Simulations for Science Token Engineering Part 3: Public Goods, Integration, and the Bigger Picture

Science Token Engineering Part 3: Public Goods, Integration, and the Bigger Picture

:church: This is Part 3 of the Science Token Engineering blog series. In this blog post, we’ll put together everything we’ve discussed so far and we’ll try to envision what an open science community could look like.

See Part 1 here and Part 2 here.

By looking at the science value flow, we have been able to identify its weaknesses and design a system that aims to solve them. Incidentally, we have also been looking for a way to maximize the creation of new knowledge and to ensure the value of this new knowledge is correctly distributed amongst those involved.


Throughout the last two posts, we have essentially been treating knowledge as any other private good, which actually isn’t a perfect analogy. The value of knowledge increases with its integration. In other words, new research is not going to bring much value to the world unless it is adopted by other people. So how do we increase the adoption of new knowledge?

Knowledge as a Public Good

A reasonable approach might be to incentivize public research, or research which will produce public knowledge assets and will not belong to a specific person (beyond the accreditation of its creators). For example, if funding should be allocated to a biomedical research project that has the potential to save millions of lives, there must be no room for malicious behavior from any party involved, and thus it might be a better idea to give this project more funding under the condition that everything will become a public good. Thinking back to Part 2 and the decentralized model of science value flow, the DAO Treasury, curated by its community, can be restricted to funding public research projects which maximize their integration within the scientific community. This in turn ensures that the incentives of the open science ecosystem are aligned so as to not only create a fair distribution of value, but to also maximize the utility of the newly created knowledge assets.

Figure 1. Schema of the public funding, profit-sharing model

Figure 1shows a possible model for an open science ecosystem that aligns the incentives of the DAO towards public research funding, but which also utilizes the effectiveness of the decentralized knowledge market in unlocking previously hidden value from the private sector. The top loop is almost identical to the profit sharing model discussed in Part 2, but this time it specifies that funding is exclusively allocated to public research projects. Furthermore, we make the distinction between different types of researchers depending on the knowledge assets they produce.

  • A data provider is somebody who runs experiments and collects data.
  • An algorithm provider is someone who uses data to create new insights.
  • A compute provider is an entity with significant amounts of data who participates in the market to receive rewards from that data.

Global Potential of the Profit-Sharing Model

:bulb: This model builds upon the realization that collaboration and data sharing are not restricted to researchers, but can expand to private research companies, entire labs, and universities. This notion is represented in the lower part of the schema in Figure 1, where private entities make use of the decentralized knowledge market to accelerate their own research.

The paradigm shift of the science value flow requires time and resources, as outlined in Part 2, but by providing the right incentives, centralized agencies can unlock previously unattainable value of their IP. On top of that, they can access new knowledge resources either from the public research sector or from other private entities, thusgreatly accelerating research and development.

Put simply, the Web3 model enables collaboration on an unprecedented scale and quick integration of new knowledge to a potentially wide range of areas, both of which increase scientific output, reduce the need for intermediaries in scientific funding and knowledge dissemination, and overall increase value that science brings into the world.

Conclusion

In a nutshell, science is broken, but not beyond repair. Web3 has the tools we need to build a better science ecosystem that is fair to its participants, sustainable in the long run, and, most importantly, best utilizes the enormous potential of science to create a better world.

Science Token Engineering Blog Series

Science Token Engineering Part 1: The Problem with Science

Science Token Engineering Part 2: The Profit Sharing Vision

Join Open Science

Building a new open science ecosystem that solves the problems outlined above is not going to happen overnight. If you want to join this exciting space, check out OpSci, say hello on Discord, and consider applying for an Open Web Fellowship.

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