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

• URL: http://arxiv.org/abs/2110.12846v1
• Date: Mon, 25 Oct 2021 12:14:47 GMT
• Title: Optimal Auction Design for the Gradual Procurement of Strategic Service Provider Agents
• Authors: Farzaneh Farhadi, Maria Chli, Nicholas R. Jennings
• Abstract summary: We consider an outsourcing problem where a software agent procures 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. 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.
• Score: 8.500525426182115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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%.

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