Björn Brembs

What do these two memes have in common?

[![falsememes](https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/falsememes-1024x354.png){.aligncenter
.size-large .wp-image-1106 loading="lazy" decoding="async" width="520"
height="180"
srcset="https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/falsememes-1024x354.png 1024w, https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/falsememes-300x104.png 300w, https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/falsememes-768x265.png 768w, https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/falsememes-50x17.png 50w, https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/falsememes.png 1558w"
sizes="(max-width: 520px) 100vw, 520px"}](https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/falsememes.png){rel="attachment wp-att-1106"}While
they may have more than one thing in common, the point important for now
is that despite both having an air of plausibility or 'truthiness'
around them, they're both false: neither has Donald Trump [ever
said](https://withoutbullshit.com/blog/donald-trump-memes-and-the-dangers-of-post-factual-politics)
these words to *People* magazine, nor do gay canvassers [have such an
effect](https://retractionwatch.com/2015/05/28/science-retracts-troubled-gay-canvassing-study-against-lacours-objections/)
on people's attitudes (even though the quoted statement about this
research was indeed published in *Science* magazine).

The issue of false facts has become so rampant in current politics, that
some have
[dubbed](https://www.huffingtonpost.com/david-sirota/welcome-to-the-postfactua_b_42527.html)
our common era "post-factual". While on the surface, "post-factual
science" appears to be an oxymoron, recent evidence raises the
tantalizing possibility that at least the literature of the
life-sciences, broadly speaking, may be on track to leaving the
reality-based community (although more research is required):

::: {#attachment_1107 .wp-caption .aligncenter style="width: 530px"}
[![Sources:
https://journals.plos.org/plosbiology/article?id=info:doi/10.1371/journal.pbio.1002165
and
https://science.sciencemag.org/content/349/6251/aac4716](https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/irreproducibility-1024x515.png){.wp-image-1107
.size-large loading="lazy" decoding="async"
aria-describedby="caption-attachment-1107" width="520" height="262"
srcset="https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/irreproducibility-1024x515.png 1024w, https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/irreproducibility-300x151.png 300w, https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/irreproducibility-768x386.png 768w, https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/irreproducibility-50x25.png 50w"
sizes="(max-width: 520px) 100vw, 520px"}](https://bjoern.brembs.net/wp/wp-content/uploads/2016/02/irreproducibility.png){rel="attachment wp-att-1107"}

Irreproducibility loosely defined as in "not replicated", or "difficult
to replicate with imprecise methods". Note that not all of these are
replication studies and not all of the replication studies are properly
published themselves, with data available etc. To my knowledge, only the
Open Science study is reliable in that respect. Sources:
https://journals.plos.org/plosbiology/article?id=info:doi/10.1371/journal.pbio.1002165
and https://science.sciencemag.org/content/349/6251/aac4716
:::

Obviously, this data can only hold for experimental sciences and even
there I would expect huge differences between the different sub-fields.
Nevertheless, the frequency of retractions in biomedicine is rising
exponentially, the 'majority of research findings' was
[estimated](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124)
and more recently supported (at least tentatively for the findings
analyzed/replicated in the above six studies) to be false and the public
trust in science is
[eroding](https://chronicle.com/article/The-Water-Next-Time-Professor/235136).
Maybe it is not too early to start asking the question: if we can't even
trust the scientific literature, what information is trustworthy? Or,
put differently: if traditional (some would say legacy) strategies for
assigning credibility are failing us, are there more modern strategies
which can replace or at least support them?

Personally, I think this is one of the most urgent and important
challenges of the post-internet globalized society. It may well be that
science, which brought us the internet in the first place, may also be
the right place to start looking for a solution.

Truth has never been an easy concept. Some may even argue that a large
portion of humanity is probably quite happy with it being rather
malleable and pliable. It wasn't really until the 20th century that
[epistemologists](http://brembs.net/metabiology/) have explicitly
formulated a convention of what constitutes a scientific fact and how
they can be used to derive and test scientific statements. Obviously, in
life outside of science we are still far from an explicit convention,
which complicates matters even further.

Be that as it may, neither within nor outside of science can we expect
every individual to always fact-check, question and investigate every
single statement or bit of information ever encountered, no matter how
much that may be desired. There will always, inevitably have to be
shortcuts and we have been taking them, legitimately, I would argue,
since the beginning of humanity.

Initially, these shortcuts involved authority. With enlightenment and
the shedding of both religious, societal and political shackles, a
(competing?) more democratic and informal shortcut was conceived
(without ever completely replacing authority): popularity. If a
sufficiently large number of information outlets confirmed a statement,
it was considered sufficient for believing it. Both shortcuts are still
being used today both inside and outside of science, for very legitimate
reasons. However, in part because the choice of authority is often
arbitrary and may even be subject to partisan dispute, there are few, if
any, universal authorities left. Even in science, the outlets with the
most authority have been shown to publish the [least
reliable](https://journal.frontiersin.org/article/10.3389/fnhum.2013.00291/full)
science. This erosion of the 'authority' shortcut accelerated with the
dawn of the internet age: never before was it so easy to get so many
people to repeat so many falsehoods with such conviction.

The 'wisdom of the crowd' seems to suggest that a  sufficiently large
crowd can be at least as accurate as a small number of expert
authorities, if not more so. Social media have the uncanny ability of
always aggregating what subjectively feels like a "sufficiently large
crowd" to solidly refute any and all authority, whether that would be on
the moon landings, the 9/11 attacks, vaccination effectiveness/risk,
climate change, crime/gun control, or spherical earth. Obviously, this
not only constitutes a fatal misunderstanding of how the crowd becomes
wise, it also contributes to an unjustified, exaggerated distrust in
entities which do have significant expertise and hence credibility.

There are several reasons as to why social media, as currently
implemented are notoriously incapable of getting anywhere near an ideal
'wisdom of the crowd' effect. For one, social feedback loops tend to
aggregate people who think alike, i.e., reduce heterogeneity, when
[diversity](https://books.google.de/books?id=afqfFW8WV9cC&pg=PA26&lpg=PA26&dq=micro-expertise&source=bl&ots=p-CtYBlgFM&sig=3BbQj7-n1ZICJ1qbPXONXvKsubk&hl=en&sa=X&redir_esc=y#v=onepage&q=micro-expertise&f=false)
is one of the most decisive factors in achieving crowd wisdom. Second,
with our stone-age concept of a crowd, we may intellectually understand,
but fail to intuitively grasp that any group of less than a few tens of
millions is more of an intimate gathering rather than a crowd, on
internet scales. With today's social media implementation, it is
comparatively easy to quickly gather a few million people who all share
some fringe belief.

For instance, if we round the incidence of schizophrenia to about 1% of
the population and assume, for simplicity's sake, that all of them
harbor either auditory hallucinations or some other forms of delusions.
Hence, if 1% of all [internet
users](https://www.statista.com/statistics/273018/number-of-internet-users-worldwide/)
were delusional in some way or form and only half of them aggregated in
an online patient forum, we'd be talking about more than 15 million
individuals. I'm pretty sure that such a patient site would deliver some
quite astounding news feed with an amazing commentariat. And this is
just one of a growing list of psychiatric disorders. How many nominally
healthy users would feel compelled to believe the news from this site,
re-share items and comment approvingly? Most far-out-there communities
have orders of magnitudes less users but disproportionately large
visibility. Indeed, some of those communities may appear just like a
subforum of such a patient site, but without a single user actually
being diagnosed with any psychiatric disorder whatsoever.

So if we want to take advantage of the
[micro-expertise](https://books.google.de/books?id=afqfFW8WV9cC&pg=PA26&lpg=PA26&dq=micro-expertise&source=bl&ots=p-CtYBlgFM&sig=3BbQj7-n1ZICJ1qbPXONXvKsubk&hl=en&sa=X&redir_esc=y#v=onepage&q=micro-expertise&f=false)
of the individuals in a crowd, that crowd needs to be not only
'sufficiently' large for the task at hand, it more importantly needs to
be sufficiently diverse, or size quickly becomes almost completely
irrelevant. From numerous examples in science and elsewhere, it seems
straightforward to argue that we need to harness the individual
micro-expertise that *anyone* can have, without making the mistake to
attribute this expertise to *everyone*. Authority alone cannot serve as
a reliable shortcut for credibility, but neither can popularity alone.
Here is an idea of how one might combine them.

I may be wrong, but at least for now I would argue that we probably
cannot start from scratch, assigning every institution, organization and
individual the same kind of credibility. We cannot and should not undo
history and track records based in evidence: there are information
sources that have a higher credibility than others.

Further, we probably need a score or at least ranks that get computed
iteratively and recursively. For any individual or piece of information
to gather points or climb ranks, there need to be arbiters that already
have some credibility -- another reason why we likely won't be able to
start with a level playing field. What is less clear is how such a
scoring/ranking system ought to be designed: I somehow have the
impression that it ought to be difficult to earn credibility, shouldn't
it? Of course, it's usually always "innocent until proven guilty", but
is this a practical approach when doling out credibility? Should we all
start with a credibility of 100 and then lose it? Or should we start
with 0 and then gain? Does such a score have to go negative?

So far, these ideas have been very vague and general. Here are my first
thoughts on how one may go about implementing such a system in science.
A prerequisite for such a system is, of course, a
[modern](https://csarven.ca/enabling-accessible-knowledge)
[scholarly](https://billmills.github.io/blog/full-stack/)
[information](https://bjoern.brembs.net/2015/04/what-should-a-modern-scientific-infrastructure-look-like/)
[infrastructure](https://cameronneylon.net/blog/principles-for-open-scholarly-infrastructures/).
This won't work with the 350 year-old technology we call 'journals'.

Because we need diversity and inclusiveness, one would never prevent
anybody from posting whatever they have discovered. However, if someone
described a discovery from a known research institution, that discovery
would receive more credibility than if it were posted by a layman
without a track record (even though both scores would still be
relatively low at the point of publication). Similarly, if the author
list contained professors, the article would receive more credibility
than if there were only graduate student authors. Yet more credibility
would be assigned if the data and code relevant for the discovery were
openly available as well. Once this initial stage had been completed,
the document and its affiliated authors and institutions can earn even
more credibility, for instance if the code gets verified, or the data
checked for completeness and documentation. Those doing the verification
and reviewing also need to be diverse, so also here there should not be
a principle limit. However, the weight given to each verification (or
lack thereof) will be different according to the scores of the person
doing the verification.  More credit for reproducible data analysis
(e.g. via [docker](https://www.docker.com/) et al.) and if the narrative
accompanying the data/code is supported by them. This whole process
would be similar to current peer-review, albeit more fine-grained,
likely by more people (each contributing a smaller fraction of the
reviewing work) and not necessarily on each and every article.

This process continues to accrue (or lose) credibility inasmuch as the
article is receiving attention with consequences, e.g., how many views
for each citation (in accordance with a citation typology, e.g.
[CiTO](https://purl.org/spar/cito/)), how many views for each comment,
endorsement or recommendation, etc. This is one possible way of
normalizing for field size, another could be by analyzing citation
networks (as in, e.g.
[RCR](https://biorxiv.org/content/early/2015/10/22/029629)). Clearly,
most credibility ought to be associated with the experiments in each
article being actually reproduced by independent laboratories (i.e., a
special kind of citation).

In this iterative process, each participant receives credit in various
ways for their various activities. Credibility would be just one of
several factors being constantly monitored. Points can be awarded both
for receiving (and passing!) scrutiny from others as well as
scrutinizing other people's work. The resulting system is thought to
allow everyone to check the track record of everyone else for some data
on how reliable the work of this person (or institution, or community)
has been so far, along with more details on how the track record was
generated.

Obviously, there are plenty of feedback loops involved in this system,
so care has to be taken to make these loops balance each other. The
feedback loops found in many biological processes would serve as
excellent examples of how to accomplish this. Complex systems like this
are also known to be notoriously difficult to game.

Those are still very rough ideas, without a clear picture of the most
suitable or effective implementation, yet, or whether the desired
outcomes can actually be achieved in this way. I also have no good idea
how one would take such a system to leverage it outside of science. I
would like to hope, however, that by starting on the easier case of
science, we may be able to approach a related system for the society at
large.


Messaggi di Rogue Scholar

Earning credibility in post-factual science?