ANSWER TO COMMENTS – CONSTRUCTIVIST FOUNDATIONS

LEARNING BY COMPUTATION ON INFORMATION NETWORKS

How knowledge network of Info-computational Constructivism relates to the knowledge network of Constructivism and how foundations of matter and cognition may be understood info-computationally

When I sent in my target article to Constructivist Foundations, I was trying to anticipate what sort of commentaries it would elicit. However optimistic I was, I would never dare to expect such richness and diversity of encouraging responses that I received. It is my first and pleasant duty to thank all commentators for their attentive and insightful contributions. I learned a lot following their arguments and numerous instructive references. It will keep me busy for a while to address them all in depth and they will certainly inspire my future work. Among the topics commentators addressed there are several fundamental ones that deserve a full article each, not just a brief exchange. My goal is both to illuminate some general topics of info-computationalism, and to answer specific questions in that context.

1. Why IC and in what why is it constructivist?

Before we even start the discussion, one may ask the question: why? Manfred Füllsack in §2 asks this question in his commentary: Why a new framework, a new variety of constructivism?The simple answer may be that one could see it as an update. World is changing and our knowledge is changing constantly and rapidly. We have to make updates in order to bring different pieces of information into a well-connected system. While Herbert Wiener, Ernst von Glasersfeld, Heinz von Foerster, Humberto Maturana and Francisco Varela were pioneers of a new comprehension of knowledge generation, our picture of the world continues to grow in many related directions. The most obvious sources of the current growth of knowledge: information and communication/computation theory and technology, information science and computing, neuroscience and study of brain, physics with its new approaches to matter-energy in terms of information, but also to the question of observer in physics QBism, "subjective Bayesian account of quantum probability" (Baeyer von, 2013a, 2013b), Endophysics (Rössler, 1998), cognitive sciences, cognitive robotics, bioinformatics, … the list goes on. All that new knowledge forms elaborate networks of information, which connect researchers, schools, methodologies, theories, and data. Those networks of new knowledge from variety of fields should be connected to the rest of existing knowledge, including networks of constructivism as a living adaptive learning system.

The raison d'être of IC as a new constructive approach is that it can address in a productive way questions important for constructivism. IC itself is constructive in several respects: the theory is constructed from two basic concepts – information and computation, representing two

complementary phenomena: structure and process. It is thus a bottom-up synthetic process that aims at re-constructing physics and biology (cognition) in common terms. It connects matter-energy with agency and biology/cognition with consciousness as the highest level of information integration in living agents with nervous systems. Connections are made based on processes of natural computation on structures of natural information on variety of levels of organization of information.

Søren Brier’s comment addresses the relationship between IC and different branches of constructivist network of networks:“(3) Philosophically, it does not sufficiently accept the deep ontological differences between various paradigms such as von Foerster’s second- order cybernetics and Maturana and Varela’s theory of autopoiesis, which both are erroneously taken to support info-computationalism.”

The above observation states that IC in an eclectic way uses elements from different approaches and incorporates them into its own construction against the fact that in the new context those elements borrowed from Maturana and Varela’s autopoiesis combined with von Foerster’s second- order cybernetics necessarily get different meaning.My response is that the theory building must follow knowledge accumulation that has been made since the time learning theories of von Glasersfeld and von Foerster have been developed as well as cognitive theory of Maturana and Varela. For example Maturana’s unwillingness to base his theory on the concept of information as in first-order cybernetics probably have been based on the fact that “information” at that time was symbolic information and computation (in spite of von Foerster experiments with biological computing) still 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. It should be pointed out that the field of IC is in its beginnings, and still far from its goal of modeling an autopoietic system of Maturana and Varela with the approach of biological computing of von Foerster.

IC takes constructivist position in the following. It assumes the existence of potential information of the physical world that constitutes an agent. An agent is an informational structure for other agents. An agent is an entity that can act on its own behalf. Living agents are agents characterized with self-* properties (self-organizing, self-adaptive, self- optimizing, self-protecting, self-managing, self-healing). All agents use differences that make a difference in their environment to act. Networks of data form information, and networks of data networks (i.e. networks of information) form knowledge for an agent.

Marcin Schröder reflects: §1. “ If we understand constructivist position as a view which gives to the mind the active and primary role in the process of construction of knowledge, how can we reconcile it with the view that all nature is a computational process? What can be the contribution of the mind into a universal process seemingly governed by external, independent rules?”

The explanation is that whatever mind is, in the computing nature mind is computational process. However, computation is not “a universal process seemingly governed by external, independent rules”. Rules are not external but internal and that difference is important. The logic is as follows. If

we take entire nature to compute on different levels of organization, all possible processes in nature are computations.

§ 5. “When we give the priority to the external universal process (computational, or not) in which mind can participate in various degrees, but is a subject of its rules and is not contributing to it as a creator or constructor, we deviate from the main tenet of constructivism. “ (Schroeder)

Here an assumption is made that I didn't make, namely that “we give the priority to the external universal process”. Info-computationalism is constructed as a way to dissolve the problem of matter-mind duality. Assuming that whatever the physical world is in itself (as Kant‘s noumena) for a cognizing agent it represents information. Reality for an agent is informational structure, network of networks of data obtained through interactions with the word. In (Dodig-Crnkovic, 2014) I present mechanisms of agency in the world necessary for knowledge construction. Each cognizing agent (and I argue with Maturana and Varela that it holds for every living organism) actively constructs its reality from interactions with the world. The fact that processes of construction are understood as (still theoryless) computation processes does not change anything; they are the same processes of construction that Ernst von Glasersfeld (Glasersfeld, 1995) refers to in relation to the concept of reality: "It is made up of the network of things and relationships that we rely on in our living, and on which, we believe, others rely on, too" (p.7). and "To the constructivist, concepts, models, theories, and so on are viable if they prove adequate in the contexts in which they were created" p.7. According to Glasersfeld "knowledge does not reflect an objective, ontological reality but exclusively an ordering and organization of a world constituted by our experience" (Glasersfeld, 1984) p.24.

“When I make this point it is often assumed that I deny reality. In fact, this is not so. I merely deny that we can know reality, if by “reality” we intend something that “exists” and has a structure independent of the human knower.” (Glasersfeld, 1991)

One of the criticisms put forward by Schroeder in § 6. regards my use (following Stuart Umpleby) of the concept of observer.

“Here the use of the term “observer” is curious, in the context of constructivist position, but the “damage” to the constructivist tradition seems not beyond the repair, if we substitute the words “construction” and “constructor” for the words “observed” and “observer.” “

Constructivism was never a homogeneous movement and we as interpreters actively construct our understanding of it. On a more detailed level I refer to Umpleby who originally made the distinction I refer to. Furthermore, I think that replacing “observer” with “constructor” would not be in the tradition of constructivism that I am familiar with.

In § 8. Schroeder rightly criticizes my use of the mechanism of self-organization instead of auto-poiesis. I should have spent more time explaining the difference. My position is that the basic generative process is that of self-organization. Autopoiesis is the result of self-organization in which closure has been obtained (through self-organization). A cell, even though a result of self-

organization is a very special system with autopoietic process that not only sustains pattern-formation but also organization-formation and maintenance.

“§ 9. In what sense can we say that autopoietic system is self-organizing, which means developing (i.e., changing) its organization, and at the same time conserving its organization?” The contradiction is resolved if we recall that cells are dying and being replaced all the time. Skin cells live about two or three weeks, red blood cells live some four months, while white blood cells live on average about a year. We are still the same organism even though not identical. Similar processes are going on inside the cell. In the huge dynamical machinery of a cell, molecules are transmitted, re-combined, degraded, expelled etc. And yet, dynamically, cell keeps its organization. So it is actually form that is preserved while “matter” is in flow.

For Schroeder, § 10. “It seems quite clear that the expression “autopoietic machine” in the fundamental concept of autopoiesis is not accidental, and that it refers to the fact that its ontological status is clearly rooted in the body side of Cartesian dualism, with the objective of subordination or elimination of the mind side.”

I have to confess that I feel strongly attracted by the approach of Maturana and Varela that aimed at understanding knowledge as biological phenomenon. That is a project I would like to contribute to. “Biologization” does not mean to eliminate mind, just to naturalize the mind. It just means to understand the real world (constructive) mechanisms that bring that mind about, as processes in specific kinds of living physical systems. We still have long way to go before the whole project of relating mind to biological processes is thoroughly researched.

2. The relationships between realities for agents

§3 “So if we agree with the constructivist assumption that there is no unobserved reality, we need a rather demanding theory with not just one but two “first” entities to start with. “ Manfred Füllsack

We can say that there is no unobserved reality in a sense that remembered reality is previously observed reality, while anticipated reality is a projection of the previously observed reality to the imagined potential future circumstances (scenarios).

Füllsack identifies an important modeling detail of IC – the origin of observers. Even though it would be instructive to understand the origins of observers, IC starts with the existing physical world in which different physical structures function as each other’s observers. Living world encompasses multitude of living agents who interact and thus engage in mutual “observation”. Humanity consists of seven billion people who exchange information. In what way can we say that there is no unobserved reality for humans? The claim is that nothing can be real for humans, which never have been observed by anybody or anything else. I agree, even though we can reconstruct reality that no human have (directly) observed – like the life dinosaurs may have lived. The entire

history has the character of reconstructed knowledge, not only observed but also memorized, forgotten, glued together from pieces of pottery and bones. The basis of knowledge is obviously not only a direct observation. Huge parts of modern physics are non-observable – they are logically deduced from observable data with help of theoretical models. Can deductions be seen as inevitable results of observed reality? In a sense yes, they reflect observed regularities of the physical world that also govern functioning of human body and brain.

§4 According to Füllsack, IC “implies the counter-intuitive picture of an “initially” differentiated world, or of a system that in its origins is sufficiently complex to harbour (at least) two subsystems of which one can make a difference in reaction to the difference in the other. “

Maybe we do not need to assume more than we actually can observe today? As we have multitude of different “observers” we do not need to postulate them. Reconstruction of the origin of observer assumes a lot of reasoning that goes beyond observation, and will need some elaboration.

Further on, §5 “This assumption about the existence of at least two observers ‘challenges additional explanations which might not be entirely deliverable through informational and computational theory.’ “

In what follows, I will show that info-computational approach can deal with the world where an agent (by intrinsic, natural computation) constructs his/her/its reality based on observation. Learning as conceived in cognitive developmental robotics is instructive. Learning about the environment is implemented by using sensors, actuators and information processing/decision making. In its present shape, robot is a good experimental platform for testing how an agent operates for whom reality is based both on its individual observation and on social component of knowledge that is what we have built into the robot, which is learned from previous experience. The problem of making distinctions (as aptly addressed by Spencer-Brown) is a problem of sensors for a robot. If its sensors are adequate, sensitive enough, placed in right way and connected with the control system, they will provide distinctions that will enable the robot to cope with its world. The “difference that makes a difference” for such a robot is simply the difference that its sensors can detect and process.

§9 Observed/Observer Duality through DEACON’s approachIn his Incomplete Nature (Deacon, 2011) Terrence Deacon gives a first sketch of the mechanisms of emergence of human cognition from unconscious matter via first self-sustaining and self generating self-organizing biological unit, the autogen, generated as a combination of a process that generates energy with a process that consumes energy, as processes of autocatalysis and self-assembly. Autogen is the rudimentary form of life, and it possesses "ententionality" that is rudimentary form of intentionality i.e. teleological, goal-directed behavior. Ententionality is realized through "teleodynamics", that is the behavior under constrains built by autogen. Teleodynamics creates ententionality, which creates intentionality, which creates consciousness. First rudimentary intentional observer can be identified as autogen.

The goal of IC is similar to Deacon’s project of reconstruction. Info-computational constructivism aims to provide generative mechanisms (intrinsic natural computational processes) that change informational structures and generate new forms – from the physical and chemical level to the

simplest biological structures, cells and organisms with increasingly complex cognitive capacities up to humans and human social cognition.

Marcin Schroeder in his commentary asks: who is in charge of the construction? The answer is simple: in IC agents are in charge of the construction. Internally agents construct their realities from the environmental information combined with memorized information. Externally they construct social reality. Agents exist at many levels of organization – neurons are local agents that generate the global processes in the brain.

Natural computation that drives agent behavior is an embodied, physical, intrinsic computation. The development from mathematical physics and physical mathematics as aptly observed by Gianfranco Basti reflects the shift from conventional model of computation as symbol-manipulation, to natural/ morphogenetic computation that is grounded in physical process instead of its abstract representation. That is the trend towards extension of the concept of computation to include physical (analog) computation is addressed in several articles by Barry Cooper (Calude & Cooper, 2012; Cooper, 2006, 2012a, 2012b) who’s research in the field of Computability emphasizes the importance of the difference between physical system and a mathematical model of that system.

Hugh Gash in §1 wonders “whether the IC position might offer a solution to explaining the RC position [on different realities] and make it less irritating.” The answer is yes. First, understanding similarities of mechanisms of reality construction in different kinds of cognitive agents such as animals, plants and machines can help to grasp the necessity of different realities for different agents. This does not have to do with subjectivity but with different cognitive architectures. Those architectures decide what information is possible for an agent to perceive and process. Societies of agents (in the sense of Minsky) build cognitive architectures. An isolated neuron or an isolated bacterium is cognitively very different from a brain or a bacterial colony. The essential information processing takes place through interaction in the distributed system. IC helps to demystify cognitive processes in different cognitive architectures as computational processes on variety of levels of organisation of information. Talking about the mechanisms of knowledge generation in humans, it is becoming increasingly evident from developmental robotics how cognizing agents construct knowledge through interactions with the world. We have still a long way to go before we understand exactly how this process leads to higher cognition, but it is just the matter of time and further study to understand how such a complex system as human brain handles information from molecular to cellular level, in neural circuits, brain regions and on the whole brain level.

Related to the question of reality is Søren Brier’s objection to IC:

“ (2) It cannot encompass human concepts of subjective experience and intersubjective meaningful communication, which prevents it from being genuinely transdisciplinary.”

IC addresses the phenomenon of subjective experience from the third person perspective, as IC adopts scientific approach. It is similar to the approach of medicine where pain of a patient (first person experience) is studied (and relieved) from the third-person perspective.

3. Computation and creativity. Deterministic vs. nondeterministic computation. Space for free will.

Hugh Gash in §1 remarks: “Creative problem solving often requires a flash of insight and a new conceptualisation. One feature involved in such processing is non-linearity. Are such problems and processes an inspiration or a stumbling block for new IC developments like parallel computing processes? “

Many people (including some computing professionals) believe that computation is deterministic and excludes creativity. It is a view that we need to "unhide". Thinking about determinism, probably one has in mind the Turing machine model of computation. However, there are other models of computation, especially natural computation that is the basis of IC framework which allow for indeterminism. As an example I refer to the commentary of Gianfranco Basti who shows how Quantum Field Theory can model bursts of creative activity in the brain, and Andre Ehresmann’s Memory Evolutive Neural System (MENS) theory which explains how lower levels of cognitive activities can be explained as interactions of numerous simple automata, while high-level computation that can support creativity belongs to upper levels of the layered computational architecture. Even the newly presented experimental model of brain by Subrata Ghosh et al. (Ghosh et al., 2014) supports computation beyond Turing machine, suggesting organization of the computational brain in twelve levels of information processing. So creativity, as one would expect from evolution of increasingly complex organisms, appears in the higher cognitive levels.

4. Info-computation and matter-energy

Carlos Gershenson declares his position regarding information and computation in §2: “Instead of trying to describe information in terms of matter and energy, we can describe matter and energy in terms of information (Gershenson 2012).(…) I defined information as “anything that an agent can sense, perceive, or observe” (Gershenson 2012: 102), and computation as a change in information.”

I agree with Carlos Gershenson regarding his interpretation of the relationship between matter-energy and information which I wrote about in (Dodig-Crnkovic, 2006, 2007), (Dodig-Crnkovic, 2008) (Dodig-Crnkovic, 2009) (Dodig-Crnkovic & Mueller, 2009) (Dodig-Crnkovic, 2011a) (Dodig-Crnkovic, 2011b) (Dodig-Crnkovic, 2012a) (Dodig-Crnkovic, 2012b) (Burgin & Dodig-Crnkovic, 2013) (Dodig-Crnkovic, 2014) based on work of Wheeler, von Baeyer, Floridi, Seyre (regarding information) and Zuse, Fredkin, Wolfram, Chaitin, Lloyd and others, regarding natural computation.

IC is building layered computational architecture, implemented at different levels of organization of the world. Carlos Gershenson points out the importance of physical constraints for every such computational model: §4 “Moreover, there are physical constraints that limit the living and cannot be deduced from only information.” Even Stuart Kauffman (Kauffman et al., 2008) and Terrence Deacon (Deacon, 2011) make it very clear that energy is necessary to drive thermodynamically a system in the direction of increased order and organization and to assure its continuous agency.

Epistemologically even matter-energy is information for an agent. However, there is a difference between information about energy of a system and its actual physical embodiment.

Physical perspective is often criticized for its reductionism. However, physics nowadays deals not only with reduction but also increasingly with complexity and emergency. So I would say that the following statement by Gershenson is a gross simplification of a complex issue:

§4 “From the physical perspective there is no difference between the aquarium and another object with exactly the same molecules.”

I think it must be admitted that even that physics makes the difference between different aggregate states of the same molecules. Water, H2O in a form of vapour, liquid or ice – will behave differently as a physical system. Maybe we could say: a molecular physicist is not interested in the organization of matter on the supramolecular level. But then again, physicists who study molecules differentiate between molecules bound in crystal lattice from those moving freely in a gas. It is not accurate to present physics as insensitive to the organization of matter. Besides biophysics current example of physics dealing with biological phenomena is cancer physics, see (Gravitz, 2012). What we can say is only that physics traditionally was not involved in the study of living systems, but with the development of complexity field physicists are starting to address even complex biological systems.

Regarding the physical substrate of computation, Gershenson in §4 claims that “the physical substrate of cognitive systems cannot be neglected.”

I agree with this claim, and IC indeed emphasizes necessity of physical grounding of information and its dynamics. IC relies on informational formulation of quantum physics, which is the basic level of organisation of the physical world. If we de-construct the intricate construction of physical reality, we will find the basic building blocks for a quantum-mechanical agent, qubits, that stand for the relation of an agent with the quantum physics level having underlying physical reality from which the whole of physical world is constructed through processes of self-organization.

Even Gianfranco Basti directs his attention towards the physical aspects of IC, more specifically to the question of physics of emergence. Given the layered computational architecture of IC, it is important to understand the process of emergence of higher levels from the lower ones.

Basti suggests “IC needs integration with the logical, mathematical and physical evidences coming from the Quantum Field Theory (QFT), as fundamental physics of the emergence of “complex systems” in all the realms of natural sciences.” He also makes an important remark that I have described in my target article quantum physics as Quantum Mechanics, which actually should be replaced by Quantum Field Theory. I agree and add that he makes essentially the same argument about emergence as found in the work of Xiao-Gang Wen.

Regarding the specifics of computational process, Basti in § 5. and § 9 mentions morphological computing (or as he rightly points out – morphogenetic computing) - a spontaneous, self-organized process of generation of new form - which is a nonsymbolic computing acting as intrinsic constructive process found in nature. That is not what classical constructivists had in mind, talking

about learning process in humans, but it is constructive process in the realm of physics.

In the following Gershenson comments on the relationship between physics and information: §5 “If physics describes the parts what is the “something” that makes the whole more? As I argued in Gershenson (2013), this something is information, and in particular, interactions.”

However, physics is not only about the parts but also about interactions. Physics can explain cases of emergent properties. See for example the work of Xiao-Gang Wen on emergence http://arxiv.org/a/wen_x_1. Physics can be based on information as in the work of Chiribella and Goyal (Chiribella, D’Ariano, & Perinotti, 2012) (Goyal, 2012). Regarding physics of emergence the distinction should be made between two different types of emergence:

- Weak Emergence - phenomena that are derivable from the laws or organizing principles for their constituents. (Complex phenomena of nonlinear dynamics, deterministic chaos, etc., the self-organizing processes of the formation of snow-flakes, planets, stars, or galaxies; and physical phase transitions); and

- Strong Emergence - phenomena that are (as it seems today) not derivable from the laws or organizing principles for their constituents. (Emergence of new physical laws, principles, or rules, such as the emergence of classicality from quantum mechanics; Emergence of qualitatively novel global phenomena and/or novel organizing principles, such as the superconductivity/ superfluidity; Emergence of new entities, units and fundamental concepts, such as time, space, dimensions, gravity, etc.). Strong emergence resembles very much the concept of “second order” phenomenon, that is emergence of emergence.

Reflecting over physical basis of IC, Gershenson claims §5 “Materialism, IC, and cognitive science are not separable but complementary: Objects are described by materialism, subjects by IC, and action by cognitive science.”

The point of IC is to provide a coherent framework for cognition (as biological agency) based on information (structure) and computation (process). Computation stands for physical behaviour in time (thus temporal physical aspect) while information stands for structure, morphology. What we call “mass” in physics is related to behaviour of a physical object with respect to acceleration. Info-computational description of a physical system describing moving mass would use informational structure (with respect to some agent) and computational behaviour that will depend on what kind of “matter” there is behind that informational structure.

Regarding the statement that §9 “Matter and energy (object, observed) cannot be studied without considering information (subject, observer) nor vice versa.“ (Gershenson), I would like to point out that matter and energy are not only object and observed, matter-energy is a substrate and a vehicle/ drive [source of change] of a subject and cognition. The three are truly entangled. Cognition is not only agency in general (“action” can be ascribed non-living i.e. non-cognitive entities), but cognition is self-organization of information powered by matter-energy in the process of

autopoiesis. Information is always relative to the agent. Essential mechanism that enables information to act in the world is memory that is re-configuration of matter as a result of past events (like Hebbian learning or other adaptive changes in the morphology of organisms that act as constraints for their future behaviour). An interesting article (Deffner & Jarzynski, 2013) illustrates the importance of memory in the generalization of the second law of thermodynamics which allows for heat transfer form cold to hot, with “emphasis on the limits and assumptions under which cyclic motion of the device of interest emerges from its interactions with work, heat, and information reservoirs.” This research can contribute better understanding of how living beings are capable of autopoiesis in spite of the second law of thermodynamics.

Interesting research in physics is pointing in the direction of the fundamentality of information as

well: Xiao-Gang Wen "Introduction to Quantum Many-boson Theory – a theory of almost everything" argues for long-range entanglements (topological order) as a way of unification of force and matter by quantum information. Fluctuations of the vacuum – qubits are use to knit the whole fabric of the universe. (Wen, 2012)

5. Biological aspects of info-computation

Walter Riofrio introduces the topics of meaningful information in the following passage:

§ 3 “ If clearly, one can defend that information is the base of natural computation and cognition, the following is also correct:

“The ability to detect and respond to meaningful information is essentially a biological phenomenon, since there are no inanimate information detectors in nature. Information and energy are both fundamental properties of organized matter that reflect the complexity of its organization […]” (Reading 2011: 9)”

I would say with (Deacon, 2011) and (Kauffman, 1993) who write extensively on agency, that it is rather so that living agents make sense of information they find in the environment. There is no meaningful information in the world, just potential information. That potential information actualizes and becomes meaningful in different ways for different cognizing agents. Meaning for an agent is use of information. (Cf. meaning as use, according to Wittgenstein). The role of matter-energy and information is different. From the macroscopic perspective, it is evident that agents need matter-energy as a source of their body and “fuel” for their motion, metabolism and other chemical and morphogenetic processes. But from the perspective of fundamental physics information can be used to express matter-energy, space-time and agency as mentioned in the work of Xiao-Gang Wen.

In § 4 Riofrio assumes that “some living components started 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 sufficient degree of complexity such that this new entity started to behave like an autonomous agent (Kauffman 2000).”

In my view the question may also be the opposite when some information detectors started to behave like living components in the course of (biological) evolution of matter. In an info-computational universe, simple systems are “information detectors” in the above sense of Reading. If a quant of energy (photon) hits an atom, this atom detects the photon and changes its behaviour accordingly. Molecules can be said to act as information detectors even if they are not a part of an organism. The rest are the short replies to Riofrios thought on biology and information.

§ 5. “it is important to establish a relationship between meaningful information, natural computation and evolution”

I agree with the necessity of establishment of the relationship with the evolution as meaningful information is related to living agency.

§ 6. I am not sure how exactly specifically horizontal gene transfer connects to the above argument. It seems to me that horizontal gene transfer that occurs in simple organisms probably happens due to purely chemical and physical processes.

6. Discussion of the concepts of natural information and computation

Schroeder’s § 11 contains the critique of my use of the expression “atoms of information” for data. Maybe I should have used “chunks of information” to avoid the connotation of indivisibility. Thinking in terms of zero’s and ones [signal-no signal] as atoms is possible, but perhaps not adequate.

What I meant is that for an agent, that which makes “the difference that makes the difference” depends on the agents architecture and its sensors/receptors, which typically register signals/data. The next step is that data/signal be transmitted further in the system until it reaches memory in which it will get related to and incorporated into existing information structures.

Info-computational approach is monistic because information and computation are two inseparable aspects of the same phenomenon. However, Schroeder in § 12 claims:

“The dualistic 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 adjective “physical”? “

That was an interesting observation, but I do not make any use of the idea of “mental world”. Otherwise “physical” is often contrasted with “chemical” sometimes with “logical” and sometimes with “biological”. In data modelling there are conceptual, logical, and physical data models. Emphasizing “physical world” I just wanted reader not to forget physical substrate. Within the framework of IC mental is a process in the physical and as such inseparable manifestation of physical. In present day computer metaphor mental is a virtual machine. I do not think about my computer as consisting of hardware plus virtual machine. Before I run it, it is just a configuration of hardware. When I run it, it is a process capable of simulation. But that process is inextricable from the machine.

Even the § 19 in Schroeder’s commentary addresses informational structures mentioned in my article as “Reality for an agent consists of structural objects (informational structures, data structures) with computational dynamics (information processes)” (§20). Schroeder points out that “it does not explain what these structural objects are and what kind of dynamics describes their interactions. Even worse, we have here put along each other informational structures and data structures.” Informational structures or data structures can coexist. If data structures are elements for building informational structures (while informational structures are elements for building knowledge) – the result is a structure with different granularity. We can have a system consisting of different objects – molecules, atoms and electrons – there should not be a problem.

On the other hand, according to Søren Brier, one of the main problems with info-computationalism is that:

(1) Its basic concept of natural computing has neither been defined theoretically nor implemented practically.

Natural computing is a young and very active research field presented in a comprehensive way in the Handbook of Natural Computing by (Rozenberg, Bäck, & Kok, 2012). Natural computing (including morphological computing not mentioned in the Handbook) has already various practical applications in evolutionary algorithms, swarm intelligence, artificial neural networks, artificial immune systems, artificial life, DNA computing, and quantum computing. Regarding the remark on practical implementation of IC, Andrée C. Ehresmann’s contribution answers directly this criticism.

7. Mathematical approach to CI

Andrée C. Ehresmann’s commentary presents a mathematical approach to the IC framework demonstrating how it can be interpreted rigorously in mathematical terms. It underlines the importance of multiplicity principle (MP) as an important property of natural computation that leads to the development of increasingly complex cognitive processes and structures. The extraordinary important consequence is that even though local dynamics are classically computable, the global dynamics is not. As a consequence, even though basic layers of computational behaviours can be automaton-like (autonomous nervous system?) there are higher cognitive activities that are not Turing-machine type computation based on the fact that symbols have ambiguous meanings. If they would follow natural computing in biological systems, they would also be able to change (evolve).

Ehresmann’s Memory Evolutive Neural System MENS models the cognitive system of an animal with the neural, cognitive and mental systems at (micro, meso, macro) levels of description and across different timescales. In MENS a whole network of Co-Regulators (CRs) that function asynchronously. Each CR collects and processes information and operates through a central flexible Memory, building to the knowledge of the system and changing it so to adapt to the environment.

Ehresmann makes an important observation that due to the degeneracy (synonymy) property or multiplicity principle (MP), the difference that makes a difference on one level of cognitive information processing may make no difference on the higher level (two patterns may be equivalent in the same way as two symbols can be synonymous, even though there are differences that make

difference on the lower level of information processing.

8. Ethical implications of constructivism in general and Info-computational constructivism in particular

In his commentary Hugh Gash in §5 notes that ethical implications of constructivism have a low profile in accounts of constructivism.

I find the position of Cybernetics and particularly Norbert Wiener (Wiener, 1948) inspiring examples of genuine understanding of the importance of values and ethical judgment for technology in particular. The most important new developments which I see as an integral part of info-computational approach are Computer Ethics developed by James Moor, Terrell Bynum and Deborah Johnson (Bynum & James, 2000) (Johnson, 2008), among others, and Information Ethics developed by Luciano Floridi, (Floridi, 2010) .

Furthermore, Ghosh indicates in §5 the possible contribution of IC to ethics in getting insights into mechanisms of information transfer and processing in ethical deliberation. “If this insight could be made more commonplace perhaps negotiations between opposing groups with different views on their reality would have a sounder footing. “ and §7 “Perhaps there are computer models of negotiations that can take these ideas and use them? “

It is definitely a desired and expected outcome of the study of info-computational mechanisms in knowledge construction and decision-making. IC approach and its implementation in Andrée C. Ehresmann’s Memory Evolutive Neural System can help make this process more transparent and understandable.

Already now agent-based models are used as info-computational tools for analysis of social situations. Economists and sociologists are constructing computer simulations in order to visualize and better understand possible outcomes of different scenarios. Computational models can be seen as a cognitive enhancement or augmented cognition as they can compensate for our lack of intuitions when it comes to the behavior of big and complex systems.

AcknowledgementsThe author is grateful to Alexander Riegler for his careful reading of several earlier versions of this article and numerous very helpful discussions and Tom Ziemke for constructive comments, as well as number of anonymous reviewers who contributed to improve the quality of my target article.

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