发表机构
University of Huddersfield（哈德斯菲尔德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文基于TypeSafe AI的Jev模型，提出条件性杰文斯假说，探讨机器评估成本大幅下降如何从稀缺转向丰裕，并分析其对组织判断、决策权及人类评估工作的替代与重塑影响。
AI 中文摘要
生成式人工智能降低了生产看似合理符号制品的成本，使得近期的组织学学术研究将评估与辨别视为生产丰裕条件下的约束。本文考察了一种进一步的可能性：机器评估本身变得足够廉价，可以常规化、大规模地部署。这一研究由Jev（TypeSafe AI的专用类型化概率决策模型）所引发。TypeSafe明确援引威廉·斯坦利·杰文斯（William Stanley Jevons）来论证，成本更低的机器智能可以解锁以往不经济的使用场景。本文将这一主张视为一种技术挑衅而非既定的实证结果，并针对机器评估提出了一个条件性的杰文斯假说：可用机器评估的总边际成本若出现足够大幅度的下降，在潜在需求可观且互补成本不占主导地位的情况下，可能会增加其在组织中的消费量。本文将反弹经济学与关于廉价预测、生产丰裕、机器评估、决策分配、权威、依赖及高管判断的研究相结合，以考察这一可能的稀缺性转变。本文区分了预测、机器评估、组织判断与授权这四类功能活动，它们的成本不必同步下降。评估可以共享证据、标准与错误；可以扩展错误设定的评分标准；可以在那些关键资格特征已消失的表征上运作；并通过阈值与异常路由改变实际的决策权。由此产生的研究问题是：廉价的机器评估何时替代人类评估工作，何时重新分配或创造对判断的需求，以及它如何影响在重大组织承诺时可获得的依据。
英文摘要
Generative artificial intelligence has reduced the cost of producing plausible symbolic artefacts, leading recent organisation scholarship to identify evaluation and discernment as constraints under conditions of production abundance. This perspective examines a further possibility: that machine evaluation itself becomes inexpensive enough to be deployed routinely and at scale. The investigation is prompted by Jev, TypeSafe AI's specialised model for typed probabilistic decisions. TypeSafe explicitly invokes William Stanley Jevons to argue that lower-cost machine intelligence can unlock previously uneconomic uses. Treating this as a technological provocation rather than an established empirical result, the article formulates a conditional Jevons hypothesis for machine evaluation: sufficiently large reductions in the total marginal cost of usable machine evaluation may increase its organisational consumption where latent demand is substantial and complementary costs do not dominate. The article integrates rebound economics with research on cheap prediction, production abundance, machine evaluation, decision allocation, authority, reliance and Executive Judgement to examine this possible scarcity transition. It distinguishes prediction, machine evaluation, organisational judgement and authorisation as functional activities whose costs need not fall together. Evaluations can share evidence, criteria and errors; scale mis-specified rubrics; operate on representations from which consequential qualifications have disappeared; and change practical decision rights through thresholds and exception routing. The resulting research problem is when cheap machine evaluation substitutes for human evaluative work, when it redistributes or creates demands for judgement, and how it affects the grounds available at consequential organisational commitment.
Comments19 pages
