AI时代的学术努力与招聘博弈论
Game theory of academic effort and hiring in the age of AI
AI总结：
本文构建博弈论模型分析AI时代学术招聘的双向困境，分类纯策略均衡并通过模拟确定群体行为，为学术招聘适应AI提供评估深度理解价值实践的框架。
摘要：
许多学术学科（尤其是数学和理论科学）面临着如何调整自身实践以适应高能力人工智能（AI）兴起的难题。在AI出现前，一篇包含强理论结果的优质研究论文是作者深入理解其领域的可靠信号。如今AI已广泛普及，这类信号的生成成本大幅降低，因此不再能可靠区分学者的理解水平，这对学术招聘产生了直接影响。AI时代给学术就业市场的供需双方都提出了问题：早期职业研究者应投入精力深入理解领域，还是利用AI构建包含大量论文的更长简历？招聘方应青睐论文数量少但通过考察理解能力的面试的申请者，还是坚持要求既有发表量又通过面试？本文构建了这种双向困境的博弈论模型，分类了该博弈中可能出现的所有纯策略均衡，并通过学习模拟确定了申请者和招聘者群体可能习得采用的行为。我们的分析提供了一个框架，用于推理学术招聘如何适应AI，以及评估能保留深度理解价值的实践。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
