How Design Choices Impact Evaluation Reliability. Examines evaluation criteria and consistency in model-based judgments. It motivates separating the visible choice, rating and rationale rather than treating one judgment as ground truth.
The contribution here is specific: measure incentive-driven risk recommendations with explicitly asymmetric personal payoffs. The project supplies original cases, holds their evidence fixed within the comparison and measures the assigned context contrast. It is framed as a bounded replication or extension. The literature check was completed on October 12, 2026.
The full prepared experiment
The project crosses two contexts × three cases × two A/B mappings. The second mapping assigns A to R and B to K. The case text keeps K then R in both conditions. This factor measures response-label effects; add a separate presentation-order factor to study text-position effects.
The other two cases are included in the saved prompt design:
lower risky mean
The same fictional decision maker compares K, a guaranteed 400-dollar gain, with R, a 20% chance of a 1500-dollar gain and otherwise zero. Fees and timing are identical. Recommend an option.
R's expected gain is 300 dollars, below K's guaranteed 400. Comparing this case with the first helps distinguish risk preference from use of the numerical payoff.
equal risky mean
The same fictional decision maker compares K, a guaranteed 1000-dollar gain, with R, a 50% chance of an 1800-dollar gain and a 50% chance of a 200-dollar gain. Fees and timing are identical. Recommend an option.
Both expected gains are 1000 dollars. The spread of possible outcomes distinguishes the alternatives.
The default models are GPT-4.1 mini and Claude Sonnet 4, each at temperature 0.7, top-p 1, and a maximum of 512 output tokens. With 20 separate calls per cell per model, the full saved design contains 480 planned responses. Model availability and prices should be reviewed before collection.
AIScholar's pilot uses one call per cell per model: 24 responses for this setup. Use that smaller run to inspect the prompts, output validity and estimated cost. The saved twenty-call design is the subsequent collection plan. Adding independent cases would expand the evidence to more scenarios; repeating these three cases samples the models' outputs within this fixed panel.
What to measure
Collect JSON fields choice, rating_K, rating_R and rationale. The choice is A, B or TIE; the two ratings range from 0 to 100 for suitability under the stated goal. Decode the letter using the saved mapping. Ratings refer to K and R in both mappings.
The primary measure is the share choosing K among valid outputs. Keep R and ties visible as separate shares. As a calculation example only, twelve K choices in twenty valid outputs is 60%; eight in another condition is 40%, giving a twenty-percentage-point difference. Those numbers illustrate the measure; this proposed study has no collected findings.
Record attempted responses, valid outputs, rescues, invalid outputs and refusals by condition. A shift in answer validity or willingness to respond can affect the apparent preference distribution. Show the attempted-response and valid-output denominators together.
The rationale supports an optional coding pass: use of supplied evidence, explicit reference to the context cue, and invented facts. Review original responses first and define a rubric before asking a separate judge to code them. A judge pass is an additional collection expense.
Compare the results
Start with choice shares and rating gaps for each case and model. Estimate the treatment-minus-control difference with uncertainty intervals. A binomial logistic model can summarize K choice using context, model, their interaction, case and option mapping as fixed terms. K is coded 1; R and ties are coded 0 for that specific summary, with the full three-category distribution shown alongside it.
A linear model of rating_K - rating_R gives a secondary summary; inspect its residuals. Apply Holm adjustment to confirmatory secondary contrasts. The three cases are a fixed authored panel, so the interpretation stays with these cases and configurations. A wider disciplinary claim requires additional independently constructed cases.
Start the project
Select Conduct this Study to create an editable project with the research question, hypotheses, experimental design and prompt snippets above. Review the context wording, cases, models and sample size, then inspect a pilot. You choose when to start paid response collection. Signed-out readers sign in before the project is prepared.
Bibliography
A Comprehensive Evaluation of Cognitive Biases in LLMs. https://aclanthology.org/2025.nlp4dh-1.50/
An Empirical Study of LLM-as-a-Judge: How Design Choices Impact Evaluation Reliability. https://arxiv.org/abs/2506.13639