Near-Optimal Online Resource Allocation in the Random-Order Model

Saar Cohen, Noa Agmon

Research output: Contribution to journalConference articlepeer-review


We study the problem of allocating either divisible or indivisible items (goods or chores) among a set of agents, where the items arrive online, one at a time. Each agent's non-negative value for an item is set by an adversary upon the item's arrival. Our focus is on a unifying algorithmic framework for finding online allocations that treats both fairness and economic efficiency. For this sake, we aim to optimize the generalized means of agents' received values, covering a spectrum of welfare functions including average utilitarian welfare and egalitarian welfare. In the traditional adversarial model, where items arrive in an arbitrary order, no algorithm can give a decent approximation to welfare in the worst case. To escape from this strong lower bound, we consider the random-order model, where items arrive in a uniformly random order. This model provides us with a major breakthrough: we devise algorithms that guarantee a nearly-optimal competitive ratio for certain welfare functions, if the welfare obtained by the optimal allocation is sufficiently large. We prove that our results are almost tight: if the optimal solution's welfare is strictly below a certain threshold, then no nearly-optimal algorithm exists, even in the random-order model.

Original languageEnglish
Pages (from-to)2219-2221
Number of pages3
JournalProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
StatePublished - 2024
Event23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024 - Auckland, New Zealand
Duration: 6 May 202410 May 2024


  • Generalized Means
  • Online Fair Division
  • Random-Order Model
  • Resource Allocation

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering


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