Accepted papers and working drafts from the lab.

Adrasteia: AI Agent Workflow for Economic Paper Writing

Heikichi Hayashi, Huanxi Zhang (University of Wisconsin-Madison), Karl Yu (Boston College), Ruoran Lai (Sun Yat-sen University), and Jiazhuo Li (University of Michigan)

Accepted at the ICML 2026 Workshop on AI for Math

Presented at: EC'26 Workshop on AI-Driven Research in EconCS (oral contributed talk); Can AI Do Theory? Workshop at STOC TheoryFest 2026 (Google Research); AEA Annual Meeting 2027 (scheduled)

Adrasteia presents a human-in-the-loop AI agent workflow for economic paper writing, spanning assumption search, model structuring, argument revision, and referee-style critique.

Smart Contracts, Dumb Money: Open Source Lemons

Heikichi Hayashi, Weiyu Qi (University of Chicago), Karl Yu (Boston College), and Huanxi Zhang (University of Wisconsin-Madison) (authors in alphabetical order)

Kilts Center at Chicago Booth Marketing Data Center Paper

Presented at: Bank of Italy ToDeFi 2026*; CCER Summer Institute (* presented by coauthor)

The project examines information quality, adverse selection, and investor behavior in open-source smart-contract environments and digital asset markets.

Smoothing the Cliff: Fair and Incentive-Compatible Priority Allocation via Randomized Mechanisms

Heikichi Hayashi, Weiyu Qi (University of Chicago), Karl Yu (Boston College), and Huanxi Zhang (University of Wisconsin-Madison) (authors in alphabetical order)

Presented at: EC'26 Workshop on Online Learning and Economics; 12th Tsinghua University SEM Graduate Workshop (Outstanding Presentation Paper Award); EEA-ESEM*; ESIF Economics and AI+ML Meeting (Cornell University)*; KOF Swiss Economic Institute at ETH Zurich*; Peking University National School of Development (* presented by coauthor)

This paper studies randomized allocation mechanisms designed to smooth cliff effects while preserving fairness and incentive compatibility in priority-based environments.

A Blessing in Disguise? DeFi Exploits and Short-Horizon Responses in U.S. Commercial Paper Spreads

Heikichi Hayashi and Tse Hou (University of Minnesota and Federal Reserve Bank of Minneapolis) (authors in alphabetical order)

Working Paper

Presented at: Yale Pre-Doctoral Economics Conference (Yale Tobin Center); Asia Meeting of the Econometric Society, China (HKU Business School); FMA International European Conference*; 5th Annual Hong Kong Conference on FinTech and AI in Finance (City University of Hong Kong) (* presented by coauthor)

DeFi exploits generate short-run spillovers into traditional financial markets via money market fund reallocations, creating a measurable cross-market transmission channel.

The Private Enforcer: Algorithmic Deterrence and the Shadow Tax on Insider Trading

Heikichi Hayashi, Ruoran Lai (Sun Yat-sen University), Weiyu Qi (University of Chicago), and Karl Yu (Boston College) (authors in alphabetical order)

Working Paper

Presented at: Federal Reserve Board Summer Workshop on Money, Banking, Payments, and Finance (Gerzensee, Switzerland); China Financial Research Conference (Tsinghua PBCSF); Asia Meeting of the Econometric Society, China (HKU Business School); 38th Asian Finance Association Conference* (* presented by coauthor)

In DeFi markets, MEV acts as a private enforcement mechanism that penalizes informed trading and creates a shadow tax on strategic trading behavior.

Carbon vs. Silicon: Can AI Outperform Top-Tier Economists in Scientific Knowledge Production?

Heikichi Hayashi

Work in Progress

An AI-and-economics project examining whether advanced AI systems can match or outperform top-tier economists in originality, technical rigor, and heuristic value.