The knowledge economy is a complex and dynamical system, where knowledge and skills are discovered through research, diffused via education, and deployed by industry. Dynamically aligning the supply of new knowledge with the demand for practical skills through education is critical for developing national innovation systems that maximize human flourishing. In this paper, we evaluate the complex alignment of skills across the knowledge economy by creating an integrated semantic model that neurally encodes invented, instructed, and instituted skills across three major datasets: research abstracts from the Web of Science, teaching syllabi from the Open Syllabus Project, and job advertisements from Burning Glass. Analyzing the high dimensional knowledge and skills space inscribed by these data, we draw critical insight about systemic misalignment between the diversity of skills supplied and demanded in the knowledge economy. Consistent with insights from economic geography, demand for skills from industry exhibits high entropy (diversity) at local, regional, and national levels, demonstrating dense complementarities between them at all levels of the economy. Consistent with the economics and sociology of innovation, we find low entropy in the invention of new knowledge and skills through research, as specialist researchers cluster within universities. We provide new evidence, however, for the low entropy of skills taught at local, regional, and national levels, illustrating a massive mismatch between diversity in skills supplied versus demanded. This misalignment is sustained by the spatial and institutional mismatch in the organization of education by researchers at the site of skill invention over use. Our findings suggestively trace the societal costs of tethering education to researchers with narrow knowledge rather than students with broad skill needs.

Social computing is ubiquitous and intensifying in the 21st Century. Originally used to reference computational augmentation of social interaction through collaborative filtering, social media, wikis, and crowdsourcing, here I propose to expand the concept to cover the complete dynamic interface between social interaction and computation, including computationally enhanced sociality and social science, socially enhanced computing and computer science, and their increasingly complex combination for mutual enhancement. This recommends that we reimagine Computational Social Science as Social Computing, not merely using computational tools to make sense of the contemporary explosion of social data, but also recognizing societies as emergent computers of more or less collective intelligence, innovation and flourishing. It further proposes we imagine a socially inspired computer science that takes these insights into account as we build machines not merely to substitute for human cognition, but radically complement it. This leads to a vision of social computing as an extreme form of human computer interaction, whereby machines and persons recursively combine to augment one another in generating collective intelligence, enhanced knowledge, and other social goods unattainable without each other. Using the example of science and technology, I illustrate how progress in each of these areas unleash advances in the others and the beneficial relationship between the technology and science of social computing, which reveals limits of sociality and computation, and stimulates our imagination about how they can reach past those limits together.