arXiv cs.CY· Saptarshi Pal, Peter S. Park·· 12 小时前AI 评分31
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 导读
一篇 arXiv 论文用博弈论模型分析 AI 时代学术招聘的双边困境：AI 让论文产出不再可靠地反映学者对领域的理解深度，早期研究者面临深耕理解还是用 AI 堆论文的选择，招聘方则要在少论文但通过理解考察的候选人与论文数量加考察双重要求之间取舍。研究对全部纯策略均衡进行了分类，并用学习模拟识别申请人与招聘方群体可能习得的行为。
正文
Abstract: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.
| Comments: | 30 pages, seven visualizations (four figures, three tables) |
| Subjects: | Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT) |
| Cite as: | arXiv:2610.35802 [cs.AI] |
| (or arXiv:2610.35802v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35802 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Peter S. Park [view email]
[v1]
Mon, 26 Oct 2026 17:35:30 UTC (310 KB)
来源：arXiv cs.CY · arxiv.org