Online Resource Allocation via Static Bundle Pricing
Dimitris Fotakis, Charalampos Platanos, Thanos Tolias

TL;DR
This paper develops static bundle pricing mechanisms for online resource allocation with complementarities, achieving performance guarantees that improve exponentially with item multiplicity, and establishes fundamental lower bounds for such problems.
Contribution
It introduces a unified framework for static bundle pricing in online resource allocation with complementarities, providing mechanisms with exponential improvements and matching lower bounds.
Findings
Achieves $O(d^{1/B})$-competitive mechanism for $d$-single-minded settings.
Provides $O(m^{1/(B+1)})$-competitive mechanisms for general cases.
Establishes lower bounds showing no online algorithm can do better than $ ilde{ ext{o}}(m^{1/(B+2)})$.
Abstract
Online Resource Allocation addresses the problem of efficiently allocating limited resources to buyers with incomplete knowledge of future requests. In our setting, buyers arrive sequentially requesting a set of items, each with a value drawn from a known distribution. We study the efficiency of static and anonymous bundle pricing in environments where the buyers' valuations exhibit strong complementarities. In such settings, standard item pricing fails to leverage item multiplicities, while static bundle pricing mechanisms are only known for very restricted domains and their analysis relies on domain-specific arguments. We develop a unified bundle pricing framework for online resource allocation in three well-studied domains with complementarities: (i) single-minded combinatorial auctions with maximum bundle size ; (ii) general single-minded combinatorial auctions; and (iii)…
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Taxonomy
TopicsAuction Theory and Applications · Optimization and Search Problems · Advanced Bandit Algorithms Research
