Optimal unbiased estimators for evaluating agent performance
Bibliographic record
Abstract
Evaluating the performance of an agent or group of agents can be, by itself, a very challenging problem. The stochastic nature of the environment plus the stochastic nature of agents ’ decisions can result in estimates with intractably large variances. This paper examines the problem of finding low variance estimates of agent performance. In particular, we assume that some agent-environment dynamics are known, such as the random outcome of drawing a card or rolling a die. Other dynamics are unknown, such as the reasoning of a human or other black-box agent. Using the known dynamics, we describe the complete set of all unbiased estimators, that is, for any possible unknown dynamics the estimate’s expectation is always the agent’s expected utility. Then, given a belief about the unknown dynamics, we identify the unbiased estimator with minimum variance. If the belief is correct our estimate is optimal, and if the belief is wrong it is at least unbiased. Finally, we apply our unbiased estimator to the game of poker, demonstrating dramatically reduced variance and faster evaluation.
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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 | 0.014 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".