On the economics of knowledge creation and sharing

TL;DR

This paper explores how distributed computing and blockchain can transform knowledge creation and sharing in academia, healthcare, and industry by addressing data centralization and funding challenges.

Contribution

It bridges technical concepts with socioeconomic implications, proposing decentralized data sharing models to improve research and healthcare outcomes.

Findings

Data centralization limits knowledge sharing and innovation.

Blockchain can enable secure, decentralized data exchange.

Decentralized models can reduce costs and improve data utilization.

Abstract

This work bridges the technical concepts underlying distributed computing and blockchain technologies with their profound socioeconomic and sociopolitical implications, particularly on academic research and the healthcare industry. Several examples from academia, industry, and healthcare are explored throughout this paper. The limiting factor in contemporary life sciences research is often funding: for example, to purchase expensive laboratory equipment and materials, to hire skilled researchers and technicians, and to acquire and disseminate data through established academic channels. In the case of the U.S. healthcare system, hospitals generate massive amounts of data, only a small minority of which is utilized to inform current and future medical practice. Similarly, corporations too expend large amounts of money to collect, secure and transmit data from one centralized source to another. In all three scenarios, data moves under the traditional paradigm of centralization, in which data is hosted and curated by individuals and organizations and of benefit to only a small subset of people.