[Fis] Contingency signals: AI Information, Decision and Learning

[Krassimir Markov]

Dear Mark and Bill,

I would like to talk about the information later; I am currently seriously
busy with two large projects that are taking up a lot of my time.

***

Dear Mark,

Thank you for sharing your paper. I found your approach to indicating the
degrees of contingency in decision-making very thought-provoking. The
emphasis on areas of uncertainty, rather than definitive answers, seems to
me to be a key step in designing meaningful human-machine collaboration.

Your perspective on contingency indicators also got me thinking about a few
possible extensions. One is organizational design: such indicators could be
used not only in learning environments but also in real-time dashboards,
helping leaders decide when to escalate issues to human debate and when
automated processes are sufficient. Another is the parallel with law and
politics, where “gray areas” of interpretation could benefit from such an
indication to highlight where human deliberation is needed. In pedagogy,
indicators of contingency could be embedded in simulations or case studies,
prompting learners to think explicitly about where uncertainty lies and how
to allocate expertise. Finally, your biological analogy is intriguing: if
cells themselves operate with internal and external indicators of
contingency, then perhaps management learning could take the form of a kind
of “organizational metabolism” where uncertainty is not a disadvantage but
a vital indicator of adaptation.

I will be very interested to continue to follow how these ideas might
connect cybernetics, information theory, and experimental learning design.

***

Dear Emanuel ,

I am sorry that you still feel offended that your article was not accepted
for publication in an ITHEA journal. I remember this case because then as
editor-in-chief I had to make the binding decision, as there was a serious
dispute between the author and the reviewers. Below I will remind you of my
opinion, which has not changed to this day. As can be seen from your
letter, the problem with the title that I mention below was fixed by you.
Your idea was published at the next IS4SI summit, so you achieve this goal.

However, my essential remark regarding the definition of the concept of
information remains valid to this day. Here is my opinion, which was the
basis for my decision not to accept your article.



1. The title of the article — “The brain is processing information, not
data: Does anybody knows about that?” — is problematic for a scientific
journal. Rhetorical questions and colloquial phrases (“Does anybody knows
about that?”) are not appropriate for academic publishing, where clarity,
precision, and neutrality are expected. The scientific title should
indicate the focus or hypothesis of the study. Here, the title resembles a
polemical statement rather than a research statement, which undermines the
credibility of the article.



2. The central definition – “Information is a linguistic description of
structures observable in a given data set” – is both original and limiting.

It correctly emphasizes that information is not identical with raw data and
that meaning derives from the structured description. This distinction is
valuable, especially when criticizing Shannon’s purely statistical concept.

At the same time, this definition has a number of limitations.

The definition reduces information to linguistic description, giving
precedence to textual representation. This is too narrow, since information
can be generated by images, sounds, gestures, and other nonverbal
modalities.

Human cognition is not entirely text-based. The left hemisphere of the
brain is indeed associated with language processing, but the right
hemisphere is crucial for visual, auditory, and spatial reasoning.
Information should therefore be understood as encompassing symbolic,
figurative, and embodied forms, not just linguistic ones.

Nonhuman intelligence further challenges the definition. Animals such as
chimpanzees, elephants, and dolphins demonstrate self-awareness (e.g.,
passing the mirror test) and solve complex problems without relying on
textual or linguistic systems. Their information processing is based on
perception, memory, and social interaction, but not on verbal language.



3. By equating information with linguistic description, the article risks
confusing semantic representation with information itself.

Information can be encoded in mathematical models, visual diagrams, musical
notation, or even tacit bodily knowledge.

Limiting information to text ignores multimodal cognition and the diversity
of representational systems across species.



4. A more comprehensive definition of information must recognize that it is
neither uniquely human nor uniquely linguistic; it is a property of
adaptive systems, biological and artificial.



While your article raised important concerns about the conflation of data
and information, its framework was weakened by an inappropriate title and
an overly narrow definition.

Insisting on linguistic description excludes vast areas of human and
nonhuman cognition. A richer conception of information would integrate
textual, figurative, and embodied forms, recognizing that meaning is not
limited to words but emerges in multiple representational modalities.

***

Dear Steve and Joseph,

I would like to comment on autopoiesis and autopoietic systems. But that
would be a relatively long text, which is better left in a separate letter.
Therefore, I will return to these concepts later, as well as to Steve's
article and book, which are interesting and debatable.



With respect,

Krassimir

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