Re: [Fis] Is Dataism the end of classical hypothesis-driven research and the beginning of data-correlation-driven research?

[Mark Johnson]

Dear Alberto,

Thank you for this topic – it cuts to the heart of why we think the
study of information really matters, and most importantly, brings to
the fore the thorny issue of technology.

It has become commonplace to say that our digital computers have
changed the world profoundly. Yet at a deep level it has left us very
confused and disorientated, and we struggle to articulate exactly how
the world has been transformed. Norbert Wiener once remarked in the
wake of cybernetics, “We have changed the world. Now we have to change
ourselves to survive in it”. Things haven’t got any easier in the
intervening decades; quite the reverse.

The principal manifestation of the effects of technology is confusion
and ambiguity. In this context, it seems that the main human challenge
to which the topic of information has the greatest bearing is not
“information” per se, but decision. That, in a large part, depends of
hypothesis and the judgement of the human intellect.

The reaction to confusion and ambiguity is that some people and most
institutions acquire misplaced confidence in making decisions about
“the way forwards”, usually invoking some new tool or device as a
solution to the problem of dealing with ambiguity (right now, it’s
blockchain and big data). We - and particularly our institutions -
remain allergic to uncertainty. To what extent is “data-ism” a
reaction to the confusion produced by technology? Von Foerster sounded
the alarm in the 1970s:

“we have, hopefully only temporarily, relinquished our responsibility
to ask for a technology that will solve existent problems. Instead we
have allowed existent technology to create problems it can solve.” (in
Von Foerster, H (1981) "Observing Systems")

With every technical advance, there is an institutional reaction. The
Catholic church reacted to printing; Universities reacted to the
microscope and other empirical apparatus; political institutions
reacted to the steam engine, and so on. Today it is the institution of
science itself which reacts to the uncertainty it finds itself in. In
each case, technology introduces new options for doing things, and the
increased uncertainty of choice between an increased number of options
means that an attenuative process must ensue as the institution seeks
to preserve its identity. Technology in modern universities is a
particularly powerful example: what a stupid use of technology to
reproduce the ancient practices of the “classroom” online?! How
ridiculous in an age of self-publishing that academic journals seek to
use technology to maintain the “scarcity” (and cost) of their
publications through paywalls? And what is it about machine learning
and big data (I'm struggling with this in a project I'm doing at the
moment - the machine learning thing is not all it's cracked up to be!)

Judgement and decision are at the heart of this. Technologies do not
make people redundant: it is the decisions of leaders of companies and
institutions who do that. Technology does not poison the planet;
again, that process results from ineffective global political
decisions. Technology also sits in the context for decision-making,
and as Cohen and March pointed out in 1971, the process of
decision-making about technology is anything but rational (see “The
Garbage Can Model of Organisational Decision-making”
https://www.jstor.org/stable/2392088). Today we see “Blockchain” and
“big data” in Cohen and March’s Garbage can. It is the reached-for
"existent technology which creates problems it can solve".

My colleague Peter Rowlands, who some of you know, puts the blame on
our current way of thinking in science: most scientific methodologies
are "synthetic" - they attempt to amalgamate existing theory and
manifest phenomena into a coherent whole. Peter's view is that an
analytic approach is required, which thinks back to originating
mechanisms. Of course, our current institutions of science make such
analytical approaches very difficult, with few journals prepared to
publish the work. That's because they are struggling to manage their
own uncertainty.

So I want to ask a deeper question: Effective science and effective
decision-making go hand-in-hand. What does an effective society
operating in a highly ambiguous and technologically abundant
environment look like? How does it use its technology for effective
decision-making? My betting is it doesn’t look anything like what
we’ve currently got!

Best wishes,

Mark

On 6 March 2018 at 20:23, Alberto J. Schuhmacher  wrote:
> Dear FIS Colleagues,
>
> I very much appreciate this opportunity to discuss with all of you.
>
> My mentors and science teachers taught me that Science had a method, rules
> and procedures that should be followed and pursued rigorously and with
> perseverance. The scientific research needed to be preceded by one or
> several hypotheses that should be subjected to validation or refutation
> through experiments designed and carried out in a laboratory. The 

Re: [Fis] Welcome to Knowledge Market and the FIS Sci-coins

[Loet Leydesdorff]

Dear Krassimir and colleagues,

Our mental model can entertain discursive models reflexively. Thus, our 
models are (at least partly) discursively mediated and hence the result 
of communication. The development of discursive knowledge is thus 
liberated from biologically given constraints; it has a dynamic of its 
own. This is the source of progress in a knowledge-based economy. The 
models are evolving, whereas we are essentially the same.


When Julius Caesar said "veni, vidi, vici" he entertained a mental 
model, but he could not understand gravity. The history of mankind is 
driven from the next-order level and not by its genesis.


Best,
Loet


Loet Leydesdorff

Professor emeritus, University of Amsterdam
Amsterdam School of Communication Research (ASCoR)

l...@leydesdorff.net ; 
http://www.leydesdorff.net/
Associate Faculty, SPRU, University of 
Sussex;


Guest Professor Zhejiang Univ. , 
Hangzhou; Visiting Professor, ISTIC, 
Beijing;


Visiting Fellow, Birkbeck , University of London;

http://scholar.google.com/citations?user=ych9gNYJ=en


-- Original Message --
From: "Krassimir Markov" 
To: "FIS" 
Sent: 3/11/2018 11:34:12 PM
Subject: [Fis] Welcome to Knowledge Market and the FIS Sci-coins




Dear Colleagues,



This letter contains more than one theme, so it is structured as 
follow:


- next step in “mental model” explanation;

- about “Knowledge market”, FIS letters’ sequences and FIS Sci-coins.



1. The next step in “mental model” explanation:



Let remember shortly my letter from 05.03.2018.



To avoid misunderstandings with concepts Subject, agent, animal, human, 
society, humanity, living creatures, etc., in [1] we use the abstract 
concept “INFOS” to denote every of them as well as all of artificial 
creatures which has features similar to the former ones.




Infos has possibility to reflect the reality via receptors and to 
operate with received reflections in its memory. The opposite is 
possible - via effectors Infos has possibility to realize in reality 
some of its (self-) reflections from its consciousness.




The commutative diagram on Figure 1 represents modeling relations. In 
the frame of diagram:


- in reality: real models: s is a model of r,

- in consciousness: mental models: si is a mental model of ri;

- between reality and consciousness: perceiving data and creating 
mental models:  triple (si, ei, ri) is a mental model of triple (s, e, 
r).




It is easy to imagine the case when the Infos realizes its reflections 
using its effectors, i.e. relation between consciousness and reality: 
realizing mental models and creating data. In this case the receptors’ 
arrows should be replaces by opposite effectors’ arrows. In this case 
triple (s, e, r) is a realization of the mental model (si, ei, ri).







Figure 1





After creating the mental model it may be reflected by other levels of 
consciousness. In literature several such levels are described. For 
instance, in [2], six levels are separated for humans (Figure 2). The 
complexity of Infos determines the levels. For instance, for societies 
the levels are much more, for animals with no neo-cortex the levels a 
less.













Figure 2.   [2]



This means that the mental models are on different consciousness levels 
and different types (for instance - touch, audition, vision).




In [2], Jeff Hawkins had remarked: “The transformation— from fast 
changing to slow changing and from spatially specific to spatially 
invariant— is well documented for vision. And although there is a 
smaller body of evidence to prove it, many neuroscientists believe 
you'd find the same thing happening in all the sensory areas of your 
cortex, not just in vision” [2].




As it is shown on Figure 2 mental models are in very large range from 
spatially specific to spatially invariant; from fast changing to slow 
changing; from “features” and “details” to objects”.


To be continued...



2.Aabout “Knowledge market”, FIS letters’ sequences and FIS Sci-coins.



The block-chain idea is not new. All forums and mailing lists have the 
possibility to organize incoming messages in internally connected 
sequences. The new is the Bit-coin, i.e. the price for including a 
message in the sequence received after successful solving a difficult 
task.




What we have in FIS are letters’ sequences already created for many 
years. What is needed to start using them is to be strictly when we 
answer to any letter not to change the “Subject” of the letter. The 
list archive may help us to follow the sequences - only what is needed 
to ask sorting by [ Subject ] 
. We 
may sort by [ Thread ]