Welfare Maximization with Limited Interaction

TL;DR

This paper investigates the limits of distributed welfare maximization in matching markets, establishing lower bounds on approximation ratios and convergence rates for multi-round communication protocols.

Contribution

It provides the first multi-round lower bounds for welfare maximization, showing fundamental limits on approximation quality and convergence speed in distributed settings.

Findings

Multi-round protocols cannot surpass certain approximation bounds.

Convergence to stable states requires at least logarithmic rounds.

Lower bounds apply even with increased per-round bandwidth.

Abstract

We continue the study of welfare maximization in unit-demand (matching) markets, in a distributed information model where agent's valuations are unknown to the central planner, and therefore communication is required to determine an efficient allocation. Dobzinski, Nisan and Oren (STOC'14) showed that if the market size is $n$, then $r$ rounds of interaction (with logarithmic bandwidth) suffice to obtain an $n^{1/(r+1)}$-approximation to the optimal social welfare. In particular, this implies that such markets converge to a stable state (constant approximation) in time logarithmic in the market size. We obtain the first multi-round lower bound for this setup. We show that even if the allowable per-round bandwidth of each agent is $n^{\epsilon(r)}$, the approximation ratio of any $r$-round (randomized) protocol is no better than $\Omega(n^{1/5^{r+1}})$, implying an $\Omega(\log \log n)$ lower bound on the rate of convergence of the market to equilibrium. Our construction and technique may be of interest to round-communication tradeoffs in the more general setting of combinatorial auctions, for which the only known lower bound is for simultaneous ($r=1$) protocols [DNO14].