Evaluating the Performance of Approximation Mechanisms under Budget Constraints
Juan Carlos Carbajal, Ahuva Mualem

TL;DR
This paper analyzes how simple approximation mechanisms perform in revenue maximization when buyers have private valuations and budgets, revealing fundamental limits based on distribution support and correlation.
Contribution
It provides a comprehensive evaluation of the robustness and limitations of simple mechanisms under private budgets across different distribution scenarios.
Findings
Polylogarithmic menu size mechanisms approximate revenue well with bounded support distributions.
No finite-menu mechanism guarantees positive revenue fraction for unbounded or certain bounded distributions.
Relaxations can lead to unbounded revenue gains under negative correlation.
Abstract
We study revenue maximization in a buyer-seller setting where the seller has a single object and the buyer has both a private valuation and a private budget. Private budgets complicate the classic single-product monopoly problem, making optimal mechanisms difficult to characterize. To address this, we evaluate the robust performance of approximation mechanisms relative to optimal mechanisms using three performance measures: the guaranteed fraction of optimal revenue, the maximal value of relaxation, and a revenue non-monotonicity gap. Our analysis reveals sharp contrasts. For distributions with bounded support, simple mechanisms with polylogarithmic menu size can approximate optimal revenue arbitrarily well, even when valuations and budgets are correlated. By contrast, for distributions with unbounded support, and even for bounded distributions concentrated in the unit square, no simple…
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