# Online Algorithms for the Santa Claus Problem

Published in *NeurIPS*, 2022

Recommended citation: Hajiaghayi. et al (2022), Online Algorithms for the Santa Claus Problem (NeurIPS 2022)

The Santa Claus problem is a fundamental problem in *fair division*: the goal is to partition a set of *heterogeneous* items among *heterogeneous* agents so as to maximize the minimum value of items received by any agent. In this paper, we study the online version of this problem where the items are not known in advance and have to be assigned to agents as they arrive over time. If the arrival order of items is arbitrary, then no good assignment exists in the worst case. However, we show that even for arbitrary items, if their arrival order is random, then for any $\varepsilon > 0$, we can obtain a competitive ratio of $1-\varepsilon$ when the optimal assignment gives value at least $\Omega(\log n / \varepsilon^2)$ to every agent. We also show that this result is almost tight: namely, if the optimal solution has value at most $C \ln n / \varepsilon$ for some constant $C$, then there is no $(1-\varepsilon)$-competitive algorithm even with random arrival order.