Information Acquisition in Interdependent Value Auctions
Bibliographic record
Abstract
Consider, for example, the situation in which a number of established companies bid for the takeover of a target company. At the outset, all bidders are symmetrically informed but before submitting a bid, each bidder may hire a consulting firm to assess the value of the target company. The value of the target company may depend on the extent to which the asset (or activity) of the target firm matches that of the acquiring firms. In addition, if the bidders are competing with each other in the (product) market, then the quality of the match between the target firm and firm i also matters for firm j. In this paper, we analyze the bidders ’ private incentives to acquire information in such interdependent value models and establish a comparison between the equilibrium level and the socially optimal level of information acquisition. If the auction designer has a utilitarian welfare objective and valuations are private, it is transparent to see that the agents have the correct incentives to acquire information in a socially optimal manner. In the Vickrey auction, individual payoffs, when viewed as functions of own type only, coincide with the sum of payoffs
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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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".