用大数据和大模型构建创新研究的数字实验室，实现精准观测与虚拟实验。
Big Data and the Computational Social Science of Entrepreneurship and Innovation
- 结合机器学习与海量数据，构建社会层面的创新观测体系。
- 利用大模型生成技术与企业的数字孪生体，支持虚拟实验与预测。
- 适合关注创新机制、政策模拟的学者与政策制定者参考。
随着大规模社会数据激增和机器学习方法演进，创业与创新研究面临新机遇与挑战。本文探讨如何利用大规模文本、网络、图像、音频和视频数据，识别技术与商业新颖性、追踪新创企业起源，并预测新技术与商业模式间的竞争。提出两种路径：一是通过机器学习模型与大数据结合，构建跨人类社会的系统级创新观测体系；二是借助大数据驱动的人工智能模型，生成技术与企业的‘数字双胞胎’，形成虚拟实验环境以探索创新过程与政策效果。文章主张通过大数据与大模型融合，推动创业与创新理论的发展与检验。
原文摘要 · Abstract (English)
As large-scale social data explode and machine-learning methods evolve, scholars of entrepreneurship and innovation face new research opportunities but also unique challenges. This chapter discusses the difficulties of leveraging large-scale data to identify technological and commercial novelty, document new venture origins, and forecast competition between new technologies and commercial forms. It suggests how scholars can take advantage of new text, network, image, audio, and video data in two distinct ways that advance innovation and entrepreneurship research. First, machine-learning models, combined with large-scale data, enable the construction of precision measurements that function as system-level observatories of innovation and entrepreneurship across human societies. Second, new artificial intelligence models fueled by big data generate 'digital doubles' of technology and business, forming laboratories for virtual experimentation about innovation and entrepreneurship processes and policies. The chapter argues for the advancement of theory development and testing in entrepreneurship and innovation by coupling big data with big models.
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