The system of science has reached a bottleneck. The structures that emerged hundreds of years ago are failing. Their mechanisms, manifested in a world of third party arbiters, are robbing humanity of critical advancements in medicine, energy production, materials, exploration, mathematics, and countless other areas of scientific development.
Gates to knowledge, resources, and tools prevent some of humanity’s brightest and most motivated minds from utilizing the process of science to solve our most urgent needs or to discover the next groundbreaking truth of the universe.
Without the ability to participate in and understand the scientific process, the general public is taught to view it with distrust and apathy. At best we ignore the outcomes of science. At worst we actively reject its results.
As humanity stares down increasing existential threats, some of which are already coming to fruition, the only system we rely on for impartial discovery and production is losing its credibility and functionality.
Now, more than ever, humanity needs science. And so too does science need us.
The contemporary system of science emerged as a result of converging technological, political, and economic advancements of the 16th century. In short, those with money and privilege had time to access, consolidate, and profit from novel technology used to produce, share, and consume knowledge. Producers of knowledge profited by industrializing science through societies, institutions, and universities. Aggregators of knowledge profited by industrializing the publication, credit, and reputation mechanisms through journals and impact scores. Consumers of knowledge profited by owning and controlling the knowledge-to-technology translation pipeline.
Closed and permissioned processes of knowledge production, consumption, and translation created a system that incentivizes consolidated control of its mechanisms. To this day, and despite several significant advancements in knowledge-sharing technology, these arbiters of trust, capital, power, and culture choose who has access to the resources, tools, and outcomes of science.
On top of it all, the general public has never had a choice but to live with the outcomes of science, over which they had no control. While still a nascent political truism during the emergence of the system of science, it is obvious today that excluding forced consumers from the process that creates the necessary product leads to untenable levels of distrust and unrest.
Contemporary science is haunted by its legacy as an emergent system of the 16th century.
The technological innovations at hand today offer the opportunity to expand the scale of science in ways not seen since its emergence. During this process, many, if not all of the mechanisms that comprise the system of science can be renovated or entirely rebuilt. Instead of emerging as a product of the times, we can build the system of science to actively engage and empower all stakeholders, producers, and consumers within the system.
We can root science in mechanisms that incentivize public engagement instead of public exclusion, that encourage knowledge sharing instead of knowledge hoarding, that support independent production and experimentation instead of conformity, that credit collaboration instead of siloing, that reward quality and replicability instead of quantity;
Technological, economic, and political advancements are once again converging. This time however, we have the opportunity to control the outcomes. This time we can move with intention.
There are many mechanisms in the system of science. Most are interwoven with one another. Several have become cornerstones of entire structures of the system.
This intersectionality means that almost every mechanism of the system will need to change simultaneously to truly crack the bottleneck threatening science.
The changes will not come from one source. Many projects must design different iterations of alternative mechanisms. The projects must work together to design the edges by which the iterations interact. The ideal outcome is a permissionless system where new iterations are seamlessly injected into the larger network while failing iterations drop away without causing disruption.
Below are a list of mechanisms and some questions that might help inform the intention of architects.
- Who has the tools to produce data and knowledge?
- What assets of data and knowledge are made accessible?
- Who owns the data and the knowledge?
- How is the data and knowledge referenced in future research?
- At what point in the creation process is data and knowledge shared?
- How easily can data and knowledge be reproduced?
- Who has the tools to reproduce data and knowledge?
- What role does replication play in the legacy system?
- What role should replication play in the digital system?
- What defines the impact of a discovery?
- Who decides the impact of a discovery?
- What role does impact play in the discovery’s standing?
- How is impact related to the other mechanisms?
- Who gets credit for the work required to produce data and knowledge?
- What right or privilege does credit grant the owner?
- Who disseminates credit?
- How is credit disseminated?
- How can the credit mechanism encourage collaboration?
- How does credit relate to income or funding?
- Can credit for foundational discoveries produce income if that foundational discovery is used to produce a translated product?
- How is credit related to the other mechanisms?
- What does expertise mean?
- Who defines expertise?
- Does the legacy model exclude populations?
- Does the legacy model limit the number of positions of employment in pure research?
- Can expertise be granted on a network level?
- What rights or privileges does accreditation grant a participant?
- Can network accreditation translate to steady reliable funding or income?
- How does accreditation relate to the other mechanisms?
- What defines the reputation of a participant?
- How can reputation be defined to encourage collaboration?
- How does reputation relate to network rights and privileges?
- How does reputation relate to funding or income?
- How is reputation related to the other mechanisms?
- Where do the funds for research come from?
- How are funds received?
- Who decides what gets funded?
- Who are the benefactors?
- Who covers the “loss” for non-translatable outcomes?
- Can everyone be made a benefactor of research?
- When are funds distributed to a researcher?
- How is continued funding achieved?
- How is funding related to the other mechanisms?
- What is the purpose of formal publication?
- In a system with open ledgers, economics, and incentives why is formal publication necessary?
- What can replace the modern publication model to ensure everyone can share their research and receive the proper credit and accolade?
- Can a publication mechanism incentivize interdisciplinary discussion and collaboration?
- What does a “publication” look like in a trustless digital world?
- What is the purpose of peer review?
- In a system where anyone can freely produce, publish, access, and replicate data and knowledge, is formal review necessary?
- What can replace the modern review model to ensure data and knowledge production can be trusted and is quality?
- What is the purpose of IP?
- In a system where credit is guaranteed and verifiable at publication, and where remuneration can occur automatically and upon use of data and knowledge, is IP necessary?
- What can replace the modern IP model to ensure research is monetarily rewarded?
- What are the physical resources required for research?
- Who has access to them?
- How can access to these tools be granted to more people?
Imagine a scenario in which a system participant creates and publishes foundational data, methodology, and other research assets to an immutable ledger that forever recognizes the research “object” as belonging to that participant. Anyone can access the object, reproduce the data, and build on the knowledge. Let’s say four separate participants access the research object and contribute toward translating it into a product on the market. Each contributor posts their outcomes to the network creating a “stack” of research objects. When brought to market, credit and profit is shared among all four contributors without the need for legal intermediation. Profits continue to trickle to all contributors of the stack so long as the product is “syndicated”. If necessary, all participants can point to their contribution and timestamp on the ledger to prove their role in the creation of the translated product.
Now imagine that each of the four contributors receives verifiable and non-transferrable credit based on their contribution to the discovery and production of the translated product. This credit might give the holder specific privileges, responsibilities, and opportunities within and without the network in which they participated. Perhaps they can elevate the voices of new network participants, perhaps they have a louder voice in the funding mechanism, perhaps they are given resource priority, perhaps they are even given network and legally recognized accreditation which itself comes with specific benefits.
Continuing the example, each contribution to the knowledge stack that resulted in a translated product continues to exist as its own object. This means that the foundational object can be referenced again to produce a second knowledge stack that might produce a second translated product. The impact of that original research object could be determined by the number of times it is used in a knowledge stack. Each time it is used or results in profit, it generates income for the contributors that produced it. Imagine the impact the theory of general relativity would have today, and the royalties it would produce for the contributors to its stack and original research objects.
This is a brief example of how some aspects of an open, trustless, permissionless, and digital system of science might operate. It is up to the architects to design as many networks as possible, and for those networks to interact, collaborate, and compete to form a marketplace of system iterations in which participants can choose to engage.
The world was a certain way when the legacy system of science emerged. Now we have the opportunity to rebuild the system with that legacy in mind. Architects can choose to proactively seek diverse perspectives when designing their mechanisms. A permissionless system guarantees that any new iteration from any perspective is given a chance in the marketplace of mechanisms.
Cryptocurrency generating distributed ledgers can be viewed as open economic networks (OEN). The network mints a currency based on variables defined in software. The currency is distributed to network participants based on rules defined in the software. This is similar to how a central bank might mint and distribute currency. The difference is in the open and permissionless nature of the predictable software which governs cryptocurrency generating distributed ledgers. Anyone can make an OEN at any time and the marketplace of currencies will decide which networks are more valuable at a given point in time.
A cryptocurrency generating distributed ledger that is rooted in a system of science would essentially create an OEN rooted in scientific outcomes. The ledger could consist of pointers, research objects, credits, accreditation, and other traceable objects. The currency would be generated and distributed based on instruments of science production. Contrast this with a currency based on instruments of debt creation.
For example, the software might mint and distribute currency directly to participants that produce an impactful research object, contribute computational resources to discovery, become accredited on approved networks, or contribute quality improvements as defined by a weighted web of trusted peers and network participants.
Many of the mechanisms of science control the potential engagement, education, and participation of the general public. They can be designed to incentivize proactive and synergistic relationships between knowledge producers, aggregators, and consumers.
Ultimately, an actively engaged, educated, and participating general population is more likely to appreciate and accept the outcomes of science. The technology at hand offers us the opportunity to build economies and societies based on science-literacy and science-participacy.
For example, imagine there is an OEN that funds science production and consits of both scientists and a general population. The OEN distributes currency to researchers based on the evenly weighted decision of all network participants. Let’s say a scientist wants to receive funding from the OEN. To receive funding, the scientist must convince the general population of the network that their research is worthwhile and network-value-aligned. To ensure network-value-alignment, the general population must learn about the research and understand its goals and methodology. Both parties are thereby indirectly incentivized to interact, educate, and engage. Contrast this with the current tax/corporate model of funding where a researcher convinces only other researchers or administrators that their science is valuable.
Now imagine that the research itself requires resources that are more efficiently obtained through distributed processes. Distributed computing, crowd-sourcing data, and monetary funding are three examples. The general population of the network can be incentivized to directly participate in the creation of a research object. The OEN can distribute currency directly to resource contributors. Resource contributors can also receive credit and accolades for their contributions. In this scenario as well, the general public will seek education from the researcher while the researcher seeks the general public’s engagement and participation; ignoring some interesting potential distribution models, the better a researcher can convince contributors of the value of their research, the more resources will be allocated to that research.
More avenues of income, publication, respect, and participation means more minds will enter the field of science. More jobs besides the limited tenured position will emerge and enable stable income. More edge-case experiments and discoveries will move forward. More young researchers will use their energy for exploration of high-risk questions. Anonymous contributors will build without fear of persecution from overreaching authority or arbiters of culture.
Furthermore, if science production is the root of currency creation and distribution, individuals seeking solely profit will focus on producing and translating knowledge. Greed can be utilized to move science forward.
Some networks might tune their mechanisms in ways that incentivizes collaboration, collaboration being one of the practices that greatly increases productivity and efficiency.
An appropriately tuned network might also encourage single experiment publishing, incentivizing researchers to open up their research after each step. The network might encourage, acknowledge, and value negative outcomes. It might create replication that can occur on the fly.
In a network iteration of research objects, knowledge stacks, and syndicated funding, the original research object of multiple knowledge stacks becomes a very valuable outcome of the system. If I create the foundational research that is used to create 50 translated products, I receive passive income and credit from each of those products.
A significant problem of the legacy system of science stems from its single iteration. It emerged, it progressed, it is reaching a logical conclusion, and now there is no competing system to save it from itself.
The open, trustless, permissionless, and digital nature of the technology on which an open system of science is forming demands multiple iterations of scientific processes. If one OEN fails, participants can move to another. Participants can choose to engage with multiple OENs simultaneously. OENs can interact, learn from, and compete with one another to build the most efficient mechanisms. Different OENs can develop different value-sets, and each value-set can compete and shift in societal-value over time based on social, political, and economic context. All of this without disrupting the continued flow of knowledge creation and translation.
In a marketplace of interoperable currencies defined by different mechanistic iterations, stability reigns.
An open system of science is not a new idea or movement. Over the past several decades, open access, knowledge, data, education, and publication movements have attempted to build new ways to produce and translate knowledge. Many have succeeded in several key arenas. Similar to the precursors of Bitcoin, however, their ongoing struggles stem largely due to the limitations of the technology they have at hand. Distributed ledger technology offers new tools with which the same ideas can be successfully implemented along-side new visions of a more open, inclusive, and participation-based system of science.
Whether it was through securing a ledger by finding prime numbers (Primecoin), building economic networks rooted in scientific outcomes (Gridcoin), incentivizing distributed computing networks (Gridcoin, Curecoin, Foldingcoin), raising funds for research (Pinkcoin, Einsteinium), or building distributed and cloud computing marketplaces (Golem, SPARC, iExec), people working with distributed ledger technology have been experimenting with these tools since the beginning of crypto, some with measured success.
While building trustless science you will find yourself next to contributors who have dedicated their careers to open science. You will find contributors who have dedicated their time to advancing trustless technology. You will find some of the most engaging discussions centered around solving some of the most fulfilling challenges, challenges that offer the best opportunities to build a world where the production and translation of knowledge belongs to and is accessible by everyone.
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