Research map / Multi-agent systems and games / Games and equilibria
A Cramér–von Mises Approach to Incentivizing Truthful Data Sharing
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Proposes Cramér–von Mises-inspired loss mechanisms that reward agents for truthful, larger data submissions in data-sharing settings, with exact (Bayesian) and approximate (prior-agnostic) Nash truthfulness.
Problem
Incentivizing truthful data sharing in marketplaces and consortia where agents may fabricate or misreport data, without ground truth and without strong distributional assumptions.
Approach
A two-sample CvM-style loss that compares an agent's submission to others' pooled data; in the Bayesian version the loss uses the conditional expectation of the other agents' ECDF given the agent's data (ensuring truthfulness), and a prior-agnostic version replaces it with the ECDF of the agent's data augmented by held-out data, yielding epsilon-approximate truthfulness that vanishes with data siz
Key results
- On SQuAD with DistilBERT features (500 sentences), the proposed mechanism gives truthful loss 0.0003 vs untruthful 0.0011, and the CvM baseline gives 0.1547 vs 0.8598.
- On Flowers-102 with 100 images, the proposed mechanism gives truthful loss 0.0015 vs untruthful 0.0040; the KS baseline gives 0.0833 vs 0.0993.
- In the beta-Bernoulli synthetic experiment, fabricated data always increases loss under the proposed method, while baselines yield lower loss for at least one fabrication strategy.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add LLM-fabricated sentences instead of reporting truthfully | Algorithm · SQuAD 500 sentences | loss: 0.0003 -> 0.0011 | Hurt |
| add LLM-fabricated sentences instead of reporting truthfully | KS-test · SQuAD 500 sentences | loss: 0.0379 -> 0.0524 | Hurt |
| add LLM-fabricated sentences instead of reporting truthfully | CvM-test · SQuAD 500 sentences | loss: 0.1547 -> 0.8598 | Hurt |
| add LLM-fabricated sentences instead of reporting truthfully | Mean diff. · SQuAD 500 sentences | loss: 0.0043 -> 0.0095 | Hurt |
| add LLM-fabricated sentences instead of reporting truthfully | Algorithm · SQuAD 2500 sentences | loss: 0.00003 -> 0.0005 | Hurt |
| add LLM-fabricated sentences instead of reporting truthfully | KS-test · SQuAD 2500 sentences | loss: 0.0127 -> 0.0309 | Hurt |
| add LLM-fabricated sentences instead of reporting truthfully | CvM-test · SQuAD 2500 sentences | loss: 0.1609 -> 3.2760 | Hurt |
| add LLM-fabricated sentences instead of reporting truthfully | Mean diff. · SQuAD 2500 sentences | loss: 0.0015 -> 0.0069 | Hurt |
| add diffusion-generated images instead of reporting truthfully | Algorithm · Oxford Flowers-102 100 images | loss: 0.0015 -> 0.0040 | Hurt |
| add diffusion-generated images instead of reporting truthfully | KS-test · Oxford Flowers-102 100 images | loss: 0.0833 -> 0.0993 | Hurt |
| add diffusion-generated images instead of reporting truthfully | CvM-test · Oxford Flowers-102 100 images | loss: 0.1491 -> 0.7730 | Hurt |
| add diffusion-generated images instead of reporting truthfully | Mean diff. · Oxford Flowers-102 100 images | loss: 0.0462 -> 0.0953 | Hurt |
Surprising findings
- The mean-difference test, used in prior Gaussian-based truthful data sharing work, fails to be truthful under fabrication outside Gaussian settings.
- The KS test gives lower loss to fabricated normal-normal data than to truthful data, showing its susceptibility.
Limitations
- Bayesian version requires posterior computations that may be expensive for complex priors
- Feature maps must be specified and there is no universally optimal choice
- Only approximate truthfulness in the prior-agnostic version
Experimental setup
Datasets: SQuAD Oxford Flowers-102
Benchmarks: SQuAD Oxford Flowers-102 synthetic beta-Bernoulli synthetic normal-normal
Metrics: average loss for truthful vs untruthful submission
Baselines: KS test CvM test Mean diff
Builds on: CvM two-sample test peer prediction Gaussian mean-estimation truthful data sharing
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