Research map / Learning theory and algorithms / Submodular and game algorithms
Walrasian Dynamics in Multi-Unit Markets
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Start best-response dynamics from truthful reports | Any envy-free pricing mechanism · Truth-telling start | convergence to pure Nash equilibrium | Helped |
| Require the mechanism to be consistent | Envy-free pricing mechanisms · Truth-telling start | convergence time: at most n steps | Helped |
| Allow a non-truthful starting profile | General envy-free pricing mechanisms · Non-truthful starting profiles | convergence | Hurt |
| Use the All-or-Nothing mechanism | Best-response dynamics · Any starting profile, n=2 buyers | convergence to pure Nash equilibrium | Helped |
| Require price-monotonicity and supply-monotonicity | Envy-free pricing mechanisms · Non-overbidding pure Nash equilibria | social welfare loss: at most γ_A · B* | Helped |
| Use a price-monotone welfare-maximizing mechanism | Envy-free pricing mechanisms · Non-overbidding pure Nash equilibria | social welfare loss: at most the maximum budget | Helped |
| Use S-Greedy allocation for semi-hungry buyers | Price-monotone revenue-approximating mechanisms · Non-overbidding pure Nash equilibria | revenue approximation: (β-α)/2 | Helped |
| Use All-or-Nothing instead of a general mechanism | Envy-free pricing · Non-overbidding equilibria, any number of buyers | social welfare: at least w* - B* | Helped |
| Use All-or-Nothing instead of a general mechanism | Envy-free pricing · Non-overbidding equilibria, any number of buyers | revenue approximation: min {0.25, (1-α)/2} - α/2 | Helped |
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