改善今日，收窄明日：集体学习、多样性与生成力

中文摘要 AI 辅助

生成式人工智能使一个悖论变得清晰可见：从共同来源学习可以改善每个企业今天的行为，同时却收窄了可供明日发现所用的多样性。这种张力超出了人工智能领域。各领域将成功组织的反复出现的特征转化为可移植的做法，例如准时制生产、免费增值商业模式或混合专家架构。这些集体抽象是他人可以适应的部分模板，而非设计或普遍处方。我们研究了它们如何在一个企业搜索相互依赖景观的模型中塑造发现。集体知识库从领先配置所共享的模式中提取做法；企业在本地评估这些做法的方式各不相同。模型揭示了一条生成力链条：群体多样性提供异质经验，集体抽象将其转化为可复用选项，而情境判断将选项与本地需求匹配。集体学习并非固有地同质化。模仿领导者会迅速消耗多样性，而与本地判断配对的知识库则保留了生成能力。环境扰动暴露了后果：它贬低了积累的知识，但重新开启了本地搜索，帮助同质化、耗尽的行业，同时损害了其知识库仍然有用的多样化行业，尽管多样化行业整体上仍然表现更好。反复出现的做法也可能比其有用性更长久，使得流行度在变化之后成为有效性的一种糟糕检验。这些发现对使用生成式人工智能的组织、最佳实践社区以及汇聚于主导方向的研究领域具有重要意义。生成力要求治理滋养知识的多样性以及应用于知识的判断。学习系统不仅应根据它们今天传播的答案来评判，还应根据它们是否保留了可以从中制造明日答案的差异来评判。

英文摘要

Generative AI makes a paradox newly visible: learning from a common source can improve what each firm does today while narrowing the variety available for tomorrow's discoveries. The tension extends beyond AI. Fields turn recurring features of successful organizations into portable practices, such as just-in-time production, freemium business models, or mixture-of-experts architectures. These collective abstractions are partial templates others can adapt, not designs or universal prescriptions. We study how they shape discovery in a model of firms searching interdependent landscapes. A collective repertoire extracts practices from patterns shared by leading configurations; firms vary in how they evaluate those practices locally. The model reveals a generativity chain: population diversity supplies heterogeneous experience, collective abstraction turns it into reusable options, and situated judgment matches options to local needs. Collective learning is not inherently homogenizing. Copying leaders consumes diversity quickly, whereas repertoires paired with local judgment preserve generative capacity. Environmental disruption exposes the consequences: it devalues accumulated knowledge but reopens local search, helping homogeneous, exhausted industries while harming diverse industries whose repertoire remains useful, even though diverse industries still perform better overall. Recurring practices may also outlive their usefulness, making popularity a poor test of validity after change. These findings matter for organizations using generative AI, best-practice communities, and research fields converging on dominant strands. Generativity requires governing the diversity feeding knowledge and the judgment applied to it. Learning systems should be judged not only by the answers they spread today, but by whether they preserve the differences from which tomorrow's answers can be made.

发表机构

• ESADE（伊斯卡德商学院）

机构由 AI 辅助整理，请以论文原文为准。