A Study of Limited-Precision, Incremental Elicitation in Auctions: Extended Abstract
Bibliographic record
Abstract
Day-to-day business transactions have come to rely on the computer networks that link market participants by providing fast, seamless communication and negotiation channels. This move to online negotiation has led to the development of more and more sophisticated software agents that mediate such transactions. However, since the interests of the parties on whose behalf such agents act generally conflict, ideally such agents should reason strategically according to the well-studied principles of game theory and economics. As such, recent research in computer science and economics has focused on the design of economic agents and the mechanisms through which they interact. Mechanism design [3] has played a central role in much of this research. Recently, limitations of standard approaches to mechanism design have been identified, and are starting to be addressed. Chief among these is the computational complexity of the problems faced by interacting software agents. For instance, mechanisms based on the revelation principle must reveal their type (often, the utility function) accurately. This presents a problem in circumstances where utility functions are large and difficult to communicate effectively and/or hard to compute accurately. Recent research has begun to examine methods involving limited or incremental elicitation of types to circumvent some of these difficulties [1, 2, 5, 4], specifically in the context of (single-good or combinatorial) auctions. In this paper, we pursue the same line of research. Specifically, in the context of single-good auctions, we analyze mechanisms that allow bids with limited precision and that elicit bids by allowing bidders to sequentially refine their bids. We propose various natural constraints on such incremental mechanisms and show that any mechanism satisfying these constraints, and having dominant strategy equilibria, must have a very restricted form. We then present one sample mechanism of this form and show that it can be optimized for various social objectives.
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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 | 0.013 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".