Chain: A Dynamic Double Auction Framework for Matching Patient Agents

TL;DR

This paper introduces Chain, a novel framework for constructing truthful dynamic double auctions from static rules, improving efficiency in markets with arriving and departing agents.

Contribution

It presents the first method to build truthful dynamic double auctions from static single-period rules, with unique pricing and matching mechanisms.

Findings

Chain performs well compared to other schemes.

Efficiency improves with lower arrival intensity.

Volatility in valuations affects auction performance.

Abstract

In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic, two-sided market. A single commodity, such as a resource or a task, is bought and sold by multiple buyers and sellers that arrive and depart over time. Our algorithm, Chain, provides the first framework that allows a truthful dynamic double auction (DA) to be constructed from a truthful, single-period (i.e. static) double-auction rule. The pricing and matching method of the Chain construction is unique amongst dynamic-auction rules that adopt the same building block. We examine experimentally the allocative efficiency of Chain when instantiated on various single-period rules, including the canonical McAfee double-auction rule. For a baseline we also consider non-truthful double auctions populated with zero-intelligence plus"-style learning agents. Chain-based auctions perform well in comparison with other schemes, especially as arrival intensity falls and agent valuations become more volatile.