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> Context • At present, we lack a common understanding of both the process of cognition in living organisms and the construction of knowledge in embodied, embedded cognizing agents in general, including future artifactual cogni-tive agents under development, such as cognitive robots and softbots. > Purpose • This paper aims to show how the info-computational approach (IC) can reinforce constructivist ideas about the nature of cognition and knowledge and, conversely, how constructivist insights (such as that the process of cognition is the process of life) can inspire new models of computing. > Method • The info-computational constructive framework is presented for the modeling of cognitive processes in cognizing agents. Parallels are drawn with other constructivist approaches to cognition and knowledge generation. We describe how cognition as a process of life itself functions based on info-computation and how the process of knowledge generation proceeds through interactions with the environment and among agents. > Results • Cognition and knowledge generation in a cognizing agent is understood as interaction with the world (potential information), which by processes of natural computation becomes actual information. That actual infor-mation after integration becomes knowledge for the agent. Heinz von Foerster is identified as a precursor of natural computing, in particular bio computing. > Implications • IC provides a framework for unified study of cognition in living organisms (from the simplest ones, such as bacteria, to the most complex ones) as well as in artifactual cogni-tive systems. > Constructivist content • It supports the constructivist view that knowledge is actively constructed by cognizing agents and shared in a process of social cognition. IC argues that this process can be modeled as info-computation. > Key words • Constructivism, info-computationalism, computing nature, morphological computing, self-organization, autopoiesis (12) (PDF) Why we need info-computational constructivism. Available from: [accessed Oct 07 2020].
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Info-computational Constructivism
and Cognition
Gordana Dodig-Crnkovic • Mälardalen University, Sweden • gordana.dodig-crnkovic/at/
> Context • At present, we lack a common understanding of both the process of cognition in living organisms and the
construction of knowledge in embodied, embedded cognizing agents in general, including future artifactual cogni-
tive agents under development, such as cognitive robots and softbots. > PurposeThis paper aims to show how the
info-computational approach (IC) can reinforce constructivist ideas about the nature of cognition and knowledge and,
conversely, how constructivist insights (such as that the process of cognition is the process of life) can inspire new
models of computing. > MethodThe info-computational constructive framework is presented for the modeling of
cognitive processes in cognizing agents. Parallels are drawn with other constructivist approaches to cognition and
knowledge generation. We describe how cognition as a process of life itself functions based on info-computation and
how the process of knowledge generation proceeds through interactions with the environment and among agents.
> Results • Cognition and knowledge generation in a cognizing agent is understood as interaction with the world
(potential information), which by processes of natural computation becomes actual information. That actual infor-
mation after integration becomes knowledge for the agent. Heinz von Foerster is identied as a precursor of natural
computing, in particular bio computing. > Implications IC provides a framework for unied study of cognition in
living organisms (from the simplest ones, such as bacteria, to the most complex ones) as well as in artifactual cogni-
tive systems. > Constructivist content • It supports the constructivist view that knowledge is actively constructed
by cognizing agents and shared in a process of social cognition. IC argues that this process can be modeled as info-
computation. > Key wordsConstructivism, info-computationalism, computing nature, morphological computing,
self-organization, autopoiesis.
1 Introduction
« 1 » Info-computationalism (IC) is a
variety of natural computationalism, which
understands the whole of nature as a com-
putational process. It asserts that, as living
organisms, we humans are cognizing agents
who construct knowledge through interac-
tions with their environment, processing
information within our cognitive apparatus
and through information communication
with other humans. erefore, the episte-
mology of info-computationalism is info-
computational constructivism, and it de-
scribes the ways agents process information
and generate new information that steadily
changes and evolves by natural computa-
« 2 » Processes of cognition, together
with other processes in the info-computa-
tional model of nature, are computational
processes. is is a generalized type of
computation, natural computation, which
is dened as information self-structuring.
Information is also a generalized concept
in the context of IC, and it is always agent-
dependent: information is a dierence (iden-
tied in the world) that makes a dierence
for an agent, to paraphrase Gregory Bateson
(1972). For dierent types of agents, the
same data input (where data are atoms of
information) will result in dierent infor-
mation. A light presents a source of energy
for a plant; for a human, the same light
enables navigation in the environment,
while it brings no information at all to a
bat, which is not sensitive to light. Hence
the same world for dierent agents appears
dierently. We want to understand mecha-
nisms that relate an agent with its environ-
ment as a source of information.
« 3 » e historical roots of info-com-
putational constructivism can be traced
back to cybernetics, which evolved through
three main periods, according to Umpleby
(2002): the rst period, engineering cyber-
netics, or rst order cybernetics spanned the
1950s to 1960s, and was dedicated to the
design of control systems and machines
to emulate human reasoning (in the sense
of Norbert Wiener); the second period,
biological cybernetics, or second-order cy-
bernetics, developed during the 1970s and
1980s, and was dominated by biology of
cognition and constructivist philosophy
(notably by Humberto Maturana, Heinz
von Foerster, and Ernst von Glasersfeld);
and the most recent, third period, social
cybernetics, or third order cybernetics, con-
cerns modeling of social systems (Niklas
Luhmann and Stuart Umpleby).
« 4 » During the engineering period,
the object of observation, the observed was
central. In the second phase, with research
in biology of cognition, the core interest
shied from what is observed to the ob-
server. In the third phase, the domain of
social cybernetics focus moved further to
models of groups of observers (Umpleby
2001, 2002). e achievements of the rst
period have been largely assimilated into
engineering, automation, robotics, arti-
cial intelligence (AI), articial life (ALife),
and related elds, while the second period
inuenced cognitive science and AI. e
third period is still under development,
labeled as, among other names, social cog-
nition, social computing or multi-agent
« 5 » Info-computational constructiv-
ism builds on insights gained in all three
phases of the development of cybernet-
ics, combined with results from AI, ALife,
theory of computation (especially from the
nascent eld of natural computation), sci-
ence of information, information physics,
neuroscience, bioinformatics, and more.
is article concentrates on connections
between IC and biology of cognition and
the constructivist approaches, with Mat-
urana, von Foerster, and von Glasersfeld
as the main representatives. Based on ar-
guments developed in my earlier work, I
will examine how info-computational con-
structivism relates to other constructivist
« 6 » In what follows, the next chapter
on “Natural information and Natural com-
putation” presents the basic tenets of IC.
It expounds two basic concepts of IC and
explains how they dier from common, ev-
eryday notions of information as a message
and computation as symbol-manipulation.
In the third chapter, I address information
and computation in cognizing agents, and
argue that IC provides a common frame-
work for biological and artifactual cogni-
tion. Chapter 4 addresses self-organization
and autopoiesis in relation to IC and the
construction of the reality for an agent. In
Chapter 5 I discuss several criticisms of
1 | e description of the conceptual frame-
work of info-computationalism can be found in
(Dodig-Crnkovic & Müller 2011; Dodig-Crnkov-
ic 2006, 2009). e relationship between natural
computing (such as biocomputing, DNA-com-
puting, social computing, quantum computing,
etc.) and the traditional Turing machine model
of computation is elaborated in (Dodig-Crnkovic
2010a, 2011a, 2011b, 2012). Construction/gen-
eration of knowledge within info-computational
framework is discussed in (Dodig-Crnkovic 2008,
2010b, 2010c).
2 Natural information and
natural computation
« 7 » In 1967, computer pioneer Konrad
Zuse was the rst to suggest that the physi-
cal behavior of the entire universe is being
computed on a basic level by the universe
itself, which he referred to as Rechnender
Raum [“Computing Space”] (Zuse 1969).
Consequently, Zuse was the rst pancompu-
tationalist, or natural computationalist, fol-
lowed by many others such as Ed Fredkin,
Stephen Wolfram and Seth Lloyd. Accord-
ing to the idea of natural computation, one
can view the dynamics of physical states in
nature as information processing. Such pro-
cesses include self-assembly, developmental
processes, gene regulation networks, gene
assembly in unicellular organisms, protein-
protein interaction networks, biological
transport networks, processes of individual
and social cognition, etc. (Dodig-Crnkovic
& Giovagnoli 2013; Zenil 2012).
« 8 » e traditional theoretical model
of computation corresponds to symbol
manipulation in a form of the Turing ma-
chine model. It is a theoretical device for
the execution of an algorithm. However, if
we want to model adequately natural com-
putation, including biological structures and
processes understood as embodied physical
information processing, highly interactive
and networked computing models beyond
Turing machines are needed, as argued in
(Dodig-Crnkovic 2011a; Dodig-Crnkovic &
Giovagnoli 2013). Besides physical, chemi-
cal, and biological processes in nature, there
are also concurrent computational devices
today (such as the Internet) for which the
Turing machine as a sequential model of
computation is not adequate (Sloman 1996;
Burgin 2005).2
« 9 » Physical processes observed in
nature and described as dierent forms of
natural computation can be understood as
morphological computing, i.e., computation
governed by underlying physical laws, lead-
ing to change and growth of form. e rst
ideas of morphological computing can be
2 | e Universal Turing Machine (UTM)
is sometimes thought of as a universal model of
computation. However, the UTM can only com-
pute what any other TM can compute, and no
found in Alan Turings work on morphogen-
esis (Turing 1952). Turing moved towards
exploration of natural forms of computing at
the end of his life, and his unorganized ma-
chines were forerunners of neural networks.
« 10 » Based on the same physical sub-
strate, dierent computations can be per-
formed and those can appear at dierent
levels of organization. at is how the same
conventional digital computer can run the
Windows operating system and, on top of
that, a Unix virtual machine. Each virtual
machine always relies on the basic physical
computation. Aaron Sloman (2002) devel-
oped interesting ideas about the computa-
tion of virtual machines and about the mind
as a virtual machine running on the brain
substrate. Computation observed in the
brain is based on the physical computation
of its molecules, cell organelles, cells, and
neural circuits, as neurons are organized
into ensembles/circuits that process specic
types of information (Purves, Augustine &
Fitzpatrick 2001).3
« 11 » e dierence between morpho-
logical computation and our conventional
computers (articial symbol manipulators
implemented in specic types of physical
systems and governed by an executing pro-
gram) is that morphological computation
takes place spontaneously in nature through
physical/chemical/biological processes. Our
conventional computers are designed to use
physical (fundamentally computational)
processes (intrinsic computation) to ma-
nipulate symbols (designed computation).
« 12 » e current understanding of
morphological computation on the neural
level is expressed in the following passage:
Neuroscience studies cell types, tissues, and or-
gans that ostensibly evolved to store, transmit, and
process information. at is, the behavior and or-
ganization of neural systems support computation
in the service of adaptation and intelligence.
(Crutcheld, Ditto & Sinha 2010: 037101-1)
3 | In his new research program, which ad-
dresses the evolution of organic forms, Sloman
(2013) goes a step further by studying meta-mor-
phogenesis, which is the morphogenesis of mor-
phogenesis – a way of thinking that is in the spirit
of second-order cybernetics, i.e., the cybernetics
of cybernetics.
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
e central question that arises from this is:
How are the intricate physical, biochemical,
and biological components structured and
coordinated to support natural, intrinsic neu-
ral computation? Currently, huge research
projects in Europe (Human Brain Project),
the USA (the BRAIN initiative, Allen Brain
Atlas), and Japan have been launched with
the aim of addressing this question.
« 13 » Von Foerster was an early repre-
sentative of natural computation through
his work at the Biological Computer Lab
at the University of Illinois between 1958
and 1975, where he studied ideas of self-
reference, feedback, and adaptive behavior
found in computational implementations of
second-order cybernetics (Asaro 2007). He
dierentiated between symbol manipula-
tion and physical computation, which is ev-
ident from his denition of computation, as:
any operation (not necessarily numerical) that
transforms, modies, rearranges, orders, and so
on, observed physical entities (‘objects’) or their
representations (‘symbols’). (Foerster 2003c:
« 14 » In IC, everything that exists for an
agent is interpreted as potential information
(see also the next chapter), while representa-
tions actualized in an agent are information-
al structures. If we compare von Foerster’s
above denition of computation with the
basic denition of computation used within
the IC approach:
Computation is information processing. (Bur-
gin 2010: xiii)
we see that information processing corre-
sponds to von Foerster’s operation on “‘ob-
jects,or their representations, ‘symbols’.In
the next chapter we will say more about this
connection between what are considered
“physical objects” and information.
« 15 » Von Foerster also emphasizes the
important dierence between his general
notion of computation and computation per-
formed by a conventional computer:
Computation takes place in the nervous sys-
tem. erefore, we can say the nervous system is
a computer or computing system. But this is cor-
rect only if one understands the general notion of
computation. (Segal 2001: 74)
Oen arguments have been made against
computational models of cognition, based
on the idea that cognitive processes are com-
putational in the conventional sense. Scheutz
(2002) argues against this misconception
and for the idea of new computationalism,
based on the general notion of computation.
« 16 » In order to specify the models
of computation that may be more gen-
eral in their information processing capa-
bilities than the Turing machine, IC adopts
Carl Hewitt’s Actor Model of computation
(Hewitt, Bishop & Steiger 1973; Hewitt
2010), as described in the following:
In the Actor Model, computation is conceived as
distributed in space, where computational devices
communicate asynchronously and the entire com-
putation is not in any well-dened state. (An Actor
can have information about other Actors that it has
received in a message about what it was like when
the message was sent.) Turings Model is a special
case of the Actor Model. (Hewitt 2012: 161, my
Hewitts “computational devices” are con-
ceived as computational agents – informa-
tional structures capable of acting on their
own behalf.
« 17 » Within the info-computational
framework, the denition of information is
adopted from informational structural real-
ism4 (Floridi 2003). According to this deni-
tion, for an agent, information is the fabric of
the universe. is denition may cause mis-
understandings and deserves clarication.
Information that is the fabric of the universe
is potential information before any interac-
tion with an (observing) agent. IC charac-
terizes this kind of potential information5
4 | “Informational structural realism” is a
variant of epistemic structural realism, which is
dened as follows: “(Epistemic) Structural realism
is oen characterized as the view that scientic
theories tell us only about the form or structure of
the unobservable world and not about its nature”
(Ladyman 2013). A constructivist reconciliation
with structural realism would see “realist struc-
ture” as the best available model. anks are given
to an anonymous reviewer for this observation.
5 | It would be more correct to say (poten-
tial) data. Von Foerster rightly pointed out that
computers do not process information but data.
However, this term is already widely used. Data
as proto information (or proto data). One
might insist that for information to actual-
ize, some agent must be there to relate to it.
An additional complication is that the terms
information and data (as atoms of informa-
tion) are used interchangeably. So the world
can be characterized either as a potential in-
formational structure or as a potential data
structure for an agent.
« 18 » e process of dynamical chang-
es of structures as (potential) information
makes the universe a huge computational
network where computation is information
processing. e computational universe is,
by its construction, necessarily both discrete
and continuous, and exists on both a sym-
bolic and a sub-symbolic level. Information
is structure, which exists either potentially
outside of the agent (as the structures of
its environment) or inside an agent (in the
agents own bodily structures, which contain
memories of previous experiences with its
environment). Messages are just a very spe-
cial kind of information that is exchanged
between communicating agents. ey can
be carried by chemical molecules, pictures,
sounds, written symbols or similar. An agent
can be as simple as a molecule (Matsuno &
Salthe 2011) or the simplest living organism
(a bacterium) (Ben-Jacob, Shapira & Tauber
« 19 » Physicists Anton Zeilinger (2005)
and Vlatko Vedral (2010) suggest the pos-
sibility of seeing information and reality as
one.6 is agrees with informational struc-
tural realism, which says that the world is
made of proto informational structures that
are atoms of information. Information is obtained
when data becomes integrated into structure
(correlated), which happens in the interaction
with a cognizing agent.
6 | is of course does not imply that poten-
tial information from the world moves intact into
an agent. is potential (proto) information is ac-
cessed by an agent through interactions and it is
processed by the agents’ cognitive apparatus. It is
dynamically integrated and linked to other infor-
mational structures (in the memory). What is im-
portant and new about this view from physicists
is that they do not talk about matter and energy
as the primary stu of the universe (which is tra-
ditionally objectivized within the sciences). ey
talk about information, thus returning an agent
into the picture of the world.
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
agents use to construct their own reality
through interactions with the world (Floridi
2008a, 2009; Sayre 1976). Reality for an agent
is thus informational and agent-dependent.
Being agent dependent and given that every
observer is also an agent, reality is observer-
« 20 » Reality for an agent consists of
structural objects (informational structures,
data structures) with computational dynam-
ics (information processes) that are adjusted
to the shared reality of the agent’s communi-
ty of practice. is brings together the meta-
physical view of Wiener (according to whom
“information is information, not matter or
energy”) and John Wheeler (“it from bit”)8
with the view of natural computation shared
by others such as Zuse, Fredkin, Lloyd, and
Wolfram (Dodig-Crnkovic & Giovagnoli
« 21 » e world as proto information
presents the potential form of existence cor-
responding to Immanuel Kant’s Ding an sich
(thing in itself).9 at proto information be-
7 | is does not imply that reality is subjec-
tive. Observers form shared reality through pro-
cesses of social cognition, as explained in third-
order cybernetics (Johannessen & Hauan 1994).
8 | “Wheeler (1990) has suggested that infor-
mation is fundamental to the physics of the uni-
verse. According to this ‘it from bit’ doctrine, the
laws of physics can be cast in terms of informa-
tion” (Chalmers 1995: 215).
9 | Here I interpret Kant not as saying that
noumenon is something abstract without proper-
ties, but as saying it is something innitely rich
that we learn successively more and more about
(the way we can reach the world through interac-
tions – our senses and our reasoning). e world
comes information, “a dierence that makes
a dierence,” (Bateson 1972) for a cognizing
agent in a process of interaction.
« 22 » Besides Gregory Bateson’s de-
nition, there is a more general denition of
information by Carl Hewitt that makes the
fact that information is relational even more
explicit, and subsumes Batesons denition:
Information expresses the fact that a system is in
a certain conguration that is correlated to the con-
guration of another system. Any physical system
may contain information about another physical
system. (Hewitt 2007: 293, my emphasis)
« 23 » Combining Bateson and Hewitt’s
insights, on a basic level we can state:
Information is the dierence in one physi-
cal system that makes a dierence in another
physical system.
« 24 » e reality for an observer is in-
formational and information is relational:
A messages eect on a receiver can be con-
strued to include its capacity to cause a functional
or adaptive response in an organism. (Terzis &
Arp 2011: xviii)
“e receiver of information can be so-called only
if it can relate what is received to what was emit-
ted.” (ibid: 63)
exists, but changes as we interact with it. It is dif-
ferent in itself (unperturbed) and in interaction
with us. In quantum mechanics, in chaos theory,
and in relativity we see what it means. We interact
and disturb the physical system; we send a probe
and wave function collapses, chaotic system
changes regime.
What is called message or information can
be as simple as, for example, an electron that
is a dierence that makes a dierence in the
receiver molecule.
« 25 » is relational character of in-
formation has profound consequences for
epistemology and relates to ideas of a par-
ticipatory universe (Wheeler 1990) and
endophysics (Rössler 1998), with observer-
dependent knowledge production as un-
derstood in second-order cybernetics. All
information exists in relation to an observer,
or for an agent. In the words of von Foerster,
observer-dependence is described as the tru-
ism that an observation implies one who ob-
(i) Observations are not absolute but relative
to an observer’s point of view (i.e., his coordinate
system: Einstein); (ii) Observations aect the
observed so as to obliterate the observer’s hope
for prediction (i.e., his uncertainty is absolute:
Heisenberg) […] What we need now is the de-
scription of the ‘describer’ or, in other words, we
need a theory of the observer. (Foerster 2003b:
« 26 » Even though there are attempts to
dene the observer, especially in the theory
of measurement in quantum mechanics, the
common understanding of the central im-
portance of observer dependence in cog-
nition and knowledge production is still
missing. It is interesting to notice that infor-
mation-based accounts of quantum mechan-
ics emphasize the necessity of explicating the
observer, as earlier expressed by physicists
Niels Bohr and Wolfgang Pauli of the Co-
penhagen School of quantum mechanics. In
the words of Christopher Fuchs:
is professor of Computer Science at Mälardalen University, Sweden. Her research interests include
computing paradigms, natural computing, social computing and social cognition, info-computational
models, foundations of information, computational knowledge generation, computational aspects of
intelligence and cognition, theory of science/philosophy of science, computing and philosophy, and
ethics (ethics of computing, information ethics, roboethics, and engineering ethics). The most recent
events she has organized are the symposia “Natural/Unconventional Computing and its Philosophical
Significance” (co-organized with Raffaela Giovagnoli), “Social Computing – Social Cognition – Social
Networks and Multi-agent Systems” (co-organized with Judith Simon), both at the AISB/IACAP World
Congress 2012 and the Symposium “Representation and Reality: Humans, Animals and Machines at
AISB 2014.” She is the author of more than eighty international journal and conference publications.
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
e world in some very real sense is a construct
and creation of thinking beings simply because
its properties are so severely tied to the particular
questions we ask of it. But on the other hand, the
world is not completely unreal as a result of this; we
generally cannot control the outcomes of our mea-
surements. (Fuchs 2011: 151)
« 27 » Among new information-based
quantum theories, QBism takes an observer
into account when modeling physical system,
based on the Bayesian approach to probabili-
ties. ey argue that “the distinction between
classical and quantum probabilities lies not
in their denition, but in the nature of the
information they encode” (Caves, Fuchs &
Schack 2002). It is instructive to see how new
epistemic ideas take form in quantum phys-
ics based on quantum information theory. In
the years to come we can expect interesting
discussions in terms of realism vs. antireal-
ism and the role of observer in the quantum
physical realm – discussions for which ideas
of constructivism are highly relevant.
3 Information, computation,
and cognition
« 28 » e advantage of computational
approaches is their testability. Cognitive
robotics research, for example, presents
us with a sort of laboratory where our un-
derstanding of cognition can be tested in a
rigorous manner. From cognitive robotics it
is becoming evident that cognition and intel-
ligence (and especially learning) are closely
related to agency (ability to act and explore
the environment) (Pfeifer & Bongard 2006;
Pfeifer & Gomez 2009). Anticipation, plan-
ning, and control are essential features of
intelligent agency. Studies by Pfeifer et al.
show that there is a similarity between the
generation of behavior in living organisms
and the formation of control sequences in
articial systems (Pfeifer & Bongard 2006;
Pfeifer, Lungarella & Iida 2007).
« 29 » Information produced from sen-
sory data processed by an agent is a result of
the process of perception. From the point of
view of data processing, perception can be
seen as an interface10 between the proto in-
10 | It is important to note that this “inter-
face” is a complex program that transforms and
formation in the environment and an agents
behavior in the environment. is interface
is an information-processing device, which
means that information input for an agent
gets restructured and integrated with the ex-
isting information (memory). Perception is
agent-dependent. is is illustrated by Don-
ald Homans critique of the view of percep-
tion as a true picture of the world:
[O]ur perceptions constitute a species-specic
user interface that guides behavior in a niche. Just
as the icons of a PC’s interface hide the complex-
ity of the computer, so our perceptions usefully
hide the complexity of the world, and guide adap-
tive behavior. is interface theory of perception
oers a framework, motivated by evolution, to
guide research in object categorization. (Ho-
man 2009: 148)
« 30 » us, perception produces inter-
related informational structures that con-
nect inside cognitive informational struc-
tures with outside informational structures
through dynamic information processing.
Cognition cannot be decoupled from the
other side of the interface (the environ-
ment) and isolated inside an agent and its
brain. Patterns of potential information
(potential data) are both in the world and
in the structures of the agent, which are
connected through dynamical processes of
self-structuring (self-organization) of infor-
mation. e computational mechanism of
self-structuring of information is presented
in Bonsignorio (2013), Lungarella & Sporns
(2005), and Dodig-Crnkovic (2012).
« 31 » Perception has co-evolved with
the sensorimotor skills of an organism.
e enactive approach to perception (Noë
re-structures information entering an agent’s sen-
sory apparatus and gets connected with the rest of
cognitive architecture. It is not just direct one-to
one mapping. e claim that patterns of informa-
tion must be on both sides of the “interface” comes
from enactivism. I should make it clear that this
“interfaceis an active one. In computing it is a
program that rearranges input information so that
it can be accepted on the other side of the inter-
face. It is absolutely not an identity relation. In oth-
er words, this is simply the statement that an agent
and its niche, or environment, mutually form each
other. If we adopt structuralism, then structures
are on both sides and they aect each other.
2004) emphasizes the role of sensorimotor
abilities, which can be connected with the
changing informational interface between
an agent and the world, increasing informa-
tion exchange. e enactivism of Francisco
Varela, Evan ompson, and Eleanor Rosch
(1991) underlines that cognizing agents self-
organize through interaction with their envi-
ronment. It is an approach closely related to
situated cognition and embodied cognition,
and is supported by the current research in
robotics (Pfeifer & Bongard 2006; Pfeifer,
Lungarella & Iida 2007; Pfeifer & Gomez
« 32 » Traditionally, symbolic AI was an
attempt to model cognition and intelligence
as symbol manipulation, which turned out
to be insucient (Clark 1989). In order to
improve and complement symbolic ap-
proaches to animal cognition, Paul Smolen-
sky proposed the mechanism of an intuitive
processor inaccessible to symbolic intuition
as a program for a conscious rule interpreter
and basis for
all of animal behavior and a huge proportion
of human behavior: Perception, practiced mo-
tor behavior, uent linguistic behavior, intuition
in problem-solving and game-playing – in short,
practically all skilled performance. (Smolensky
1988: 5)
is non-symbolic processor is a neural net-
work type of computation.
« 33 » In natural computation, cognition
and knowledge are studied as natural process-
es in biological agents. at is the main idea
of naturalized epistemology (Harms 2006),
where the subject matter is not our concept
of knowledge (or how we talk and reason
about knowledge), but knowledge as physi-
cally existing in a cognizing agent as specic
biological informational structures.11
« 34 » We know little about the origin of
knowledge in rst living agents, and the still
dominant idea is that knowledge is possessed
only by humans. However, there are dierent
types of knowledge and we have good rea-
sons to ascribe “knowledge how” and even
simpler kinds of “knowledge that” to other
living beings. Plants can be said to possess
memory (in their bodily structures) and the
11 | Maturana (1970) was the rst to suggest
that knowledge is a biological phenomenon.
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
ability to learn (adapt, change their morphol-
ogy) and can be argued to possess rudimen-
tary forms of knowledge.12 In his book An-
ticipatory Systems, Robert Rosen claims:
I cast about for possible biological instances
of control of behavior through the utilization of
predictive models. To my astonishment I found
them everywhere […] the tree possesses a model,
which anticipates low temperature on the basis of
shortening days. (Rosen 1985: 7)
« 35 » Karl Popper ascribes the ability to
know to all living things:
Obviously, in the biological and evolutionary
sense in which I speak of knowledge, not only
animals and men have expectations and therefore
(unconscious) knowledge, but also plants; and,
indeed, all organisms. (Popper 1999: 61)
And similarly Maturana and Varela:
Living systems are cognitive systems, and living
as a process is a process of cognition. is state-
ment is valid for all organisms, with or without a
nervous system. (Maturana & Varela 1980: 13)
« 36 » e above understanding of cog-
nition is adopted by info-computational
constructivism as it provides a notion of
cognition in degrees, which bridges from
human-level cognition to minimal cogni-
tion in the simplest biological forms and
intelligent machines. For a cognizing agent,
information is meaningful data, which can
be turned into knowledge by interactive
computational process. Information is al-
ways embedded in a physical substrate: sig-
nal, molecule, particle or event (Landauer
1991), which will induce a change in a struc-
ture or a behavior of an agent. For IC this is
important: we must know how to construct
cognitive articial agents that are able to
function adequately in their environment,
so we must know how to treat information
acquired, stored, processed or used by an
« 37 » e information-processing view
should be identied neither with classical
cognitive science, nor with the related no-
tions of input–output and symbolic repre-
12 | On the topic of plant cognition, see Gar-
zón (2012: 121–137).
sentations. It is important to recognize that
connectionist models are also computational
as they are based on information processing
(Scheutz 2002; Dodig-Crnkovic 2009; Clark
1989). e basis for the capacity to acquire
knowledge is in the specic morphology of
organisms that enables perception, memory,
and adequate information processing. at
morphology is a result of the evolution of
living organisms in the interaction with the
« 38 » William Harms (2004) proved a
theorem showing that under certain condi-
tions, by nature, the total amount of infor-
mation in the living system will always in-
crease, which will always lead a population
to accumulate information, and so to “learn
about its environment. Samir Okasha sum-
marizes Harms’ results:
any evolving population ‘learns’ about its envi-
ronment, in Harms’ sense, even if the population
is composed of organisms that lack minds entire-
ly, hence lack the ability to have representations
of the external world at all. (Okasha 2005: §10)
4 Construction of “reality”
as info-computation in
an agent via “structural
« 39 » In order to understand cogni-
tion and knowledge as a natural phenom-
enon, the process of re-construction of the
origins, development and present forms and
existence of life, processes of evolution, and
development based on self-organization are
central. e work of Maturana and Varela
on the constructivist understanding of life
is of fundamental importance. ey dene
autopoiesis as a network of processes in the
“autopoietic machine” (a unity in space con-
stituted by its components) that govern pro-
duction, transformation, and destruction of
those components and so enable incessant
regeneration and maintenance of the au-
topoietic machine as a whole (Maturana &
Varela 1980: 78). Or, in the words of Milan
A cell produces cell-forming molecules, an or-
ganism keeps renewing its dening organs, a social
group ‘produces’ group-maintaining individuals,
etc. Such autopoietic systems are organization-
ally closed and structurally state-determined…
(Zeleny 1977: 13)
« 40 » What does it mean that an au-
topoietic system is organizationally closed?
It means that it conserves its organization.
However, this applies only to the snapshot of
an organisms inner operation. In the course
of evolution, organisms change their struc-
ture through interactions with the environ-
ment and successively, as they evolve, even
change their organization. In other words,
organisms tend to preserve their organiza-
tion, but that organization evolves on an evo-
lutionary time scale. at is what Maturana
calls “ontogenic structural dri” (Maturana
2002: 17).
« 41 » e information-processing pic-
ture of organisms incorporates basic ideas
of autopoiesis and life, from the sub-cellular
to the multi-cellular level. Being processes of
cognition, life processes are dierent sorts
of morphological computing that, on evolu-
tionary time scales, aect even the structures
and organization of living beings in the sense
of meta-morphogenesis. In Slomans work on
meta-morphogenesis, understood as evolu-
tion and development of information-pro-
cessing machinery,” he presents…
a rst-dra rudimentary theory of ‘meta-
morphogenesis’ that may one day show how, over
generations, interactions between changing envi-
ronments, changing animal morphologies, and
previously evolved information-processing ca-
pabilities might combine to produce increasingly
complex forms of ‘informed control,’ starting with
control of various kinds of physical behaviour,
then later also informed control of information
processing. (Sloman 2013: 849)
e ideas of morphogenesis and meta-
morphogenesis are attempts at computation-
al modeling of cognitive processes and their
evolution in all living organisms within the
same computational framework, where the
computational model is not a simple Turing
machine computing function but morpho-
logical computation that represents biologi-
cal information processing.
« 42 » In the process of living, interac-
tion between an organism and its environ-
ment is a source of new information that
results in learning and adaptation. Maturana
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
explains the relationship between an auto-
poietic system and its environment in the
I have called the dynamics of congruent struc-
tural changes that take place spontaneously be-
tween systems in recurrent (in fact recursive)
interactions, as well as the coherent structural
dynamics that result, structural coupling. (Mat-
urana 2002:16)
« 43 » Autopoietic processes with struc-
tural coupling can be described within the
IC model as changes of structures in the bio-
logical system resulting from the exchange
of information with the environment and
thus the information processing patterns in
a self-reective, recursive computation. Self-
organization with natural selection of organ-
isms is responsible for information that liv-
ing systems have built up in their genotypes
and phenotypes, as a simple but costly meth-
od to develop knowledge capacities. Higher
organisms (which are “more expensive” to
evolve13) have grown learning and reasoning
capabilities as a more ecient way to accu-
mulate knowledge. e step from “genetic +
epigenetic learning” (typical of more primi-
tive forms of life, Ben-Jacob, Shapira & Tau-
ber 2006) to the acquisition of cognitive skills
on higher levels of organisation of the ner-
vous system (behavioral and symbolic) are
the next topic to explore in the project of cog-
nitive info-computation, following the ideas
of Eva Jablonka & Marion Lamb (2005), who
distinguish genetic, epigenetic, behavioral,
and symbolic evolution. A study of cogni-
tive skills of increasingly more complex or-
ganisms is the next project for naturalized
epistemology in terms of info-computational
constructivism. Understanding of the roots
and evolution of cognition are relevant for
cognitive robotics and cognitive computing,
which are also useful for implementing ideas
and testing hypotheses.
« 44 » As already mentioned, for Mat-
urana & Varela, the process of living is a pro-
cess of cognition. In the info-computational
formulation, life corresponds to information
13 | More expensive in this context means
that they take more time and other natural re-
sources to develop. A human compared to a
bacterium is considerably more expensive” to
processing in a hierarchy of levels of organi-
zation, from molecular networks, to cells and
their organizations, to organisms and their
networks/societies (Dodig-Crnkovic 2008).
In that way, fundamental-level proto infor-
mation (structural information) correspond-
ing to the physical structure, is a “raw mate-
rial” for cognition as a process of life with
variety of self-* properties in a living system:
self-reproduction, self-regeneration, self-
defense, self-control/self-regulation (plants);
self-movement/locomotion, and self-aware-
ness (animals); and self-consciousness
(humans).14 Survival, homeostasis, learning,
self-maintenance, and self-repair appear as
a product of evolution in complex biologi-
cal systems, which can be modeled compu-
tationally, as argued in (Dodig-Crnkovic &
Hoirchner 2011).
« 45 » Expressed in terms of von Foer-
ster’s notions of eigenvalues (stable struc-
tures) and eigenbehaviors (stable behaviors
established in the interaction with the envi-
Any system, cognitive or biological, which
is able to relate internally, self-organized, stable
structures (eigenvalues) to constant aspects of its
own interaction with an environment can be said
to observe eigenbehavior. Such systems are dened
as organizationally closed because their stable in-
ternal states can only be dened in terms of the
overall dynamic structure that supports them.
(Rocha 1998: 342)
« 46 » Even though organizationally
closed, living systems are informationally
open (Pask 1992). ey communicate and
form emergent representations of their en-
vironment through processes of informa-
tion self-organization. Rocha denes self-
organization as the “spontaneous formation
of well-organized structures, patterns, or
behaviors, from random initial conditions.
(Rocha 1998: 343). Learning, as a self-orga-
nized process, requires that the system “be
informationally open, that is, for it to be able
to classify its own interaction with an envi-
14 | Even though some plants possess the ca-
pacity for self-movement it is not a typical charac-
teristic of plants. Despite animals possessing de-
grees of self-consciousness/self-awareness (Allen
2010), it is commonly not considered as dominant
characteristic of animals.
ronment, it must be able to change its struc-
ture…” (ibid: 344).
« 47 » Observation is one of many possi-
ble ways of interacting with the environment,
and von Foresters’ notion of observation re-
ceives the following illuminating interpreta-
tion: “observables do not refer directly to real
world objects, but are instead the result of
an innite cascade of cognitive and sensory-
motor operations in some environment/
subject coupling” (Rocha 1998: 341). In prin-
ciple, those cascades are innite because of
self-reference, while in practice they succes-
sively die obecause of energy dissipation.
us von Foerster’s eigenvalues represent the
externally observable manifestations of cog-
nitive operations.
« 48 » Von Foerster’s insight that identi-
es the ability of an organization to classify
its environment as a consequence of forma-
tion of stable structures (eigenvalues) in the
dynamics of its organization, agrees with
current understanding of dynamic systems
(Smolensky & Legendre 2006; Crutcheld,
Ditto & Sinha 2010; Juarrero 2002). Dynami-
cal system theory establishes the connection
between the brain as a dynamical system
and the environment, while the details of the
connection of the body of an agent and the
environment are modeled as morphological
« 49 » Von Foerster’s view of observables
casts doubts on the belief that we humans
can directly interact with the “real world as
it is.” One of the reasons is that it takes time
for an agent to integrate information. Dana
Ballard explains:
Our seamless perception of the world depends
very much on the slow time scales used by con-
scious perception. Time scales longer than one
second are needed to assemble conscious expe-
rience. At time scales shorter than one second,
this seamlessness quickly deteriorates. Numerous
experiments reveal the fragmentary nature of the
visual information used to construct visual experi-
ence. (Ballard 2002: 54)
« 50 » Already Kant argued that “phe-
nomena,” or things as they appear to us,
and which constitute the world of common
experience, are an illusion. Consciousness
provides only a rough sense of what is go-
ing on in and around us, primarily what we
take to be essential for us. e world as it
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
appears for our consciousness is a sketchy
simulation, which is a computational con-
struction. e belief that we can ever experi-
ence the world “directly as it is” is an illu-
sion (Nørretranders 1999). We change the
world through interactions and, moreover,
the world is always more than we can ob-
serve and interpret in any given moment.
What would it mean anyway to experience
the world directly as it is,” without ourselves
being part of the process? Who would expe-
rience that world without us?
« 51 » e positivist optimism about
observations independent of the observer
proved problematic in many elds of physics
such as quantum mechanics (wave function
collapse aer interaction), relativity (speed-
dependent length contraction and time
dilatation) and chaos (a minor perturbation
caused by measurement sucient to switch
the system to a dierent attractor). In gen-
eral, the observer and the systems observed
are related (Foerster 2003a) and by under-
standing their relationship we can gain in-
sights into limitations and power of models
and simulations as knowledge generators.
e interaction of an agent with the envi-
ronment eliminates inadequate cognitive
models. Model construction thus proceeds
through variation and selection. is agrees
with von Glasersfelds second principle of
e function of cognition is adaptive and serves
the organization of the experiential world, not the
discovery of ontological reality. (Glasersfeld
1995b: 18)
5 Some criticisms
of the info-computational
approach to cognition and
their refutation
No information without humans?
« 52 » A typical criticism of the informa-
tional nature of reality originates from the be-
lief that the world without cognizing agents
would lose its content because there would be
no one to observe it. e view is that “if all the
humans in the world vanished tomorrow, all
the information would vanish, too.
« 53 » In response to this criticism, let
me point out that not only is information
physical (Landauer 1996), but the opposite
also holds: “things physical are reducible to
informationfor an agent. Quantum phys-
ics can be formulated in terms of informa-
tion for an agent (Chiribella, D’Ariano &
Perinotti 2012; Goyal 2012; Caves, Fuchs &
Schack 2002; Baeyer 2013). Physical reality
as information for an observer makes this
observer-dependence of the physical model
both explicit and natural.
« 54 » Clearly, if there are no cognizing
agents in the world, the world remains proto
information, das Ding an sich, and never
turns into actual information for an agent.
But, in the same way as the world does not
disappear when we close our eyes, it does
not disappear when we look back in his-
tory to when no living beings were present
to observe it. Moreover, given the fact that
there are cognitive agents besides humans,
living beings (animals, plants, microorgan-
isms, and even machines capable of cogni-
tive computing, i.e., processing information
and making sense of it), information for all
those agents continues to exist even if no hu-
man is present.
« 55 » It is not necessary for an agent to
be conscious on a human level in order to
make use of the world as proto information/
potential information. e fundamental in-
sight of Maturana and Varela that not every-
body has yet realized is that life in itself is a
cognitive process (Maturana & Varela 1992).
Metabolism is a basic aspect of cognition,
along with sensorimotor functions and im-
mune system processes. No nervous system
or free will is needed for the information pro-
cessing that goes on in all living organisms.
ose processes can be understood as com-
putational in the sense of natural computa-
tion (Ben-Jacob, Shapira & Tauber 2006).
« 56 » Koichiro Matsuno and Stanley
Salthe (2011) go one step further, in a search
of the origins of life, attributing material
agency and information processing ability
even to such simple systems as molecules.
We can apply Hewitt’s Actor Model to the
computation found in nature and say that
even elementary particles possess material
agency, as they are capable of acting on their
own. e step from material agency to life is
a big one, and goes via chemical computing
of more and more complex molecular struc-
tures, leading to the rst autopoietic systems
(Gánti 2003).
Can information bridge the
Cartesian gap?
« 57 » Søren Brier criticizes the idea of
information as used in IC, since in his view,
“it is not information that is transmitted
through the channel in Shannons theory,
but signals” (Brier 2013a: 242).
« 58 » As an answer to this criticism, I
refer to the work of Brian Skyrms (2010) and
Bateson (1972). It is possible that we should
see Bateson’s “dierences that make a dier-
ence” as data or signals even though they are
usually called information:
Kant argued long ago that this piece of chalk
contains a million potential facts (Tatsachen) but
that only a very few of these become truly facts
by aecting the behavior of entities capable of re-
sponding to facts. For Kant’s Tatsachen, I would
substitute dierences and point out that the number
of potential dierences in this chalk is innite but
that very few of them become eective dierences
(i.e., items of information) in the mental process of
any larger entity. Information consist of dierences
that make a dierence. (Bateson 1979: 110, my
« 59 » But those dierences, “items of
information or “atoms of information,
become information when they trigger an
agent’s inner structures and cause changes
in its informational networks. ose chang-
es may be relatively simple for relatively
simple living agents such as bacteria, while
they become more complex for increasingly
complex living organisms.
« 60 » Brier continues his critical ex-
amination of IC, which in his view does not
provide an account for…
how the processes of cognition and communi-
cation develop beyond their basis in the pertur-
bation of and between closed systems and into
a theory of feeling, awareness, qualia and mean-
ing. (Brier 2013a: 243)
is criticism may be applicable to some
computational approaches but not to IC
based on natural computation and the idea
of the world as proto informational structure.
Information is not only suitable for the fun-
damental reformulation of physics, but even,
as David Chalmers aptly noted, a natural can-
didate for a theory of consciousness bridging
that Cartesian gap between mind and matter:
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
Info-computational Constructivism and Cognition Gordana Dodig-Crnkovic
We are led to a conception of the world on
which information is truly fundamental, and on
which it has two basic aspects, corresponding to
the physical and the phenomenal features of the
world. (Chalmers 1995: 215)
« 61 » e phenomenal aspect of in-
formation (vs. physical aspect) can be in-
terpreted as a version of the endogenous
(within an agent) vs. exogenous (outside an
agent) aspect of cognition. Endogenous in-
formation constitutes what can be identied
as the agent itself/himself/herself and consti-
tutes the inner “subjective,” while exogenous
information corresponds to the network of
relationships with the outside world, which
denes the negotiated “intersubjective/ob-
jective.” is approach gets its natural for-
mulation if we chose the relational denition
of information proposed in the introduction.
« 62 » Von Foerster oers illuminating
analysis of “intersubjective/objective and
asks what is then objective about an object
seen as an eigenvalue or a result of a process
of classication in the dynamic structures
of a cognizing agent? His answer is inter-
subjectivity through other agents, when ei-
genbehaviors of one participant generate
(recursively) those for the other.” Meaning-
ful communication is a result of inter-sub-
jectivity. e gap between inner and outer is
bridged by information as the fabric of real-
ity for cognizing agents – both “objective
and “subjective” reality.
A third-person vs. first person
approach and emergence of
meaning for a cognizing agent
« 63 » Brier (2013a) discusses Den-
nett’s (1993) endeavor to explain subjective
consciousness and the qualia by explaining
“subjective” phenomena in “objective” terms
of “the objective, materialistic, third-person
world of the physical sciences” (Dennett
1978: 5). Brier’s argument is that this project
is not viable “since the language of physics
does not include the notion of agent (agen-
cy) and meaning.” (Brier 2013a: 242). is
would imply that any translation between
dierent levels of description is in principle
impossible. However, there are macroscopic
phenomena that can be explained by micro-
scopic physical theories in the language of
quantum mechanics, such as superconduc-
tivity, ferromagnetism, and atomic spectral
lines. e macroscopic phenomenon of heat
can be explained in terms of microscopic
kinematics of molecules. It is not necessary
that the same vocabulary be used at each
level of description. At the single-neuron
level there is no cognition. At dierent levels
of organization, dierent vocabularies are
appropriate. Vocabulary is not intrinsic to
the domain, but imposed by human observ-
ers who interact with it and also construct
connections between vocabularies.
« 64 » e way of interpreting Dennett’s
research program would be to equate objec-
tive with inter-subjective and material with
physical, which makes it agree with modern
cognitive science approaches, as presented,
for example, in Clark (1989). Physics has no
notion of meaning (more than the intrinsic
meaning of its own theory), but meaning
in living organisms emerges from physical
substrate. Information plays a role of estab-
lishing relations.
« 65 » Subjective experience has no spe-
cial privileged position in relation to other
types of cognition. It is by no means cog-
nitively superior and cannot replace third-
person understanding of that experience
(established socially). Subjective experience
is informational like all other aspects of re-
ality for an agent, and we have no reason to
believe that it is dierent from the rest of
cognitive processes.
« 66 » From neuroscience we learn
that processes of listening/hearing/seeing/
etc. all correspond to physical states of the
brain (von Foerster talks about eigenstates
with regard to perception). What happens
at the physical level in our body, at some
higher level of information processing, gets
observed as subjective experience. What
arrives as photons to our visual apparatus
causes processes that lead to dynamically
stable states in our brains (Foerster 2003b;
Juarrero 1999). ose processes in our phys-
ical body give us subjective experience of the
world. Without the third-person insight we
would not be able to share the knowledge
about the existence of other rst-person ex-
periences. To base a research program on a
third-person perspective, inter-subjective
knowledge and physical foundations are
necessary for a scientist. Take, for example,
a psychologist who deals with people and
their rst-person experiences by using a
third person approach. Likewise, it is im-
possible for a physician to have a rst-per-
son experience of the pain of a patient. It is
more useful if he/she can help the patient by
sharing the kind of third-person knowledge
about rst-person pain that people typically
share in similar situations. For similar rea-
sons, info-computational constructivism
builds on a scientic approach and takes a
third-person approach to subjective aspects
of cognition.
6 Conclusion
« 67 » No philosophical approach or
scientic eld can exhaust all the aspects
of one phenomenon – that is why we need
transdisciplinarity and collaboration in a
constructive project. Constructivist ap-
proaches are important because elements of
knowledge produced in specialist elds are
used in the building of a common knowl-
edge network in which elements being con-
nected gain new meaning from their new
common context. In order to understand
the result of the construction it is important
to understand its process.
« 68 » e IC framework needs to ll
many explanatory gaps. Based on neurosci-
ence, biology, bioinformatics, biosemiotics,
cognitive computing, etc., it needs to pro-
vide computational models of phenomena
of mind for which we still lack proper sci-
entic models. e concept of natural com-
putation as presented in Dodig-Crnkovic &
Giovagnoli (2013) provides some hints on
how to ll those gaps within the computa-
tional framework, proposing the concept of
nature as a network of networks of concur-
rent information processes. Even though the
rst steps towards a unied understanding
of natural computation have already been
made (in particular the contributions in
Hector Zenil’s 2012 book A Computable
Universe), a lot of work remains to be done
for a full picture to emerge and connect both
to its predecessors in the work of construc-
tivists – von Foerster, Maturana and Varela,
von Glasersfeld, and others – and to antici-
pated results from, among other disciplines,
the brain sciences, cognitive computing,
synthetic biology, and studies in the origins
of life.
R:  J 
A:  J 
IC and the Observed/Observer Duality Manfred Füllsack
IC and the Observed/
Observer Duality
Manfred Füllsack
ISIS, University of Graz, Austria
> Upshot • While I agree with Gordana
Dodig-Crnkovic’s IC approach, I am un-
certain about two points: rst about
whether constructivism needs yet an-
other etiquette in order to be considered
a viable conception, and second whether
the focus on information and computa-
tion carries the risk of directing atten-
tion away from other crucial aspects of
the approach.
« 1 » Gordana Dodig-Crnkovic’s paper
provides a comprehensive survey of what
could be called the state of the art construc-
tivist conception arising from the compi-
lation of second-order-cybernetic, com-
putational, informational, and cognitive
approaches. I have no doubt that this survey
indeed outlines a framework for the unied
study of cognition in living organisms as
well as in articial cognitive systems.
« 2 » e one point, however, that I
am a bit skeptical about is the question of
whether it really is necessary to tag this
compilation with yet another label, the la-
bel of “info-computationalism” (IC). I agree
that information and computation are cru-
cial aspects in this framework and denitely
play an essential role. And of course, it might
also be strategically gainful to establish a
rather complex and controversial approach
by way of using new labels. But in my opin-
ion, this strategy also carries the risk of di-
recting attention away from another aspect
of this framework that is mentioned in the
paper but not further discussed in terms of
its consequences. is is the aspect of dual-
ity or concurrency, as implicitly alluded to in
the sentence: “Information is the dierence
in one physical system that makes a dier-
ence in another physical system” (§23). In
its consequences, this aspect could be more
crucial for the acceptance of this frame-
work than information and computation.
It seems to stir up those objections against
constructivism that, in spite of the favorable
evidences gained from computation (Füll-
sack 2013), still render it a controversial
theory in the eyes of many. In the following,
I will briey expand on this aspect.
« 3 » Information, as a dierence that
makes a dierence, is, according to Gregory
Bateson and, as emphasized in the paper
(§21), always a dierence to someone or to
something that is able to perceive this dier-
ence as such. is denition of information
hence implies – dierent to the denition of
Claude Shannon – observation. It implies a
dierence of an observed and an observer,
or in other words, a dierence of a system
that is able to change, albeit slightly, in re-
action to a change in another system. As a
philosophical minimum condition from
this, one bit of information needs two enti-
ties (or systems or whatever) in order to be.
A dierence in one system would not be a
dierence (that makes a dierence) without
the other system for which this makes a dif-
ference. So if we agree with the constructiv-
ist assumption that there is no unobserved
reality, we need a rather demanding theory
with not just one but two “rst” entities
to start with. e info-computational ap-
proach (or however it may be called) hence
implies the counter-intuitive picture of an
“initially” dierentiated world, or of a sys-
tem that in its origins is suciently com-
plex to harbor (at least) two subsystems, of
which one can make a dierence in reaction
to the dierence in the other.
« 4 » is is not to say that I consider
this option less attractive than the assump-
tion of a reality existing beyond observa-
tion quite the opposite. But it challenges
additional explanations that might not be
entirely deliverable through informational
and computational theory.
« 5 » Two theoretical approaches that
might provide helpful building blocks in
this regard seem to be, on the one hand, the
dierentiation theory of George Spencer
Brown (1969) as interpreted by Niklas Luh-
mann (1995), and, on the other hand, a no-
tion by Francesco Varela (1992) about the
possibility to regard observation as a kind
of capitalization of advantages that might be
interpreted in terms of Kolmogorov com-
plexity. Since I intend to elaborate on these
modules and their implications for a con-
sistent second-order science in a separate
paper, I will just briey summarize these
aspects in the following.
« 6 » A consequence of dening infor-
mation in the above sense can be seen in
the fact that any observer – whatever basic
Open Peer Commentaries
on Gordana Dodig-Crnkovic’s
“Info-computational Constructivism and Cognition”
IC and the Observed/Observer Duality Manfred Füllsack
conception one is willing to use – cannot be
conceived dierently than as being depend-
ent on being observed itself. e observer
hence forces its scientic explanation into
circular reasoning (Foerster 1981; Luhmann
1995; Kauman 2009), or as philosophers
call it when rejecting it, into a vicious circle.
A fundamental conception for bootstrap-
ping the observer in this sense has been
suggested with the distinction/indication
dual of Spencer Brown (1969). is concept
conceives observation as a formal duality of
drawing a distinction and indicating one of
the distinct parts as the currently relevant
one, i.e., as a basic binary choice. Its circu-
larity arises from the fact that each obser-
vation builds on (presupposed) preceding
observations that cannot be observed in the
current act of observation, thereby generat-
ing an “unmarked space” in each observa-
tion. Observations hence carry uncertainty
with respect to their own constitution. ey
are built, so to speak, on the anticipation
of being conrmed in the next step. In the
same manner as the nodes of a network
depend on other nodes, the distinction/
indication-dual hence founds a procedural
approach that considers potentially innite
webs of recurrent observations that cannot
be reduced to any “rst” (cf. Füllsack 2011).
Each observation remains conditioned on
observation itself, implying a process of on-
going interaction of observed and observer.
« 7 » A mathematical concept that cap-
tures the recursive interaction of observed
and observer has been brought forth by
Heinz von Foerster (1976), following Jean
Piaget’s considerations on cognitive devel-
opment via ongoing sensorimotor inter-
actions (Abraham & Shaw 1999). As this
recursive interaction of observation of
a new-born baby for instance – and sub-
sequent coordinative movement tend to
render its initial value (its “rst”) irrelevant,
it seems to oer a chance to conceive ob-
served and observer in terms of the gen-
eration of what von Foerster (1976) called
“objects as tokens for eigenbehaviors.”
Mathematically, these objects correspond to
attractors that the “bottomless” interaction
of non-linear dynamics runs up (Strogatz
1994). Seen as the expression of an asym-
metric statistical tendency of dynamical
systems, the concept of “strange” or “itiner-
ant” attractors in particular seems to pro-
vide an appropriate template for scientic
explanations of phenomena that emerge
in the ongoing interaction of observed and
« 8 » is conception of attractors
combined with the formal conception of the
Spencer Brownian observer might allow the
observed/observer duality to be re-dened
in terms of what philosophers discuss as
“intentionality.” is can be considered as
a temporarily viable “interpretation” of an
observer (an “ascription” in the sense of
Ernst von Glasersfeld 1995b), in the course
of which something becomes a “resource”
(towards which intention is directed) if it
is observed in the presence of an entity (an
organism, for instance) that depends on
it (Varela 1992). is generalizes the ob-
served/observer duality, since the entity, as
observed as intentionally relating to the re-
source, could be an organism on the search
for nourishment as well as a network of
catalysts forming into an autocatalytic loop
(Kauman 2000) or the Game of Life glider1
reacting to the state of its adjacent cells.
« 9 » Conceiving the observed-observ-
er duality in this way seems to allow Va-
rela’s notion of something capitalizing on
a resource” to be connected to the concept
of algorithmic complexity (Kolmogorov
1965). Using this conception, observation
(in the formal sense) can be conceived as a
way of compressing regularities into some
kind of viable algorithm, as for instance the
rule Fn = Fn–1 + Fn–2 (with F0 = 0 and F1 = 1)
does with the regularities of the Fibonacci-
sequence. As this compression (or model)
frees computational power (i.e., reduces
complexity), it counteracts entropy and
thus implies (temporal) order, which in the
next step itself can be capitalized on at an-
other trophic level. From this, a “metabolic”
network becomes conceivable that grows
through the emergence of entities nding
ways to capitalize on respective regularities
(or, just as well, on regularities of irregulari-
ties by establishing control and monitoring
mechanisms) – with the caveat, however,
that the expression “nding ways” and the
1 | e cell constellation called “glider” in
John Conway’s Cellular automaton “Game of Life”
self-replicates according to the state of its neigh-
boring cells. It thus could be observed as “capital-
izing on” the state of its neighboring cells.
intentionality it implies has to be taken as
observed itself, i.e., as second-order observed
(Foerster 1981). While on the level of rst-
order observation, this network would be
nothing but coincidental (i.e., unintended)
a “cut-out” (in the sense of James 1983)
provided by natural selection with inten-
tionality only retrospectively ascribed – the
required second-order observation would
need a network that includes concurrently
operating strong-tie clusters that them-
selves serve as observers by compressing
aspects and dynamics of the rest of the net-
work into a concept that otherwise would
remain dispersed and overly complex. Or
in other words, it necessitates a modular-
ized network of looser and tighter coupled
nodes (weak and strong ties, Granovetter
1973; Csermely 2009), of which some form
clusters that, by taking in the “intentional
stance” (Dennett 1987), capitalize on the
(perceived) order of others, thereby free-
ing computational power, generating order
themselves and hence becoming observable
(i.e., capitalizable) in their own turn. Free-
ing computational power in this sense could
then be understood as being “productive,
and a web of mutual observations could be
conceived as a “food web” of some sort, with
each observation reducing complexity and
thereby providing “resources.
« 10 » I intend to elaborate on this con-
ception in the near future in a more com-
prehensive publication. For the moment,
this commentary might serve as a sup-
portive reference to one of the directions in
which the conception of Dodig-Crnkovic
might be fruitfully expanded.
Manfred Füllsack is Professor of Systems Sciences at
the University of Graz. His research includes: systems,
complexity, networks, games and computational
theory, work – its history, its sociology, its economy,
and its philosophy, and computer-based simulations.
R:  F 
A:  F 
Søren Brier
Copenhagen Business School,
Denmark • sb.ikk/at/
> Upshot • The main problems with
info-computationalism are: (1) Its ba-
sic concept of natural computing has
neither been dened theoretically or
implemented practically. (2). It cannot
encompass human concepts of sub-
jective experience and intersubjective
meaningful communication, which pre-
vents it from being genuinely transdis-
ciplinary. (3) Philosophically, it does not
suciently accept the deep ontological
dierences between various paradigms
such as von Foerster’s second- order
cybernetics and Maturana and Varela’s
theory of autopoiesis, which are both er-
roneously taken to support info-compu-
« 1 » I have had the pleasure of discuss-
ing the info-computational (or pan-com-
putational) paradigm several times before
(Brier 2011a, 2013a, 2013b) in writing, and
orally at several meetings and conferences,
with my colleague Gordana Dodig-Crnkov-
ic, and watched her paradigm develop to the
present stage. See, in particular, Brier (2008),
where most of my arguments present here
are developed in greater detail.
« 2 » I nd this articles transdisciplinary
goal admirable, but also nd its idea of an
all-encompassing computation process for
nature, society and consciousness to be too
reductionist. is is rst of all because the
paradigm does not include rst person ex-
perience or the phenomenological aspect,
which I nd crucial for human intersubjec-
tive production of knowledge and mean-
ing. Secondly, because its idea of natural
computation is a mere postulate based on
a reductionist belief in present computers’
production of what is called articial intelli-
gence to be the core of human cognition is
paradigm gave rise to the reductionist view
of cognitive science based on information
processing. In latter years, the development
of cognitive science has moved into brain
sciences. It is now trying to model and emu-
late human emotions on one hand and one
the other to correlate registration of neural
activity with human rst person experience,
comparing analysis of behavior and linguis-
tically based reports of experience – not the
experience itself, which we cannot measure.
But the idea of a general info-computation
is a research program without any theory
of what such a common denominator for
all natural, social and conscious processes
that have to go beyond the possibilities of a
Universal Turing Machine should be, except
some sort of universal concept of informa-
tion processing. So far, it does not contain a
theory of conscious awareness and meaning.
e whole phenomenological and herme-
neutical aspect of reality is not only missing,
but simply not recognized and accepted as
crucial to such a transdisciplinary paradigm.
is is a considerable blow to its transdis-
ciplinary aspiration in the sense of Basarab
Nicolescus (2002) Manifesto of Transdisci-
plinarity. To put it in another way, I do not
think that “Messages are just a very special
kind of information that is exchanged be-
tween communicating agents” (§18) but on
the contrary, that information is a part of
meaningful cognition and communication.
« 3 » I also nd info-computationalism’s
blend of a sort of computational realism
– even if it is only a variant of epistemic
structural realism – with a declared con-
structivism based on, especially, second and
third order cybernetics, paradoxical and
confusing. is is of course because I base
my views on a Peircean triadic pragmaticist
semiotic realism that considers information
only as a component of semiotic processes,
which always include meaning.
« 4 » I am also a doubtful about the
soundness of combining the idea of com-
putation with the self-organizing paradigms
of general system science and non-equilib-
rium thermodynamics, as long as this new
conception of natural computation – call
it actor-model or a general notion of com-
putation – is not produced. It is like selling
the skin before the bear is shot. Aer all, the
concept of computation is developed on the
basis of the Turing machine, which is not
self-organizing but a xed structure cre-
ated and organized by the human mind. Al-
though robots can be programmed to func-
tion with each other in self-organizing ways,
the Turing machine in itself is sequential
and linear; the problem is that most natural
processes of the living systems are not. ere
is a huge gap between these two conceptual
worlds. I do understand the need to bridge
or merge them. But the mere talk of “if we
had a model for natural computation” is not
enough. It rather avoids the deep problem in
my view. See, for instance, the many discus-
sions about this in Swan (2013).
« 5 » As part of the group that has devel-
oped the idea of biosemiotics, I am inclined
to believe that biosemiotics is a much bet-
ter research strategy for understanding what
sets the processes in living nature apart from
computers and the processes in inanimate
nature, namely that they are Peircean triadic
semiotic. Heinz von Foerster is used as part
of Dodig-Crnkovic’s argument such as in
§14: …we see that information processing
corresponds to von Foerster’s operation on
‘objects,’ or their representations, ‘symbols’.
However, he did not see computation as
information processing either (Brier 1996).
He wrote very critically against the general
information concept. I therefore think he is
misused here as a supporter of info-compu-
« 6 » From a Peircean ontology of conti-
nuity and view point of fallibility of all gen-
eral knowledge, it is also worth remarking
that mathematics and science are nite dis-
ciplines and are not identical with or prior
to reality as such. We live in an immanent
frame, which we continually expand and
attempt to understand. Experience and
cognizing reality is the starting point of all
thought and cognition – not computation in
my view.
« 7 » In the same way, I wonder how
Dodig-Crnkovic uses the concept of “ob-
server” (is a robot an observer?) and I do
not think she interprets Floridi correctly
here (§19) or Wheeler just aer that (§20).
His “it from bit” is based on a participatory
universe, not a computer metaphor. Deep
ontological issues seem to be treated a little
supercially here. Pan- and info-compu-
tation views attempt to remove all mystery
from the world by postulating computation-
al agents without any experiential aware-
ness. In §23 Dodig-Crnkovic claims: “In-
formation is the dierence in one physical
system that makes a dierence in another
physical system,” and a little later speaks of
functional responses only. But then she re-
A Mathematical Model for Info-computationalism Andrée C. Ehresmann
turns to her inspiration from second-order
cybernetics that all information is observer
dependent but that observer is never an
experiential phenomenological rst person
one. In some other places Dodig-Crnkovic
writes about perception as if subjective ex-
perience is taken for granted, but it does
not really exist in the implicit paradigm the
whole paper is written on. It is much as in
Ernesto Laclau and Chantal Moue’s (1985)
discourse analysis, where the subject is what
lls out the holes in a chain of arguments
(Laclau 1990). It works like a negative de-
nition in the hope of an “intuitive processor”
as a form of neural network non-symbolic
processor type of computation (§32) – now
introducing biological (probably cybernet-
ic) agents. As a biosemiotician, I agree that
all biological systems produce knowledge,
but not from the understanding of them as
autopoietic machines (Brier 1995, 2011b).
« 8 » ere are some further cases in
which Dodig-Crnkovic may have misquoted
other scholars. Humberto Maturana does
not accept an information processing view
either; neither did Francisco Varela, who was
inuenced by phenomenology. So they are
misquoted here, even though their insights
t well with von Foerster’s eigen-values and
eigen-behaviors, and Luis Rochas further
development of his cognitive cybernet-
ics. In §56 Stanley Salthes pan-semiotism
is ignored and instead he is portrayed as
supporting constructivist info-computa-
tionalism. In reply to my earlier criticisms,
Dodig-Crnkovic uses David Chalmers infor-
mational model of consciousness but misses
mentioning his doublet aspect theory of
information, which is pretty dierent from
hers (although I do not agree myself with the
way he introduces the experiential aspect).
She deals with the doublet aspect philosophy
in §62 with the help of the concepts exo- and
endogenic, thereby dodging the experiential
aspect of awareness. Dodig-Crnkovic com-
bines the endo-exo-model with Gregory
Batesons “information as a dierence, which
makes a dierence” omitting the fact that it
applies only for a cybernetic mind that does
not contain rst person experience and qua-
lia (Brier 1992). In §64, subjectivity becomes
a question of levels, though such a qualita-
tive emergent ontological organismic system
thinking is not introduced or argued, but is
again postulated in §65.
« 9 » In §66, intersubjectivity is seen as
primary to rst person subjectivity, which
to me is the prerequisite for intersubjectiv-
ity and language. Here, however, it is made
informational. is is an interesting attempt
to place rst person experience and percep-
tion as well as meaningful communication
in a corner of a basic physicalistic informa-
tion world view. But rst person experi-
ence and meaningful communication are
the prerequisite for the information science
from which the info-computational view is
argued. It is not the other way round.
« 10 » In general, I cannot help the im-
pression that the philosophy behind info-
computation is mixing apples, pears, and
bananas by arguing that no matter how their
taste is experienced, they are all fruits and
that is the basic fact on which we should
build transdisciplinarity.
Søren Brier is Professor in the Semiotics of
Information, Cognition, and Communication Sciences
at Copenhagen Business School. He is the editor
of Cybernetics & Human Knowing, a fellow of the
American Society for Cybernetics, and a member
of the board of the International Association for
Biosemiotic Studies and its journal, Biosemiotics.
R:  F 
A:  F 
A Mathematical Model for
Andrée C. Ehresmann
Université de Picardie Jules Verne,
France • ehres/at/
> Upshot • I propose a mathematical ap-
proach to the framework developed in
Dodig-Crnkovic’s target article. It points
to an important property of natural com-
putation, called the multiplicity principle
(MP), which allows the development of
increasingly complex cognitive processes
and knowledge. While local dynamics are
classically computable, a consequence of
the MP is that the global dynamics is not,
thus raising the problem of developing
more elaborate computations, perhaps
with the help of Turing oracles.
How can a mathematical approach
to info-computationalism be
« 1 » Gordana Dodig-Crnkovic pro-
poses an info-computational framework
for approaching cognition in living organ-
isms and in embodied cognitive agents of
any kind: the environment aords potential
information that the agent can integrate
into actual information and transform into
knowledge by natural computation; per-
ception acts as an information-processing
and learning device, through dynamical
processes of self-organization of the agent.
While the objective is clear, the article re-
mains in an abstract setting, without il-
lustrating it with specic situations, and it
does not raise the problem of mathematical
modeling, with its possible contributions to
a better understanding of the situation.
« 2 » Here I propose such a mathe-
matical approach, namely the bio-inspired
Memory Evolutive Systems (MES) method-
ology, which we have been developing for
25 years (cf. Ehresmann & Vanbremeersch
2007). It is based on a “dynamic” category
theory, a recent mathematical domain (in-
troduced by Samuel Eilenberg and Saun-
ders MacLane in 1945) that stresses the
role of relations over structures. It identies
some important properties of information
processing and natural computation not
discussed in the article, and shows their
role in the non-(Turing-)computability of
the global dynamics of the system.
Memory Evolutive Systems
« 3 » An MES gives a constructive
model for a self-organized multi-scale cog-
nitive system that is able to interact with
its environment through information pro-
cessing, such as a living organism or an
articial cognitive system. Its dynamics
is modulated by the interactions of a net-
work of specialized internal agents called
co-regulators (CRs). Each CR operates at its
own rhythm to collect and process external
and/or internal information related to its
function, and possibly to select appropriate
procedures. e co-regulators operate with
the help of a central, exible memory con-
taining the knowledge of the system, which
they contribute to develop and adapt to a
changing environment.
A Mathematical Model for Info-computationalism Andrée C. Ehresmann
Information, Computation and Mind Marcin J. Schroeder
« 4 » In an MES, a central role is played
by the following properties of information
processing in living systems: (i) e sys-
tem not only processes isolated information
items, but also takes their interactions into
account by processing information patterns,
that is patterns of interconnected informa-
tion items. (ii) e MES satises a multi-
plicity principle (MP), asserting that sev-
eral such information patterns may play the
same functional role once actualized, with
the possibility of a switch between them
during processing operations. is principle
formalizes the degeneracy property that is
ubiquitous in biological systems, as empha-
sized by Edelman (1989; Edelman & Gally
2001). It permits Gregory Batesons sentence
(§21) to be completed into “a dierence that
makes a dierence, but also may not make a
dierence.” e MP is at the root of the ex-
ibility and adaptability of an MES; it will also
be responsible for the non-computability of
its global dynamics.
« 5 » Once actualized in the MES, an in-
formation pattern P will take its own identity
as a new component cP of a higher complexity
order, which “binds” the pattern, for instance
as a record of P in the memory. e binding
process is modeled by the categorical colimit
operation (Kan 1958): cP becomes the colimit
of P and also of each of the other function-
ally-equivalent information patterns; thus
it acts as a multi-facetted component. Such
multi-facetted components are constructed
through successive complexication processes
(Ehresmann & Vanbremeersch 2007). e
complexication also constructs the links
interconnecting two multi-facetted compo-
nents cP and cQ. ere are simple links, which
bind together a cluster of links between the
information items constituting the patterns P
and Q. However, the MP makes also possible
the emergence of complex links, composed of
simple links binding non-adjacent clusters,
for instance a simple link binding a cluster
from P to and a simple link from Qʹ to Q if
and are functionally equivalent patterns
with colimit cQʹ = cPʹ (cf. Figure 1). Com-
plex links reect “changes in the conditions
of change” (Popper 1957). ey are at the
root of the emergence theorem (Ehresmann
& Vanbremeersch 2007): the MP allows the
development over time of components of in-
creasing orders of complexity, such as more
and more elaborate knowledge and cognitive
The model MENS for
a neuro-cognitive system
« 6 » To describe the functioning of an
MES more explicitly, we restrict ourselves to a
particular MES, the memory evolutive neural
system (MENS), which models the cognitive
system of an animal (up to man). MENS gives
a framework comprising the neural, cogni-
tive and mental systems at dierent (micro,
meso, macro) levels of description and across
dierent timescales. Its construction takes
account of the following properties of the
neural system: (i) as already noted by Hebb
(1949), there is formation, persistence and
intertwining of distributed neural patterns
whose synchronous activation is associated
to a specic mental process; (ii) this associa-
tion is not one-to-one due to the “degeneracy
property of the neural code,emphasized by
Edelman (1989: 50). In MENS, this degener-
acy is formalized by the MP, and a mental ob-
ject or process is represented by the common
binding (formally colimit) of the more or less
dierent neural patterns that it can synchro-
nously activate at dierent times, and that
constitute its several “physical” realizations.
« 7 » MENS is a hierarchical evolutive
system (Ehresmann & Vanbremeersch 2007)
that sizes up the system “in the making,
with variation over time of its conguration
categories, its information processing. Its
memory stores dierent data, knowledge,
experiences and procedures in a exible
manner, to be later recalled or actualized in
changing conditions. e evolutive system
Neur of neurons (and synapses) constitutes
the lower level of MENS. e higher levels
are constructed by successive complexica-
tions of Neur that add new components,
called “category neurons, that represent
mental objects or processes and are obtained
from the binding (= colimit) of synchronous
neural patterns. Due to the emergence theo-
rem, these complexications generate an “al-
gebra of mental objects” (Changeux 1983),
up to exible higher mental and cognitive
processes. us MENS processes more and
more complex information over time (cf.
§38). However, the complexications may
also destroy some existing category neurons,
in particular records in the memory that are
no longer adapted to the context.
Dynamics of MENS
« 8 » “Potential information” (§14) con-
sisting of some change in the environment
can be actualized in MENS only if it interacts
with the system by activating some neural
patterns in specialized brain areas acting as
co-regulators. ese co-regulators operate
stepwise as an “interface” (§29) between the
environment and the systems behavior. Sev-
eral co-regulators “perceivedierent parts
of incoming information through specic
patterns; for instance, a co-regulator dealing
with colors will only perceive the color of an
object O, while a shape co-regulator will only
perceive the shape of O. At a time t, a co-reg-
ulator collects the dierent information re-
ceived from its external and/or internal envi-
ronment into its landscape at t (modeled by a
category). Using its dierential access to the
memory, it processes the information and re-
acts to it by selecting an adequate procedure:
if the color of O is already known, the color
CR will “recognize” it and activate its record;
if the color is not yet known, it will com-
mand the synchronization of the color pat-
tern P (by strengthening its synapses), lead-
ing to its binding into a new category-neuron
(colimit of P), which will memorize the color
of O. e synchronization of an assembly
of neurons is a kind of natural computation
that is reducible to a classical computation in
the usual Turing sense; if there was only one
co-regulator, its dynamics during one step
could be computed by classical means (e.g.,
via dierential equations).
« 9 » However, there is a whole net-
work of co-regulators that can function a-
synchronously (as in Hewitts actor model,
cf. §16). At t, the dierent procedures they
try to implement can be conicting, thus
cP cP= colimitQ
simple link
simple link
complex link
Figure 1: Complex links in MES
Information, Computation and Mind Marcin J. Schroeder
requiring some “interplay” among them to
harmonize them. MP lends exibility to this
process, since a procedure Pr can be “physi-
cally” realized through the activation of any
one of the neural patterns it binds. As there
is no central co-regulator, this interplay may
necessitate cascades of natural computation
of various kinds, making it not computable
in the usual sense. At the moment we do not
know of any mathematical models for such a
kind of natural computation, where there are
possibilities for switching between dierent
physical realizations of the same procedure.
It would necessitate the use of more sophis-
ticated methods, for instance using Turing
machines with oracles (cf. Soare 2009, or
the DIME method proposed by Mikkilineni
2011). e idea would be that each co-regu-
lator acts as an oracle, possibly interrupting
the local dynamics of another co-regulator,
with cascades of such operations up to the
attainment of a common solution.
« 10 » e MES methodology aords
partial constructive mathematical approach-
es to the development of cognition in Dodig-
Crnkovic’s general info-computational
framework. It also characterizes the multi-
plicity principle as the root of both the emer-
gence of increasingly complex knowledge
and of the non-computability (in a classical
sense) of natural computation. e colimit
(or binding) operation translates the actu-
alization of information patterns into new
components that, thanks to the multiplicity
principle, take their multi-facetted individu-
ation over time. While the local dynamics
of one co-regulator during one step is clas-
sically computable, the global dynamics is
not. ere is a need for more elaborate math-
ematical models, thus opening new horizons
for research.
Andrée Ehresmann (née Bastiani) is Professeur Emérite
at the Université de Picardie Jules Verne and Director of
the mathematical journal CTGDC. Author and editor of
several books, she has more than 100 publications in
mathematical or multidisciplinary domains: analysis;
category theory; and self-organized multi-scale systems
with applications to biology, cognition, innovative
design. Website:
R:  F 
A:  F 
Information, Computation
and Mind: Who Is in Charge
of the Construction?
Marcin J. Schroeder
Akita International University, Japan
> Upshot • Focusing on the relationship
between info-computationalism and
constructivism, I point out that there is
a need to clarify fundamental concepts
such as information, informational struc-
tures, and computation that obscure the
theses regarding the relationship with
constructivist thought. In particular, I
wonder how we can reconcile construc-
tivism with the view that all nature is a
computational process.
« 1 » Gordana Dodig-Crnkovic’s “Info-
computational Constructivism and Cogni-
tionpresents a comprehensive program of
cognitive studies combining constructivist
methodology with an info-computational
ontological framework. e main line of
thought is well documented and supported
by solid argumentation, but there are some
points which aroused objections or ques-
tions in the present author. us, although
the following is not intended as a criticism of
the program, it is a call for further explana-
tion or clarication of confusing statements.
e need for further explanation may be a
result of the immense task of comparing and
correlating the two extensive directions of
thought that Dodig-Crnkovic ventured and
achieved with impressive results, but that
did not allow more detailed explanation to
be entered into. However, in the opinion of
the present author, the comparison becomes
confusing without clarication of the fun-
damental concepts, such as information or
Questions regarding the ontology of
« 2 » e main question regarding the
program presented by Dodig-Crnkovic is
about the degree to and manner in which
the view of reality in terms of information
and computation, at least as presented in the
article, is consistent with the constructivist
point of view (hence the subtitle of this com-
mentary: Who is in charge of the construc-
tion?) Unfortunately, neither info-com-
putationalism (IC) nor constructivism are
homogeneous, uniform schools of thought,
and the perception of the need for the ques-
tion above can be just a matter of equivoca-
tion. But even then, it is worth attempting to
clarify this issue.
« 3 » e article declares in the rst
paragraph that
It [IC] asserts that, as living organisms, we
humans are cognizing agents who construct
knowledge through interactions with their envi-
ronment, processing information within our cog-
nitive apparatus and through information com-
munication with other humans. (§1)
However, this sentence is preceded by
Info-computationalism (IC) is a variety of
natural computationalism, which understands
the whole of nature as a computational process.
« 4 » I nd this innocent looking jux-
taposition puzzling. If we understand the
constructivist position as a view that gives
the active and primary role in the process
of construction of knowledge to the mind,
how can we reconcile it with the view that all
nature is a computational process? What can
be the contribution of the mind to a univer-
sal process seemingly governed by external,
independent rules?
« 5 » e long tradition of construc-
tivism, going back at least to Giambattista
Vico, opposes the view of learning through
observation (even when understood as ex-
ploration necessarily involving interaction),
promoting the view that knowing means
creation, that truth is an invention or gen-
eration, not an acquisition. Aer all, the
constructivist tradition was intended as a
way to avoid the Cartesian duality of body
and mind, res extensa and res cogitans. We
know something when we can construct it,
not when we recognize a pattern through
observation. e involvement of the mind in
the construction of what we are learning dis-
solves the division between the mental and
the physical. When we give priority to the
external universal process (computational
or not), in which the mind can participate
in various degrees but is subject to its rules
and does not contribute to it as a creator or
constructor, we deviate from the main tenet
of constructivism.
« 6 » e target article refers to the
views of Stuart Umpleby (2002) as the his-
torical roots of info-computational con-
structivism, which included the division of
the development of this direction of thought
into the three periods of so-called engineer-
ing, cybernetics, biological cybernetics and
social cybernetics:
During the engineering period, the object of
observation, the observed was central. In the sec-
ond phase, with research in biology of cognition,
the core interest shied from what is observed to
the observer. In the third phase, the domain of so-
cial cybernetics focus moved further to models of
groups of observers. (§1)
Here the use of the termobserver” is curi-
ous, in the context of the constructivist po-
sition. But the “damage” to the constructiv-
ist tradition does not seems beyond repair
if we substitute the words “construction
and “constructor” for the words “observed”
and “observer.” However, to include cy-
bernetics (at least its rst stage) into the
main stream of constructivism, we have
to redene its philosophical foundations
and interpret it as modeling the construc-
tivist view within a very dierent or even
opposite paradigm. e original position
of cybernetics, i.e., rst-order cybernetics,
was rather to eliminate the mind from the
Cartesian mind-body dualism by giving
priority to the body (embodiment), not by
elevating the mind to the role of a creator,
inventor or constructor.
« 7 » e reference to rst-order cyber-
netics, or even its later forms, as the initial
source of info-computational constructiv-
ism brings out the possibility that this posi-
tion should rather be considered a mirror
reection of the traditional position of con-
structivism, closing the gap in the dualis-
tic view not from the side of the mind but
from the side of the body. It can be seen, for
instance, in the views of Humberto Mat-
urana and Francisco Varela, whose main
concept is called an “autopoietic machine,
not “autopoietic mind” (Maturana & Varela
1980: 78). us, the ontological foundation
is built on the assumption of the existence
of the “physical world” in which some
structure is constructing mind out of its
cognitive functions.
« 8 » Someone could look for more con-
structivist ontological foundations for au-
topoiesis, detaching it from dualistic ontol-
ogy by the assumption of a shi of emphasis
from the body (machine) side of the dualism
to the mind side. It could do so by interpret-
ing autopoiesis as self-organization, where
organization could be made independent
from its physical substratum and could as-
sume an active, constructive role. is is
how we could understand the statement
from the target article:
In order to understand cognition and knowl-
edge as a natural phenomenon, the process of
re-construction of the origins, development and
present forms and existence of life, processes of
evolution, and development based on self-orga-
nization are central. e work of Maturana and
Varela on the constructivist understanding of life
is of fundamental importance. (§39)
However, the interpretation of autopoiesis
as self-organization is not consistent with
the views of the original authors of this
concept. Maturana explicitly opposed such
interpretation, writing that he would “never
use the notion of self-organization, because
it cannot be the case … it is impossible. at
is, if the organization of a thing changes, the
thing changes” (Maturana 1987: 71).
« 9 » e target article also refers to Mi-
lan Zeleny’s view:
Such autopoietic systems are organization-
ally closed and state-determined… What does it
mean that an autopoietic system is organization-
ally closed? It means it conserves its organiza-
tion. (§39)
In what sense can we say that an autopoi-
etic system is self-organizing, which means
developing (i.e., changing) its organiza-
tion, and at the same time conserving its
organization? is contradiction cannot be
resolved by a distinction between the con-
servation of organization at the phenotypic
level and change at the genetic level of spe-
cies, as phenotype and species are two dif-
ferent systems.
« 10 » It seems quite clear that the ex-
pression autopoietic machine in the fun-
damental concept of autopoiesis is not ac-
cidental, and that it refers to the fact that
its ontological status is clearly rooted in the
body side of Cartesian dualism, with the ob-
jective of subordinating or eliminating the
mind side.
Questions regarding understanding
« 11 » Similar ontological assumptions
can be found in Dodig-Crnkovic’s article
regarding the fundamental concepts of IC.
It starts at the most fundamental level by
dening the concept of information (pre-
ceding the concept of computation) in her
paraphrase of Gregory Bateson’s denition:
Information is also a generalized concept in
the context of IC, and it is always agent-depen-
dent: information is a dierence (identied in the
world) that makes a dierence for an agent. […]
For dierent types of agents, the same data input
(where data are atoms of information) will result
in dierent information. (§2)
e concept of “atoms of information” is
questionable and dicult to understand in
the context of her denition (are there any
indivisible dierences?). But whatever the
data are, this means their existence is within
the world and seemingly they are indepen-
dent from agents. us, it is just a matter
of what agents do with the data (how they
interpret data), and this suggests that actu-
ally Dodig-Crnkovic is writing not about the
data–information relationship, but about
the meaning of information.
« 12 » e dualistic (physicalistic) on-
tological position can be seen even more
clearly in her next paraphrase of Batesons
denition, to combine it with Hewitt’s rela-
tional view of information: “Information is
the dierence in one physical system that
makes a dierence in another physical sys-
tem” (§23, emphasis in the original).e du-
alistic ontology is already present in the use
of expression “physical world” as it requires
a complement in the form of the mental
world (what other complement is possible?).
If not, what is the reason for using the adjec-
tive “physical”?
« 13 » e relationship with the world is
described in the target article as follows:
e world as proto information presents the
potential form of existence corresponding to Im-
Information, Computation and Mind Marcin J. Schroeder
manuel Kant’s Ding an sich (thing in itself). at
proto information becomes information, ‘a dier-
ence that makes a dierence,’ […] for a cognizing
agent in the process of interaction. (§21)
« 14 » It is dicult to nd out how an
agent is involved in creation of information
beyond the fact that it is involved in obser-
vation through interaction, which changes
proto information into information. But this
was just the denition of proto information
involving an “(observing) agent” as neces-
sary for transition into information (§17).
It is, of course, an expression of the view
that an observer is necessary to transform
potential existence into actual existence, but
it hardly reects tenets of constructivism.
Once again, it seems likely that this process
is actually not a transition from proto infor-
mation to information, but rather interpre-
tation that gives meaning to information;
however, there is nothing in these passages
regarding creation of the meaning for infor-
mation (which actually could be considered
a weak form of constructivism, if the role of
construction is given to the mind).
Questions regarding computation
« 15 » ere is a similar problem with
understanding how to identify the construc-
tivist character of the second fundamental
concept of computation. e quotation of
Mark Burgin’s denition Computation is
information processing” does not make it
easier (§14, emphasis in the original). To say
that “computation is information process-
ing” is to say nothing except that there is
some vague relationship between computa-
tion and information (computation is doing
something to information), unless someone
clearly denes the term “processing.
« 16 » We can learn more from the quo-
tation of Heinz von Foerster’s denition of
computation as “any operation (not neces-
sarily numerical) that transforms, modi-
es, rearranges, orders, and so on, observed
physical entities (‘objects’) or their represen-
tations (‘symbols’)” (§13). It seems that von
Foerster means that computation is simply
any change of some entities, their relations,
or representations, or actually any change in
general, as every change is either of entities,
their relations or representations. Changes
of accidental or essential properties of en-
tities are just specications of the types of
changes of entities. is, however, is a gross
over-generalization, as what would be the
reason to use two dierent terms “change”
and “computation” in the same meaning?
Change is a natural candidate for the genus
for computation, but we need a non-trivial
« 17 » Since the very concept of an agent
has its most general meaning as something
that makes changes, the reference to “com-
putational agents” does not help much in
understanding computation: “Hewitts ‘com-
putational devices’ are conceived as compu-
tational agents – informational structures
capable of acting on their behalf ” (§16).
Here, as well as in many other places in the
target article, appears the expression “infor-
mational structures.” It is not clear what they
are and how they relate to the constructiv-
ist view of reality. ere is a short passage
in a footnote, which seems to be the most
important in the entire paper, which refers
to the problem:
Von Foerster rightly pointed out that comput-
ers do not process information but data. However,
this term is already widely used. Data are atoms of
information. Information is obtained when data
becomes integrated into structure (correlated),
which happens in the interaction with a cogniz-
ing agent. (Footnote 5)
« 18 » In the opinion of the present au-
thor, this is the point where we can nd a
connection between information, computa-
tion, and constructivism. In the target ar-
ticle, information integration and its struc-
tural characterization are le without more
detailed description, but the recognition of
the role of a cognizing agent in the integra-
tion of information seems to point at the
active role of the mind in seeking knowl-
edge. is point of view is close to the views
presented by the present author in his ear-
lier publications (Schroeder 2011). But even
the footnote is confusing and apparently
involves a vicious circle. We learn that data
are atoms of information, but information is
obtained only when data are integrated into
a structure in the interaction with an agent.
ere is another passage that refers to infor-
mational structures: “Reality for an agent
consists of structural objects (informational
structures, data structures) with computa-
tional dynamics (information processes)
that are adjusted to the shared reality of the
agent’s community of practice” (§20). How-
ever, it does not explain what these struc-
tural objects are and what kind of dynam-
ics describes their interactions. Even worse,
here we have put informational structures
and data structures alongside each other.
« 19 » us, when the concept of mor-
phological computing appears in the text,
we can guess that it is some type of struc-
tural change involving informational struc-
tures. But it is not clear at all what these in-
formational structures are or how they come
into existence, except that it happens in the
interaction (of the data?) with a cognizing
agent. en, the dynamics of informational
structures is also le without explanation.
Dynamics means interaction, in this case in-
teraction between informational structures
(or possibly within, but in this case between
what?). At the same time we have an interac-
tion with a cognizing agent that constitutes
information, which itself requires some
form of dynamics.
« 20 » Confusions regarding the con-
cepts of information, informational struc-
tures, and computation and their relation-
ship to constructivism make understanding
the relationship between info-computa-
tionalism and constructivist thought very
dicult. It is possible that it is a matter of
dierence in the understanding of info-
computationalism. In fact, for the present
author, the denition of info-computation-
alism as “understanding of the whole nature
as a computational process is not clear as
long as a computational process (i.e., pre-
sumably computation) is just any change of
unclearly dened informational structures.
e way from information understood as
a dierence that makes a dierence (notice
the idiomatic character of this expression!)
to informational structure to computation
is too long to be le to individual interpre-
tations if we want to have some identiable
direction of thought.
« 21 » Info-computationalism can be
related to constructivist approaches only
when its fundamental concepts are dened
in a suciently clear philosophical frame-
work. Otherwise, we risk inconsistency in
relating constructivist epistemology to a
dualistic ontology of info-computational-
ism. e denitions and their interpreta-
tions used in the target article are too nar-
row, too general, or too far removed from
the philosophical background to satisfy this
Marcin J. Schroeder is a Professor at Akita
International University, Akita Japan. His educational
and professional-academic background includes
theoretical physics (M.Sc. University of Wroclaw,
Poland) and mathematics (Ph.D. SIU-Carbondale,
USA). His current long-term research interests
focus on the philosophical and mathematical
aspects of information and computation.
R:  F 
A:  F 
Modelling Realities
Hugh Gash
St Patrick’s College, Ireland
> Upshot • Gordana Dodig-Crnkovic pro-
poses that radical constructivism and
info-computational (IC) processes have
a synergy that can be productive. Two is-
sues are proposed here: can constructiv-
ism help IC to model creative thinking,
and can IC help constructivism to model
conflict resolution?
« 1 » Classical introductions to con-
structivism are presented in human terms.
Humberto Maturanas account of cognition
begins with observation. More generally,
cognition depends on noticing dierences
and we make choices based on past experi-
ence. ese choices are based on decisions
concerning what is best for us (Glasser 1985)
and the heuristics on which these decisions
are based have been studied (Gopnik et al.
2004). Ernst von Glasersfeld (1974) intro-
duced radical constructivism (RC), em-
phasising that our cognitive rational ways
of understanding the world did not refer to
a Reality as it was always beyond our sense
receptors. is implied that classical notions
of truth that presumed a matching of cogni-
tion with Reality require rethinking and von
Glasersfeld proposed instead that viability
ensured that our understandings worked.
e concepts of truth viability and certain-
ty are linked because survival depends on
knowledge. So the ways an individual and
her social group understand reality are vi-
tally important.
« 2 » Siegfried Schmidt (2011) recently
proposed that constructivism should focus
on processes rather than entities. Gordana
Dodig-Crnkovic here makes a similar pro-
posal that the info-computational approach
(IC), which also emphasises processes, has a
synergy with constructivism that can be mu-
tually benecial to both approaches. In her
article she describes how computing agents
interact with their environments in intelli-
gent ways. Agents and robots that compute
do so with a limited but eective notion of
their environment. ey are regulatory sys-
tems that are increasingly becoming com-
monplace in our experience of our worlds,
from thermostats to computer assisted
braking systems to apps in phones. ese
regulatory systems with cybernetic features
use feedback from specic sensors and have
been compared to cognitive processes since
the time of Ross Ashby (1960) and the Macy
Conferences in the middle of the last century.
« 3 » In modelling cognition with com-
puting systems, there are two issues on
which I would like to comment. One goes
back to the conicting approaches of René
Descartes and Giambattista Vico. ese are
whether thinking is better-modelled as de-
ductive (Descartes), or whether the creative
processes involved in constructing new ways
of understanding phenomena should be
emphasised (Vico). is issue is one that in-
vites comment from the IC approach. Since
Charles Sanders Peirce and John Dewey,
processes of deduction and induction have
been accompanied by abduction as ways of
explaining creative processes. So, I wonder
if computing systems that use parallel com-
puting can, or will soon, simulate this type
of creativity. Dodig-Crnkovic cites the inad-
equacy of earlier eorts to model cognition
(§32). Constructivism has a strong history of
emphasising creativity in learning; in an ap-
propriate example, the Empowering Minds
Project used Lego robotics with children in
schools (Butler & Gash 2003). One feature
in this project that required creative prob-
lem solving for novices was the problem of
changing the direction of power using gears,
as in cars. Creative problem solving oen
requires a ash of insight and a new con-
ceptualisation and a feature involved in such
processing is non-linearity. Are such prob-
lems and processes an inspiration or a stum-
bling block for new IC developments, such as
parallel computing processes?
« 4 » A second issue is to explain dier-
ent realities. is theme seemed to preoc-
cupy von Glasersfeld. It is a theme that since
then has been a constant source of irritation
(e.g., Boghossian 2006). However, it must be
stated, our concept of reality is intimately as-
sociated with our notion of self and responsi-
bility, thus it is intimately related to our iden-
tity. It is also central to so many conicts,
both intercultural and interpersonal. I want
to explain this and then ask whether the IC
position might oer a solution to explaining
the RC position and make it less irritating.
« 5 » Taking responsibility for one’s own
ideas has been central to the constructivist
position, and dierent writers give dierent
reasons for this. Von Glasersfeld (2010) ar-
rived at his RC position on account of both
his philosophical readings and his living in
more than one language. Humberto Matu-
ranas (1988) explanation of cognition shows
how taking responsibility for our acts and
thoughts implied acceptance of the con-
structivist position. Finally, Andreas Quale’s
target article on ethics implies that our inter-
actions with each other inuence the forms
of responsibility we adopt in our daily lives.
It is clear that our ethical values arise during,
and are inuenced by, our experiences with
others. Like our sense of self, ethical values
and responsibility belong in the relational
domain (Glasersfeld 1979). e problem is
how to share these ideas with the wider pub-
lic. Civic responsibility has received serious
attention, especially in the need to promote
social capital (Putnam 2000). However, these
ethical implications of constructivism have
a low prole in accounts of constructivism.
« 6 » A largely ignored implication of
constructivist thinking is that two realities
are uncomfortable and potentially danger-
ous. Gregory Bateson (1979) referred to this
when discussing heresy. However, we do not
need to discuss religious or political dier-
ences in the past or present to appreciate how
two visions of how things are or should be
can be divisive. Yet RC proposes that these
ideas about dierent realities depend on the
choices and past experience of their propo-
Info-computationalism or Materialism? Neither and Both Carlos Gershenson
nents, and this proposition always seems to
require explanation. If this insight could be
made more commonplace, perhaps nego-
tiations between opposing groups with dif-
ferent views on their reality would have a
sounder footing.
« 7 » Is it possible that the info-com-
putational approach could model opposing
views in ways that would facilitate negotia-
tions between rival groups? It is well known
that contact between groups has the poten-
tial to facilitate the emergence of mutual
respect. If the idea that dierent versions of
reality are what divide two groups is more
generally accepted, and if this reality can be
modelled with computer assisted represen-
tations, this might assist negotiations. e
work done in IC seems to hold this out as a
possibility, and two models seem appropriate
one a simulation model (Riegler & Douven
2009) and the other theoretical (Josué Anto-
nio Nescolarde-Selva & Josep-Lluis Usó-Do-
ménech 2013). e latter proposed model of
belief systems is mathematical, with specic
properties for understanding expressions of
culture, including text. is model should al-
low precise specication of the belief systems
of two groups who are trying to negotiate.
When groups dier in their visions of reality,
one diculty is to persuade each group that
things can be seen dierently. Is it too much
to hope that the commonplace gadgets that
are so useful to us can serve as models of lim-
ited realities? IC seems to hold much prom-
ise for facilitating a model of understanding
constructivism. Schmidt (2011) has shown
how this works cognitively. Can IC contrib-
ute? Perhaps there are computer models of
negotiations that can take these ideas and
develop them?
Hugh Gash obtained his doctorate at State University
at Buffalo in 1974 with a thesis on moral judgment.
He was a post-doctoral researcher at the University
of Georgia with Charles Smock and then worked at
St Patrick’s College, Dublin until 2010. Gash is a
member of the International Institute for Advanced
Studies in Systems Research and Cybernetics. He has
published extensively on educational applications of
constructivism, with a particular interest in changing
attitudes and children’s representations of different
others. Homepage:
R:  F 
A:  F 
or Materialism?
Neither and Both
Carlos Gershenson
Universidad Nacional Autónoma de
México • cgg/at/
> Upshot • The limitations of material-
ism for studying cognition have moti-
vated alternative epistemologies based
on information and computation. I argue
that these alternatives are also inherent-
ly limited and that these limits can only
be overcome by considering materialism,
info-computationalism, and cognition at
the same time.
« 1 » Gordana Dodig-Crnkovic argues
convincingly that materialism is insucient
for studying cognition. As an alternative,
an epistemology based on information and
computation is oered.
« 2 » Materialism has been successful in
describing physical phenomena (matter and
energy), but it cannot explain phenomena
such as cognition, life, meaning, and agen-
cy, oen falling into a mind/body dualism
(Kauman 2010). e problem with a dual-
istic perspective is that it cannot relate phys-
ics and cognition (nor life, §35); nor can it
explain how cognition depends on a physi-
cal substrate or how cognition can aect the
physical world.
« 3 » Instead of trying to describe in-
formation in terms of matter and energy,
we can describe matter and energy in terms
of information (Gershenson 2012). is
allows us to explore potential laws that ap-
ply to phenomena at all observable scales,
including the biological and the cognitive.
I dened information as “anything that an
agent can sense, perceive, or observe” (Ger-
shenson 2012: 102), and computation as a
change in information.
« 4 » IC can oer a novel perspective
on cognition, but it also has its limitations.
Even though in principle it encompasses
materialism, physics cannot be ignored, as
it can be argued that meanings are ground-
ed in a common physical space (matter and
energy), mediated by social interactions.
Intersubjectivity (§63) requires a physi-
cal medium to share and change informa-
tion. Moreover, there are physical con-
straints that limit the living and cannot be
deduced from only information. Looking
only at molecules, one cannot distinguish
living systems from non-living ones. Take,
for example, an aquarium with sh, algae,
and bacteria. From the physical perspective
there is no dierence between the aquarium
with its contents and another object with
exactly the same molecules. e dierence
lies in the organization of the components
(§39; Varela, Maturana & Uribe 1974). Con-
sidering only information, one cannot dis-
tinguish the physical from the virtual, as in
a computer simulation. If we have a physical
description of matter and energy, this can
be also described in terms of information
(Gershenson 2012), as matter and energy
can be seen as particular types of infor-
mation. Nevertheless, my argument is that
the physical substrate of cognitive systems
cannot be neglected. I claim that within a
constructivist worldview, it is not enough to
consider only the organization/information
of systems; their substrate and their rela-
tion must also be considered, as will be ex-
panded on below. is is not an ontological
claim, but an epistemological one.
« 5 » e “conict” between material-
ism and IC can be traced back to the cen-
turies-old discussion related to the concept
of emergence, i.e., that the whole is not the
sum of its parts. If physics describes the
parts, what is the “something” that makes
the whole more? As I argued in Gershen-
son (2013), this something is information,
and in particular, interactions.
« 6 » e concept of emergence seems
to be problematic in terms of causality: can
the parts cause the whole? Can the whole
cause the parts? (Bar-Yam, 2004b; Hey-
lighen, Cilliers & Gershenson 2007). Philip
Anderson (1972) showed that properties of
systems cannot be reduced to the proper-
ties of their components. And it is common
sense to agree that even when a system can
inuence its components, these may have
certain autonomy, such as an individual
in a society. Because of this, when study-
ing complex systems, parts and whole and
their interactions should be considered
at the same time in order to have a more
complete description. Multiscale perspec-
tives attempt to address this issue (Bar-Yam
2004a; Gershenson 2011).
Info-computational Constructivism and Quantum Field Theory Gianfranco Basti
Info-computational Constructivism and Quantum Field Theory Gianfranco Basti
« 7 » In a similar line of thought,
Buddhist philosophy maintains that ob-
ject, subject and the action of the subject
perceiving the object are not separable
(Nydahl 2008). is is because we cannot
describe an object without a subjective ob-
server, while subjective description is con-
strained by the object, and their relation is
mediated by the action. Focussing only on
objects we fall into materialism, with all the
limitations exposed by Gordana Dodig-
Crnkovic. Focussing only on the descrip-
tions, we fall into subjectivism, which has
also its drawbacks, as we know from the
limitations of postmodernism (Cilliers
2002). Constructivism proposes to go be-
yond these limitations by relating the social
construction of the descriptions, mediated
by shared objects and through the action of
« 8 » Materialism, IC, and cognitive
science are not separable but are comple-
mentary: objects are described by material-
ism, subjects by IC, and action by cognitive
science. e latter is not an epistemological
position, but is required to link the other
two, as it is cognitive systems that produce
information from a material substrate and
use the information to change the sub-
strate. is also considers the social aspect
of cognition, as meaning is made not only
by the interaction between object and sub-
ject, but also between subjects (§22; §56;
Froese, Gershenson & Rosenblueth 2013).
« 9 » Matter and energy (object, ob-
served) cannot be studied without consid-
ering information (subject, observer), nor
vice versa. Cognition (action, observing) is
precisely the process that relates the physi-
cal and the mental, the material and the
informational. It might not seem so, but
this is actually a monist perspective, as this
triadic interdependence does not allow us
to study dierent aspects separately. “Mat-
ter” and “energy” are concepts relative to
the cognitive subject, who constructs them
out of her experience. Only by considering
matter, information, and cognition at the
same time, will we have a better under-
standing of them all.
Carlos Gershenson is a full time researcher and
head of the computer science department of the
Instituto de Investigaciones en Matemáticas
Aplicadas y en Sistemas at the Universidad Nacional
Autónoma de México (UNAM), where he leads the
Self-organizing Systems Lab. He is also an affiliated
researcher and member of the directive council
at the Center for Complexity Sciences at UNAM.
R:  F 
A:  F 
Constructivism and
Quantum Field Theory
Gianfranco Basti
Pontical Lateran University, Italy
> Upshot • Dodig-Crnkovic’s “info-com-
putational constructivism” (IC), as an es-
sential part of a constructivist approach,
needs integration with the logical, math-
ematical and physical evidence coming
from quantum eld theory (QFT) as the
fundamental physics of the emergence
of “complex systems” in all realms of
natural sciences.
« 1 » In this commentary I suggest how
QFT, with its logic and its epistemology, can
support, integrate or even correct some IC
notions, always clarifying them at the fun-
damental levels – logical, mathematical, and
A change of paradigm: From
mathematical physics to physical
« 2 » In the second section (§§7–27),
Gordana Dodig-Crnkovic “expounds the
two basic concepts of IC” (§6). ese are the
notions of “natural information” and “natu-
ral computation,” as far as they are based
on the information approach to quantum
physics, and hence distinguished from their
usual notions, respectively, of symbol trans-
mission (information) and symbol manipu-
lation (computation).
« 3 » ere are several theoretical ver-
sions of the information theoretic approach
to quantum physics (cf. Fields 2012), which,
for the present purpose, can be reduced to
essentially two.
« 4 » e rst one is related to a classical
“innitistic” approach to the mathematical
physics of information in quantum mechan-
ics (QM). Its proponents include Heinz-
Dieter Zeh (2004, 2010) and Max Tegmark
(2011). Typical of this approach is the notion
of the unitary evolution of the wave func-
tion, with the connected, supposed innite
amount of information it “contains,“made
available” in dierent spatio-temporal cells
via the mechanism of the “decoherence” of
the wave function. is approach needs to
assume an external observer (“information
for whom?” Fields 2012). It uses Claude
Shannons purely syntactic measure and no-
tion of information (Rovelli 1996).
« 5 » Dodig-Crnkovic refers essen-
tially to this innitistic approach when she
speaks about natural information/computa-
tion, equating “natural computation” with
“morphological computing, i.e., computa-
tion governed by underlying physical laws,
leading to change and growth of form” (§9).
at is, physical/chemical/biological proc-
esses relate to the progressive emergence
of ever more complex natural structures of
matter, from hadrons and leptons to atoms,
to molecules, to cells, tissues, organs, and
organisms, up to social groups (§11).
« 6 » Assuming that the mathemati-
cal laws of nature “produce” the ever more
complex structures characterizing our
evolving universe seems in contradiction
with constructivism. “Eects” are produced
by “causes” not by “laws.” ey “rule” a caus-
al process, making its evolution in time pre-
dictable (or, conversely, retro-dictable) to
observers. Hence, it is not “kinetics, dened
as the geometrical laws of mechanics, but
“dynamics,” dened as the dierent types
of forces and force elds, “causally” acting
on material things (processes, particles, sys-
tems, etc.), that produces the dierent forms
of “orders.” ey can be “quantied” through
their proper “order parameters,” character-
izing the emergence of ever more complex
systems at all levels of matter organization
in nature and self-organization. is also
holds in quantum physics and explains the
epistemological dierence between QM and
Info-computational Constructivism and Quantum Field Theory Gianfranco Basti
Info-computational Constructivism and Quantum Field Theory Gianfranco Basti
QFT. It justies the evolutionary emergence
of the same mathematical laws of nature
with the processes they rule and therefore
contradicts such laws’ “immutability, as-
sumed by the dualistic Platonic ontology
underlying the Newtonian paradigm since
the beginning of modern science. is sug-
gests changing the mathematical physics
of the Newtonian approach to the physical
mathematics of constructivism.
« 7 » Such an alternative is related to
QFT, which is a “nitistic” approach to the
physical mathematics of information, taken
as a fundamental physical magnitude to-
gether with energy. QFT makes it possible
to span the microphysical, macrophysical,
and even the cosmological realms within
a single quantum theoretical framework,
which is dierent from QM (Blasone, Jizba
& Vitiello 2011).
« 8 » In contrast to QM, in QFT sys-
tems, the number of degrees of freedom
is not nite, “so that innitely many uni-
tarily inequivalent representations of the
canonical commutation (bosons) and
anti-commutation (fermions) relations ex-
ist” (Blasone, Jizba & Vitiello: 18). Indeed,
through the principle of spontaneous sym-
metry breaking (SSB) in the “ground state”
(i.e., in the state at 0 energy of the system),
innitely (not denumerable) many quan-
tum vacua conditions compatible with the
ground state exist. Moreover, this holds not
only in the relativistic (microscopic) domain
but also applies to non-relativistic many-
body systems in condensed matter physics,
i.e., in the macroscopic domain, and even
on the cosmological scale (Blasone, Jizba &
Vitiello 2011: 53–96).
« 9 » Several phenomena related to
what Dodig-Crnkovic calls “morphological
computing” can be found in QFT, and in
the SSB of quantum vacuums as their fun-
damental explanatory dynamic framework.
is includes: the thermal eld theory; the
phase transitions in a variety of problems at
any scale; and the process of defect forma-
tion during the process of non-equilibrium
symmetry breaking in the phase transi-
tions, characterized by an order parameter.
All these phenomena and many others are
fruitfully approachable by using the same
principle of “nonequivalent representa-
tions” in QFT. For the same reason, and to
go back to Turing’s early suggestion, even
though on a dierent basis (see below), I
suggest using the notion of “morphogenetic
computing” in IC.
« 10 » Another fundamental charac-
ter of IC mentioned right at the beginning
in §1 has its proper fundamental dynamic
explanation in the QFT approach. It is the
IC principle inspired by Gregory Bateson’s
seminal idea of the “necessary unity be-
tween a biological (and hence cognitive)
system and nature” (Bateson 2002), accord-
ing to which,
for dierent type of agents, the same data input
[…] will result in dierent information. […] e
same world for dierent agents appears dier-
ently. (§2)
is principle has its proper causal and
mathematical explanation in the QFT for-
malism of “algebra doubling” for justifying
the intrinsic character of the thermal bath
in QFT systems.
« 11 » In the context of QFT, the notion
of non-symbolic, “morphogenetic computa-
tion,” which has its proper ancestor in Alan
Turing’s pioneering work on “morphogen-
esis” (Turing 1952; see §9), has its deepest
justication at the level of fundamental
physics. In fact, it concerns the various dif-
ferent physical interpretation of the Heisen-
berg uncertainty principle and of the related
particle-wave duality.
Wigner functions, quasi-
probabilities and the notion
of “natural information”
« 12 » QFT may also oer a rigorous
pathway for a quantitative denition of the
IC notion and measurement of “natural in-
formation” (§7), as distinct from the syn-
tactic notion and measurement of Shannon
information used in QM, and that cannot
justify in principle any constructive, causal
approach to complexity.
« 13 » Indeed, because of the intrinsic
openness to the quantum vacuum uctua-
tions of any QFT system, and because of
the associated thermal bath, it is possible
in QFT to dene thermodynamic operators
such as “entropy” and “free energy,as well
as the dynamic role they play in the dierent
QFT systems. Schrödinger “negentropy” is
indeed “free energy,” that is energy “properly
channeled” toward the “right places” where
it can perform “work.” e “free energy” is
thus “ordered energy.
« 14 » e widespread applicability of
QFT is claimed by Massimo Blasone and
colleagues, who address an important as-
pect, i.e., that quantum eld dynamics is
not conned to the microscopic world only
but rather includes the whole domain of
fundamental physics, from cosmology to
the physics of condensed matter, living, and
neural systems:
[C]rystals, ferromagnets, superconductors,
etc. are macroscopic quantum systems. ey are
quantum systems not in the trivial sense that
they are made by quantum components (like
any physical system), but in the sense that their
macroscopic properties, accounted for by the or-
der parameter eld, cannot be explained without
recourse to the underlying quantum dynamics
(Blasone, Jizba & Vitiello 2011: ix).
« 15 » From the computability theory
standpoint, this means that a physical sys-
tem in QFT, in contrast to the Turing Ma-
chine paradigm, is able to change dynami-
cally the basic symbols of its computations,
since – according to the QFT uncertainty
principle – new collective behaviors can
emerge from individual ones, or vice versa.
is justies the denition of the informa-
tion associated with a Wigner distribution
as a semantic (non-syntactic) information
content, since the system is able to change
dynamically the codes of its computations,
so to suggest a new, semantic sense of the
notion of “computational dynamics.2
« 16 » In Basti (2014), I demonstrated
that in formal logic an inference process,
based on such a probability calculus, in
which the basic symbols – and hence “truth
– between the antecedent and the conse-
quent are not conserved cannot satisfy the
logical connective of the material implica-
tion (pq (1011)). On the contrary, it sat-
ises the logical connective of the converse
implication (pq (1101)), i.e., the connec-
tive of all the “form generation” or morpho-
genetic processes. However, it is the logic of
2 | To avoid misunderstandings, the notion
of “semantic” information and computation al-
lowed by the QFT notion of “coherence domain
constitution” has nothing to do with Tarski’s truth
On the Emergence of Meaningful Information and Computing in Biology Walter Riofrío
On the Emergenceof Meaningful Information and Computing in Biology Walter Riofrío
an inductive inference, not as a logic of the
(empirical) corroboration of true proposi-
tions already given, but as the logic of the
Aristotelian (onto-logical) constitution of
new true propositions. is means that the
IC notion of “morphogenetic computation
is non-symbolic in the syntactic TM sense
(see §32), because it is the computational
dynamics process of new symbol dynamic
generation, and not of the syntactic symbol
Conclusion: Toward a constructivist
change of paradigm in modern
« 17 » e novelty of the constructivist
approach, with the support of IC and QFT,
can be summarized in the slogan “from
mathematical physics to physical math-
ematics.Paul Davies describes it in the fol-
lowing way:
In a universe limited in resources and time –
for example, in a universe subject to the Lloyd’s
cosmic information bound – concepts such as
real numbers, innitely precise parameter values,
dierentiable functions and the unitary evolution
of the wave function are a ction: a useful ction
to be sure, but a ction nevertheless. (Davies
2010: 82)
« 18 » In other words, the change of
paradigm consists in turning the dualistic
“Platonic” relationship, characterizing the
Galilean-Newtonian beginning of the mod-
ern science:
Mathematics Physical Laws Information
into the QFT one, which has a greater heu-
ristic power:
Information Mathematics Physical Laws
« 19 » e key problems for further re-
search are about the notion and measure of
“natural information” in QFT, in as far as it
the notion and measure of natural in-
formation, based on the notion and
measure of quasi-probability,” typical of
WF, and of a QFT approach to quantum
computing, and hence,
the morphogenetic computational para-
digm with its proper logic, and mathe-
matics – set theory (meta-mathematics)
is is an amazing, huge, constructivist, re-
search project for several future works.
Gianfranco Basti is Full Professor of Philosophy
of Nature and of Science at the Pontifical Lateran
University in Rome. His research interests are mainly
directed to the formal logic and the formal ontology
of the cognitive neurosciences. He is the author of
five books and of more than one hundred and twenty
scientific and philosophical papers on philosophy of
logic, cognitive sciences, and philosophy of mind.
R:  F 
A:  F 
On the Emergence
of Meaningful Information
and Computing in Biology
Walter Riofrío
Cayetano Heredia University, Peru
> Upshot • Info-computational con-
structivism calls attention to some of
the open questions about the origins of
information and computation in the liv-
ing realm. It remains unclear whether
both were developed and shaped by evo-
lution by natural selection or if they ap-
peared in living systems independently
of it. If the former, it is possible to sketch
a scenario with a certain degree of rea-
sonableness and postulate some of the
conditions that triggered the emergence
of these biological properties.
« 1 » e evolution of the rst living
cells began about 3.8 billion years ago and
the rst multicellular organisms appeared
nearly 1 billion years ago. ese facts tell us
that the time to evolve from simple cells to
more complex cellular systems was almost
three times more than that for the evolution
of all the multicellular organisms (includ-
ing humans). e great complexity within
modern cells expresses very soundly a need
for new approaches to understanding the
most central properties of living systems
(Riofrio 2007) and conditions for the emer-
gence of cognition in evolution (Heyes &
Huber 2000; Gontier 2010). One interest-
ing alternative in this direction is the info-
computational constructivism proposal. For
instance, in §3, Gordana Dodig-Crnkovic
claims that computation is information
processing (a reformulation of Heinz von
Foerster’s physical computation). Her aim is
to develop a model of natural computation
that is more general than that of the infor-
mation processing capabilities in the Turing
« 2 » On the other hand, looking at
the dynamics of microorganisms, we ob-
serve the massive acquisition of new genes
through horizontal transfer3 (Jain, Rivera &
Lake 1999; Sowers & Schreier 1999). Hori-
zontal gene transfer is an important evolu-
tionary driving force in microorganisms.
Although gene exchange is easier in closely
related organisms, it is proposed that hori-
zontal gene transfer played a central role in
the evolution of archaea and bacteria (Boto
2010). Moreover, according to Carl Woese
(2002), the “origin of speciation” is marked
by the shi in early phylogenic adaptation
from vertical to horizontal gene transfer.
is picture leads to the postulate that the
time of the major transition of evolution-
ary mechanisms was the passage from hori-
zontal transfer to the beginnings of vertical
« 3 » Pursuing the connection between
information, natural computation and cog-
nition within a broad framework allows at-
tention to be put on the conceptual necessity
of capturing the semantic aspect of these.
Clearly, if one can defend information being
the base of natural computation and cogni-
tion, the following is also correct:
e ability to detect and respond to meaningful
information is essentially a biological phenom-
enon, since there are no inanimate information
detectors in nature. Information and energy are
both fundamental properties of organized mat-
ter that reect the complexity of its organization
[…] (Reading 2011: 9)
3 | Horizontal gene transfer (HGT) refers
to the transfer of genes between organisms in a
manner other than traditional reproduction. It
has played a major role in bacteria and archaea
evolution and is fairly common in certain unicel-
lular eukaryotes.
On the Emergence of Meaningful Information and Computing in Biology Walter Riofrío
On the Emergenceof Meaningful Information and Computing in Biology Walter Riofrío
« 4 » e important thing is the way in
which biological entities are self-organized,
because inside these complex macromolec-
ular connections certain kinds of informa-
tion detectors have appeared in evolution,
such that:
Meaningful information can thus be dened
as a pattern of organized matter or energy that is
detected by an animate or manufactured recep-
tor, which then triggers a change in the behavior,
functioning, or structure of the detecting entity
[…] If there is no eect on the detecting entity’s
behavior, functioning or structure, the infor-
mation is considered to be meaningless […]
(Reading 2012: 638).
« 5 » Certainly, this pattern of organized
matter or energy is a kind of “pattern” only
to the biological entities that have the capac-
ity to detect it. An interesting question is
that of when some living components start-
ed to behave like information detectors in
the course of biological evolution. It seems
the answer is again related to the epoch in
which the self-organization of intertwined
macromolecular connections reached a suf-
cient degree of complexity such that this
new entity started to behave as an autono-
mous agent (Kauman 2000).
« 6 » In order to integrate these aspects
into a possible scenario, it is important to
establish a relationship between meaningful
information, natural computation and evo-
lution as follows.
« 7 » If one claims the hypothesis that
the emergence of cellularity was earlier in
evolution than previously thought (Mo-
rowitz 1992), then my proposal of a kind
of dynamic self-organization originating at
the dawn of the prebiotic world is feasible.
It could contain the most basic properties
of living systems: information, function and
autonomy (Riofrio 2007). If this is correct,
it is rational to contend that what is men-
tioned above could signal the beginnings of
a kind of prebiotic evolution that led, very
much later, to the rst horizontal gene trans-
fer dynamics (Riofrio 2010).
« 8 » Furthermore, sharing certain com-
ponents and structures acquired and trans-
mitted through these sources was possibly
the way that the most ancient populations
of protocells evolved. Maybe this was also
the way that novel structures, components,
molecular networks, characteristics, proper-
ties and the like were generated by the rst
dynamic protocells (Riofrio 2011).
« 9 » Moreover, in agreement with
Dodig-Crnkovic and Anthony Reading’s
quote above with respect to meaningful in-
formation, my proposal of biological infor-
mation as a relational notion will depend on
biological processes and is related to what-
ever kind of energy variation might occur
in a biological system. If this kind of energy
variation is incorporated into the system
as a variation – with the capacity to become
part of the systems processes, the system
will have the capability to react accordingly.
On the other hand, if an energy variation
does not have the capacity to be incorporat-
ed in the form of a variation in the system,
the system cannot develop a response. is
is the way that information emerges in the
biological world as meaningful information,
as information with biological meaning or
“bio-meaning” (Riofrio 2008: 365–366).
« 10 » e minimum complexity dis-
cussed above would be necessary for con-
ditions to be ripe for the emergence of the
most fundamental properties of life. It
would have to be possible to contend the ex-
istence of two very interconnected processes
behaving as the rst prebiotic constraints:
(1) a container made of amphiphilic mol-
ecules4 and (2) a micro cycle, driving the
protocell far away from thermodynamic
equilibrium. is latter constraint would
then cause a change in the system’s free en-
ergy, i.e., a trend towards negative values,
and turn into an unavoidable checkpoint
along the pathway of creating a future set
of responses that are generated in another
part of the interconnected and interdepend-
ent processing network. In consequence, it
would have provided the conditions for the
emergence of the rst small world structures
as core characteristics of the way in which
the biological realm computes. And some
kind of “horizontal-like” evolution may have
been the rule in those remote epochs (Riof-
rio 2012).
« 11 » Taking into account the above-
mentioned sketch of my proposal, together
with the info-computational approach, it is
4 | I.e., molecules having a polar water-solu-
ble group (hydrophilic) attached to a water-insol-
uble hydrocarbon non-polar part (hydrophobic).
possible to discern some directions in future
research in the growing eld of biological
information. is eld is visualized as the
structure in which biological computation
is dened as its dynamics, inside the bio-
logical realm. Firstly, it seems important to
study the character of biological processes
understood as non-algorithmic computa-
tion and the nature of some kind of ecient
formalization able to represent the major
points of this dynamic in order to reproduce
it in simulations. Secondly, it is important
clarify to what extent biological computa-
tion could show us the central aspects of a
universal model underlying all natural com-
putation. irdly, it is the idea that the info-
computational model includes open systems
in communication with the environment. In
other words, the proposal that the environ-
ment is constitutive to an open, complex,
info-computational system could shed more
light on certain important problems in biol-
ogy, for example, the elaboration of a theo-
retical biology and the origin of a signaling
network (Dodig-Crnkovic 2010a). Finally,
focusing on the study of evolutionary dy-
namics in prebiotic systems may widen the
framework and application of some notions
involved in the combinatorial optimization
problem such as evolutionary computation
(Riofrio 2013).
Walter Riofrío is Associate Research Professor
in the Neuroscience and Behavior Laboratory-LID,
Faculty of Science and Philosophy at Cayetano
Heredia University. He is working on the framework
of complex systems science in topics such as the
origins and properties of prebiotic systems, the
structure of cell signaling networks and the nature
of neural information processing, among others.
R:  F 
A:  F 
Author’s Response Gordana Dodig-Crnkovic
Author’s Response
Why We Need Info-
computational Constructivism
Gordana Dodig-Crnkovic
Mälardalen University, Sweden
> UpshotThe variety of commentaries
has shown that IC impacts on many dis-
ciplines, from physics to biology, to cog-
nitive science, to ethics. Given its young
age, IC still needs to ll in many gaps,
some of which were pointed out by the
commentators. My goal is both to illumi-
nate some general topics of info-compu-
tationalism, and to answer specic ques-
tions in that context.
« 1 » It is my rst and pleasant duty to
thank all commentators for their attentive
and insightful contributions. I learned a lot
following their arguments and numerous in-
structive references. No doubt many of the
topics addressed by the commentators are
fundamental and would deserve a full article
of their own. Here, I can just focus on the
main criticisms and sketch future lines of
research for info-computational construc-
tivism (IC).
Why IC and what makes
it constructivist?
« 2 » e probably most fundamen-
tal question was raised by Manfred Füllsack:
Why a new framework, a new variety of con-
structivism? For me, IC is not so much a new
theory but rather a theoretical framework
constructed such as to accommodate up-
grades. Our (scientic) knowledge is rapidly
changing. So we have to make updates to
keep various pieces of knowledge in a well-
connected system. In the context of IC, this
addresses, in particular, theories of infor-
mation and computation, communication,
computability theory, neuroscience, new
branches of physics (including the question
of the observer in physics1), theory of knowl-
edge, and cognitive science, among others.
IC tries to integrate all this new knowledge
1 | Cf. the subjective Bayesian account of
quantum probability (Baeyer 2013) and Otto
Rössler’s Endophysics (Rössler 1998).
in a coherent epistemological framework.
It is constructed from two basic concepts,
information and computation, represent-
ing two complementary phenomena: struc-
ture and process, being and becoming. As a
bottom-up synthetic process, IC aims at re-
constructing knowledge production, starting
from physics, via chemistry then biology, up
to cognition in terms of info-computation.
It connects matter/energy with agency and
biology/cognition with consciousness as
the highest level of information integration
in living agents with nervous systems. Con-
nections are based on processes of natural
computation on structures of natural infor-
mation on a variety of levels of organization.
« 3 » Søren Brier is critical when it comes
to placing IC among constructivist ap-
proaches. He argues that
Philosophically, it does not suciently accept
the deep ontological dierences between various
paradigms such as von Foerster’s second- order
cybernetics and Maturana and Varelas theory of
autopoiesis, which are both erroneously taken to
support info-computationalism. (Upshot)
« 4 » I am aware that using elements
from dierent approaches and incorporat-
ing them into IC results in new contexts
in which those elements acquire dierent
meanings. For example, Maturanas reluc-
tance to base his theory of autopoiesis on
the concept of information may be related
to the fact that in the time of rst-order
cybernetics and the early days of articial
intelligence, “information meant “sym-
bolic information and computation was
conceived as symbolic program execution.
IC, on the other hand, is built upon natural
information and natural computation, which
are much broader concepts that allow us to
develop models of biological systems. is
clearly relates to Heinz von Foerster’s re-
search in biological computing in the 1960s
and 1970s, which opposed symbolic arti-
cial intelligence. However, I must add that
IC is in its beginnings, and is still far from
being able to model autopoietic systems in
detail. Nevertheless, the work of Andrée C.
Ehresmann presented in this issue as well as
in Ehresmann (2012) shows the direction for
how this can be done mathematically.
« 5 » e constructivist character of IC
can be characterized as follows. It assumes
the existence of potential information. is
potential information actualizes through
interaction with an agent. An agent is an
entity that can act on its own behalf. It is
also an informational structure for other
agents. Living agents are agents character-
ized with self-* properties (self-organizing,
self-adaptive, self- optimizing, self-protect-
ing, self-managing, self-healing). All agents
use dierences that make a dierence in
their environment (Bateson 1972) to con-
struct their realities and to act based on that.
rough interaction with the environment,
living agents modify their morphology
based on self-organization and autopoiesis
and evolve through constructive processes.
Networks of data form information, and
networks of data networks (i.e., networks of
information) self-organize as knowledge for
an agent.
« 6 » For Marcin Schroeder, such a char-
acterization seems in contradiction with
the constructivist position, which “gives
the active and primary role in the process
of construction of knowledge to the mind
(§4). He wonders how this position can be
reconciled with the view that all nature is
a computational process: “What can be the
contribution of the mind to a universal pro-
cess seemingly governed by external, inde-
pendent rules?” (ibid.)
« 7 » Whatever mind is, in the comput-
ing nature mind is computational process.
However, computation does not refer to a
universal process seemingly governed by
external, independent rules.” Rules are not
external but internal to the mind and its
substrate. I make a distinction between cog-
nition as a property of any living organism
and mind as a specic info-computational
process that is essential for living beings
with nervous systems. Mind is a result of
active engagement of an agent with the en-
vironment. It is evolutionary, morphological
process of intrinsic, natural computation of
a kind that Ehresmann describes in her com-
« 8 » Schroeder continues that we de-
viate from the main tenet of constructiv-