Xiang Hui

Xiang Hui

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I study credible exchange in markets, with a focus on the economics of AI and digital platforms. My research asks how markets sustain trust when information, expertise, effort, identity, or intent are difficult to verify. I examine how trust mechanisms and algorithmic systems shape what firms, consumers, workers, and intermediaries can credibly claim, observe, learn, and verify, and how these mechanisms affect platform governance, market performance, and welfare.

One stream of my research studies digital platforms. I examine mechanisms such as certification, ratings, reputation, verified data, and platform rules, and study how they influence consumer response, seller behavior, market efficiency, and welfare. This work speaks to a broader question: how do markets create credible signals when direct verification is costly?

My recent work extends this agenda to AI. AI reduces the cost of generating claims, recommendations, advice, synthetic evidence, and persuasive interactions at scale. As a result, the central challenge in many AI-mediated markets is not only prediction or automation, but credibility and verification. I study how AI reshapes expertise, market communication, platform governance, and the institutions needed to support welfare-enhancing exchange. In related work, I also study how decentralized systems, including blockchain-based mechanisms, can support verification, coordination, and trust when traditional intermediaries are limited or costly.

Academic/Professional Activities

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• Editor, Associate Editor, Decision Sciences
• Editorial Review Board Member, Marketing Science

Awards/Honors

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• Olin Award, Olin Business School, Washington University, 2026
• Olin Award, Olin Business School, Washington University, 2025
• Research Excellence Faculty Recognition, Office of the Provost, Washington University, 2025
• Marketing Science Service Awards, Marketing Science, 2023

Research Interests

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Trust Mechanisms, Economics of AI, Digital Platforms