A duality-based unified approach to Bayesian mechanism design
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
In this letter we briefly survey our recent work [Cai et al. 2016]. In it, we provide a new duality theory for Bayesian mechanism design which is quite general, and applies for any objective the designer wishes to optimize, and for arbitrary agent valuations. We then apply our theory to auction design settings with many independent buyers who have independent values for many items, and are able to provide a unified proof of several recent exciting works on this front [Hart and Nisan 2012; Li and Yao 2013; Babaioff et al. 2014; Yao 2015; Chawla et al. 2007; Chawla et al. 2010; Chawla et al. 2015]. These works all show that simple mechanisms are approximately optimal in various settings. In some cases, our principled approach yields greatly improved approximation ratios as well.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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