Building Systems of Trustless Science


The Problem

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.

The Mechanisms

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.

Data and Knowledge Creation

  • 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?

Access to Physical Resources

  • What are the physical resources required for research?
  • Who has access to them?
  • How can access to these tools be granted to more people?

A simplified example incorporating a few mechanisms

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 Effects of an Open System of Science


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.

An Economy of Science

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.

Engagement, Education, and Participation

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.

Speed and Anonymity

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.

Foundational Research is Valuable

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 Marketplace of Networks

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.

Why Choose to Work Toward Trustless Science

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.

Continue the conversation:


@jringo thanks for bringing this into forum! What do you make of the argument that the current scientific funding model seems to work pretty well, despite its flaws? It seems like for engineering, there’s a lot of good research going on in industry and academia, and the same is probably true for other fields. Does the current system really block much useful research? I’m definitely persuaded by the human factor, I think STEM PhDs get a raw deal, but I’m not convinced that society is missing out on a bunch of breakthroughs… there’s a pretty compelling argument for there being too much scientific publication (c.f. Too much academic research is being published)


It’s a very complex problem, but neither argument stands up to scrutiny, as outlined in the writing. Yes the current system blocks an unimaginable amount of research. Underappreciated negative outcomes alone should force skepticism. If not that, then the literal restriction on pure research positions in the system.

As mentioned in the write-up, quality and replicability are far more important than quantity. A system that rewards quantity is not a good system. That’s the point = )

I have a fun metaphor, and I apologize in advance for its language.

I think the outhouse works pretty well, despite its flaws. Plenty of shit gets deposited in it, maybe too much shit. Still, solid technology. Works fine. Keep it.


This is pretty sound in your discussion. As we look at our science today its commercialized and it is mostly used for profit. There have been some innovations that have been for the betterment of humanity. But time and time again we see it get bogged down as we compete with one another to not let the other one advance. Ranging from pharmaceutical companies to advances in biology.

The systems your mentioning is quite fascinating and which I can see this creating more freedom for not only scientist but innovators as well that can share there information without an institution trying to stop them. That it could very well possible speed up the rate in which we discover things and solve complex problems.


I appreciate the metaphor, and for what it’s worth the modern research university emerged in the 19th Century. Even then there were criticisms about the deleterious effects of industry on ‘pure learning.’ I appreciate your commitment to DeSci and am largely on your side. In this forum, we rely on evidence and prior scholarship. So I think it would really make your post more compelling if you could cite a few studies. I’m sure they exist. Could you point to evidence suggesting that universities and scientific publications are the bottleneck for other research?


Happy to be wrong (and probably am), but my understanding is that rich and powerful people and societies started universities and “science” through land grants and whatnot in the early 1000s. Knowledge production and translation got decentralized in the 1500/1600s with the convergence of societal advancements. Then decentralized and consolidated again a couple more times to where we are now, at another inflection point. The 1800s could be a good target of discussion around inflection, but industrialization in the 1800s was not the same as the 1500s – physical production vs knowledge production. That cycle of science was still informed by its past which was based on wealthy/powerful arbiters.

Separating into “contemporary” or “modern” seems to me like ignoring the past, which is similar to accepting a non-replicated result from the early 1900s only to find out 100 years later that the methodology was flawed and everything built on top of it is wrong.

Universities and publications are not the bottlenecks of research. They are parts of a complex systems failure. This is similar to when a financial or political system collapses. The premise is that every mechanism of knowledge production and translation is reaching a logical conclusion, weakening the structures, and collapsing the system. As an example: if we recall the 2008 financial collapse, MBS was a mechanism that was abused and reached a logical conclusion, collapsing a structure (or two).

Regardless, the point of the post is not to argue, prove, or convince of a largely accepted problem: I would challenge someone to find a researcher that loves spending more than half their time searching for funding, for example; or find a doctor that loves explaining to a patient that no, mRNA vaccines do not change your DNA; or find a meteorologist that loves explaining that they cannot say it will be “rain” or “sun” with confidence because their work is based on probabilities; or find a smoker that believed the research funded by the tobacco industry, or funded by the leaded gasoline industry… etc.

I understand the goal of including academic-based evidence, and such evidence exists I’m sure… people have been opening science for decades, but what’s the point of finding a study to prove the earth is round?

The point of this writing is to help guide people designing the new systems of a digital, trustless reality, and given their choices, to encourage them to help with redesigning science. Secondary, the point is to highlight the mechanisms of science and the questions surrounding them. Tertiary, to discuss answers to the questions and possible implementations.

Regarding citations on this forum, I was encouraged to post a random comment about 99% of DAOs being likely to fail. This is a much more cogent post about something much more important than the adoption cycles of DLT. So color me confused, but happy to oblige any requests to remove the post = )


Oh, please don’t remove your post! One of the things we’re doing here at SCRF is using the forum format to test open peer review, so think of the comments here – and requests for citations – as a communal attempt to help you develop your project.

Aside from scoping out the problem, which is why I was asking you for citations (you need to show your work, even when something does seem obvious), how are you imagining tackling this project? Is it a guide, is it a wiki, or something closer to a DAO?

(And speaking of, we’d love to hear why you think 99% of DAOs will probably fail, which I also agree with)

Happy to describe the roots of scientific university research if you like. Humboldt University in Berlin (1810) was the first to combine teaching and research into a single institution, which became the model for incorporating scientific research into universities in the 20th century. You’re absolutely right that there were ecclesiastical and liberal arts colleges for a millennium before (such as Oxford, which I think was founded in the 11th century), but the way that universities and government labs do research today came from Wilhelm von Humboldt’s ideas.


There is no project, as described in the original post.

Besides the three goals I already mentioned, I don’t know how else to describe the intention.

I think we might disagree on the definition of “roots”.

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Inherently, if the original post makes claims that are uncited; time will have to be spent clarifying those claims. I don’t think @jmcgirk was suggesting that you remove the original post, but the original post makes many unfounded claims (that may in fact be true) and moves forward assuming those claims to be true.

We would not have to spend time in the comments going back and forth about the claims if there was a peer-reviewed article which could be referenced to prevent this type of back and forth about individual aspects of the post. The original post has many useful questions that could be examined to help further the space. On the other hand, the intermixed opinions that do not have citations make it more difficult to get to the questions that really need to be addressed.

I think it would really increase the chances of a project happening if you were able to identify your claims and substantiate them so the forum comments could focus on the subsequent questions that are not rooted in potential logical fallacies. On the one hand, I think many people would agree with some of the claims you have made; but on the other hand we would not have to spend any time debating those aspects if there were references.

In this context, I think it may be useful to examine your original post and look at what is clearly an opinion and try to find a peer-reviewed article to substantiate it. Where you can’t prove a claim, it might warrant deletion of that claim, but not the entire post. I am not certain if you had your original medium post edited or reviewed before posting it here, but the process of editing and review is the step which usually points out those needs for citations so that by the time the piece gets published the discussion is about the intended subject rather than trying to clarify uncited claims.

All that said, I think there are many useful questions in the original post that could guide project development. The main problem is that they are coming from unsubstantiated claims which ultimately leads to an ever-fragmented discussion. I believe it would go a long way for the potential for any subsequent project to occur if you could just take a few days and cite or delete the unsubstantiated claims in the OP.


The point being, if we’re trying to get to a certain point, we cannot start from incorrect assumptions and presume to get to the desired goal. I’m definitely not trying to be pedantic about picking apart your posts. The issue is that a reference goes a long way in preventing derailing the discourse. Some references for the original post would be a great start to directing the questions that have been posed. As I stated earlier, I am not sure if there was any review between when you posted originally on Medium and when you reposted it here; but if you consider this feedback as “review,” you could edit the original post into a much more compelling framework from which projects could occur if there were citations.


I think there is confusion around what this is. I am not looking for review. I was told to post a random thought to the forum, so I did not expect the forum to be a place for peer-review.

It’s difficult to not sound rude saying this, so please appreciate that I’m not trying to be rude but: I don’t really care if people think these things are unsubstantiated. That’s not the conversation nor audience I’m looking for and will no longer engage with it.

If anyone wants to discuss alternative mechanism designs, on the other hand, I’m game!


I do want to jump in here because I think there is some strong alignment with what everyone here is posting about and I’m concerned we might lose that agreement and ability to move forward in the conversation toward actionable outcomes based on @jringo’s ideas. Also, I think this is a valuable discussion to have about the nature of knowledge creation, distribution, and engagement.

At the core, I think the main premise here is that there are a variety of flaws in the current system in that information is often behind paywalls, access to tools or legitimacy are often secluded within institutions, and contributions are not always appropriately rewarded. Additionally, this is proposing that a trustless and open system would correct these failings. Or maybe even that a new system is wholly needed and that a trustless system of science is what replaces the current status quo altogether.

It’s at this point that I think both @Larry_Bates and @jmcgirk are making some valuable contributions. It seems there is agreement that these flaws exist, but if we are in the pursuit of solutions to problems, then we need to precisely define and understand both the problems and the solutions. For example, the claim that

would greatly benefit from some additional development and support. My reading of this is that science as an endeavor is monolithic, and I’m not sure I agree. From my own experiences, I suspect that there are both between and within variations of centralism and consolidation in academic/knowledge specific disciplines. To my mind, that would have a significant impact on mechanism design choices and considerations.

This post might get us started on addressing some of the questions posed by @jringo in the Mechanisms section of the post. Many of the questions posed have some robust discussions already occurring within the legacy system and are scattered across a variety of disciplines. This section in particular strikes me as having a lot of potential to become a Key Questions in DeSci and Notable Works in DeSci if SCRF creates a DeSci category on the forum. From what I could tell in the chat, this seems likely and something that I would hope @jringo would contribute to and potentially lead the initial populating of these future posts.

I do agree that a discussion about alternative mechanism design might also be a fruitful direction for this conversation, so I’m looking forward to that unfolding as well. I’m going to come back to this post soon as I would particularly like to explore mechanisms of reputation related to the key questions in Publication and Review.


Very much appreciated. Looking forward to it. Happy to contribute where I can.


I’m going to pen down my thoughts and contribute to the discussion here as I’m fascinated by trustless systems.

I do concur with @jringo on the emergence ‘roots’ of science. Modern universities and current systems may be pioneered in the 1800s as @jmcgirk points out but the roots for scientific knowledge began in the 16th century. I would like to reference here a short paragraph from the chapter of Marriage of Science and Empire from Harari’s book - Sapiens

European imperialism was entirely unlike all other imperial projects in history.
Previous seekers of empire tended to assume that they already understood the
world. Conquest merely utilised and spread their view of the world. The Arabs, to
name one example, did not conquer Egypt, Spain or India in order to discover
something they did not know. The Romans, Mongols and Aztecs voraciously
conquered new lands in search of power and wealth – not of knowledge. In
contrast, European imperialists set out to distant shores in the hope of obtaining
new knowledge along with new territories.

This gave birth to Europe leading the technological race which was built on the knowledge foundation which was then centered in Europe. The Europeans funded a lot of research through which amazing results were produced including the Theory of evolution as we know it today from Charles Darwin. We should keep in mind that these fundings were not altruistic but had a political, religious or economic goal behind it. Without those funds, we might not have had advancement in knowledge and hence in societies and systems

Similarly, even today, funding is the bottle neck of research. I think @jringo is referring to this complex intertwined systems of research funding which revolves around external influences which are result oriented.

I am very much interested in the further development of such proposed systems. @jringo Would like to hear your thoughts on these,

  • In your system, from which date would one start incorporating the research ‘object’ ? Most of the research now is built on preexisting knowledge, how can we ensure that the system recognizes previous work? Or do we just start afresh.

  • How do you define a research ‘object’ ? Are they peer reviewed papers?

  • Ref. your publication questions, I think SCRF can be a platform for initial experiment on discussion and collaboration over publications. What is your take on this?

I’m however concerned about,

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.

Since the learning curve for generic population will be pretty steep and might lead to false, wrong or misinformed decisions of the groups. I think this is pretty evident in today’s democracy. How many people actually read or completely understand the Manifesto’s or similarly in DAOs? I’m afraid this needs a different solution.


I think that it is almost impossible for us to understand or predict what will make an effective trustless system. We live in gated systems and suddenly have the technology to build something never really seen before.

It’s like creating the 3d printer and celebrating that you’ll always be able to make a screw whenever you need one. The shift of the technology, though, is that it makes objects that don’t require fasteners.

The way forward is to enable experimentation on mass scale, which is what Bitcoin did. “Here is an economic model based on technology that enables permissionless and trustless systems, here is how it works, try to do something else with it.” We have had tens of thousands of experiments over the years since and every single one, even the intentional rug-pulls and scams, play with the potential of the technology. That play has produced some very exciting projects.

This is what needs to be done by those interested in using the technology to improve knowledge creation and translation. Thousands of iterations.

All this to say:

  • I don’t have a preferred system. I think the questions of how an object begins and how to maximize recognition are good ones. I imagine some iterations will use centralized bootstrapping mechanics, others will start completely fresh; some will incorporate legacy research and attempt to retroactively reward contributors from the past, others will not; and others might use a combination of extremes. For the time being I think building future systems first and porting in legacy knowledge trees makes the most sense. Maybe in the end there will be multiple open-source repositories of knowledge and recognition along-side centralized repositories, each with different views and value-sets. Different iterations can then choose to use one or more of these repositories or develop their own. The team at Foresight is doing some fascinating work on the subject.

  • The constitution of a research object is another great question. The incorporation of peer-review will undoubtedly be critical to some iterations. At the moment, however, my view is that peer-review structures are not required in trustless and permissionless systems. The goals of legacy peer-review structures can probably be achieved in novel ways that simultaneously increase access to and participation within the system; Peer-review structures might be like the screws that hold the current system together – maybe we don’t need screws anymore. What I can say for certain is that the contemporary “research object” is a .pdf and there is so much more to research than a .pdf. Research objects can be interactive, include software, include multiple translations, include multiple specialized language versions, be verified, validated, and elevated in a thousand different ways, and much more. DeSci Labs is doing some awesome work with research objects.

Regarding your concern:

First off, and probably most importantly, I imagine there will be thousands of iterations of decision making structures. Some might completely exclude the general public from decision making. Others might only have the wealthy make decisions, or maybe only tik-tok influencers make decisions. Others might let only specialists in a field make decisions on matters related to their own field. Other iterations might weight specialists differently than the general public based on contributions or reputation. The possibilities are unknown until people start playing around.

This is the point where I have a very strong opinion = ).

I think concerns around public inclusion in decision making are valid, but are based more in taught fear and exclusivity than any valid foundation. We are all human. We were all at some point the general public.

One of the critical failures of the contemporary system of science (along with most contemporary institutions and systems) is its active exclusion of the public. The difference between a specialist and the general public is the level of exposure and access to a system. So if a system perpetuates a gyre of exclusion, it reduces exposure, access, and ultimately interest to the point that the general public actively rejects the outcomes of the system. Eventually, the center can no longer hold the system together and things fall apart. That is what we see playing out all around us, not a learning curve that leads to misinformed decisions. It is active rejection of the systems that control our lives and to which the vast majority of society has absolutely no connection. We might be pleasantly surprised how quickly active inclusion can change someone’s level of acceptance of misinformation; If I am actively included in the creation of a vaccine (a stakeholder… at any level), I might more readily accept the technology behind it – If I am actively included in democracy, I might more readily accept democracy’s outcomes.

Regarding how many people understand the specialized language or read-the-whole-thing: It only needs to be one for the information to spread accurately. That one person needs to teach the information to a communicator who then translates it to unspecialized language. Communication is critical to the adoption of a system. With Bitcoin, for example, people like Andreas Antonopolous have been just as important as Jakobsson and Juels.

Another thing to keep in mind with regard to the fear of the general public making bad decisions. The specialized public is part of the general public. In an open iteration, specialized individuals would be making decisions along-side everyone else. How that looks will depend on the choices of the network and its iteration.

In the original post I imagine indirect incentives reinforcing education and communication between the specialized and the general. This might lead to more informed decisions from the public. It needs to be played around with. We do a lot with this concept at Gridcoin where the head of a computing project must convince the entire network that their project is worthy of resources, in our case computational power. The mechanism leads to very intense and educational discussions around science. Even the general public that might only be interested in making money through Gridcoin must be sure that the science done does not hurt the value-proposition of the network at large (does something illegal, or has junk methodology, for example).

Regarding SCRF:

This is a think-piece. It doesn’t belong on a forum that is dedicated to strict experiment and research discussion and collaboration. At the same time, think-pieces can be very engaging and inspire research and collaboration. The two contribution-types come with their own pros and cons. There are a couple ways I can think of to get the benefits of both worlds – engage researchers and engage the public in their research which in turn communicates the research and might engage industry and inspire more research. Whatever SCRF decides, and it’s not an easy decision to be sure, it needs to commit.


I totally agree with you @Yesh

Those questions need to be answered.

Plus, the excerpt you added gave me more insight into how Europe was able to have such economic, technological and political impact during those times.