Love your beautiful “No Man’s Land Insights”

There is a certain kind of insight that I’d like to give a name to because it matters. And ideally I’d love to collect examples of this kind of insight from you and from other experts.

I’ll call them “No Man’s Land insights”.
NML insights for short.

NML insights are born out of expertise and experience. I could have called them “expert insights” or “experience insights” (or arguable just “insights“). But that would have been too general.

For instance, as an experienced expert in the field of Boolean Automata Networks, I have had plenty insights. Most of them eventually get translated into the mathematics that I publish in papers. Some don’t however. Those that don’t are my NML insights. Like the rest of my insights, my NML insights are enabled by my immersion in the field of Boolean Automata Networks. But unlike the rest of my insights, my NML insights are too difficult to formalise to the degree of formalisation that math demands. Or at least, they are not yet ready for that level of formalisation (several years ago I tried my best in ‘Perspectives and networks‘, and ‘Causality and networks‘). In capacity of insights, they nonetheless still fuel the math that I publish even if they aren’t published themselves.

There is no platform for me to publish my NML insights. I have evoked some of them in introductions and conclusions of papers. But I think I’ve noticed my papers are more quickly accepted when I don’t do that, and when on the contrary,  I invest the extra effort to make my papers look more brutally mathematical. Despite the key enabler role my NML insights play with regards to the brutal math I publish, they seem unwelcome in the usual math venues of my field. I’ve tried once to publish in a philosophy venue. They hated it. Clearly, I know nothing of philosophers’ lingo. And probably the little nuts and bolts of Boolean Automata Networks don’t interest philosophers half as much as they interest me.


So the kind of insights I want to make matter are insights that might be somewhat personal and definitely very specific to the way you approach a certain abstract piece of knowledge or concept in your field. Perhaps they generalise outside of your niche, but the most accurate way you have of expressing them today remains by using very specific concepts — similar to how my NML insights are most accurately expressed in terms of Boolean Automata Networks even though they are very general ideas concerning the circulation of information across networks of interacting objects.

Your NML insights are very useful to you on a daily basis for what you do, or very obvious. But there is no dedicated public venue to express them. Typically, you will share your NML insights with some of your colleagues. Not necessarily all your colleagues. Your NML insights will be just as obvious to some of your colleagues as they are to you. But to some other colleagues, they will be less intuitive, perhaps overshadowed by different NML insights, closest to those colleagues’ hearts minds.


I couldn’t recommend enough the beautiful article written by Andy Matuschak and Michael Nielsen : “How can we develop transformative tools for thought?”. In it, the authors give several examples of NML insights, some personal, some fictional, some real. One insight is Michael Nielsen’s, relative to what gets students stuck when learning quantum mechanics. Another NML insight evoked in the article relates to the “many non-obvious ideas” captured in the design of Hindu-Arabic numerals.


There are probably almost as many university level courses of classical mechanics as their are teachers of classical mechanics. Why is that? Why isn’t there exactly 1 course of classical mechanics? Why do we need any more than 1? Why aren’t all physics teachers using exactly the same courses, at least for the well established knowledge they teach? The reason can be summed up in terms of NML insights. NML insights are dense in the teaching profession.

My physicist friend Guillaume Chevereau teaches physics in an engineering school. He feels the need to rewrite the courses he teaches. He can’t recycle existing courses — existing textbooks — without significantly adapting them. Most of what he teaches is established knowledge. Whether the knowledge is new and still evolving or not, isn’t the question here. The reason why existing courses can’t be recycled as they are, is rather a question of modularity and granularity in the textbook documentation of knowledge. Existing courses are narratives. The narratives are not exactly the knowledge itself. Textbooks record narratives which in turn convey knowledge. Knowledge and narratives are meshed together. We haven’t yet got the format that allows the kind of modularity and granularity in the expression of knowledge, that would allow to easily, systematically, distinguish between knowledge and narrative (I’m working on it by the way).


In presenting scientific knowledge, a lot of the choices we make are part of the narrative. The order in which we choose to present different parts of the knowledge is part of the narrative. Factors that determine the order are for instance prerequisite notions, and a teacher’s experience of what is easier for a particular kind of student, what pre-existing intuitions to build on, what pre-existing intuitions to avoid invoking. Definitions and notations are also part of the narrative. Notations, as suggested by Andy Matuschak and Michael Nielsen, have deep ramifications. They are deeply tied to definitions.


To teach the notion of force in classical mechanics, you usually need the notion of projection, and in particular the Euclidian scalar product. Mathematically, there are different ways you can define the scalar product, depending on the context. One definition takes a geometric perspective requiring a prior understanding of the notions of magnitude and angle. The scalar product can alternatively be defined in terms of vector coefficients. Guillaume Chevereau tells me that in this context, he prefers the algebraic definition. The geometrical definition is then presented as a property that follows from the algebraic definition, rather than as a definition per se. There are many reasons for GC to do that. One of them is that the algebraic definition doesn’t require a deep dive into geometry. It is more self contained. It doesn’t invoke a prior understanding of what an angle is. Another reason has to do with minimising the amount of student memory space used. Also this approach makes GC’s course more self-contained in a rigorous manner. It minimises the amount of prerequisite assumptions. This allows to have better control over which questions students are likely to ask in order to have more time to address each question thoroughly. In different contexts, GC doesn’t especially focus on the algbraic definition.

This is an example of how an NML insights apply and shape the narrative. GC knows the scientific content of his course, the official objectives of his course, he knows his students and their relationship to math, etc. All this knowledge translates into the narrative he chooses to transfer knowledge to his students. But none of this is explicitly documented in GC’s courses.

The knowledge expert is not just expert of the knowledge. They are also expert in how knowledge is accessed and expressed. They have intuitions on what works and what doesn’t. They make informed decisions based not only on their familiarity with the knowledge but also their familiarity in manipulating the knowledge in many different contexts, under a variety of different conditions, towards (slightly) differing objectives, and observing it being manipulated by a variety of different people. Knowledge experts are experts at so much more than the knowledge of their field. I’m almost tempted to say knowledge experts are fields in themselves. But that would ruin the way we usually use the word “field” to circumscribe experts who share comparable expertise. Still, there is a lot to be said about knowledge that isn’t written in textbooks and articles but that directly influences the expression of the knowledge that is written in textbooks and articles. Knowledge experts are fine masters of a collection of subtle parameters that other people often don’t even suspect. The knowledge is just one of the parameters, albeit the central parameter. The formalism and notations in which the knowledge is expressed is another. The profound relationships between the different parts of the knowledge is another. Mastery of these parameters allows researchers to formulate the right new questions to derive new answers and it allows teachers to adapt their presentations to their students.


If you are reading this, I’d love it if you could help make NML insights and knowledge expertise matter by sharing your own examples.


I mean to equip myself with a collection of examples in order to do the following: add an optimistic nuance to one specific idea that Andy Matuschak and Michael Nielsen convey in their paper.

Like them I understand how human thinking can be impacted by “tools”, be it software tools, number systems or just language. I am also convinced that even though plenty tools for thought are actually being produced, especially software tools, the ability of these new tools to impact our civilisation’s thought habits is very limited. Optimising tools for thought for civilisational transformation requires a combination of conditions. One of them is simply work. Experimentation and open-ended exploration. Another is expert insight, typically NML insight since regular expert insight already tends to get translated into the usual field-specific venues. Andy Matuschak and Michael Nielsen call attention to the “insight-through-making loop”. In very short, synergising thinkers and makers more systematically could restart our civilisation’s ability to produce transformative thought tools for itself.

The idea I want to nuance is expressed in the following quote:


“The musician and comedian Martin Mull has observed that “writing about music is like dancing about architecture”. In a similar way, there’s an inherent inadequacy in writing about tools for thought. To the extent that such a tool succeeds, it expands your thinking beyond what can be achieved using existing tools, including writing. The more transformative the tool, the larger the gap that is opened. Conversely, the larger the gap, the more difficult the new tool is to evoke in writing.”

The idea is echoed further down in the article:

““What will new tools for thought be like?” is a question we hear often. And yet, almost by definition, we cannot say. As we noted earlier, if we could communicate the experience in an essay, then the tools would be failing at their job; they would not be transforming a person’s thinking, or even their consciousness. […]. One of the most famous papers in the philosophy of consciousness is entitled “What is it like to be a bat?” Each tool for thought poses a similar question, near impossible to answer without immersion in the tool: “What is it like to be a language user? A musician?” and so on.”


We might not have the tools yet but we already have plenty raw material to inspire and produce them: the NML insights of experts. Experts’ thinking already is expanded in a certain direction beyond the present civilisational status quo. In the distant future, transformative tools for thought will impact our thinking in ways that are ineffable for now. But perhaps we don’t need to think of tools so long in advance that we can’t even formulate what they will be like. Perhaps there are tools with huge transformative power in the near future that we can already describe the core of because the core is NML insights. With all the raw material available in the form of NML insights, we have so much to exploit already. The raw material is not exactly ineffable. It is challenging to express, especially outside of the domain in which it emerges. But perhaps the main reason little ado is made about NML insights is because of whose hands it is in:  typically, domain specific experts who aren’t interested in building tools because they already are busy doing something else — they are more interested in using their NML insights to do what they are expected to do, to inspire new mathematical proofs and to tailor new physics courses. So NML insights stay caught in the silos that allowed them to be had instead of getting translating into generalisable tools for thought that could be transformative. Still, we have going for ourselves that experts, by definition, are already experiencing some sort of immersion. The ‘mediums’ they are immersed in are not yet realised in software form but they do have form as long as the experts can at least talk about their NML insights.


And I cycle back to one of the main arguments of Andy Matuschak and Michael Nielsen’s paper: a well oiled “insight-through-making loop” could do wonders.


Andy Matuschak and Michael Nielsen, “How can we develop transformative tools for thought?”,, San Francisco (2019).

NB: this post only refers to one of the many ideas discussed in Andy Matuschak and Michael Nielsen’s article. The article has much more to give…

How to Fight Misinformation

Insights from dealing with the pandemic, for dealing with the infodemic

As a thought experiment, consider misinformation as a virus.


Like a virus, misinformation is undesirable. At least at first sight. Like a person who is contaminated with a virus, a person contaminated with misinformation can end up being less functional than usual in navigating the world. Also because misinformation is somewhat contagious, she can be a risk to other people she is in contact with.


There are several ways to deal with viruses. Depending on the evolution of the epidemic, the state of the world and the particular virus, all tactics won’t necessarily be equally relevant. Some of them can be complementary. But nothing is obvious. Some solutions might be unsustainable and others deleterious. We need to check. The infodemic is serious.  Before we rush into anything, I propose we think our options through carefully and push our imaginations and our reasoning past the knee-jerk reactions to misinformation and the righteous wishful thinking. We are probably not going to solve the infodemic with some people’s opinions on other people’s opinions.


One way to deal with a biological virus is sanitisation.


Of course, in case of an epidemic, if we’re only counting on sanitisation then it’s very heavy duty mass sanistisation that is required. Like what some countries tried last year to fight the coronavirus.


That certainly wasn’t a definite solution.

Similarly, in case of an infodemic we can put heavy-duty fact-checking in place to get rid of all the tiny little misinformation viral particles lying around on our information spaces…


It’s good to have hope.

It’s smart to have alternatives.


Another option is lock-down.


You thought lock-down was tough? The informational analog of lock-down would probably be something else…


There’s no reason to go there now. We haven’t considered all our options. Precisely, I’m writing this post to suggest that we methodically consider our options before we rush into one approach, or resign ourselves to one for lack of foresight. Some approaches will certainly be more efficient, more ethical and more pleasant than others. Many solutions are presently being discussed and proposed to address the problem of infodemic. We might not understand the full ramifications of each one of them yet. Some solutions might seem logical at first sight, quick fixes with possible deleterious effects if we somehow let ourselves get used to them.

Another option is social distancing.


In battling misinformation, an equivalent of social-distancing might be limiting digital contact between people (in some or in all circumstances), to limit informational closeness between them and reduce the risk of informational exchange. In this day and age marked by digital networking, this approach would certainly struggle from going directly against the tide. It would also be a dramatic step back in the overall history of humanity. And certainly, just like social distancing, it wouldn’t be sustainable. But unlike social distancing, we might lack instincts to notice it soon enough and to resist it. Anything that looks like the informational equivalent of social-distancing should therefore be considered with circumspection.

Another option is wearing masks.


The interesting thing about masks (other than FFP2) is that a mask protects you less against the virus outside of you than it protects others from the virus inside of you. It’s a solidarity based solution. One of the most efficient, remarkably.

Interestingly, coronavirus particles aren’t all equally dangerous. The millions of ones that have been sitting on the door handle of your building since last Thursday probably aren’t as dangerous as the millions of ones that your sick boss coughed into your salad. Viral particles don’t ‘live’ forever. They can decay. And they decay more or less quickly depending on where they are — eg what surface (plastic, metal, your skin) they are sitting on. Probably the temperature, humidity, exposure to sunlight of the room factor in as well.

Also, probabilities have a role to play here. Your sick neighbour touching the building door handle last Thursday was a one time event. The millions of viral particles he left have been decaying ever since Thursday, not reproducing.

Now take a 3 hour flight with 3 contaminated passengers and that’s another story. The odds are turning against you now. You are breathing an air that is being fed by 3 virus-factories.


Similarly, not all misinformation is equally dangerous. It might be time to start nuancing our understanding of the risk misinformation represents. Like coronavirus particles, pieces of misinformation don’t necessarily present that much risk unless they are “in the air”. Certainly, dealing with misinformation, is not a simple black or white situation.


What is the equivalent of masks for information? You tell me…


In any case, face masks are not a sustainable solution. At some point we have to start spitting on each other’s salads again. We need our immunity stimulated. We need to be infected sometimes so we don’t become asepticised infirms, who are only functional under very limited conditions. The same goes for censorship. It’s not sustainable and it’s not even safe in the long term. It will eventually come back to bite us.

Another option is to treat the symptoms of COVID-19. Some people will react more or less well to a treatment. Different treatments might be possible.


Of course that’s just a local solution. It can stop your personal lungs and other organs from shutting down and you from dying. But it does nothing against the epidemic.


I’m not sure what exactly is the informational equivalent of local symptomatic treatment. Perhaps individual education against misinformation?

Locally, treatment of symptoms can be absolutely necessary.

The local problem of COVID-19 symptoms and the global coronavirus pandemic, are however not the same problem exactly. And solutions to the local problem are not exactly solutions to the global problem. To keep everyone safe, it is essential to distinguish between the two and make sure we do also have solutions to the global problem.

Systematic palliative measures are not to be confused with sustainable measures. Fighting against death and sickness is not exactly fighting for health and immunity. Similarly, fighting against misinformation is not exactly fighting for information.


There is a last option and it is very different from the ones listed above.

As I’m writing this text, my civilisation is coming out victorious, albeit severely worn out, from an unprecedented coronavirus pandemic. In 2 years, 2020-2021, my civilisation, using science to its advantage, has curved the course of history. What could have been billions of deaths was limited to millions (billions and millions both sound synonymous of “a lot” but the difference is pretty huge). We are now almost ready to get back to normal life, having added COVID-19 to the list of colds we’re going to be catching once in a while. This was made possible because of the set of measures that were implemented (albeit in tragically chaotic and discordant ways at times). The key setting us free from lock-downs etc being vaccination.

Why is vaccination such a special kind of solution against the viral inconveniences?

  • Vaccination doesn’t address the cause of the problem (the virus). Instead it makes us insensitive to it’s consequences so that we can co-exist with it.
  • Vaccination doesn’t treat the undesirable symptoms (of COVID-19). And people don’t have to wait until they are sick to get vaccinated.
  • Vaccination works based on a collaboration with the natural immunity of individuals. The vaccine boosts the vaccinated individual’s capacity to know-how to deal with a viral attack. And it decreases their capacity to participate to the pandemic by multiplying and relaying the virus. The vaccine prepares individuals’ organisms to act quick and resist acting as powerful ‘virus factories’ for days on end. It’s like a motivation making human organisms mettle and consequently less contagious.
  • Vaccination is a global solution. When the vaccine is massively inoculated to the population, it may favour herd immunity, or relieve congestion in hospitals. This benefits not only the vaccinated but also the unvaccinated, and changes the course of human history.
  • Vaccination turns a vicious cycle into a virtuous one. The vaccination solution is special also because of the new relationship it favours with what used to be the cause of the problem (the virus). Once vaccinated and safe(r) from getting killed by the virus, the population and each individual have in their best interests to be regularly exposed to the virus.

All other measures than vaccination are either local or punctual or both. They are therefore not sustainable solutions. Vaccination is a sustainable global solution, other than letting everybody getting sick.

We need an informational vaccine which satisfies the exact same properties listed above for the viral vaccine.


NB: An informational vaccine will certainly look nothing like a biological vaccine, other than it satisfying the properties listed above. Obviously, at some point, the biological/informational analogy must be dropped.

We’re almost done with it, but not quite yet…


If we have a vaccine, then we also need a vaccination plan.


Recent vaccination against coronavirus benefited from fear.  People who were afraid of being sick or getting their loved ones sick got themselves vaccinated.


The vaccination campaign also benefited from the political strong-arming of the population.


Not everybody loved being told what to do with their own bodies.


Likewise, not everybody loves being told what to do with their minds.

Who can blame them?


This time around, let’s try something else…

There is a difference between biological viruses and misinformation. We can use it to our advantage when implementing an informational vaccination campaign.


Most people don’t have much emotional attachment to knowledge of how immunity operates inside their bodies. Whereas, fear of getting (someone) sick comes easy. With information, the situation is reversed. Most people are very attached to what goes on inside their minds. And they have limited intuition of what suffering from misinformation feels like.

Locally, people don’t think of misinformation as a dangerous virus. They often simply confuse it with information. Infection with misinformation doesn’t feel like sickness. So surely, not all strategies that work against biological viruses are going to work as well against misinformation. We need to think this over. It could be easier than we think to devise an informational vaccine and a vaccination plan (it could also be harder than we think, especially if we think good intentions and trendy tech can cut it). It is worth noting that people aren’t that afraid of misinformation and they already have a clear interest in the thoughts happening in their own minds. So to get people informationally vaccinated, there is no reason to resort to fear any more than to force.

If there is anything to take away from social media, it’s the ambition to resort to fun and benefit instead — and the ambition to influence our civilisation’s informational habits to a determinative extent, within much less than a generation, using humanmade systems that amplify existing human tendencies and capabilities, appealing to what humans already love to do, can do, and are doing.


The project I’m working on, called the MMM plan, is a system-based, fun and benefit resorting, informational vaccination plan. Feel free to contact me if you want to know more.

A Proof that Misinformation is Not Untrue

This is a draft

This post starts the same way as a previous post about communication problems vs information problems. Here is the beginning again:

Misinformation is consensually defined as untruthful, misleading messages. We would rather have information than misinformation.

Meanwhile, the task of defining information remains classified as a challenge or a matter of context. 21 years ago, Wikipedia started. The same year, an outline for the encyclopedic Wikipedia article on ‘Information’ was drafted. 21 years of discussion later, Wikipedians are still trying to agree on what information is.

“Conceptually, information is the message (utterance or expression) being conveyed. Therefore, in a general sense, information is “Knowledge communicated or received concerning a particular fact or circumstance”.”

“Viewing information as the conveyor of a message contradicts my experience on how this word is used. But your experience may be different from mine.

“Information, in its most restricted technical sense, is a sequence of symbols that can be interpreted as a message. Information can be recorded as signs, or transmitted as signals.”

In this post, I will not assume nor propose a definition of information. I don’t want to add yet another necessarily opinionated theory of what information is. I mean to illustrate in a later post how analytical theories and fully formalized definitions aren’t always appropriate at a given time in the history of human understanding. Sometimes we are not ready for them yet. Our understanding first needs to mature through a combination of intuitive and half-baked analytical steps. This, I think, is where our understanding of information presently is at. We’re not ready for a consensual formal analytical definition of what information is. So let’s humbly respect the present, ambient state of confusion and indecision around information and cautiously explore our logical options.

Let’s start simple:

First Question:

Is information the container or the content, or a bit of both?

The question is inspired by Wikipedians’ traditional worry (cf quotes above): is information the conveyor of meaning or is it itself the meaning conveyed.

The container ≈ the type of things like  symbols, language, encoding that contain/convey/communicate meaning


The content ≈ the message / the interpretation or meaning of symbols / the  sense we make of them

Second Question:

What about misinformation?


In the spirit of starting simple:

ASSUMPTION NO 1: Misinformation is different from information.


ASSUMPTION NO 2: We prefer information to misinformation.

ASSUMPTION NO 2 (reformulated): We would prefer misinformation be replaced with information.

Based on Assumption No 2, we have:

THEOREM NO 1: Misinformation is of the same type as information.

where :

DEFINITION NO 1: X and Y are of the same type if and only if X can replace Y and Y can replace X.


Same type: okay!
Not the same type: not okay! Violates Theorem 1
Same type: okay!

DEFINITION NO 2: We say that X is of type 'container' if X contains/conveys/expresses meaning.


DEFINITION NO 3: We say X is of type 'content' if X is meaning.


See above, first figure of this post.

Of course all of these considerations are only worth having under the following assumption:

ASSUMPTION NO 3: Meaning is different from the expression of meaning.

Ideally, one is distinguishable from the other in practice. Content is distinguishable from container of content.


Now contrary to the message itself (cf fig 1), symbols and series of symbols conveying the message can be jumbled and unreadable, but are neither true nor false in themselves.

ASSUMPTION NO 4: If X can be qualified of being either true or false, then X is not of type container.

Assumption NO 4 reinforces the parallel with glasses of wine: it’s not the glass itself that may be good or bad, it’s the wine in it. Similarly it’s not a series of symbols that are true or false, it’s our interpretation of them, the meaning we give to them.

That said, you are extremely welcome to pinpoint fallacies in any of this post’s assumptions and reasoning!

Based on Theorem No 1, we have 3 possible ways of answering the two questions raised above about (mis)information and their definitions in terms of container/content:

1. Either information and misinformation are both of type container
2. Either they are both of type content
3. Or they are both a bit of both.


Both container
Both content
Both both
First Option:

Information is of type container.


Information is a certain way in which we arrange symbols to express a message. It isn’t the meaning of the message (that would be the content conveyed by the symbols).


Misinformation is comparable to information yet different from it (Assumption 1 + Theorem 1). Misinformation is also of type container. What we want to do with it is eradicate it or replace it with information (Assumption 2).

We are beating around the bush…

LEMMA NO 1: If information is of type container then, misinformation can't be false.

Proof: If information is of type container then, misinformation is of type container by Theorem 1. And by Assumption 4, misinformation can’t be qualified of false, nor of true for that matter.

Not okay
Not okay
Not okay

Deep down if we are honest with ourselves, our problem with misinformation has nothing to do with untruthfulness. What we really are out to eradicate is a language, not the French language nor Javascript, but still a means of expressing operational content. Too bad we’re still unclear as to how misinformation as a container, a language can be recognised.

The things we say about misinformation being wrong are just to give ourselves good conscience. The dirty truth is that it’s what we are doing that is wrong.

No wonder there isn’t a UN meeting about misinformation that doesn’t cycle back to the topic of censorship and freedom of speech! Those are all indeed the same topic under the previous set of assumptions.

Second Option:

Both information and misinformation are of type content. But I’m assuming that by now, everyone equipped with a relatively functional mind, has noticed that the notion of truth becomes very silly, very slippery and vacuous and dysfunctional when it is generalised and stripped from the context in which it makes sense.

Of course, the container conveys part of the context.


So assuming information and misinformation are of the content type i.e. pure meaning makes us say really stupid things — drawing on an extremist view of Absolute Truthhood / Falsehood.


ASSUMPTION NO 5: There is no sense in discussing things that are absolutely false (respectively absolutely true) independently of how they are expressed.

Forget about identifying them, forget about arguing that they exist or that they don’t. Things that are true/false independently of how you express them, by definition are not things that can even be discussed. Something that is true/false independently of its experssion, is experienced independently of any form of language. It is essentially ineffable.


LEMMA NO 2: If information is of type content and not ineffable, then misinformation can't be false in itself.

Proof: By Theorem 1, misinformation is like information: of type content and not ineffable.  Lemma No 2 follows from Assumption No 5 and the fact that it is presently making sense to us to discuss misinformation.

Third Option:

Information is neither purely container nor purely content. It’s a bit of both. As is misinformation (by Theorem No 1).


I hate drinking good wine out of a water glass. I don’t know if it changes the taste enough for me to notice or if I can’t help but imagine I’m drinking cheap table wine, the kind my grandpa used to drink out of a water glass at every meal.


Perhaps the problem with misinformation is similar. It’s not so much a problem of container or of content. It rather is a misfit between container and content.

Maybe my palate is sensitive enough to detect a difference in the degree of ventilation of the wine depending on which glass it is served in. Maybe my mind is suggestible enough to change the way the wine tastes.

If there is something similar happening with information, then once again, things are not as simple as a true/false dichotomy. If misinformation denotes a misfit between container and content, then qualifying misinformation of misleading is more appropriate than qualifying it of untrue. Perhaps the activists of our time fighting against misinformation should make sure they use the right language and clear out the ambiguity. “Misleading information” has a very different vibe than “false information”.


NB: No relativism here. Some wines definitely are better than others. Further, some palates certainly are more trained than mine. And some minds more suggestible than mine. The most important remains that not all wines are created equal. Similarly, not all information is created equal. Shitty wine exists. Wine can be spoiled but it also can be shitty from the start. Of course taste is very subjective. But there is no logical implication between subjectivity and the absence of consensual criteria of evaluation. If anything, my subjective preference for dry wine is a motivation to formulate a dryness criteria useful to all wine drinkers who want to communicate their wine preferences with others.

I’ve been writing posts about misinformation because I am sick of prehistoric dichotomies underlying common discourse on misinformation. The great true/false dichotomy is turning us into intellectual savages, incisively oscillating between two uncivilised extremes: (1) rudimentary relativism which grants expertise the same amount of speaking time it grants each kind of thoughtlessness wallowing anywhere on the informational landscape; (2) inquisitional thought police ready to trade free speech for more of their own ideas of what is right.

LEMMA NO 3: If information is a combination of both container and content, then misinformation can be misleading but not false.

Proof: If information is a combination of both content and container, the same goes for misinformation (Theorem 1). A combination in itself can’t be false. The combination can be relevant, efficient… or misleading. Not false.


If misinformation is a misfit between container and content, what does it mean to replace misinformation with information? It means refitting the content to the container or vice versa. It doesn’t mean that we necessarily want to change the meaning of the content. We want to replace the way it fits with its container. The meaning can stay the same. The refitting can preserve the falsehood or truth of the content.

Perhaps what we don’t want is something true to seem false and something false to seem true?

(Re)fitting is different from sorting content (Option 2). Fitting requires subjectivity to be addressed. The fit is to be appreciated by a recipient. The recipient is the one who decodes the container to access the content. They must have the decoding manual corresponding to the particular fit that they receive.

If misinformation is a misfit between container and content, we should be going about it very differently than we are.  Fact-checking might be a solution to some problem. But it’s not a solution to the misinformation problem under this assumption. Fact-checking is like hunting down all glasses of shitty wine to toss their content, and possibly fill the glasses back up with good wine instead, regardless of parameters such as people’s palates and imagination.

We don’t necessarily need to settle on a definition of information right now. But we do need to be mindful that as long as we are not ready for formal functioning definitions, we are lying in a grey zone, with consequences. Our half-baked understanding of information can make or break our solutions to misinformation. Depending on how we look at information, it can look obvious that our solutions to misinformation are silly, evil or besides the point.

Having said all that perhaps this whole discussion is founded on invalid assumptions. Perhaps the silliness we want to eradicate is captured in one or several of the assumptions above. You tell me…


How to Get an Information Age

Lessons from the Stone Age to the Information Age

10 million years ago, there were lots of stones, but the Stone Age wouldn’t start for another 7 million years.

Today, there is lots of information. And we might be getting a little ahead of ourselves saying it’s the Information Age.


5 million years ago, one of our bipedal ancestors is sitting idle by the side of a river. Let’s call her Lucy. Lucy picks up a pebble on the ground and notices its shape. It is smooth, and lovely and a little warm from the sun. It fits comfortably in the palm of her hand. She picks up a second pebble. She switches the two pebbles from one hand to another playfully. The pebbles knock against each other making a pleasant round sound. Lucy wants more of the sound. She deliberately makes the pebbles knock against each other. Harder, slower, faster … The rythm changes. It’s pleasant. She gets excited. One pebble smashes against the other. A piece of it chips off. The shape of the pebble has changed. Lucy marvels at the change. The pebble has been improved: it is now more extraordinary, not like other smooth pebbles all around. This one now has a sharp edge. Lucy feels its sharpness on the skin of her hand. The pebble is now a dangerous pebble. Dangerous like an angry wildebeest… — for sure, more dangerous than a dead angry wildebeest, Lucy salivates. This improved pebble could surely pierce the skin of the animal, even tear its fresh meat into pieces. And certainly it could chop branches and carve wood and do many more things that other pebbles don’t do! This is fantastic! This is literally the discovery of the Megannum! Lucy calls her brother and shows him the improved pebble. Brother runs his fingers over it. It definitely is an unusually shaped pebble… A heron flies close by and lands on the opposite river bank. Brother points at it. They marvel together at the phlegmatic bird which has turned still as a statue. A given pebble is tossed into the river next to the bird, to challenge its immobility. The moment for talking about lithic tech had passed. Sharing the process by which the shape of the pebble was improved and sharing the notions of what might have been done with the improved pebble will have to wait. The Stone Age will have to wait — another 2 million years


Proto-humanity might be ready to pick up pebbles and to notice fortuitous improvements of pebbles. But it is not even remotely ready to organise a pebble-improving industry. Over the course of the next 2 megannums, Lucy’s discovery will be rediscovered again and again, countless times. Herons are marvelous birds however, undoubtedly deserving our immediate attention. As are many other things in the world. As it turns out, it takes more than doing exciting stuff with stone to start a stone age.

It's not that easy to start an Age of Humanity

Likewise, it probably takes more than doing exciting stuff with information to start an information age. 


Lesson No 1:
Don’t just hand over the improved stone to your brother, show him the process, how the stone is improved.

The heron made Lucy’s job impossible. She had time to show the improved pebble to her brother but she didn’t get to demonstrate how she had done it. Her brother saw an unusually shaped pebble, rather than an improved pebble.

Lesson No 2:
Beware of distracting herons. They come easily and can appear to be so much more deserving of our immediate attention than stone. Don’t let them distract you from showing more than just a stylish pebble.

Showing the improved stone is not showing the smashing. However many more people than just Lucy’s brother would have seen Lucy’s improved pebble, however many of Lucy’s aunts and uncles would have been told and believed that Lucy had the power to transform ordinary stone into dangerous stone, the Stone Age would not have started then and there. To start a pebble-improving industry, it’s not enough for you, your tribe, your species to know of improved pebbles. Nor is it enough to know that pebbles can be improved. The process by which a stone is improved must be made to matter. The know-how must permeate society. As long we keep forgetting to tell show our brothers and sisters how we got to improve a stone, the Stone Age is not about to start. Wherever there is a discontinuity in the sharing of the process, there is an interruption of the civilisational learning favourable to a new civilisational age.


It’s not enough to be surrounded by information. The process of improving information must pervade the diversity of our human experiences.


Lesson No 3:
Smash stones. Because you can. Learn through practice.

Perhaps not all people of the Stone Age invested the hours of work needed to make a good tool out of stone. But most people had an idea of the basics of stone improvement, i.e. stone smashing. Because stone smashing is within reach of most people. Master stone workers are not sorcerers.


Lesson No 4:
Respect but don’t worship stone workers.

In the Age of Information, information professions will be treasured mundanely. It will not remotely feel like the Age of Degenerescence of Information. Information improvements will be much appreciated and made practical use of. Information workers — teachers, scientists, journalists — will not be confused with sorcerers. They will not be locked in ivory towers. They will walk among the people, highly respected for the work they do. Information won’t be considered any more sacred and precious than stone. Respect for information workers will come from normalised appreciation of the added value that information improvement brings to society. Respect for information workers will also come from the people’s familiarity with the sport of information improvement. Appreciation of the added expertise, experience, effort and time invested by information professionals into doing something that everyone can do to some extent, will come easy.


Back to Lesson No 2:

Lesson No 2:
Beware of distracting herons. They come easily and can appear to be so much more deserving of our immediate attention than stone. Don’t let them distract you from showing more than just a stylish pebble.

Lesson No 2 — about distracting herons that systematically interfere with the propagation of stone-smashing know-how — may translate several ways in terms of information. We can take pebbles to represent published content. The distracting herons of our time could be: publication itself, IP, authorship. As we ‘toss’ information towards publication etc, our attention strays away from the content itself. With this interpretation, Lesson No 2 warns against letting the heron of publication distract you from the fact that in publishing new information, you’ve shared an end result not the process that got you the end result. You’re not participating in the Information Age. Nor are you making it more likely to come any time soon.


If we want to provoke the advent of the Information Age rather than wait for it or pretend it’s already here, this is what we have to do: build ourselves means to propagate the know-how of information-improvement in such an efficient way that learning the know-how preempts distracting herons of our time. Considering our present digital networking industry/mania, this certainly seems like something we can do.


What happened during the Stone Age is that hitting stones to deliberately change their shapes became commonplace, and methods were developed over time for improving the quality of the results and the efficiency of the process.

We started fortuitously improving stones by breaking pieces off of them, then we discovered that hitting stones methodically could actually be used to sculpt stone into more deliberate shapes with more specific planned uses. That’s when manufactured symmetry and manufactured flatness entered human history.


Lesson No 5 :
Reap a wealth of brand new concepts, understanding and sensitivity.

Later humanity experienced the Bronze age, the Iron Age, the Age of Exploration, the Age of Printed text, the Age of Reason, the Space Age, the Digital Age, the Data Age, the New Media Age… Every time a similar thing happened: we learned, as a civilisation, to relate to something in a more deliberate and a more methodical manner than we did before — whether it be to stone, copper, bronze, iron, the geography of our planet, printed text, our own reason as opposed to our senses, space, computation, data or social media.  

Lesson No 6 :
Learn and teach to relate to stone in a more deliberate and methodical manner than your ancestors.

Lesson No 7:
Don’t be a snob. Welcome anyone who can help improve stone. Welcome any improvement.

Of course it’s hard to make sense of Lesson No 7 if you don’t know how to recognise stone improvement.

A Flat Earther experimenting to disprove the roundness of the Earth may produce a bit of improvement even if our snobbish era will automatically ridicule them without factoring in the initiative, design and context of the experiment. Noticing information improvement requires a minimum amount of benevolent attention to pierce through the haze of indetermination. Information snobs find more righteousness in calling people liars than in experimenting themselves. They expect Truth and Falsehood to be sorted out into two columns.


The least we can say of each named age of humanity is that it marked a time when humans acquired a precise idea of what something was and how to use it. Humans of the bronze age acquired a pretty good idea of what bronze is, how to use it, and how it differs from copper. Humans of the space age had a pretty good idea of what space is. Humans of the computer age have a pretty good idea of what a computer is, even though computers keep changing in form, shape and utility. Humans of the data age know what data is. A lot of people’s jobs involve manipulating some sort of data; data isn’t a vague and abstract concept to them. Arguably, not all humans of the social media age are aware and understand what social media is. But enough of them do for social media to have a huge structural impact on our civilisation.

We know what we are talking about


Structural societal changes

Indeed, every named age of humanity resulted in the structure of our society being deeply and durably impacted. The age of printed text introduced the start of mass literacy, catalysed religious reformation, and seeded notions of intellectual property and even celebrity that we still have today. The Age of Enlightenment formed notions of reason, science and knowledge that still deeply shape our thinking today, centuries later. The Age of Enlightenment made such a fuss about the reason / sense dichotomy, that it drove us to have to claim control over the interplay between reason and sensory observation of the physical world when we are drawing information out of our interactions with the physical world. Out of that we got a scientific method and modern science which the empirical sciences still diligently abide by. And generally we got so called “modernity”. The digital age, like the age of printed press, brought change in the way humans communicate among each other, and it changed the whole economy of our society.


Wikipedia editors have spent 21 years trying to agree on the content of the Wikipedia article on the topic of ‘information‘. That is: 21 years trying to answer this question What is information? They never found a consensus that would work for an encyclopedia. One editor said this question is one of the most challenging questions for science to answer in the 21st century. And regularly, someone in the community would say something like “Give it up”.

Unlike the Wikipedia articles on the topics of ‘computer‘ and ‘data‘, the Wikipedia article on the topic of ‘information‘ does not settle on a definition. It gives a list of definitions: information as per Information theory, information as a sensory input, information as representation of complexity, information as a pattern, information as a message, information as an influence that leads to transformation, information as a property of physics, … 

We know what data is, and it is generally accepted that data is not quite the exact same thing as information. We know what misinformation is and, strangely enough, as discussed in another post, we find it easier to agree on the definition of misinformation than on that of information. Certainly, Stone Age people had incomparably more collective understanding of what stone is than we currently have collective understanding of what information is.

This civilisation doesn't yet know what information is

Lesson No 8:
Know what stone is.


Millions of years ago, as long as there were only few distinctions people could make relatively to stone — eg as long as they could only distinguish between stone being there or not there — and as long as the distinctions they made weren’t especially characteristic of stone — eg the stone is warm or not warm, much like water can be warm or not warm — certainly, the Stone Age hadn’t started.


Stone There


Stone not There


Industry based on a certain stuff requires to know that stuff beyond its superficial properties. To have a stone based industry, humanity must have come up with a minimum amount of concepts relative to stone, be it:

  • concepts denoting different qualities of stone,
  • different sorts of stones,
  • actionable differences between stones, or
  • different actions to be performed on/with stone…

If not concepts, then at least awareness. Familiarity with certain categories of stone must have become easier to spread. Necessity to communicate stone related ideas and know-how must have called for enrichment of stone related vocabulary. People of the Stone Age must have noticed stone had different colour, weight, porosity, dryness, sharpness, fragility, resistance to smashing, resistance to scraping…

Lesson No 9:
Know your stone.


We have ‘misinformation’ and ‘information’.


Clearly we’re not there yet.

As long as the term misinformation is in use, and as long as it makes sense for us to rely on some sort of prehistoric dichotomy between the good and the bad information, we certainly are not yet in the Information Age. I would go so far as to propose using the prevalence of the word “misinformation” as a telltale of the Information Age still being ahead of us.

Some say there has been two information ages. The first was the age of the printing press started over half a millennium ago by Gutemberg’s innovation. The second was started 50 years ago by the advent of the internet. There is indeed a strong commonality between these 2 ages. Both have taught us to relate to a form of communication in a more deliberate and methodical manner.  A form of communication intimately delineated by the new technology. Thus we have gained a pretty good understanding of communication as allowed by the printing press such as books and articles. And we have gained a pretty good understanding of communication as allowed by the internet such as emails and the web.

In the mainstream understanding, “information age” is taken as a generic term amalgamating the concepts of computer age, data age and the age of social media. As if all those things were one thing, or as if all those things were categories or properties of information, or as if information was the intersection of all those things, as if all those things were digital and information was digital too …


We’re clearly not there yet.

We can do much better than that. We might not be there yet but we can already start looking forward to the times when the word “information” will be as consensually clear and functional to us as are the terms “communication”, “social media”, “data” and “stone.

Wikipedia harbingers those times.

If it weren’t for Wikipedia, the Information Age would seem so far, I probably wouldn’t bother advocating for investing in making it come faster.

But Wikipedia is a very serious announcement of the Information Age.

Wikipedia has successfully integrated Lesson 7 (“Don’t be a snob…etc“). That in itself is a powerful sign that the winds of change have begun to blow.

Very importantly, although less famously, Wikipedia has also managed to partially address the tricky problem of “distracting herons” (cf Lesson 2). Many people still ignore this but Wikipedia doesn’t just have encyclopedic articles, it also has talk pages associated with the articles. The talk pages are the most important part of Wikipedia. They are where the information published in each article is discussed. They provide a way of determining the quality and actuality of the information published in the main encyclopedic article. Use them to see if the article is an opinionated piece written by a single partial writer, or if it is the result of a consensual approach to a topic, or if it is the continually changing reflection of collective uncertainty and intellectual immaturity on a topic (cf the talk pages for the article on the topic of information). Wikipedia’s talk pages are of utmost importance because they pave the way to a brand new approach to information: information as a dynamic of object, not fixed in print once and for all, but continually evolving, updating, improving, reformulating. Wikipedia has opened a door revealing that it is possible to relate to information in an interactive manner and still have high(er) standards in terms of quality of information. Wikipedia shows it is possible to organise systems that avoid the traditional herons of publication and promote focused attention on content and its continual improvement. Unfortunately, Wikipedia’s talk pages are not conspicuous and Wikipedia is still limited to encyclopedic information. There are further steps to take. And more lessons to learn. But we definitely are on the way now…


The most important lesson to take away for now remains this one:

Lesson No 10:
Distinguish between the instrinsic properties of stone and the extrinsic properties of stone.

The fact that your brother likes your sharp stone is an extrinsic property of the stone. The fact that the stone is sharp is an intrinsic property. You don’t work with extrinsic properties the same way you do with intrinsic properties. The fact that the sun shines on the stone: extrinsic. The fact that the stone stores warmth for a while: intrinsic. The fact that all your tribe prefers flint to chert: extrinsic. Your tribe lives in a place where grayish chert is commonplace so they find dark opaque flint extraordinarily beautiful. The fact that flint is a very fine-grained variety of quartz just like chert: intrinsic.

Working with the extrinsic properties of stone means working on things that relate to stone but aren’t stone — like your brother, the sun, your tribe and the place your tribe has chosen to settle — and working on the relationship between those things and the stone. Changing extrinsic properties of stone usually boils down to changing properties of people (what they like, where they live…) or waiting for a sunny day.

Changing intrinsic properties of stone requires to change the stone itself, eg by smashing it with another stone. It is worth noting however that many intrinsic properties of stone cannot be changed, or at least not without a great deal of insight into the nature of the stone.


Similarly, the fact that you trust a certain source for information is an extrinsic property of information. The fact that the information follows from a modus ponens applied to two other pieces of information is an intrinsic property of the information. The fact that a certain result about memory was proven by an experiment with sleeping humans rather than sleeping rats: intrinsic property of that result. The fact that this result was proven in a prestigious scientific venue: extrinsic.

After WIkipedia’s foot in the door, I have no doubt:  the next lesson and the most pressing one to learn on our way to the Information Age, is Lesson No 10 (“Distinguish between extrinsic and intrinsic”). Once this particular lesson starts seeping through, impacting on the way we talk about information and about the great information-related issues our civilisation faces, the other lessons will follow suit. Lesson 8 (“Know what stone is.”) will become trivial. Lesson 9 (“Know your stone.”) will be within reach.  And as our societies gradually reorganise in response to this new perspective on information, Lessons 1 to 4 and 6 and 7 will find their way to our collective brain too. Finally, Lesson 5 will bring happy unexpected bonuses.


In the meantime, here’s a little



What does an Information Age have in common with a Stone Age?


  • It doesn’t just start because there is plenty of the stuff in our lives, be it stone or information.
  • It doesn’t just start because some people have figured out how to improve some stones/information.
  • It requires the process of improving stone/information to pervade the diversity of our human experiences.
  • It marks our learning to relate to stone/information in a more deliberate and methodical manner than we did before.
  • It marks a time where humanity makes stone/information matter.
  • It marks a time when we know what stone/information is and are long past the stage where we confuse it with other things — like metal and data or knowledge.
  • By the time we are in the Stone/Information Age, we have been manipulating stone/information so much and in so many different context that we have discerned a number of different categories and properties of stone/information and the vocabulary we have is very rich. → means that the vocabulary we have to describe properties of stone/information is very rich. Its not limited to just stone is there or not there. Information is correct or incorrect.
  • It deeply and durably impacts on the structure of our society.


The Stone Age is long past us. The Information Age might be on the verge of starting.

Have no Fear, Science is Certain

In a previous post, I discussed the following implication:

science ⇒ misinformation

i.e., if you have science, then you necessarily have misinformation.

In this post, I want to point out that the converse implication also holds, substantiating the idea that science is natural and not fragile at all:

misinformation ⇒ science

i.e., science is necessary in case of misinformation.

Science follows from misinformation. Humans will be humans. They will use language to express their understanding and produce information. The world will be complex, it will change. Unlikely events will eventually occur for the first time.  Understanding will be updated because new experiences will need explaining. What was once satisfactory information will become outdated, incorrect, decontextualised, misleading information, reflecting the understanding of another time, another place, another people. Misinformation will inevitably come out of information. And science will necessarily happen again. Because it is humans’ natural response to the world acting independently of the information we have on it. The complexity, evolution and multiplicity of the world and of humans will test and degrade information that is not updated continuously and carefully recontextualised. Humans will be humans, they will notice. They will be curious and creative and try to get ahead of time to further test the information they have now, and find out how close it is to obsolescence and if they can already go forward and improve on it.

Play the game.

Seeing misinformation as the enemy of science and reason has clearly done nothing towards promoting a scientific mindset among the public. No great surprises there. The enemy of science is considering science as something sacred we have to protect and adhere to.

Science is not sacred and needs no adhering to. It’s natural, it’s mundane, it can’t be helped. Think of it: if you were very powerful and could travel back in time to prevent science from even emerging in the history of humanity, which point in time would you choose to go to and what would you do there? When were the seeds of science not yet in humans? When was the time where humans were humans — or on their way to becoming humans comparable to us — and they did not already observe the world, the sun, the moon, the weather, the seasons, what makes crop grow, what makes it die…?

Encyclopedia Britannica’s article “history of science” written by W. L. Pearce answers:

Science […] is universal among humankind, and it has existed since the dawn of human existence.

Science is a form of play and a way to exercise a power over the world. Humans will be humans, they will observe, they will experiment and test their ability to predict and to impose their will on parts of the world. When humans are discouraged from playing, a little angst is bound to build up.

Misinformation is part of the game. It’s misalignement with logic and observation that stimulates and makes the game worth playing: because there is improvement to be produced and because it’s in our power to produce it.

Seeing science as the inevitable natural pleasurable response to misinformation is worth a try, especially given how miserably the alternative, traditional view is failing to promote the scientific mindset among the general public. Misinformation might be worth seeing as a pretext to play the worldly game of science. We could reframe misinformation as an opportunity to encourage a scientific mindset rather than a threat to reason. That might make us come up with very different kinds of solutions and approaches to managing online information…

Fact-Checking is Fatal to Science

Misinformation is commonly construed as the opposite of information. But information and misinformation aren’t related the way public discourse suggests they are.

The common view

Experience in how scientific information is made suggests that the difference between misinformation and information is tenuous. Scientists make science bit by bit, by saying lots of slightly wrong things all the time, often even completely wrong things, and diligently adjusting them.  If we take “misinformation” to mean “wrong, incomplete, messy or somehow imperfect information“, then, in science, there is no information without misinformation being worked on. If there was a ban on saying wrong or messy things, then scientists (and science) would be among the first to suffer. They would lose their job which would stop existing. Good information just doesn’t get produced without saying wrong and messy things.  So wherever there is scientific information you can be sure there also is the ability to work with misinformation: the ability to challenge it, to contextualise it, to reformulate it, to compare it, to decompose it, to substantiate it, to update it, to complete it, to refine it, to ditch it, to replace it, to recycle it … Without those abilities, misinformation is not the problem. The problem is the wrong – unable – hands it’s in.

The scientific reality

The norm in science is misinformation being meticulously processed and improved using a battery of analytical tools. This is why science is so successful: on a given question, it either has nothing to say or it’s ahead of the game in terms of misinformation management. Because of this lead, scientific information can be described as the most exquisite and cutting edge misinformation you can find at any point in time.

Let's not pretend it's so clear

It’s unclear that there can even be information without misinformation, nor that we can rid our information spaces of misinformation without as well suspending use of the analytical tools with which slightly inaccurate information gets processed.

In short, in terms of logic:

science ⇒ misinformation

If you have science, then you necessarily have misinformation, i.e.:

no misinformation ⇒ no science

Eradicating misinformation is enough to eradicate science.

We need to be careful what we wish for. Is a world without misinformation really what we want?

Fact-checking is evidently failing at raising the quality of information circulating on our public information spaces. So There is no general improvement of quality so far. This doesn’t mean fact-checking is impactless. Fact-checking affirms and further normalizes some extremist ideas on information and society:  that there are two kinds of information, the trustworthy and the untrustworthy, that there are ‘information authorities’ who distinguish between the two kinds of information for the commoners who don’t, and that the commoner’s only liberty and responsibility is to chose who feeds them information

Being informed requires work. Significantly more work than resorting to the argument from authority. You’re not informed if you have let someone else do all the work. You haven’t gotten smarter. You haven’t earned a perspective. You haven’t earned nuance. You haven’t earned a new skill that you can use next time you are faced with information.

If science manages to produce vaccines that turn a severe health hazard into a regular cold, it’s because of work. Not truth. Not trust. Not fact.

Understanding is not the same as trusting, nor even as knowing.

Anything like-fact-checking that promotes the argument from authority massively at the expense of promoting the work and the analytical skills required to perform the work is a crime against information.

It doesn’t matter if someone is an expert if they are not the right kind of expert: if they haven’t done the relevant work, if they haven’t striven to tie their work with the work already done by generations of scientists. Without minimal experience of the kind of work that produces good quality information, one can’t be aware of what work that is. One can’t begin to appreciate nor select expertise. One can’t appreciate the significance of expert consensus and of lack of it. One can’t appreciate imperfect information, not to mention the imperfection in it. One can’t appreciate information.

closing remarks

There seams to be a comfortable consensus on the definition of “misinformation” nowadays. Detecting and avoiding misinformation is still a struggle. But many people are using the term “misinformation” as if there was no question about what “misinformation” means and what needs to be done with it, eg avoid it. Meanwhile, we are nowhere close to having a consensual definition of “information”. And most people have no idea what to do with it.  I find the difference in treatment strange and very telling of our times. Information-wise, this is prehistory.


Forget all this. Take your favourite difinitions of “misinformation” and “information”. 

Fighting against misinformation still doesn’t mean fighting for information, does it?

Problems of Communication VS Problems of Information

This is a draft

Misinformation is consensually defined as untruthful, misleading messages. We would rather have information than misinformation.

Meanwhile, the task of defining information remains classified as a challenge or a matter of context. 21 years ago, Wikipedia started. The same year, an outline for the encyclopedic Wikipedia article on ‘Information’ was drafted. 21 years of discussion later, Wikipedians are still trying to agree on what information is.

“Conceptually, information is the message (utterance or expression) being conveyed. Therefore, in a general sense, information is “Knowledge communicated or received concerning a particular fact or circumstance”.”

“Viewing information as the conveyor of a message contradicts my experience on how this word is used. But your experience may be different from mine.

“Information, in its most restricted technical sense, is a sequence of symbols that can be interpreted as a message. Information can be recorded as signs, or transmitted as signals.”

The main concern around misinformation seems to be to eradicate it, which entails telling misinformation from information — so telling misinformation from something we’re not confident to define. Meekly, we resort to assuming someone else than us —”the experts”— have the confidence to define information for us and to weed out the misinformation.

Is fighting against the one, equivalent to fighting for the other?

When misinformation is regarded as a problem, it is regarded as a communication problem rather than an information problem. I think it’s worth making the distinction.

Misinformation as a communication problem := wrong or misleading messages being communicated, especially massively and on public channels.

A communication problem is one where there is a discrepancy between the message emitted and the message received or a discrepancy between the intended recipient and the actual recipient, or possibly lack of clarity regarding where the message comes from. Typically, this kind of problem is solved by adding mechanisms to verify the transaction between emitter and receiver. The type of “mechanisms” added depend on the kind of message being communicated. Messages related to financial operations need cybersecurity technology. Marital communication problems, need tricks that a couple therapist might suggest. The solution to misinformation — the communication problem — is checking the provenance of messages and the trustworthiness/authority of their sources, possibly deleting the message altogether, and replacing it with another.


Framing misinformation as a communication problem entails resorting to the argument from authority in the solution. Asserting the prevalence of the argument from authority is exactly the worst possible thing that can possibly be done for promoting critical thinking in the general public.


As a scientist, as a theoretical computer scientist, the argument from authority is my worst nightmare. It is the gate at which my intellectual spunk dies from the hypostimulation of another mind I’m trying to interact with. It’s where intellectual activity goes to die, handing over the intellectual life torch to an ethreal “more expert mind”.


I hate the argument from authority. There are plenty commendable tendencies in common reasoning. But the argument from authority incarnates everything that is wrong with common reasoning. It’s okay sometimes to defer to reasoning performed at a different time and place by someone different, under different conditions. It’s not okay to do it without putting all those differences on the table for inspection. By inspection I don’t mean the mindless task of determining what makes the information more trustworthy, what makes it less. Evaluating information isn’t like sorting dirty laundry, grading how much it smells like feet.


Information is contextual. If you take it out of the context in which it was built to make sense, you’ve done something to the information. It no longer is the same information. Perhaps it’s close enough. But perhaps it isn’t. You can’t just inspect where the information came from, you must also inspect how it traveled. This is no small task for an active analytical mind. It’s an absolute impossibility for anyone who has given up the intellectual life torch through the argument from authority.


Authority denigration is a tragic symptom of overexposure to the argument from authority. Authority denigration is fishing for reasons to defend our beliefs and continue distrusting the things we are prepared and motivated to distrust. Invoking authorities / third-parties (experts) as “gods of information” has consequences. It means that if we don’t like the information we are given, or if we simply don’t get it, then there’s nothing left for us to do than invoke different gods. The remaining way for us to apply free will is in choosing our information gods. Unsurprisingly, that’s what is happening now.

Not only does the argument from authority deprive me from active analytical minds with which to interact, by making information a matter of experts, it also devastates respect for expertise.


Now what is an information problem?

Precisely, we are missing a consensual definition of what information is. We can’t expect to easily agree on what an information problem is. We’re not there yet.

Missing consensual, fully formalised analytical definitions shouldn’t stop us from pushing our imaginations and our logic. We might not understand what information is, but we can still raise questions about it, and take the time to explore the notion from different angles in the meantime.

The main raison d’être of this post is for me to ask you to please take some time to consider the following question:

Where does one find information problems tackled as information problems rather than as communication problems?


A second raison d’être of this post is for me to suggest looking in the whereabouts of scientists’ whiteboards. It’s a good place to start: where the art of detecting the imperfections in imperfect information is practiced on a daily basis, and where troubleshooting information is constructive rather than sacramental.

This raises the next question: why is it “constructive”? Why do scientists manage to consistently produce quality information out of bits of outdated and incomplete information? Why does scientific reasoning produce concrete results such as vaccines and automatic translation? Why does science work?

Reasons are as far as it gets from trust.

Science works because scientists use very simple tools. And they make sure to apply those tools in very simple situations that they master. The world is very complex, but no scientist ever deals with all the complexity in the world. Practising science means resisting all urges to bite off more than one can chew in terms of information. Locally, this seams to be very unambitious, but globally, it makes for a very reliable process that works.

Note that there are many scientists. At first sight it might seem more efficient in terms of scientific progress, to have them all work independently on different scientific questions. That would make sense if scientists made shoes. We wouldn’t want two workers hammering the same nail into the same sole. But science is something different from shoes. Making reliable information has a primary requirement: that trust not be used as an ingredient in producing information. In every day life, we are constantly resorting to trust. So the requirement in science to do without trust calls for an exceptional workaround:

In science, we don’t trust. We agree instead.

Science works because humans can agree and when they do, even only momentarily, they manage to do things together that they wouldn’t otherwise, if only, they manage to momentarily stand on the same ground or look in the same direction and make sense of what they see in relatable ways. The industry that systematically leverages the ability of humans to agree is called science (mastery of the art of human agreement is called computer science 😉 ). Circumscribing objects of agreement is the basis of science-making. Usually agreement requires work.

Humans also have the ability to disagree. Mastering the art of human agreement means excelling at circumscribing objects of agreement, and knowing how to work with bordering disagreement and contradiction. Disagreement is an opportunity to progress together — as long as it isn’t found too far out from a well-circumscribed area of agreement. Agreement is a prerequisite providing solid common ground we can build on and tether bordering disagreement to.


Here are my two concluding cents:

Smothering disagreement with trust is not encouraging the distrustful building of agreement.

Fighting against misinformation is not fighting for information.

Tackling misinformation as a communication problem is not tackling it as an information problem.

We are bound to find better solutions if we stop wallowing in clichés.


Container := place to document a piece of information or content.

Here are some of the containers traditionally used for documenting scientific content:

  • Book
  • Page
  • Blank sheet of paper
  • Article
  • Article title
  • Article keywords
  • Article abstract
  • Article introduction
  • Article section 1
  • Article conclusion
  • Article list of references

Here are some potential alternative containers (listed here for the sake of the thought experiment, not as a concrete proposition):

  • Blank page
  • A pdf
  • Question
  • Statement
  • Equivalence
  • Definition
  • Translation
  • Conclusion

Consider an arbitrary piece of information or piece of content that I’ll name ‘x‘ to avoid making x explicit. How much (scientifically meaningful/useful) information do you gain on x from me telling you: 

  • It is possible to formulate x as an article” ?
  • x figures in the introduction of an article” ?
  • x is the kind of information that can be documented in a section numbered 1″
  • x figures in the conclusion of an article” ?
  • x is a keyword” ?
  • x is a reference” ?
  • It is possible to formulate x as a question” ?
  • x is a definition” ?
  • x is a conclusion” ?
  • x conveys an equivalence” ?
  • x is translation” ?


x has to be documented somewhere, in some container. The natural question now is “What constitutes a good container?

Sometimes, knowing what container x is documented in doesn’t prepare the reader at all to the reading and understanding of x. This is the case with the article and abstract containers.

Sometimes, knowing what container x is documented in helps understand the role of x. This is the case with the ‘question container‘. 

It all depends on the container.

In the case of the ‘keyword container’, knowing that “x is a keyword” helps you understand that “x denotes a concept worth studying“. It also informs you that “you are not expected to understand anything new by reading x: it’s just a word”. The keyword container prepares the reader to understanding the piece of content she is about to receive. But in this case, there is nothing to understand. Arguably, the ‘keyword container’ isn’t very scientifically useful/meaningful.

The primary role of containers is to contain content.

To rely on a system of containers to express the logic that is traditionally expressed by the content inside the container, is something more ambitious. It requires a sophisticated system of containers. And solutions to make people want to face the ensuing complexity and inflexibility.

This post is about finding a good system of containers, not finding a universal way to express logic and content.

Ideally we want to strike a balance. More meaning than the traditional containers. Much more flexibility than an ontology.

Currencies in academia

I had an interesting chat with Björn Brembs this morning evoking the subject of currencies in the public research sector.

The public research sector is notorious for systematically incentivising undesirable behaviours from researchers. The incentives are established through the system’s way of rewarding researchers’ work. It is customary to think of this in terms of currencies.

A currency is a prevalent medium of exchange. With currencies in circulation in academia, one buys academic positions and funding. Having such means to easily acquire academic positions and funding easily is the academic version of being rich.

The richer you are

The more executive power you have

Your incentive as a researcher is to become rich

The problem is simple: in the public research sector, presently, becoming rich is different from doing good research.

Activities you do to get rich


Activities you do to make research progress

There is an overlap. There also are discrepancies.


Today there are several kinds of currencies in circulation in the public research sector:

Number of articles: the more articles you have in your name, the richer you are in academia
Number of citations of each of your articles: the more cited are your papers, the richer you are in academia
Prestige of the journals in which you have published your articles: the more prestigious the journals, the richer you are in academia
Number of co-authors of each of your articles: the less co-authors you have, the more your name stands out, the richer you are in academia
Order of your name in the list of co-authors: the earlier it appears, the richer you are in academia

All these currencies are based on peer-reviewed articles. If peer-reviewed articles didn’t matter, these currencies wouldn’t make sense the way they do.

I will distinguish between ‘currency’ and ‘currency basis’: at present peer-reviewed articles are the basis for the currencies listed above. I will call that “the old basis“.

I write this post to propose to build a new currency basis. I will call it “the new basis” and introduce some propositions and requirements for it.

Incentivisation of desirable behaviours in science: drop it already!

Björn insisted that if we want to incentivise a behaviour, we might first want to ask ourselves why this behaviour needs to be incentivised in the first place. Is it an unpleasant behaviour? Is it detrimental? If the behaviour was pleasant and self-serving, people would already be motivated and no further incentivisation would be needed. Incentivising new behaviours might not be the smartest thing to do, nor the easiest.

There are things to try before…

I’d add a cautionary “be careful what you wish for“. If you incentivise new behaviour among researchers, and you succeed, then you’ve just changed the profession a little, and possibly the reasons why people enroll in it. I find the idea of gamification (gamification of peer-review, gamification of open access publishing…) ostentatiously naive for this reason. If you change the rules of the game, you might change the name of the game too.

For now, the game is called science. It’s a good game. Many of its rules already make it simultaneously exciting and useful to play. The idea is to empower not to incentivise desirable scientific.

Elizabeth Gadd’s complementary arguments on incentivisation are worth considering as well →  How (not) to incentivise open research.

The new basis does not change "the name of the game."

Incentives to do science are not tampered with.

The new basis supports behaviours that researchers already find pleasant and self-serving.

What’s the point in a new basis if it doesn’t change what people want to do? → To change what people can do.
How are we to change anything for the better without generating new behaviours, only promoting old behaviours? → By enhancing the old desirable behaviours, and making them matter more so that the old undesirable behaviours matter less.


By Björn’s remark, we hardly have a choice anyway. Other than temporarily forcing people to do what they don’t want to do and jeopardising the profession in the process of tinkering with it, we are left with encouraging scientists to do what they already want to do.

We all have ideas on what it is scientists want to do. We are looking for something more specific than that however: what scientists want to do that simultaneously serves a bigger picture such as scientific progress and society.  Determining what that is, isn’t trivial. Let’s save the task for later. For now let’s complete Proposition 2:

The new basis supports behaviours that researchers already find pleasant and self-serving and that serve the bigger picture of science and society.
The new basis must not make things harder for researchers. It must not add to the workload of researchers.
The new basis is defined in terms of work that researchers already do.
The new basis must be institution-agnostic to allow for currencies to be portable from one institution/department/country to another.

The old basis is already like that.  All research institutions in all countries are aware of the actual importance of article publications. The new basis should benefit from similar portability in order to allow researchers to keep moving. Institution-agnosticism is also important to recognise the contributions of non-affiliated researchers as well as researchers who make non-mainstream science with little institutional support.

Another remark Björn made this morning motivates the distinction between “currency basis” and currency . A single currency is not enough. Biologists have different scientific cultures than mathematicians. Scientific value is not assigned the same way in math and in biology. 

At first sight, it looks like all sciences do actually agree on one currency, namely “the number of publications”. But  there is nothing remotely scientific about this agreement (cf below: paragraph about “wrapping”).

An example of disagreement between scientific fields: In Björn’s field, one paper per year is good. Björn says “more than that, and you’re not thinking far enough, less than that and you’re not pushing yourself enough“. In my field, a paper a year is considered too much to allow for anything else than redundant or vacuous content. The old basis failed to make the rate of publication matter as well.

It must remain possible for different fields to assign value differently. As Björn said, we need multiple currencies.

The new basis must allow for multiple currencies.

The different things valued by different scientific fields can be matched by different currencies.

→  complementary currencies.

The example of differing rate of publication suggests that further than that, the old basis must also the same things to be valued differently… This requirement will be covered below.

The new basis must not be exclusively about articles.

The old basis is already almost exclusively about articles. For a reshuffle, the new basis has to be about something different.

Changing or removing some of the current article-based currencies is not enough. This is because those currencies aren’t universal, apart perhaps from the  “number of article publications” currency. For instance, in my field, journal prestige hardly counts. Conference papers, which don’t count in other fields, do count in mine. The conferences tend to be rather small and focused on the work of a certain community. This bounds competition to a reasonable scale and scientific scope. It is clear that my field is hugely more comfortable than scientific fields in which journal prestige counts.  Comparatively to life science fields, mine is a scientific Eldorado. However in absolute terms, my field is not a scientific Eldorado at all. The “number of publications” currency is enough in itself to ruin the profession. The inflation of this currency changes the name of the game. It promotes the optimisation of research output at the expense of the optimisation of research. And as argued further down, this annoying aspect of the “number of publications” currency  is an intrinsic property of the old basis.

So indeed, changing or removing some currencies can make a huge positive difference. But if we pay attention to the different configurations represented by the different research fields of academia, we soon notice that attacking lone currencies will only get us so far and “so far” is insufficient and it’s already getting uncontrollably worse in some fields. It’s not only the worst case scenarios that should inform our solutions. The best case scenarios should inform them to.

Dynamics between the different scientific fields need to be considered. What happens to one field can propagate to other fields. An effect that is obvious in one field can be an underlying  effect we underestimate in an other field.

The new basis must not be exclusively about data.

Otherwise the new basis would be a basis only for currencies that matter to some research fields, those that manipulate data. Many research fields don’t manipulate data including mine: ‘data’ is a word I never ever use as part of my research work. If academia starts rewarding researchers based on criteria defined in terms of data, I won’t get rewarded. As discussed elsewhere, if our solutions to academic dysfunctions, exclude some sciences by design, we might as well reconsider our common scientific identity and discuss the option of a schism in our profession.

The new basis must not be exclusively about data and articles.

Otherwise the change would only affect data-oriented sciences. Other sciences would continue operating on the basis of articles.

For now, let’s refuse the option of a schism. We want biologists and mathematicians etc to keep operating under the same roof, governed by the same institutions, to keep collaborating and considering each other as colleagues. Let’s also refuse the abolition of interdisciplinary collaborations. Then, as long as some sciences are still driven by article publications, I wager that all sciences will continue being driven by article publications. The continued interest of some sciences in the old article based currencies, will cause those currencies to continue being substantiated and general interest in them to continue being fueled.

At the start of my career, I decided not to engage in the publication race. The strategy has served me well, but it suffers a hard limit: not caring about article counts myself does not stop my collaborators from worrying about them. There always comes a point when I’m working with someone at the whiteboard and they bring up the subject of publication. With regards to the science we are making and have not yet finished making, I find the subject is usually brought up far too early. This discrepancy in our concerns interferes very concretely in the work we do at the whiteboard. Proving a theorem is not the same thing as preparing for a paper.

The new basis must be universal.

Just like the old basis of peer-reviewed articles, the new basis must be common to all fields of scientific research.

The system of currencies should avoid straining (interdisciplinary) collaborations with conflicting incentives /interests For any pair of (interdisciplinary) collaborators working together at the whiteboard, this can mean either of two things:

1. There exists a common currency that both researchers can be rewarded in for the work they do together (probably different from the currency rewarding a different pair of collaborating scientists)

2. There exist different currencies to reward each of the two researchers’ contributions, and the currencies are compatible: the currencies recognise different aspects of the same collaboration, or different kinds of input to the same output.

The new basis must be science-agnostic to allow for currencies recognised in all scientific fields.
The new basis must allow for a continuum of compatible currencies.

Different scientific fields call for different competences in scientists. Further, different scientists have different qualities. Some are good at generalistic thinking, some are good at formulating questions etc. Further, one scientist may change what she is good at from one period of her life to the next. Perhaps even, she is be better at formulating questions before lunch and better at seeing the big picture while digesting her lunch. Arguably, a good currency is one that helps make the most of scientists by finely recognising and rewarding scientists’ different qualities/roles.

If none of those roles and qualities transcend disciplinary fields, a schism is probably in order.

I don’t think this is the case. If there were no role/quality/sort of input a scientist could contribute, that would be recognisable outside of her field of expertise, then interdisciplinary collaboration would be impossible. No mathematician could ever really collaborate with a biologist. It would be impossible for them to make a line of reasoning progress collaboratively, as neither would recognise what the other brings to the table. The relation would be purely transactional: one kind of scientist placing an order for the other kind of scientist to deliver (“Dear mathematician, please produce a theoretical model for my data“, “Dear biologist, please produce a justification of my theoretical work“).  I have experienced enough true (/cybernetic) interdisciplinary collaboration to have no worries about that. It is possible for a computer scientist, biologically unsavvy, to formulate profound questions that will encourage a biologist to change their perspective on the biological reality. And a biologist can formulate fundamental questions that can inspire brand new avenues of thought for the computer scientist.

For any true scientific collaboration involving 2 fields of expertise, there should be currencies reflecting what each party in the collaboration contributes. Further than that. in order to substantiate a common scientific identity and justify the use of a shared basis, there should be universal currencies that work for all scientific disciplines not just pairwise (requirement 6). They don’t have to be the preferred currency of all fields. They don’t even have to be the preferred currency of any field. But they must exist to materialise interdisciplinary appreciation and to support a common scientific identity.

This relies on the variety of research fields sharing at least some commonality. Defining a new currency basis is an opportunity to highlight a meaningful commonality. Something more meaningful than the fact that we all write papers. Something more true than the fact that we all output data. It’s the whole point of a currency basis.

Currencies and their importance can vary from field to field, but the basis must be common.

Commonalities & Opportunities for relay

The previous requirements suggest we should take interest in:

  1. Commonalities shared by all scienceseg all sciences make intensive use of questions; in all sciences, there are circumstances in which acknowledgment for the formulation of a good question is more fitting than acknowledgement for the publication of a good paper.
  2. Opportunities for sciences and scientists to relay each othereg a biologist might give meaning to a formal system by turning it into a model of some biological system, thereby constructively framing the study of the model, while a computer scientist might frame the good use of the model by expliciting the formal system’s limited expression power.

What is a currency?

The dictionary definition works fine in this context. Let’s just add the following precision to narrow down to currencies of the public research sector:

Currency := a quantifiable property of research or of a researcher.

Here are some more examples, to extend the list of article-based currencies above:

Number of collaborators
Number of questions raised that have opened a line of inquiry
Number of bridges built across otherwise independent research fields
Number of indirect contributions to praised publications
Amount of constructive feedback provided to peers
Depth of scientific proofs constructed
Distance of contributions to hot topics like the SDGs
Degree of precision in the definition of the context of application of a model

This says nothing about how those quantities could be measured.

This also says nothing about what is a good currency. Not all currencies are good, even when they represent something we want to capitalize on. We used to think more papers was necessarily a good thing until we realised there was a bug in our logic. As producing more science translated into producing more papers, we thought that conversely, producing more papers translated into producing more science. It turns out papers can be produced independently of science.

According, to Elizabeth Gadd, ‘measures of openness’ are another example of currencies that aren’t as good as they first seemed to be. And openness is thus another example of flawed currency basis.

Let’s leave for later the important discussions of how to define good currencies and how to measure them. For now we still have a new basis to find.

Björn highlighted the risk of inflation of currencies. This is the situation we have today with journal prestige and number of articles. Both these currencies have come to matter so much to researchers, that the profession has changed as a result of it. Research is now a profession dedicated to the writing of articles, preferably in the most prestigious journals.

Björn and I each came up with a solution to the problem of inflation:

Multiply currencies so there are so many of them in circulation that traditional article-based currencies lose traction and no other takes over
Use a basis that is not in itself obviously countable (unlike articles which are easily countable)

These must be two formulations of the same idea:

B → M : As long as we have a countable basis (like articles), we have a privileged currency (like the number of articles), and a risk of inflation.

M → B : A new basis that is not obviously countable must allow for multiple currencies for reasons detailed below.

Articles are easy to count. Further, articles are such that it is easy to say lots of things about them (how many of them there are, where they are published, who published them, how many pages they have…) without ever saying anything remotely scientific. Articles wrap up scientific content and make it perfectly natural to talk more about the wrapping than about the content. They are such that you either talk about the wrapping or you talk about the content but can’t talk about both at once. They make you choose between your position in the economy of public research and science.  In that sense, they are a poor currency basis, bound to promote and entertain the notoriously bad situation academic research is in now.

The new basis mustn't wrap content in such a generic way that we can talk about the wrapping without consideration of the content. Content must be wrapped up in scientifically meaningful/useful containers.

This doesn’t say what scientifically meaningful/useful containers are. Just like the question of good currencies, the question of good containers is important and non-trivial. Let’s leave it for later. For now, let’s continue exploring the question of a good basis.

The following requirements follow from Proposition 3.

The new basis must not make the traditional way of publishing harder than it is. Ideally, it should facilitate it. At the very least, the new basis should be compatible with the old basis (coexist without competition).
The new basis must provide an optional bonus in addition to the old basis.

Putting together previous requirements for the new basis, especially:

  • that it needs to relate to work researchers already do — work that is neither data nor article related —
  • and that the new basis must not find itself competing with the old… :
The new basis must involve the daily scientific production of researchers, especially the neglected bits that don't end up in traditional publications.

What those neglected bits are has to be determined. For now let’s just say that we will be looking for bits:

  • that researchers are happy to produce and already are producing
  • that are useful and serve the big picture
  • that we can build a new basis out of that satisfies the requirement of universality and science-agnosticism.

This is a demanding task. Let’s leave it for later.

Now, a new series of requirements which relate to the point made earlier about empowering existing desirable behaviours (rather than incentivising new ones) — and to propositions 1 and 2, and Corollary 1.

The new basis must not bother thinkers in their thinking, researchers in their research. The new basis must not interfere negatively with *scientific* customs, habits and practices in place in each field of scientific research.

Science is not broken. Science is something different from the environment in which science is made, the governance and administration of science, the incentivisation of science… Those things being broken don’t make science broken. Science has been successfully practiced for ages. Those who know how to practice it are highly specialised scientists. They might not know how to fix the other broken things. But they are the ones who know how to make science work.

Practicing science and managing science are two different activities. To know what interferes with a scientist’s  progress, requires to know what the scientist is working on, to understand the approach she is taking and the scientific reasons why she is taking this particular approach. In other terms, it requires being the scientist herself or being scientifically very close to her.

To know how to not bother scientist thinkers in their work requires consulting with them. This is not a trivial task but like those mentioned above, it is an essential one.

The system is now designed to optimise the production of article publications. It is not designed to optimise the advancement of good science. Dysfunctions and undesirable behaviours are bound to happen. We should not let this situation make us blind to good science-making abilities where they can be found. Who is to blame is a different question. Irrespective of how we choose to answer this question, we should take care to not disregard scientific competences even when they are held by blamable individuals.

The new basis must be mess-friendly.

Mess is an integral part of science-making. Forbidding scientists from being messy — eg by forbidding them to say wrong or irrelevant things — is interfering with the process of science-making.

If this corollary requirement worries you, it might reassure you to know further down figures a requirement about quality control.

Just like the old basis, the new basis must be very flexible.

Science is characterised by its continual self-updating.

Rigid standards risk inhibiting precious changes of perspectives and jeopardizing  scientific breakthroughs.

Flexibility is needed to ensure the new basis accommodates highly specialised scientific cultures.

Checking the new basis is flexible enough will require testing before validation.

The new basis must not get in the way of science evolving continually. Ideally, it must facilitate the continual evolution of science. It must mitigate the risk of today's science getting in the way of tomorrow's science.

The new basis must allow for what we believe to be relevant and true today, to not come in the way of brand new perspectives in the future. Ideally, it must be designed to facilitate relevant changes of perspectives while simultaneously supporting scientific explorations according to actual perspectives.
→ This relates to the concept of “information in the dark” i.e. information that is overshadowed by information we already have.

In relation to Requirement 12, Requirement 14 also concerns scientific approaches. They too evolve. And as they do, scientific fields evolve: how we define and delineate scientific fields. In defining a new basis we need to take care not to rigidify the system by unnecessarily crystallising in it some current views that could change.

The new basis must be an extension of the old basis. It must not exclude data- and article-based currencies. It must be compatible with all currencies in use.

This is needed to avoid forcing researchers to chose at their own expenses between capitalising on new currencies (what we want researchers to want) and capitalising on old ones (what researchers actually want). If there were a competition between currencies, the old ones would likely win because they already are currencies i.e. prevalent. A competition would risk making the lives of researchers harder. Researchers would be forced to choose between giving up on academic wealth and mild gilt.

Utilisation of anything new should not rely on researchers renouncing anything old they presently capitalise on.

Since the new basis must extend the old, the new basis must involve some form of publishing just like the old basis:

The new basis must involve some form of publishing.

Open Science has been operating as if it were obvious that a better future for public research entailed changes to the scholarly publication system. I have never been convinced of this.

As I already mentioned, data publication doesn’t concern me. As for articles, I spend a tiny amount of my time at work thinking about article publications. I can easily go half a year without even thinking of my next publication. I also don’t read articles that often.

In between the moments were my work brings me to read a paper or think of writing one, my work has nothing to do with papers. There is a huge amount of communication that happens between me and my peers. It just doesn’t happen through articles on a daily basis. Communication in my work only marginally overlaps publication.

Our understanding of the term publication has drifted. It could mean “offering a piece of information to the public and offering to take responsability for it as an author“. But instead it rather means “claiming a form of ownership over a piece of information, in public“.

Before I can offer the public my responsability over a piece of information, I must test that I am able to take responsability for it facing  peers who speak my scientific language. What I produce is meant to be both of quality and useful. My peers, working with me at the whiteboard, are the key partners in ensuring the quality of my work. I don’t necessarily have to communicate with them through an article. And often, an article wouldn’t be appropriate. Also, my work can only be used by people who understand the highly specialised language in which it is expressed. Writing an article is a way to reach these people but there other ways, some more direct.

The new basis must have scientific value in itself. Since it extends traditional publishing, it must also extend traditional peer-review correspondingly. The new basis must add to scientific quality control.

The new basis must involve some form of scientific quality control that adds to traditional article-based peer-review.

The old basis has gotten us used to thinking official quality control of scientific content only happens through the peer-review process at the submission of an article. We need to ensure that the new basis comes with an integrated quality control system equivalent to what peer-review is for the old basis.

This is more trivial than we think once we stop thinking in terms of the old basis and acknowledge that in reality, scientists spend their days doing quality control. Quality control is too big a part of science for it to wait for article submission.

Quality control is part of the “neglected bits” mentioned earlier in Corollary 2: the elements of science that don’t, per se, add to academic wealth as long as wealth is counted with the old currencies. Thus adding the task of ensuring quality control on the new basis has not added to the challenge introduced earlier which is to identify the “neglected bits“.


The starting point of the discussion was that despite some overlap, there are discrepancies between academic enrichment and academic research. There are two approaches to address the notorious consequences of this. I propose the second:

1.Try to deincentivise enrichment (very hard given that enrichment = acquiring the means to do research)
Activities you do to get rich
Activities you do to make research progress
2.Mitigate the discrepancies towards:
Activities you do to get rich
Activities you do to make research progress

The 2nd approach requires work and thought. — cf the icon in the right margin above. This icon corresponds to questions which now require our dedicated and methodical attention. To take care of those question appropriately, we need a plan. I have a something to propose as a starting point (topic of a next post).


The new basis does not change the name of the game. It must ensure the profession of scientific research is indeed about scientific research.

The new basis supports behaviours that researchers already find pleasant and self-serving and that serve the bigger picture of science and society.

The new basis must not make things harder for researchers. It must not add to the workload of researchers.

The new basis is defined in terms of work that researchers already do.
The new basis must be institution-agnostic to allow for currencies to be portable from one institution/department/country to another.

The new basis must allow for multiple currencies.

The new basis must not be exclusively about articles.

The new basis must not be exclusively about data.

The new basis must not be exclusively about data and articles.

The new basis must be universal.

The new basis must be science-agnostic to allow for currencies that are recognised in all scientific fields.

The new basis must allow for a continuum of compatible currencies.

The new basis mustn't wrap content in such a generic way that we can talk about the wrapping without consideration of the content. Content must be wrapped up in scientifically meaningful/useful containers.
The new basis must not make the traditional way of publishing harder than it is. Ideally, it should facilitate it. At the very least, the new basis should be compatible with the old basis (coexist without competition).

The new basis must provide an optional bonus in addition to the old basis.
The new basis must involve the daily scientific production of researchers, especially the neglected bits that don't end up in traditional publications.
The new basis must not bother thinkers in their thinking, researchers in their research. The new basis must not interfere negatively with *scientific* customs, habits and practices in place in each field of scientific research.

The new basis must be mess-friendly.

Just like the old basis, the new basis must be very flexible.

The new basis must not get in the way of science evolving continually. Ideally, it must facilitate the continual evolution of science. It must mitigate the risk of today's science getting in the way of tomorrow's science.
The new basis must be an extension of the old basis. It must not exclude data- and article-based currencies. It must be compatible with all currencies in use.

The new basis must involve some form of publishing.

The new basis must have scientific value so it must involve some form of scientific quality control.

This same boat scientists are on

Scientists are very good at analysing a situation and identifying the problems in it. The notorious situation of scholarly communication is no exception. Most scientists, individually, have an excellent understanding of the situation and how its ramifications impact on their daily practice of research. But what if, collectively, scientists weren’t as clairvoyant? What if, this situation with scholarly communication impacted in subtly different ways from one scientific field to another?

Sitting down at one of those well-meaning interdisciplinary Open Science meetings, implies accepting to talk about problems in general terms. Perhaps it also means becoming blind to the essential, idiosyncratic, scientific dimension of the problem.

Solutions abound. But solutions to what problem exactly?



As a theoretical computer scientist, I haven’t yet come across a single Open Science project offering a solution to a problem that remotely concerns my work in practice. There is no part of my work that involves data so none of the numerous solutions to openly document, organise, publish data concern me the least bit. And in terms of article publications, the only thing that would perhaps help is a cap on the number of papers allowed to be published per year per person (anyone who publishes more than a paper every 18 months in my field is probably polluting the literature with vacuous or repetitive content). And yet from where I stand, Open Science’s general principles make sense and the situation with scholarly communication very concretely and directly determines how well I can do my job.

So what? Does this mean we should give up on finding solutions that work for every science? Or maybe just give up on finding solutions that work for the formal sciences? The other sciences have more in common among themselves, like, they deal with data and publish frequently?

Giving up on common solutions (and common problems) is a small step away from giving up on a common identity.

This is the crux of the matter. Our common identity. Why wouldn’t we want to give up on it? Why do we physicists, historians, biologists, mathematicians, legal scientists… continue to want to be governed by the same institutions, as a unique profession? Why don’t we part ways? Why aren’t we even talking about it? Haven’t we noticed the way we work is so different? What we output is also extremely different. So is what we need to do our work. The present situation is terrible. Why stick together just to continue experiencing it? We’ve been trying to solve the situation for years. It’s taking so long, it might be time to say we failed.

What is this common identity of ours that glues us together?

Perhaps if “we” want to talk meaningfully about “our” common problems, we should start by clarifying who “we” is.

If “we” is the set of people suffering in some way from scholarly publication, then the continuation of “we” is tantamount to the subsistence of the problem.

Hopefully, “we” is something different, that can survive the resolution of the problem.

So what is it that scientists who suffer from the scholarly communication situation have in common other than this situation, or its causes?

Other than the object of traditional scholarly communication, what is this common “science” thing we do that justifies that we continue sharing the same problems and looking for common solutions?

Certainly, every scientist has an idea on that, but every scientist’s idea on that is necessarily anchored in one particular scientific field. The anchoring is a strength. The devil is in the details, we know it. Perhaps it’s time we stopped the superficial buzzwording generalistic interdisciplinarian unverified postulates on what science is, who scientists are, and what they all need. Perhaps it’s time we approached our scholarly problems as scientists with an attention to details, and a preference for questions and rigorous verification.

I doubt any collection of solutions can guarantee a lasting, intended effect if some sciences are dealt with as an afterthought. I also doubt that we can solve scientists’ problems without taking interest in their sciences and looking very closely at what they do in practice to make scientific understanding in their field progress. And I doubt we can avoid sacrificing the common scientist identity if we can’t find commonalities there.

But I have no doubt we will find commonalities.

Not the antiquated semi spiritual BS like “we scientists are discoverers of the Truth”. Not the folklore involving mythological boundaries getting pushed back along endless metaphorical lines.

Small practical commonalities instead. Ordinary things that don’t necessarily distinguish scientists clearly from non-scientists: common intellectual tools that scientists make special intensive use of. Like questions. In my field we can make science without articles, but we certainly can not make science without asking questions. Is this generalisable to other scientific fields?

What else? …

Here’s another one perhaps: we navigate between information expressed in intuitive everyday language, and highly formalised information.

It’s not much but consider the commonalities we’ve been running on lately: “We write articles“, “we write abstracts“, “we list references” / “We work in public institutions” / “We know what Elsevier is“…

We don’t need to aim high as long as we find some common ground. Just enough to anchor a plan of action that actually concerns us all.

To be continued…