Info-computational Constructivism

Gordana Dodig-Crnkovic

Mälardalen University, Swedengordana.dodig-crnkovic@mdh.se

Structured Abstract

Context: At present we lack both common understanding of the process of cognition in living organisms and the details of construction of knowledge in embodied, embedded cognizing agents in general, including future artifactual intelligent agents such as cognitive robots and softbots that are being developed. The info-computational approach is focused on the mechanisms behind the observed phenomena understood as computational processes on informational structures.

Purpose: This paper presents a comparative study of computational framework of info-computational constructivism and classical constructivism (Maturana and Varela, von Foerster, von Glasersfeld) through an analysis of the process of knowledge production in cognizing agents. Two basic concepts of info-computationalism information and computation are used in a generalized way. Information is defined as a structure for an agent (not only symbolic messages) and computation is defined in general as any kind of (structural) information processing (thus goes beyond Turing Machine model). Information and computation are used to solve the problem of understanding how the process of knowledge construction proceeds in cognizing agents through the interaction with the world as “protoinformation” perceived by an agent, and proceeds as information self-structuring. It provides support for the views of radical constructivism that knowledge is actively constructed by an agent and shows how this process can be modelled.

Results: We present the info-computational constructive framework used for modelling of cognitive processes as they unfold in a cognizing agents in their layered structures of nested information network hierarchies with corresponding computational dynamics (information processes) – from molecular, to cellular, organismic and social levels. We show how the process of knowledge generation proceeds through interactions with the environment and among agents.

Implications: Info-computational approach is especially suitable for modelling of phenomena where network interactions are essential such as metabolism, immune system, individual and social cognition.

Constructivist Content: The aim is to contribute to the constructivist project with new perspectives and to indicate how computational approaches, dominant in knowledge production today and new computational models under current development, together with new insights from research on theory of information and bioinformatics support constructivism.

Key Words – Constructivism, Info-computationalism, Computing nature, Morphological computing, Evolution with Self-organization and Autopoiesis.

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Introduction

The historical roots of info-computational constructivism can be traced back to cybernetics, which has evolved through three main periods, according to (Umpleby, 2002). The period of engineering cybernetics, or the first order cybernetics from 1950s to 1960s, dedicated to the design of control systems and machines to emulate human reasoning (Wiener, 1948). The second period, biological cybernetics, or second order cybernetics developed during1970s and 1980s, dominated by biology of cognition and constructivist philosophy (Maturana, 1970; von Foerster, 1981; von Glasersfeld, 1987) while in the third, most recent period of social cybernetics, modelling of social systems was developed (Luhmann)(Umpleby, 2001). During the engineering period in cybernetics, the focus was on the object of observation, the observed. In the next phase of research in biology of cognition, the focus shifted from what is observed to the observer, while in the period of social cybernetics, focus moved further from models of an isolated observer to models of groups of observers. (Umpleby, 2002)

The achievements of the first period have been largely assimilated into engineering, automation, robotics (especially autonomous robotics) and related fields, the second period influenced cognitive science while the third period is under current development labelled among others as social cognition/ social computing/ multi agent systems. Via those three different directions of development, the approaches and results have been integrated in the info - computational conceptual space, through its connections to engineering, cognitive science, bioinformatics, social computing, robotics and related fields.

This article will establish in the first place connections of info-computationalism with the second period, with the focus on biology of cognition and constructivist philosophy with Humberto Maturana, Heinz von Foerster, and Ernst von Glasersfeld as main representatives, with some connections to the engineering (object-level) basis (in the first place concerning robotic applications) as well as on social-networks aspects when it comes to the question of inter-subjective construction of knowledge and social cognition. Based on arguments developed in my earlier work I will examine how info-computational constructivism relates to different constructivist approaches1.

Info-computationalism is a variety of natural computationalism, which models the whole nature as a computational process. As all other living organisms, we humans are cognizing agents who construct knowledge through interactions with the outside world and through the processing of information within our cognitive apparatus. Processes of cognition as well as other processes in the info-computational model of the universe are computational processes. The computation going on in nature is a generalized type of computation, natural computation, which is defined as information processing. Information is also a generalized concept in this context, and it is always agent dependent. Information is a difference that makes a difference for an agent. For different types of agents the same data input will result in different information. Agent can be anything from a molecule to a plant, animal, human or robot. Thus information processing (computation) in this approach is going on in the world, and information is the fabric of the world that agents use and make sense of through interactions. What is reality depends on an agent or rather on communicating networks of agents who form an inter-subjective common framework. It happens for social animals as well as robots. That is how this info-computational framework provides a broader view that shows the mechanisms of knowledge construction in different kinds of agents. It comes to Maturana and Varela’s conclusion that life is synonymous with cognition and that to be alive means to be a cognitive agent.

This brings the question about the distinction between living and non-living agents. When is a group or “society of molecules”2 alive? The study of the origins of life makes it clear (in agreement with Maturana and Varela’s autopoetic view of life) that all living beings are open systems in the constant interaction with the environment (Prigogine, Kauffman).

1 The description of the conceptual framework of info-computationalism can be found in (Dodig-Crnkovic & Müller, 2011) (Dodig-Crnkovic, 2009) (Dodig-Crnkovic, 2006). The relationship between natural computing (such as biocomputing, DNA-computing, social computing, quantum computing, etc) and the traditional Turing machine model of computation is elaborated in (Dodig-Crnkovic, 2012a)(Dodig-Crnkovic, 2011a) (Dodig-Crnkovic, 2011b) (Dodig-Crnkovic, 2010a). Constructing/generation of knowledge within info-computational framework is discussed in (Dodig-Crnkovic, 2007) (Dodig-Crnkovic, 2010b)(Dodig-Crnkovic, 2010c)(Dodig-Crnkovic, 2008). 2 This is meant to point to Marvin Minsky’s ”society of mind”, or in general, when some interacting parts make some qualitatively different whole, or: when an emergent phenomenon emerge?

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The article starts with the presentation of the idea of computing nature and proceeds with the elaboration of two basic concepts of info-computationalism, information and computation that are more general notions than what is in common use. The chapter on info-computational epistemological constructivism establishes relationships between info-computationalism and classical constructivism. Under the heading Self-organization and autopoiesis. System vs. environment: open vs. close an overview is made how computational models provide understanding of process of cognition on several levels of organization of matter, not only on the level of symbolic representations and symbol manipulation. On the level of human cognition, we can see reality as simulation that our (naturally computational) brains play for us, in order to provide adequate basis for decision-making and other behavior. In the concluding chapter some criticisms and replies are given. Finally conclusions are made together with suggestions on future work.

The Computing Nature

Computer pioneer Zuse was the first to suggest (in 1967) that the physical behavior of the entire universe is being computed on a basic level, by the universe itself which he referred to as “Rechnender Raum” or Computing Space/Cosmos. Consequently, Zuse was the first pancomputationalist (naturalist computationalist), followed by many others like Ed Fredkin, Stephen Wolfram and Seth Lloyd – to name but a few. According to the idea of computing nature (naturalist computationalism or pancomputationalism) one can view the time development (dynamics) of physical states in nature as information processing (natural computation). Such processes include self-assembly, developmental processes, gene regulation networks, gene assembly in unicellular organisms, protein-protein interaction networks, biological transport networks, and similar. (Dodig-Crnkovic & Giovagnoli, 2013)

Within info-computationalism, two basic concepts information and computation (the dynamics of informational structure) are mutually interdependent (Dodig-Crnkovic, 2011a) (Chaitin, 2007) – so the framework is a synthesis of informational structural realism3 and natural computationalism.

Informational structural realism (Floridi, 2003) takes (proto)information4 to be the fabric of the universe (for an agent that will use the threads of this fabric to weave their own cognitive structures). As a consequence the process of dynamical changes of the universe makes the universe a huge computational network where computation is information processing. As it corresponds to the dynamic of processes that exist in the universe, it is necessarily both discrete and continuous, on both symbolic and sub-symbolic5 level. Information and computation are two fundamental and inseparable elements necessary for naturalizing cognition and knowledge. (Dodig-Crnkovic, 2009) Information is structure, that exists either potentially, outside of the agent (structures of the world) or inside an agent (agents own bodily structures that contain memories of the previous experiences with the world). Messages are just a very special kind of information that is exchanged between communicating agents. They can be chemical molecules, pictures, sounds written symbols or similar. Agents can be as simple structures as molecules or simplest living organisms. Agents are simply entities able to act on their own behalf.

Physicists Zeilinger (Zeilinger, 2005) and Vedral (Vedral, 2010) suggest the possibility of seeing information and reality as one6. This is in accord with informational structural realism which says that reality is made of informational structures (Floridi, 2009)(Floridi, 2008) (Sayre, 1976) as well as with info-computational epistemology (Dodig-Crnkovic, 2009) based on informational structural realism in

3 It is important to understand that ”realism” of Floridi’s informational structural realism is a minimal ontological commitment to the existence of the outside world. With regard to epistemology, Floridi is constructivist.4 This (proto)information presents potential for an agent which/who constructs actual information for itself/himself/herself out of this potential information of the world. (We might be talking about potentisal data as data are atoms of information, in a sense of Skyrms signals, but for historical reasons we talk about structural information.) 5 Sub-symbolic computations go on in neural networks, as signal processing.6 World for an agent is informational structure. This of course does not imply that potential information from the world moves intact into an agent as a mirror reflecting the world. The potential (proto-) information is accessed by an agent through interactions and it is processed by agent’s cognitive apparatus. It is dynamically integrated and linked to the rest of informational structures in the memory of an agent. What is important and new about this view of physicists is that they do not talk about matter and energy as the primary stuff of the universe (which traditionally is objectivized within sciences). They talk about information, thus putting back an agent into the picture of the world.

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conjunction with natural computationalism. Reality for an agent is informational and agent-dependent (observer-dependent) and consists of structural objects, which are adjusted to the shared reality of agents community of practice. This brings together metaphysical views of Wiener (“information is information, not matter or energy”) and Wheeler (“it from bit”) with Zuse, Fredkin, Lloyd, Wolfram and others view of computing nature.

In sum: for info-computationalism information is the structure, the fabric of reality. The world exists7 in the form of proto-information, the potential form of existence corresponding to Kant’s das Ding an sich. That proto-information becomes information (“a difference that makes a difference” according to (Bateson, 1972)) for a cognizing agent in a process of interaction through which specific aspects of the world get uncovered.

There is a more general definition of information that includes the fact that information is relational and subsumes Bateson’s definition:

”Information expresses the fact that a system is in a certain configuration that is correlated to the configuration of another system. Any physical system may contain information about another physical system.” (italics added) (Hewitt, 2007)

This has profound consequences for epistemology and relates to the ideas of participatory universe, (Wheeler, 1990) endophysics (Rössler, 1998) and observer-dependent knowledge production as understood in second-order cybernetics. Combining Bateson and Hewitt insights, on the basic level, information is the difference in one physical system that makes difference in another physical system. Of special interest with respect to knowledge generation are agents - systems able to act on their own behalf.

The world as it appears to an agent depends on the type of interaction through which the agent acquires information8. Potential information in the world is obviously much richer than what we observe, containing invisible worlds of molecules, atoms and sub-atomic phenomena, distant cosmological objects and similar. Our knowledge about this potential information or proto-information which reveals with help of scientific instruments continuously increase with the development of new devices and the new ways of interaction with the world, both theoretical and material constructs (Dodig-Crnkovic & Mueller, 2009).

Information and Computation in Cognizing Agents

Living organisms are adaptive systems and as a result of evolution, they have developed ability to effectively deal with complex environments. Studies in biology, ethology and neuroscience, which have increased our knowledge of biological cognitive functions, have led to the insight that the most important feature of cognition is its ability to deal efficiently with complexity, see argument by Gell Mann9in . This understanding that complexity management (thus efficient information processing) is central for survival, together with the increase in power of electronic computing and robotics, especially cognitive robotics, brings us closer to adequate modelling of intelligent behaviour.10 From the computationalist point of view intelligence may be seen as capacity based on several levels of data processing in a cognizing agent, as argued by Minsky. Data, information, perceptual images and knowledge are organized in a multiscale model of the brain and nervous system, up to the emergent level of consciousness according to (Minsky, 1986)(Minsky, 2011). Multiresolutional models have proven to be a good way of studying complexity in biological systems, and they are also being implemented in artificial intelligence, AI (Goertzel, 1993).

The advantage of computational approaches is their testability. Cognitive robotics research, e.g. presents us with a sort of laboratory where our understanding of cognition can be tested in a rigorous manner. From cognitive robotics it is becoming evident that cognition and intelligence are closely related to agency. Anticipation, planning and control are essential features of intelligent agency. A similarity has

7 This is an ontological commitment in contrast to the agnostic position of constructivism, which argues that no such metaphysics is necessary (for constructivist epistemology). In computing ontology represents all that exists for a computational system.8 For example, results of observations of the same physical object (celestial body) in different wavelengths (radio, microwave, infrared, visible, ultraviolet and X-ray) give profoundly different pictures, so agents sensitive to different wave lengths would get different notions of the same phenomenon.9 According to Gell-Mann, complex adaptive systems encode their environments into different schemata, and dealing with complexity is of prime importance for information processing of a cognitive agent.10 See for example first three chapters in Computing Nature (Cottam et al., Phillips and Lindley)

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been found between the generation of behaviour in living organisms and the formation of control sequences in artificial systems. (Pfeifer & Bongard, 2006)(Pfeifer, Lungarella, & Iida, 2007)

Information produced from sensory 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 interface between the (potential) data11 (the world) and an agent’s perception of the world. This interface is an information processing device, which means that information input for an agent gets re-structured, and linked with the existing information. This is illustrated by (Hoffman, 2009) criticizing the traditional view of perception as a true picture of the world:

“Instead, our perceptions constitute a species-specific user interface that guides behavior in a niche. Just as the icons of a PC's interface hide the complexity of the computer, so our perceptions usefully hide the complexity of the world, and guide adaptive behavior. This interface theory of perception offers a framework, motivated by evolution, to guide research in object categorization. ”

Thus, perception produces chunks of interrelated informational structures that connect the inside cognitive informational structures with the outside informational structures through a dynamic information processing matching. Cognition cannot be cut off on one side of the interface, inside an agent and its brain. Patterns of information are both in the world and in the functions and structures of the agent which are connected through dynamical information processes of self-structuring (self-organization). Information is the difference in the world that makes difference in an agent.

With perception as an interface (thus an executing program in a computationalist phrasing which performs the first part of the construction process), sensorimotor activities play a central role in realizing the function of connecting the inside with the outside worlds of an agent, endogenous with the exogenous. Perception has co-evolved with sensorimotor skills of an organism. Enactive approach to perception (Noë, 2004) emphasizes the role of sensorimotor abilities, that can be connected with the changing informational interface between an agent and the world, and thus increasing information exchange. Enactivism (Francisco Varela, Evan Thompson, and Eleanor Rosch) emphasizes that cognizing agents self-organize through interaction with their environment. It is an approach closely related to situated cognition and embodied cognition, and an alternative to Cartesian dualism, cognitivism (in its representationalist and symbolic information-processing form) and computationalism (based on the Turing Machine model of computation). However, recent developments in cognitive science and computation have evolved to encompass physical computation and non-symbolic, embodied information processing.

Both enactive approach and interface theory fit naturally into info-computational framework. As mentioned before with reference to (Minsky, 1986) and (Goertzel, 1993), the step from perception to higher cognitive processes is not trivial and detailed multiresolutional computational accounts are yet to be developed. They can be expected along the lines similar to Brier’s transdisciplinary approach of Cybersemiotics (Brier, 2013) with the difference that the connections between different branches of scientific knowledge (in the sense of “Wissenschaft”12) are here construed computationally.

Traditionally, symbolic AI was an attempt to model cognition and intelligence as symbol manipulation, which turned out insufficient. (Clark, 1989) In order to improve and complement symbolic approaches, Smolensky proposed mechanism of an intuitive processor (which is not accessible to symbolic intuition), with a conscious rule interpreter:

“What kinds of programs are responsible for behavior that is not conscious rule application? I will refer to the virtual machine that runs these programs as the *intuitive processor*. It is presumably responsible for all of animal behavior and a huge proportion of human behavior: Perception, practiced motor behavior, fluent linguistic behavior, intuition in problem solving and game-playing--in short, practically all skilled performance.” (Smolensky, 1988)

Sloman has developed interesting ideas about mind as virtual machine running on the brain in (Sloman, 2002) which also addresses the symbol grounding problem.

11 Data are atoms of information. Information is obtained when data result into structure (correlated), which happens in the interaction with a cognizing agent.12 Wissenschaft is a broader concept that the English word science, and includes besides science, also learning, knowledge, and scholarship in general. The word science can be directly translated to Naturwissenschaft.

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From the point of view of info-computationalism, a mechanism behind this virtual machine hierarchy is computational self-organization of information, i.e. morphological computing13, see (Dodig-Crnkovic, 2012b) and references therein. In his new research programme, Sloman goes a step further studying meta-morphogenesis which is the morphogenesis of morphogenesis, (Sloman, 2013) – a way of thinking in the spirit of second order cybernetics. The difference between morphological computing and our conventional computers (artificial symbol manipulators implemented in physical systems and governed by an executing program) is that that morphological computing takes place spontaneously in nature through physical/chemical/biological processes. The process of growth as well as cognitive processes can be modelled as morphological computing.

Info-Computationalist Epistemological Constructivism

“Living systems are cognitive systems, and living as a process is a process of cognition. This statement is valid for all organisms, with or without a nervous system.” (Maturana & Varela, 1980) p. 13

The above understanding of cognition is adopted by info-computationalism as it provides a notion of cognition in degrees, which provides a bridge from human-level cognition to minimal cognition in simplest biological forms and intelligent machines (under development). Within the framework of info-computational naturalism (Dodig-Crnkovic, 2009) knowledge is seen as a result of successive structuring of data, where data are simplest information units, signals acquired by a cognizing agent through the senses/ sensors/ (Dodig-Crnkovic, 2007) (Skyrms, 2010). Information is meaningful data, which can be turned into knowledge by an interactive computational process going on in the cognizing agent. Information is always embedded in a physical substrate: signal, molecule, particle or event which will induce change of a structure or a behaviour of an agent (Landauer, 1991). The world (reality) for an agent presents potential information, both outside and within an agent.14 This is a detail of implementation which classical constructivism does not study. But for a computationalist constructivism this is important as we must know how to construct cognitive artificial agents that are able to function adequately in the real world, so we must know how to treat information from the world and how to process it within an agent.

Unlike information, in its various potential and actual forms, which in the info-computationalist framework constitutes reality for an agent and can be found potentially existing also outside an agent, knowledge always resides in a cognitive agent. Semantics develops as data → information → knowledge structuring process, in which complex structures are self-organized by the computational processing from simpler ones. The meaning of information is thus defined for an agent and a group of agents in a network and it is given by the use information has for them. Knowledge generation as information processing in biological agents presupposes natural computation, defined by MacLennan (MacLennan, 2004) as computation occurring in nature or inspired by that in nature, which is the most general current computation paradigm.

Knowledge Generation as Morphological Computation

Traditional theoretical Turing machine model of computing is equivalent to algorithms/effective procedures, recursive functions or formal languages. Turing machine is a logical device, a model for execution of an algorithm. However, if we want adequately to model computing nature 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 & Giovagnoli, 2013). In order to develop general theory of the networked physical information processing, we must also generalize the ideas of what computation is and what it might be. For new computing paradigms, see for example (Rozenberg, Bäck, & Kok, 2012)(Burgin, 2005)(MacLennan, 2004) (Wegner, 1998)(Hewitt, 2012)(Abramsky, 2008). Turing machines form the proper subset of the set of information processing devices.

In the computing nature, knowledge generation should be studied as a natural process. That is the main idea of Naturalized epistemology (Harms, 2006), where the subject matter is not our concept of knowledge, but the knowledge itself as it appears in the world15 as specific informational structures of an agent. The origin of knowledge in first living agents is not well researched, and the still dominant idea is

13 Morphological computation is fundamentally information self-structuring.14 Again, this is a minimal ontological comitment to the existence of the physical world and does not contradict radical constructivism which is agnostic with respect to ontology.15 Maturana was the first to suggest that knowledge is a biological phenomenon.

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that knowledge is possessed only by humans. However, there are different types of knowledge and we have good reasons 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 ability to learn (adapt, change their morphology) and can be argued to possess rudimentary forms of knowledge. On the topic of plant cognition see Garzón in (Pombo, O., Torres J.M., Symons J., 2012) p. 121. In his Anticipatory systems (Rosen, 1985) claim as well: “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” Popper (Popper, 1999) p. 61 ascribes the ability to know to all living: ”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.”

Computation as information processing should not be identified with classical cognitive science, with the related notions of input–output and structural representations – but it is important to recognize that also connectionist models are computational as they are also based on information processing (Scheutz, 2002)(Dodig-Crnkovic, 2009). The basis for the capacity to acquire knowledge is in the specific morphology of organisms that enables perception, memory and adequate information processing that can lead to production of new knowledge out of old one. Harms16 proved a theorem showing that under certain conditions, the total amount of information in the system always increases by natural which will always lead a population to accumulate information, and so to 'learn' about its environment. Writing on Harm’s results (Okasha, 2005) points out that

“any evolving population 'learns' about its environment, in Harms' sense, even if the population is composed of organisms that lack minds entirely, hence lack the ability to have representations of the external world at all. ”

That may be seen not as a drawback of the theory but as its strength because of the generality of naturalistic approach. It shows how cognitive capacities are a matter of degree and how they slowly and successively develop with evolution. Recent empirical results suggest that even simple 'lifeless' prion molecules are capable of evolutionary change and adaptation, (Li, Browning, Mahal, Oelschlegel, & Weissmann, 2010).

However, this understanding of basic evolutionary mechanisms of accumulating information at the same time increasing information processing capacities of organisms (such as memory, anticipation, computational efficiency) is only the first step towards a full-fledged evolutionary epistemology, but the most difficult and significant one.

Self-organization and Autopoiesis. System vs. Environment: Open vs. Closed

In order to understand knowledge as 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-organization are central. Work of (Maturana & Varela, 1980) on the constructivist understanding of life processes is of fundamental importance. They define the process of autopoiesis:

“An autopoietic machine is a machine organized (defined as a unity) as a network of processes of production (transformation and destruction) of components which: (i) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them; and (ii) constitute it (the machine) as a concrete unity in space in which they (the components) exist by specifying the topological domain of its realization as such a network.” (Maturana & Varela, 1980) p. 78

What does it mean that an autopoetic system is organizationally closed? It means that it conserves its organization. That is a true of a momentaneous picture of the world in which organism lives (functions, operates). Evolution shows that organisms change their structure and successively as they evolve even organization through the interactions with the environment. In a sense organisms preserve their organisation, but that organisation is dynamic on an evolutionary time scale. Living beings constantly metabolize, communicate and exchange information with the world. We can say that there are different processes going on in an organism – on a short time scales they retain their (dynamical) organization, while exchanging information with the world. On the long time scale they evolve and thus change their organization. 16 Harms W. F., (2004) Information and Meaning in Evolutionary Processes, Cambridge University Press, chapter 5.

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Maturana and Varela’s idea of autopoetic systems, and especially the idea of life as cognition is of vital importance but it might need some reinterpretations when incorporated into the framework of info-computationalism. Even Luhmann generalized Maturana and Varela’s concept of autopoiesis to include also psychological and socio-communicative systems (Brier, 2013)

In short, the information processing picture of organisms incorporates basic ideas of autopoiesis and life, from the sub-cellular to the multi-cellular level. Being cognition, life processes are different sorts of morphological computing which on evolutionary time scales affect even the structures and organization of living beings in a sense of meta-morphogenesis.

Autopoetic processes with structural coupling (interactions with the environment) (Maturana & Varela, 1992) (Maturana & Varela, 1980) can be described within info-computational model as changes in structures in biological system and thus the information processing patterns in a self-reflective, recursive computation. Self-organization with natural selection of organisms, responsible for nearly all information that living systems have built up in their genotypes and phenotypes, is a simple but costly method to develop knowledge capacities. Higher organisms (which are “more expensive” to evolve17) have grown learning and reasoning capability as a more efficient way to accumulate knowledge. The step from “genetic learning” (typical of more primitive life forms) to acquisition of cognitive skills on higher levels of organization of the nervous system (such as found in vertebrata) will be the next step to explore in the project of naturalized epistemology, which is an implementation of info-computational constructivism to the question of knowledge generation in living agents.

Life is cognition according to Maturana (1970) and (Maturana & Varela 1980). In the info-computational formulation, this corresponds to information processing in hierarchy of levels of organization, from molecular networks, to cells and their organisations, to organisms and their networks/societies (Dodig-Crnkovic, 2008). In that way, fundamental level proto-information (structural information) corresponds to the physical structure, while cognition is a process that appears as a product of evolution in complex biological systems, as argued in (Dodig-Crnkovic & Hofkirchner, 2011).

“(I)f we see a living system behaving according to what we consider is adequate behavior in the circumstances in which we observe it, we claim that it knows. What we see in such circumstances, is: a) that the living system under our attention shows or exhibits a structural dynamics that flows in congruence with the structural dynamics of the medium in which we see it, and b) that it is through that dynamic structural congruence that the living system conserves its living. I claim that the process which gives rise to the operational congruence between an organism and its niche, the process that we distinguish in daily life either as learned or as instinctive knowing, is structural coupling.”

What does it mean for an agent to know? According to (Maturana, 2002) knowledge is precondition for adequate behaviour of an organism in its environment. For Maturana “the process which gives rise to the operational congruence between an organism and its niche, the process that we distinguish in daily life either as learned or as instinctive knowing, is structural coupling.”

Maturana’s structural determinism has its counterpart in the info-computational framework in a form of structural realism with determinism replaced with causality which may take a form of statistical laws. ADD DETAILS

Of interest for understanding of life is a system (an agent) that presents a unity for and by itself, such that can be described as constituting a whole - a property referred to as closure. (Bertschinger, Olbrich, Ay, & Jost, 2006) mention the following different notions of closure:“ autopoiesis as organizational closure (Maturana and Varela [1]), closure to efficient cause (Robert Rosen [2]), semantic closure (Howard Pattee [3]), or operational closure (Niklas Luhmann [4]). Within the info-computational framework different kinds of closures can be modelled and their roles compared.

In the info-computational constructive approach it would be instructive to study the properties and behaviours of agents under different conditions of closure and how closure affects information processing and knowledge generation in cognizing agents.

17 More expensive in this context means that they take more time and other natural resources to develop.A human compared to a bacteria is considerably more ”expensive” to develop.

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In terms of von Foerster’s notion of eigenvalues (stable structures) and eigenbehaviours (stable behaviours established in the interaction with the environment). Even though organizationally closed living systems are informationally open18, as they communicate and form emergent representations of its environment. Rocha (1998) defines self-organization as the “spontaneous formation of well organized structures, patterns, or behaviours, from random initial conditions.” (p.3). Learning, as a self-organizing process requires that the system (personal or organizational learning systems) “be informationally open, that is, for it to be able to classify its own interaction with an environment, it must be able to change its structure...” (p.4).

Self-organization of information on a cognizing agent level is a micro-process that proceeds in the interaction with the environment. of the larger self-organizing knowledge constructs created on a social level. This is in agreement with the constructivist position that "observables do not refer directly to real world objects, but are instead the result of an infinite cascade of cognitive and sensory-motor operations in some environment/subject coupling.” Eigenvalues are self-defining, or self-referent, through the imbedding dynamics – implying a complementary relationship (circularity, closure) between eigenvalues and cognitive/sensory-motor operators: one implies, or defines, the other. " (Foerster, 1977) p. 278

Reality as Simulation

We humans have an impression that we interact directly with the “real world as it is”. However that is far from accurate characterization of what is going on, as already mentioned in connection to perception as an interface.

“Of all information processing going on in our bodies, perception is only a tiny fraction. Our perception of the world depends on the relative slowness of conscious perception. Time longer than one second is needed to synthesize conscious experience. At time scales shorter than one second, the fragmentary nature of perception reveals.” (Ballard, 2002)

Already Kant argued that “phenomena” or things as they appear to us and which constitute the world of common experience are an illusion. Kaneko and Tsuda explain why:

“ (T)he brain does not directly map the external world. From this proposition follows the notion of “interpreting brain”, i.e. the notion that the brain must interpret symbols generated by itself even at the lowest level of information processing. It seems that many problems related to information processing and meaning in the brain are rooted in the problems of the mechanisms of symbol generation and meaning.” (Kaneko & Tsuda, 2001)

Consciousness provides only a rough sense of what is going on in and around us, in the first place what we take to be essential for us. The world as it appears for our consciousness is a sketchy simulation which is a computational construction. The Belief that we ever can experience the world 'directly as it is' is an illusion (Nørretranders, 1999). EXPAND: HENRY MARKRAM and details of Nörretranders

What would that mean anyway to experience the world 'directly as it is', without ourselves being part of the process? Who would experience that world without us? It is important to understand that, as (Kaneko & Tsuda, 2001) emphasize, the brain maps19 the information about the (part of the) world into itself, but the mapped information is always formed by the activity of the brain itself. This is the view of (Maturana, 2007) as well.

The positivist optimism about observations independent of the observer proved problematic in many fields of physics such as quantum mechanics (wave function collapse after interaction), relativity (speed dependent length contraction and time dilatation) and chaos (a minor perturbation caused by measurement sufficient to switch the system to a different attractor). In general, observer and the systems observed are related (Foerster, 2003) and by understanding their relationship we can gain insights into limitations and power of models and simulations as knowledge generators.

If what we perceive of the world is a simulation our brain plays for us in order to manage complexity and enable us to act efficiently, then our knowledge of the world must also be mediated by this computational modelling nature of cognition. Not even the most reliable knowledge about the physical world as it appears in sciences is independent of the modelling frameworks which impact what can be known.20

18 [Pask, 1992]19 This ”mapping” is a complex function, and definitely not identity!20 For example, within liquid-drop model of atomic nucleus, shell structure effects cannot be explained.

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Models are always simplifications made for a purpose and they ignore aspects of the system which are irrelevant to that purpose. The properties of a system itself must be clearly distinguished from the properties of its models. All our knowledge is mediated by models. We often get so familiar with a model and its functions that we frequently act as if the model was the actual reality itself (Heylighen & Joslyn, 2001).

Awareness of the modelling character of knowledge and the active role of the cognizing agent in the process of generation of knowledge is specifically addressed by second-order cybernetics. Cybernetic epistemology is constructivist in recognizing that knowledge cannot be passively transferred from the environment, but must be actively constructed by the cognizing agent based on the elements found in the environment in combination with information stored in the agent. The interaction with the environment eliminates inadequate models. Model construction thus proceeds through variation and selection. This agrees with von Glasersfeld’s two basic principles of constructivism (Glasersfeld, 1995):

“Knowledge is not passively received either through the senses or by way of communication, but is actively built up by the cognizing subject.

The function of cognition is adaptive and serves the subject's organization of the experiential world, not the discovery of an ‘objective ontological reality’ ”.

This understanding coincides with the info-computational view of knowledge generation (Dodig-Crnkovic, 2007)(Dodig-Crnkovic & Müller, 2011). In an info-computational interpretation, the process of life is

Some Criticisms of Computationalism and Replies

“1. No information without humans?”

A typical criticism of the informational nature of reality originates from the belief that the world without cognizing agents would lose its contents because there would be no one to observe it. The view that “If all the humans in the world vanished tomorrow, all the information would vanish, too.”

Let me try to reply to the above criticism. First of all, not only is information physical (Landauer, 1996), but even the opposite holds as well: “things physical are reducible to information”, quantum physics can be formulated in terms of information, for details of the argument see Goyal (Goyal, 2012) and Chiribella (Chiribella, G.; D’Ariano, G.M.; Perinotti, 2012).

Secondly, clearly, if there are no cognizing agents in the world, the world as potential information, das Ding an sich, never turns into actual information for an agent. But given the fact that there are cognitive agents besides humans, living beings – animals, plants, microorganisms and even machines capable of cognitive computing i.e. processing of information and making sense of it, information for all those agents continue to exist even if no human is present.

It is not necessary for an agent to be conscious in order to make use of the world as potential information. Maturana and Varela view life in itself as a cognitive process (Maturana & Varela, 1992). Metabolism is basic aspect of cognition along with sensorimotor functions and immune system processes. No free will is needed for information processing that goes on in all living cells. Those processes can be understood as computational in the sense of natural computation, even though those single-cellular agents cannot be ascribed consciousness. Matsuno and Salthe go one step further (Matsuno & Salthe, 2011), attributing material agency and information processing ability even to such simple systems as molecules.

When no agents are present to process protoinformation of the world, it remains potential. Epistemology of info-computationalism is info-computational constructivism, and it describes the ways cognizing agents process information and generate knowledge from the existing world21 which steadily changes and evolves through processes of natural computation.

“2. Signals rather than information?”

(Brier, 2013) criticizes the idea of information as used in info-computationalist framework:

21 The realism of this approach consists in the assumption that world (reality) exists in the form of proto-information (synonymous with potential information).

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“This is the problem that pan-computational and pan-informational theories attempt to solve with a view of the world as a grand computer and a new concept of natural computing (Dodig-Crnkovic, 2010; Dodig-Crnkovic & Müller, 2011). Given these assumptions the view of natural computing can be expressed in this way:

1. The physical world is a network of computational processes with many levels of organization.

2. Whatever changes there are in the states of the physical world, we understand them as computation.

3. Not all kinds of computations (changes in the physical world) are best represented by the Turing model.

In my view, it is not information that is transmitted through the channel in Shannon’s theory, but signals.”

As an answer this criticism I refer to the work of Skyrms (Skyrms, 2010) and Bateson (Bateson, 1972). It is possible that we should see Bateson’s “differences that make a difference” as data or signals22:

“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 affecting the behavior of entities capable of responding to facts. For Kant’s Tatsachen, I would substitute differences and point out that the number of potential differences in this chalk is infinite but that very few of them become effective differences (i.e., items of information) in the mental process of any larger entity. Information consist of differences that make a difference.” (110a, italics mine) (Bateson, 1979)

But those differences “items of information” become instantaneously information when they enter a cognitive system and get integrated into its informational networks.

Brier continues his critical view of natural computationalism:

“We must further theorize how the processes of cognition and communication develop beyond their basis in the perturbation of and between closed systems and into a theory of feeling, awareness, qualia and meaning. (…) This ontological foundation does not solve the problem of how experience and meaningful cognition and communication emerge or manifest themselves in the world.”

The above criticism may be applicable to some computational approaches but definitely not to info-computationalism based on natural computing and the idea of the world as (proto) informational structure. As Chalmers aptly remarks, information is fundamental and natural candidate for theory of consciousness:

“Wheeler (1990) has suggested that information is fundamental to the physics of the universe. According to this 'it from bit' doctrine, the laws of physics can be cast in terms of information, postulating different states that give rise to different effects without actually saying what those states are. It is only their position in an information space that counts. If so, then information is a natural candidate to also play a role in a fundamental theory of consciousness. 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.” (italics mine) (Chalmers, 1995)

The phenomenal aspect of information (vs. physical aspect) can be interpreted as a version of the endogenous vs. exogenous aspect which get its natural formulation if we chose a relational definition of information proposed in the introduction, obtained as a combination of Bateson and Hewitt definitions: on the basic level, information is the difference in one physical system that makes difference in another physical system. Phenomenal features show when this relation becomes reflexive.

Info-computationalism is agent-based (observer dependent) and takes into account context dependence of open systems.

“3. Third-person approach?”

(Brier, 2013) discusses Dennett’s project to explain subjective consciousness and the qualia in his Consciousness Explained (Dennett 1991) by explaining ‘subjective’ phenomena in

22 Data are atoms of information. Information is produced when data are interrelated (by an agent). 11

‘objective’ terms “to be the objective, materialistic, third-person world of the physical sciences.” Dennett (1987a, p. 5)

”As far as I can see, none of these endeavours are feasible, since the language of physics does not include the notion of agent (agency) and meaning.”

The way of interpreting Dennett’s research programme guided by the principle of charity would be to equal objective with inter-subjective and material to physical which makes it agree with modern cognitive science approaches as for example presented in (Clark, 1989). Physics has no notion of meaning, but meaning emerges from physical substrate. Music for a human is more than notes written down on paper and more than physical phenomena of energy transfer between different media. Yet those are all different facets/aspects/dimensions of music. Yet another is social – how music is learned, performed, organized in practice. Subjective experience is still another such aspect of the phenomenon that we call music. It has no special privileged position in relation to other aspects. Subjective experience has its own merits but it is by no means more cognitively superior or functional than third-person understanding of that experience. Without the third person we would not be able to share the knowledge about the existence of other first person experiences. To base a research programme on a third person perspective, inter-subjective knowledge and physical foundations is the most natural approach for a scientist.23

No philosophical approach or scientific field can exhaust all the aspects of one phenomenon – that is why we need transdisciplinarity and collaboration as a constructive project. Constructive approaches are important because elements of knowledge produced in specialist fields are used in the building of a common knowledge network in which elements being connected gain new meaning from their new common context.

Info-computational framework needs to fill many explanatory gaps and based on neuroscience, biology, bioinformatics, biosemiotics, cognitive computing etc. provide computational models of phenomena of mind that we still lack proper scientific models for. (Clark, 1989) and (Dodig-Crnkovic & Giovagnoli, 2013) give some hints how to fill those gaps within computational framework, but it also indicates that a lot of work remains to be done. As every model serves certain purpose it is to be expected that some models may be better than the others within different domains, on different levels of abstraction and for different purposes.

Conclusions

The fundamental second order cybernetics legacy consists of Maturana’s finding that knowledge is a biological phenomenon, von Foerster’s insight that cognitive agents construct reality in the interplay with the environment and von Glaserfeld’s argument that the most important characteristics of knowledge is its viability – practicability in the world. (Umpleby, 1997)

The info-computational framework as a conceptual system builds on the results of Maturana, von Foerster and von Glaserfeld and proposes morphological computation as information processing (computational) mechanism for knowledge generation understood as information self-structuring in cognizing agents – both biological and artificial. From current insights in the mechanisms of cognition (Smolensky & Legendre, 2006)(Clark, 1989) (Rosen, 1991)(Wiener, 1948)(Foerster, 1960)(Kampis, 1991)(Kauffman, 1993) it is becoming increasingly visible how cognizing agents construct their knowledge from information both reflexively within their own informational structures (memory, embodiment) and from the interactions with the environment (embeddedness). Knowledge provides evolutionary advantage for an agent and it is in the first place a tool of modelling of the world, thus as any model it is constructed for a purpose and can by no means be agent-independent or absolute (Dodig-Crnkovic, 2008)(Bates, 2005). Reality is a dynamical informational structure and for an agent it presents a simulation which is an interface between an inner and an outer world, resulting from both past, anticipated and current information used in producing relevant informational interface.

The present article addresses number of questions that have been posed on computational approaches to constructivism. The goal is to contribute with info - computationalist realist version of constructivism. The questions have been addressed as follows.

23 As an example I can mention psychology as a science which deals with people and their first-person experiences by using third person approach. It is impossible for a physician to feel first person pain of a patient. It is more useful if he/she can help patient by third-person socially shared knowledge about first person pain that people typically share in similar situations.

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1. Computational Constructive Realism. Reality as Simulation

Von Glasersfeld’s claim “[The world] is a black box with which we can deal remarkably well” with computational interpretation can be unpacked into several claims.

The black box of the world is protoinformation, which presents potential information for an agent capable of processing it for its own purposes. Sort of computation that “takes place in the cognizing subject that gives rise to her reality construction based on her (its24) experiences” is natural computation, as defined in the concept of Computing Nature (Dodig-Crnkovic & Giovagnoli, 2013).

The knowledge construction processes in different kinds of agents, biological and artificial are formulated computationally in (Clark, 1989)(Pombo, O., Torres J.M., Symons J., 2012)(Scheutz, 2002)(Dodig-Crnkovic & Giovagnoli, 2013).

Even though existing computational models may provide useful heuristics for simulating an individual’s construction of reality, we hope to learn more about how nature computes in order to get more detailed computational models (Dodig-Crnkovic & Giovagnoli, 2013). However, reality construction in complex cognizing agents like majority of biological systems is not possible to computationally predict, as info-computational models, though causal, are not deterministic. Nevertheless, it might be possible to reconstruct and computationally predict reality construction in simple agents such as robots or viruses in controlled environments.

2. Information: Observer-dependence of knowledge production

In words of von Foerster, observer-dependence (agent-dependence) is described as “the truism that a description (of the universe) implies one who describes it (observes it)”, which implies that:

“(O)ne had to account for an “observer” (that is at least for one subject):

(i) Observations are not absolute but relative to an observer’s point of view (i.e., his coordinate system: Einstein);

(ii) Observations affect 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 description of the “describer” or, in other words, we need a theory of the observer.” p.258 (Foerster, 1981)

Even though there are attempts to define the observer, especially in the theory of measurement in quantum mechanics, the common understanding of the central importance of agents (“observer dependence”) in knowledge production is still missing.

3. Computation: Natural/morphological computing

Present article even offers answers to the following questions: “How to define ´computational´?” (As information processing) and “Can computational models ever create something new?” (Yes, e.g. evolution shows how new species evolve in case of natural computing)

Info-computational framework enables unified understanding of knowledge generation in cognizing agents, from the simplest living forms to the most complex ones, building on two basic concepts: information (structure) and computation (process).

4. Self-organization and Autopoiesis. System vs. Environment: Open vs. Closed

This article argues that computational autopoiesis is not only possible, but that autopoiesis is fundamentally an info-computational process based on morphological computing. Information is defined as the difference in one physical system that makes the difference in another physical system.

24 in case of simpler organisms, machines or software agents, my addition.13

As natural computing is physical computing, enactivism (enactive approach to cognition)25 comes as a direct consequence of info-computationalism.

As in a short article like this all the arguments cannot be presented in detail, I refer interested reader to original articles and books (Dodig-Crnkovic & Burgin, 2011) and (Dodig-Crnkovic & Giovagnoli, 2013) and references therein all of which support the claim that info-computationalism is a way to “to set ´computational constructivism’ in motion”.

THE RELATION BETWEEN CONSTRUCTIVISM AND REALISM

COGNITION AS AN ABILITY TO MANAGE COMPLEXITY

DATA – INFORMATION - KNOWLEDGE

COMPUTATION -- MORPHOLOGICAL COMPUTATION – COMPUTATION BEYOND TURING MACHINE

OBSERVER-DEPENDENCE OF KNOWLEDGE AND CONSTRUCTIVISM OF INFO-COMPUTATIONAL TYPE

Info-computational constructivism in modelling of life as cognition

Starting with the definition of an agent as an entity capable of acting on its own behalf, as an actor in Hewitt’s Actor model of computation, even so simple systems as molecules can be modelled as actors exchanging messages (information). We adopt Kauffman’s view of a living agent as something that can reproduce and undergoes at least one thermodynamic work cycle. This definition of living agents leads to the Maturana and Varela’s identification of life with cognition.

Within the info-computational constructive approach to living beings as cognizing agents, from the simplest to the most complex living systems, mechanisms of cognition can be studied in order to construct synthetic model classes of artifactual cognizing agents on different levels of organization.

25 As mentioned before, enactive approach (Noë, 2004) emphasizes the role of sensorimotor skills of an agent, that can be connected with the changing informational interface (an information processing program) between an agent and the world, and thus increasing information exchange.

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This paper presents a study within info-computational constructive framework of the life process as <knowledge> generation in living agents from the simplest living organisms to the most complex ones. Here <knowledge> of a primitive life form is very basic indeed – it is <knowledge> how to act in the world. An amoeba <knows> how to search for food and how to avoid dangers.

An agent is defined as an entity capable of acting on its own behalf. It can be seen as an "actor" in the Actor model of computation in which "actors" are the basic elements of concurrent computation exchanging messages, capable of making local decisions and creating new actors. Computation is thus distributed in space where computational units communicate asynchronously and the entire computation is not in any well-defined 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.) (Hewitt, 2012)

A living agent is a special kind of actor that can reproduce and that undergoes at least one thermodynamic work cycle. (Kauffman, 2000) This definition differs from the common belief that (living) agency requires beliefs and desires, unless we ascribe some primitive form of <belief> and <desire> even to a very simple living agents such as bacteria. The fact is that they act on some kind of <anticipation> and according to some <preferences> which might be automatic in a sense that they directly derive from the organisms morphology. Even the simplest living beings act on their own behalf.

Summary

This article argues that computational modelling of self-organization and autopoiesis (as a special case of self-generative processes in living organism) are fundamentally info-computational processes of morphological computing that develop at different levels of organization of physical systems. The following are the main points:

1. Info-computational constructivism as a framework for the analysis:

Information: Reality as information for an agent. Information is observer-dependent.

Computation: natural/morphological computation, self-organization of information. Execution of physical laws on different levels of organization. <Knowledge> production as computation.

2. Agency as understood within the Actor model of asynchronous computation.

3. For an actor to qualify as living agent, it must be capable of sustaining at least one thermodynamic work cycle.

4. Life as a (morphological computational) process of cognition. On each level of organization corresponding level of cognition is present, from single cell to an organism and society (social cognition).

5. Knowledge generation as morphological computation of informational structures.

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The author

Gordana Dodig-Crnkovic 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). She is a co-organizer, PC member and invited speaker at numerous conferences. The most recent events she co- organized are symposia Natural/Unconventional Computing and its Philosophical Significance (co-organized with Raffaela Giovagnoli) and Social Computing - Social Cognition - Social Networks and Multiagent Systems (co-organized with Judith Simon) at AISB/IACAP World Congress 2012. She is the author of more than eighty international journal and conference publications. Her teaching includes Research Methods, Theory of Computation, Philosophy of Computing and Professional Ethics.

List of at least five potential independent reviewers

1. Sören Brier, sb.ibc@cbs.dk2. Bob Logan, logan@physics.utoronto.ca3. Ranulph Glanville, ranulph@glanville.co.uk4. Lorenzo Magnani, lmagnani@unipv.it5. Stuart Umpleby, umpleby@gwu.edu

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