Subsidized Prediction Mechanisms for Risk-Averse Agents
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we study the design and characterization of sequential prediction mechanisms in the presence of agents with unknown risk aversion. We formulate a collection of desirable properties for any sequential forecasting mechanism. We present a randomized mechanism that satisfies all of these properties, including a guarantee that it is myopically optimal for each agent to report honestly, regardless of her degree of risk aversion. We observe, however, that the mechanism has an undesirable side effect: each agent's expected reward, normalized against the inherent value of her private information, decreases exponentially with the number of agents. We prove a negative result showing that this is unavoidable: any mechanism that is myopically strategyproof for agents of all risk types, while also satisfying other natural properties of sequential forecasting mechanisms, must sometimes result in a player getting an exponentially small expected normalized reward.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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