Mitigating information asymmetries to achieve efficient peer-to-peer queries
Bibliographic record
Abstract
Querying for a particular data item is perhaps the most important feature to be supported by peer-to-peer network infrastructures, and receives the most research attention in recent literature. Most existing work follows the line of designing decentralized algorithms to maximize the performance of peer-to-peer queries. These algorithms often have specific rules that peer nodes should adhere to (e.g., placement of data items on particular nodes), and thus assume that peers are strictly cooperative. However, in realistic peer-to-peer networks, selfish and greedy peer nodes are the norm, and query strategies degenerate to random or flooding based searches. In this paper, we explore the design space with respect to query efficiency in selfish peer-to-peer networks where nodes have asymmetric information, and apply the signaling mechanism from microeconomics to facilitate the sharing of private information and thus improve search efficiency. We extensively simulate the signaling mechanism in the context of other alternative solutions in selfish networks, and show encouraging results with respect to improving query performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".