Research map / Learning theory and algorithms / Combinatorial and search optimization
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| cap each reviewer-paper assignment probability at 0.5 (randomized assignment) instead of deterministic max-similarity | reviewer assignment · ICLR 2018 reconstructed | sum-similarity (% of optimum): 90.8% of optimal at q0 = 0.5 | Mixed |
| add partition constraints with 15-reviewer random subsets on top of q0=0.5 | pairwise-constrained assignment · ICLR 2018 reconstructed | sum-similarity (% of pairwise-constrained optimum): 100% | No clear effect |
| add strict partition constraints with only 3 subsets | pairwise-constrained assignment · ICLR 2018 reconstructed | sum-similarity (% of non-partition objective): 99.1% | Hurt |
| use randomized assignment with Q=0.5 instead of standard deterministic assignment against a bidding-manipulating reviewer | reviewer assignment · ICLR 2018 with simulated bids | manipulation success probability: capped at 0.5 | Helped |
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