Resolving Failed Banks: Uncertainty, Multiple Bidding and Auction Design
Bibliographic record
Abstract
Abstract The FDIC resolves insolvent banks with scoring auctions. Although the structure of the scoring rule is known to bidders, they are uncertain about how the FDIC trades off different bid components. Scoring-rule uncertainty motivates bidders to submit multiple bids for the same failed bank. To evaluate the effects of uncertainty and multiple bidding for FDIC costs, we develop a methodology for analysing multidimensional bidding when the auctioneer’s scoring weights are unknown to bidders. We estimate private valuations for failed-bank assets during the great financial crisis and compute counterfactuals in the absence of scoring uncertainty. Our findings imply a substantial reduction in FDIC resolution costs of between 29.8% ($8.2 billion) and 44.6% ($12.3 billion). These savings can reduce policy-driven banking-sector distortions, since FDIC resolution costs are covered either through special levies on banks or through loans from the US Treasury. Our analyses also shed new light on optimal bid portfolio choice in combinatorial auctions.
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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 | 0.006 | 0.003 |
| 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 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".