Optimal Auction Design for the Gradual Procurement of Strategic Service Provider Agents

TL;DR

This paper introduces a novel auction mechanism for procuring services from strategic providers, balancing success probability and costs, with incentive compatibility and significant computational efficiency improvements.

Contribution

It presents a new auction design with a weighted threshold payment scheme and a low-complexity near-optimal auction for strategic service procurement.

Findings

Achieves up to 79% success probability

Reduces invocation costs by up to 11%

Improves performance by 59% over existing methods

Abstract

We consider an outsourcing problem where a software agent procures multiple services from providers with uncertain reliabilities to complete a computational task before a strict deadline. The service consumer requires a procurement strategy that achieves the optimal balance between success probability and invocation cost. However, the service providers are self-interested and may misrepresent their private cost information if it benefits them. For such settings, we design a novel procurement auction that provides the consumer with the highest possible revenue, while giving sufficient incentives to providers to tell the truth about their costs. This auction creates a contingent plan for gradual service procurement that suggests recruiting a new provider only when the success probability of the already hired providers drops below a time-dependent threshold. To make this auction incentive compatible, we propose a novel weighted threshold payment scheme which pays the minimum among all truthful mechanisms. Using the weighted payment scheme, we also design a low-complexity near-optimal auction that reduces the computational complexity of the optimal mechanism by 99% with only marginal performance loss (less than 1%). We demonstrate the effectiveness and strength of our proposed auctions through both game theoretical and numerical analysis. The experiment results confirm that the proposed auctions exhibit 59% improvement in performance over the current state-of-the-art, by increasing success probability up to 79% and reducing invocation cost by up to 11%.