重新定义资本：知识如何成为可积累、可管理的现代财富核心
A Knowledge Theory of Capital:The Value of Natural and Artificial Intelligence, Volume 1
- 以知识为资产，构建新型资本理论框架
- 提出知识可转化、可反馈、可封闭或共享的治理机制
- 适合关注数字经济与知识经济的学者和政策制定者
本卷构建了面向软件、数据、模型、惯例、专长、平台、组织、公共知识基础设施等日益主导生产能力的经济体系的知识资本理论。基于亚当·斯密的劳动、资本、分工与市场范围理论，探讨当知识具备类似资本的特征——可迁移、可扩展、可治理、可重组、会计中不完全可见时，经济逻辑如何演变。书中将承载知识的资本作为核心对象，分析其生成、转化为可治理形态、部署、通过反馈改进、被私有或共享、度量、损耗以及作为未来生产投入的过程。区分了嵌入型、非嵌入型、制度化、公地及公共知识形式，并提出首次转化、认知封闭、反馈捕获、暗资本与预期知识损失等概念。论证具有条件性与可检验性：现代财富不仅依赖资本积累，更取决于生产性知识的治理方式。
原文摘要 · Abstract (English)
This volume develops a knowledge theory of capital for economies in which productive capacity increasingly resides in software, data, models, routines, expertise, platforms, organizations, commons, and public epistemic infrastructure. Beginning from Adam Smith's theory of labour, stock, specialization, and market extent, it asks what changes when knowledge becomes stock-like, mobile across forms, scalable, governable, recombinable, and imperfectly visible in accounting. The book introduces knowledge-bearing stock as the central object and analyses how it is generated, converted into governable form, deployed, improved through feedback, enclosed or shared, measured, impaired, and used as input to future production. It distinguishes embodied, disembodied, institutionalized, commons, and public knowledge forms and develops concepts such as first conversion, cognitive enclosure, feedback capture, dark capital, and expected knowledge loss. The argument is conditional and testable: modern wealth depends not only on capital accumulation, but on how productive knowledge is governed.
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