Fairly Allocating (Contiguous) Dynamic Indivisible Items with Few Adjustments

TL;DR

This paper addresses dynamic fair allocation of indivisible items with minimal adjustments, achieving fairness notions like EF1 and PROPa under various valuation and contiguity constraints, with efficient algorithms and tight bounds.

Contribution

It introduces improved algorithms and bounds for fair allocations with adjustments, especially under contiguity constraints and for identical versus nonidentical valuations.

Findings

EF1 can be achieved with no adjustments for restricted valuations.

An EF1 algorithm reduces adjustments from O(nmT) to O(mT).

Achieving PROPa requires Θ(nT) adjustments for identical valuations.

Abstract

We study the problem of dynamically allocating $T$ indivisible items to $n$ agents with the restriction that the allocation is fair all the time. Due to the negative results to achieve fairness when allocations are irrevocable, we allow adjustments to make fairness attainable with the objective to minimize the number of adjustments. For restricted additive or general identical valuations, we show that envy-freeness up to one item (EF1) can be achieved with no adjustments. For additive valuations, we give an EF1 algorithm that requires $O(mT)$ adjustments, improving the previous result of $O(nmT)$ adjustments, where $m$ is the maximum number of different valuations for items among all agents. We further impose the contiguity constraint on items such that items are arranged on a line by the order they arrive and require that each agent obtains a consecutive block of items. We present extensive results to achieve either proportionality with an additive approximate factor (PROPa) or EF1, where PROPa is a weaker fairness notion than EF1. In particular, we show that for identical valuations, achieving PROPa requires $\Theta(nT)$ adjustments. Moreover, we show that it is hopeless to make any significant improvement for either PROPa or EF1 when valuations are nonidentical. Our results exhibit a large discrepancy between the identical and nonidentical cases in both contiguous and noncontiguous settings. All our positive results are computationally efficient.