博弈论模型分析 AI 时代学术招聘中的努力选择困境
Game theory of academic effort and hiring in the age of AI
搞学术的朋友看看这篇：AI 让发论文变便宜了，论文还能不能证明实力？作者用博弈论把求职和招聘两边的选择全推了一遍，还做了模拟。
arXiv 论文用博弈论建模研究 AI 对学术界的影响：当 AI 能低成本产出高质量论文后，论文数量不再能可靠区分研究者的真实理解深度。模型将求职者与招聘方刻画为博弈双方，求职者需在深耕理解与用 AI 堆论文之间选择，招聘方需在面试考察与论文数量之间取舍。作者分类了所有纯策略均衡，并通过学习模拟预测求职者与招聘者群体可能采取的行为。
Game theory of academic effort and hiring in the age of AI
Many academic disciplines (in particular, mathematics and the theoretical sciences) face difficult questions about how to adapt their practices to the rise of highly capable artificial intelligence (AI). Before AI, a well-written research paper with a strong theoretical result acted as a reliable signal that its authors understood their field in depth. Now that AI is widely accessible, such signals have become cheaper to produce. As a result, they no longer reliably sort academics by their level of understanding. This has direct consequences for academic hiring. The age of AI raises a question for each side of the academic job market. Should an early-career researcher invest in a deep understanding of their field, or instead use AI to build a longer resume with many papers? Should a recruiter favor applicants with fewer papers who pass an interview aimed at probing understanding, or insist on both publication volume and a pass? In this paper, we develop a game-theoretic model of this two-sided dilemma. We classify all pure-strategy equilibria that can arise in this game and use learning simulations to identify the behaviors that populations of applicants and recruiters may learn to adopt. Our analysis provides a framework for reasoning about how academic hiring can adapt to AI and for evaluating practices that preserve the value of deep understanding.