Zhihao Gavin Tang

Xiaohui Bei, Jiajun Ma, Zhan Jing et al.

Mathematical formalization uses interactive theorem provers to turn informal mathematical statements into machine-checkable artifacts. The success of mathlib, a large collaborative library for Lean, illustrates the potential of this approach. Recent progress in AI-assisted programming and theorem proving is also making large-scale formalization more practical. This paper presents EconCSLib, an early Lean 4 library for computational economics, as both infrastructure and a case study for AI-assisted formalization. The library aims to provide reusable definitions and theorems for game theory, mechanism design, social choice, and related areas. Beyond verified proofs of existing results, the library also aims to host machine-checked open problems and formalization of modern research papers. We discuss the design principles behind the library, the lessons learned from its development, and future directions for AI-assisted formalization in computational economics.

Chenghao Guo, Zhiyi Huang, Zhihao Gavin Tang et al.

This paper explores a theory of generalization for learning problems on product distributions, complementing the existing learning theories in the sense that it does not rely on any complexity measures of the hypothesis classes. The main contributions are two general sample complexity bounds: (1) $\tilde{O} \big( \frac{nk}{ε^2} \big)$ samples are sufficient and necessary for learning an $ε$-optimal hypothesis in any problem on an $n$-dimensional product distribution, whose marginals have finite supports of sizes at most $k$; (2) $\tilde{O} \big( \frac{n}{ε^2} \big)$ samples are sufficient and necessary for any problem on $n$-dimensional product distributions if it satisfies a notion of strong monotonicity from the algorithmic game theory literature. As applications of these theories, we match the optimal sample complexity for single-parameter revenue maximization (Guo et al., STOC 2019), improve the state-of-the-art for multi-parameter revenue maximization (Gonczarowski and Weinberg, FOCS 2018) and prophet inequality (Correa et al., EC 2019), and provide the first and tight sample complexity bound for Pandora's problem.