Dear Terrence and colleagues, 


I agree that we should not be fundamentalistic about “information”. For 
example, one can also use “uncertainty” as an alternative word to Shannon-type 
“information”. One can also make distinctions other than 
semantic/syntactic/pragmatic, such as biological information, etc.


Nevertheless, what makes this list to a common platform, in my opinion, is our 
interest in the differences and similarities in the background of these 
different notions of information. In my opinion, the status of Shannon’s 
mathematical theory of information is different  from special theories of 
information (e.g., biological ones) since the formal theory enables us to 
translate between these latter theories. The translations are heuristically 
important: they enable us to import metaphors from other backgrounds (e.g., 


For example, one of us communicated with me why I was completely wrong, and 
made the argument with reference to Kullback-Leibler divergence between two 
probability distributions. Since we probably will not have “a general theory” 
of information, the apparatus in which information is formally and 
operationally defined—Bar-Hillel once called it “information calculus”—can 
carry this interdisciplinary function with precision and rigor. Otherwise, we 
can only be respectful of each other’s research traditions. J


I wish you all a splendid 2017,




Loet Leydesdorff 

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

 <> ;  
Associate Faculty,  <> SPRU, University of Sussex; 

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

Visiting Professor,  <> Birkbeck, University of London; 



From: Fis [] On Behalf Of Terrence W. DEACON
Sent: Thursday, December 22, 2016 5:33 AM
To: fis
Subject: Re: [Fis] What is information? and What is life?


Against information fundamentalism


Rather than fighting over THE definition of information, I suggest that we 
stand back from the polemics for a moment and recognize that the term is being 
used in often quite incompatible ways in different domains, and that there may 
be value in paying attention to the advantages and costs of each. To ignore 
these differences, to fail to explore the links and dependencies between them, 
and to be indifferent to the different use values gained or sacrificed by each, 
I believe that we end up undermining the very enterprise we claim to be 


We currently lack broadly accepted terms to unambiguously distinguish these 
divergent uses and, even worse, we lack a theoretical framework for 
understanding their relationships to one another.

So provisionally I would argue that we at least need to distinguish three 
hierarchically related uses of the concept:


1. Physical information: Information as intrinsically measurable medium 
properties with respect to their capacity to support 2 or 3 irrespective of any 
specific instantiation of 2 or 3.


2. Referential information: information as a non-intrinsic relation to 
something other than medium properties (1) that a given medium can provide 
(i.e. reference or content) irrespective of any specific instantiation of 3.


3. Normative information: Information as the use value provided by a given 
referential relation (2) with respect to an end-directed dynamic that is 
susceptible to contextual factors that are not directly accessible (i.e. 
functional value or significance).


Unfortunately, because of the history of using the same term in an unmodified 
way in each relevant domain irrespective of the others there are often 
pointless arguments of a purely definitional nature.


In linguistic theory an analogous three-part hierarchic partitioning of theory 
IS widely accepted. 


1. syntax

2. semantics

3. pragmatics


Thus by analogy some have proposed the distinction between


1. syntactic information (aka Shannon)

2. semantic information (aka meaning)

3. pragmatic information (aka useful information)


This has also often been applied to the philosophy of information (e.g. see The 
Stanford Dictionary of Philosophy entry for ‘information’). Unfortunately, the 
language-centric framing of this distinction can be somewhat misleading. The 
metaphoric extension of the terms ‘syntax’ and ‘semantics’ to apply to iconic 
(e.g. pictorial) or indexical (e.g. correlational) forms of communication 
exerts a subtle procrustean influence that obscures their naturalistic and 
nondigital features. This language bias is also often introduced with the term 
‘meaning’ because of its linguistic connotations (i.e. does a sneeze have a 
meaning? Not in any standard sense. But it provides information “about” the 
state of person who sneezed.)


So as a first rough terminological distinction I propose using


1. physical information (or perhaps information1)

2. referential information (information2)

3. normative information (information3)


to avoid definitional equivocation and the loss of referential clarity.


I would argue that we use the term ‘information’ in a prescinded way in both 1 
and 2. That is, considered from the perspective of a potential interpretation 
(3) we can bracket consideration of any particular interpretation to assess the 
possible relational properties that are available to provide reference (2); and 
we can bracket both 3 and 2 to only consider the medium/signal properties 
minimally available for 2 and 3 irrespective of using them for these purposes.*


Although 2 and 3 are not quantifiable in the same sense that 1 is, neither are 
they unconstrained or merely subjective. The possible referential content of a 
given medium or sign vehicle is constrained by the physical properties of the 
medium and its relationship to its physical context. Normative information 
captures the way that referential content can be correct or incorrect, accurate 
or inaccurate, useful or useless, etc., depending on the requirements of the 
interpretive system and its relation to the context. In both cases there are 
specific unambiguously identifiable constraints on reference and normative 


There has been a prejudice in favor of 1 because of the (mistaken) view that 2 
and three are in some deep sense nonphysical and subjective. Consistent with 
this view, there have been many efforts to find a way to reduce 2 and 3 to some 
expression of 1. Although it is often remarked that introducing non reduced 
concepts of referential content (2) and normative evaluation (3) into the 
theory of information risks introducing non quantifiable (and by assumption non 
scientific) attributes, I think that this is more a prejudice than a principle 
that has been rigorously demonstrated. Even if there is currently no widely 
accepted non reductionistic formalization of reference and significance within 
the information sciences this is not evidence that it cannot be achieved. One 
thing is clear, however, until we find a way to use the term ‘information’ in a 
way that does not privilege one of these uses over the others and unequivocally 
distinguishes each and their relationships to one another, the debates we 
engage in on this forum will remain interminable.


So I suggest that we commence a discussion of how best to accomplish this 
terminological brush-clearing before further debating the relevance of 
information to physics, logic, biology, or art. I apologize if this is already 
accepted as “solved” by some readers, and would be glad to receive and share 
your different taxonomies and learn of how they are justified.


— Terry


* Stan Salthe might organize them in a subsumptive hierarchy.





On Tue, Dec 20, 2016 at 4:19 PM, Mark Johnson <> wrote:

Dear all,


It's important that one should remain practical. Shannon's formulae are 
practical. The correspondence with certain tenets of cybernetics such as 
Ashby's Law, or Maturana's "Structural Coupling" presents Shannon as a window 
for exploring *relations* empirically. This I understand to be Bob Ulanowicz's 
focus too. I think Ashby's epistemology which accompanied his championing of 
Shannon (and which seems to me to be quite radical) is worth a much deeper 
exploration (it was eclipsed by second-order cybernetics in the early 70s). 
Klaus Krippendorff wrote an excellent paper about this here: 


Information theory is counting - but it provides a way of measuring relations, 
which I think marks it out as distinct from other statistical techniques such 
as variance. It also provides the basis for questioning what we actually mean 
by counting in the first place: you might call it "critical counting". For 
example, Ashby makes the comment about "analogy" (a key concept if we are to 
say that one thing is the same class as another when we count them together)... 
(apologies because I can't find the reference to this right now, but will send 
if anyone is interested):


"The principle of analogy is founded upon the assumption that a degree of 
likeness between two objects in respect of their known qualities is some reason 
for expecting a degree of likeness between them in respect of their unknown 
qualities also, and that the probability with which unascertained similarities 
are to be expected depends upon the amount of likeness already known."


Also, just to correct a possible misconception: I don't think counting leads to 
populism. Econometrics has led to populism. Some of the greatest economists of 
the 20th century saw the problem - this is why Keynes wrote a book on 
probability, and Hayek wrote extensively criticising mathematical modelling. In 
the end, I'm afraid, it's an American problem which goes back to McCarthy and 
the distrust of criticality in the social sciences in favour of positivist 
mathematical "objectivist" approaches. Those schools in the US which championed 
mathematical approaches (Chicago, etc) got all the funding, controlled the 
journals, whilst others were starved. The legacy from the 1950s is still with 
us: it's still very hard to get an economics paper published unless it's got 
crazy equations in it. In the end, it's just bad theory - and bad mathematics.


We could well see a similar thing happen with climate science in the next four 


Best wishes,







On 20 December 2016 at 19:55, Bob Logan <> wrote:

Loet - thanks for the mention of our (Kauffman, Logan et al) definition our 
definition of information which is a qualitative description of information. As 
to whether one can measure information with our description, my response is no 
but I am not sure that one can measure information at all. What units would one 
use to measure information? E = mc 2 contains a lot of information but the 
amount of information depends on context. A McLuhan one-liner such as 'the 
medium is the message' also contains a lot of information even though it is 
only 5 words or 26 characters long. 


Hopefully I have provided some information but how much information is 
impossible to measure.








Robert K. Logan

Prof. Emeritus - Physics - U. of Toronto 

Fellow University of St. Michael's College

Chief Scientist - sLab at OCAD












On Dec 20, 2016, at 3:26 AM, Loet Leydesdorff <> wrote:


Dear colleagues, 


A distribution contains uncertainty that can be measured in terms of bits of 

Alternatively: the expected information content H of a probability distribution 
is .

H is further defined as probabilistic entropy using Gibb’s formulation of the 
entropy .


This definition of information is an operational definition. In my opinion, we 
do not need an essentialistic definition by answering the question of “what is 
information?” As the discussion on this list demonstrates, one does not easily 
agree on an essential answer; one can answer the question “how is information 
defined?” Information is not “something out there” which “exists” otherwise 
than as our construct.


Using essentialistic definitions, the discussion tends not to move forward. For 
example, Stuart Kauffman’s and Bob Logan’s (2007) definition of information “as 
natural selection assembling the very constraints on the release of energy that 
then constitutes work and the propagation of organization.” I asked several 
times what this means and how one can measure this information. Hitherto, I 
only obtained the answer that colleagues who disagree with me will be cited. J 
Another answer was that “counting” may lead to populism. J






Loet Leydesdorff 

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

 <> ;  
Associate Faculty,  <> SPRU, University of Sussex; 

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

Visiting Professor,  <> Birkbeck, University of London; 



From: Dick Stoute [ <>] 
Sent: Monday, December 19, 2016 12:48 PM
To:  <>
Cc: James Peters;  <>; Alex Hankey; FIS 
Subject: Re: [Fis] What is information? and What is life?




Please allow me to respond to Loet about the definition of information stated 


1. the definition of information as uncertainty is counter-intuitive 
("bizarre"); (p. 27)


I agree.  I struggled with this definition for a long time before realising 
that Shannon was really discussing "amount of information" or the number of 
bits needed to convey a message.  He was looking for a formula that would 
provide an accurate estimate of the number of bits needed to convey a message 
and realised that the amount of information (number of bits) needed to convey a 
message was dependent on the "amount" of uncertainty that had to be eliminated 
and so he equated these.  


It makes sense to do this, but we must distinguish between "amount of 
information" and "information".  For example, we can measure amount of water in 
liters, but this does not tell us what water is and likewise the measure we use 
for "amount of information" does not tell us what information is. We can, for 
example equate the amount of water needed to fill a container with the volume 
of the container, but we should not think that water is therefore identical to 
an empty volume.  Similarly we should not think that information is identical 
to uncertainty.


By equating the number of bits needed to convey a message with the "amount of 
uncertainty" that has to be eliminated Shannon, in effect, equated opposites so 
that he could get an estimate of the number of bits needed to eliminate the 
uncertainty.  We should not therefore consider that this equation establishes 
what information is. 





On 18 December 2016 at 15:05, Loet Leydesdorff < <>> wrote:

Dear James and colleagues, 


Weaver (1949) made two major remarks about his coauthor (Shannon)'s 


1. the definition of information as uncertainty is counter-intuitive 
("bizarre"); (p. 27)

2. "In particular, information must not be confused with meaning." (p. 8) 


The definition of information as relevant for a system of reference confuses 
information with "meaningful information" and thus sacrifices the surplus value 
of Shannon's counter-intuitive definition.


information observer


that integrates interactive processes such as 


physical interactions such photons stimulating the retina of the eye, 
human-machine interactions (this is the level that Shannon lives on), 
biological interaction such body temperature relative to touch ice or heat 
source, social interaction such as this forum started by Pedro, economic 
interaction such as the stock market, ... [Lerner, page 1].


We are in need of a theory of meaning. Otherwise, one cannot measure meaningful 
information. In a previous series of communications we discussed redundancy 
from this perspective.


Lerner introduces mathematical expectation E[Sap] (difference between of a 
priory entropy [sic] and a posteriori entropy), which is distinguished from the 
notion of relative information Iap (Learner, page 7).


) expresses in bits of information the information generated when the a priori 
distribution is turned into the a posteriori one . This follows within the 
Shannon framework without needing an observer. I use this equation, for 
example, in my 1995-book The Challenge of Scientometrics (Chapters 8 and 9), 
with a reference to Theil (1972). The relative information is defined as the 


I agree that the intuitive notion of information is derived from the Latin 
“in-formare” (Varela, 1979). But most of us do no longer use “force” and “mass” 
in the intuitive (Aristotelian) sense. J The proliferation of the meanings of 
information if confused with “meaningful information” is indicative for an 
“index sui et falsi”, in my opinion. The repetitive discussion lames the 
progression at this list. It is “like asking whether a glass is half empty or 
half full” (Hayles, 1990, p. 59). 


This act of forming forming an information process results in the construction 
of an observer that is the owner [holder] of information.


The system of reference is then no longer the message, but the observer who 
provides meaning to the information (uncertainty). I agree that this is a 
selection process, but the variation first has to be specified independently 
(before it can be selected.


And Lerner introduces the threshold between objective and subjective observes 
(page 27).   This leads to a consideration selection and cooperation that 
includes entanglement.


I don’t see a direct relation between information and entanglement. An observer 
can be entangled.





PS. Pedro: Let me assume that this is my second posting in the week which ends 
tonight. L.


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