Research map / Learning theory and algorithms / Submodular and game algorithms
PoA of Simple Auctions with Interdependent Values
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GVA under single-crossing and homogeneous influence | Single-item settings · Single-item, SC and homogeneous influence | B-POA: fully efficient | Helped |
| Use GVA with γ-heterogeneous, c-SC valuations | Single-item settings · Single-item, γ-heterogeneous, c-SC valuations | B-POA: 1 + max{γ, c} | Helped |
| Use GVA with γ-heterogeneous, SC valuations | Single-item settings · Single-item, γ-heterogeneous, SC valuations | B-POA: tightly γ | Helped |
| Use second-price auction with interdependence under γ-heterogeneity and c-SC | Single-item settings · Single-item, γ-heterogeneous, c-SC valuations | B-POA: max{γ, c} + 1 | Helped |
| Allow interdependent values with single-crossing but without information-dispersion restrictions | Highest-valued-bidder allocation auctions · Single-item, SC valuations | PoA: Ω(n) | Hurt |
| Use simultaneous privatized second-price auctions under limited information asymmetry | Multiple-item unit-demand settings with n ≥ m · Multiple items, n ≥ m | B-POA: O(dγ2) | Helped |
| Allow an extreme concentration of information in one bidder | Natural item-by-item highest-value mechanisms, including simultaneous 2PA or GVA · Multiple items, agent 1 holds almost all information | EP-POA: Ω(m) | Hurt |
| Use simultaneous item auctions in the many-items regime without additional coordination guarantees | Multi-item settings with common values · Multiple items, n ≫ m | PoA: ˜Ω(log n) | Hurt |
| Assume weighted-sum valuations with symmetric signals | Multiple-item unit-demand settings · Multiple items, weighted-sum valuations, n = m | truncated welfare approximation: (1 + e) g̃OPT ≥ OPT | Helped |
Closest papers by meaning
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