Pricing Online Decisions: Beyond Auctions

TL;DR

This paper demonstrates that well-designed posted prices in online settings with selfish agents can match the performance of optimal algorithms in minimizing social costs across various problems, offering incentive-compatible solutions.

Contribution

It introduces a general framework for using posted prices to achieve near-optimal social cost minimization in online settings, extending beyond traditional auction-based approaches.

Findings

Posted prices can match online algorithms in social cost minimization.

Efficient mechanisms are developed for metrical task systems, k-server, and matching problems.

Social costs decrease significantly with the proposed pricing strategies.

Abstract

We consider dynamic pricing schemes in online settings where selfish agents generate online events. Previous work on online mechanisms has dealt almost entirely with the goal of maximizing social welfare or revenue in an auction settings. This paper deals with quite general settings and minimizing social costs. We show that appropriately computed posted prices allow one to achieve essentially the same performance as the best online algorithm. This holds in a wide variety of settings. Unlike online algorithms that learn about the event, and then make enforceable decisions, prices are posted without knowing the future events or even the current event, and are thus inherently dominant strategy incentive compatible. In particular we show that one can give efficient posted price mechanisms for metrical task systems, some instances of the $k$-server problem, and metrical matching problems. We give both deterministic and randomized algorithms. Such posted price mechanisms decrease the social cost dramatically over selfish behavior where no decision incurs a charge. One alluring application of this is reducing the social cost of free parking exponentially.