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Info-computational constructivism in modelling of life as cognition – possibilities and limits
Anonymous
This paper addresses, within the framework of info-computational constructivism (ICONC) the open question formulated as: “Which levels of abstraction are appropriate in the synthetic modelling of life and cognition?”. ICONC treats natural phenomena as computational processes on informational structures.
At present we lack the common understanding of the processes of life and cognition in living organisms with the details of co-construction of informational structures and computational processes in embodied, embedded cognizing agents, both living and artifactual ones.
Accepting Maturana and Varela’s identification of life with cognition (as self-generating process of interaction with the environment) I present the info-computational constructive approach to living beings as cognizing agents and suggest studying mechanisms of cognition, from the simplest to the most complex living systems, in order to be able to model classes of artifactual cognizing agents on different level of organization. The argument builds on Kauffman’s understanding of agency and self-organization.
Key Words – Computing nature, Info-computationalism, Morphological computing, Information physics, Evolution with Self-organization and Autopoiesis.
Introduction. Life as info-computational generative process of cognition.
“ 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) This paper presents a study within info-computational constructive framework of the life process as
An agent is defined in a sense of Kauffman as an entity capable of acting on its own behalf. This further means that an agent is something that can reproduce and has at least one thermodynamic work cycle. (Kauffman, 2000)
This definition differs from the common belief that agency requires the capacity to act on beliefs and desires, unless we ascribe metaphorically some primitive form of belief and desire even to a very simple 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. Nevertheless even the simples living beings act on their own behalf.
Although a detailed physical account of the agents capacity to perform work and so act in the world is central for understanding of life/cognition, as (Kauffman, 2000)(Deacon, 2007) have argued in detail, this paper will be primarily interested of the info-computational aspects of life. Given that there is no information without physical implementation, computation as the dynamics of information is the implementation (execution) of physical laws – thus is based on physics.
Kauffman’s concept of agency (also adopted by Deacon) seems to suggest the possibility that life can be derived from physics. That is not the same as to claim that life can be reduced to physics that is obviously false if reduction is meant as the claim that the whole is the sum of its parts. However, in deriving life from physics one may expect that both our understanding of life as well as physics will change. We can witness the emergence of information physics (Goyal, 2012) (Chiribella, G.; D’Ariano, G.M.; Perinotti, 2012) as a possible reformulation of physics that may bring physics and life closer to each other. This development in physics smoothly connects to info-computational understanding of nature (Dodig-Crnkovic & Giovagnoli, 2013).
Since for a human it is impossible to grasp reality at once at all levels of organization, I analyze life as cognitive processes unfolding in a layered structure of nested information network hierarchies with corresponding computational dynamics (information processes) – from molecular, to cellular, organismic and social levels.
Informational structures evolve through morphological/morphogenetic computation and in that way the aspect of form is added to the fundamental aspect of energy as found in Kauffman and Deacon. Number of interconnected fields (information physics, theory of agency, theory of computation and information, general biology) are under concurrent development and may be seen as autocatalytic network where the parts rely on each other. 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).
The problem of the relationship between closed and open systems, that is complementarity of constructive and axiomatic approaches is addressed in (Burgin & Dodig-Crnkovic, 2013).
Finally the idea of computing nature and the relationships between two basic concepts of information and computation are explored in (Dodig-Crnkovic & Giovagnoli, 2013) (Dodig-Crnkovic & Burgin, 2011). (637)
The Computing Nature
This section will introduce two fundamental theories about the nature of the universe and their synthesis. The first one is the idea of computing universe (naturalist computationalism or pancomputationalism) in which one sees the time development (dynamics) of physical states in nature as information processing (natural computation): all of the universe constantly computes its own next state.
The next fundamental theory introduced is Informational structural realism of (Floridi, 2003) that takes information to be the fabric of the universe (for an agent).
As a synthesis of the two, info-computational structuralism adopts two basic concepts: information (as a structure) and computation (as a dynamics of an informational structure) (Dodig-Crnkovic, 2011a) (Chaitin, 2007). As a consequence the process of dynamical changes of the universe make the universe a huge computational network where computation is information processing. (Dodig-Crnkovic & Giovagnoli, 2013) Information and computation are two basic and inseparable elements necessary for naturalizing cognition and knowledge. (Dodig-Crnkovic, 2009)
The world exists independently from us (realist position of structural realism) 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 (living) agent in a process of interaction. There is a more general definition 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. 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 and make sense (use) of information. (336)
Information and Computation in Cognizing Agents
“Intelligence organizes the world by organizing itself.” (Piaget, 1955) The above claim by Piaget can be also rephrased as “Cognition organizes the world by organizing itself.”
Both cognition and intelligence need to be generalized in order to understand their role in living world. Maturana and Varela identify cognition with life, so we could equally say: “Life organizes the world by organizing itself.” Not only humans do so but green algae and any other organisms, in one way or the other.
But not only that! The physical world organizes itself both in living agents and as inanimate matter – all of which contributes new understanding of processes of life, its origins and evolution (Kauffman, 1993) (Kauffman, 2000).
The advantage of computational approaches is their testability. Cognitive robotics research, e.g. presents us with a 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 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 perception. From the point of view of data processing, perception can be seen as an interface between the data (the world) and an agent’s perception of the world. (Hoffman, 2009) criticizes 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 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. Information is the difference in the world that makes difference in an agent.
With perception as an interface, sensorimotor activities play a central role in realizing this 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. 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. From the point of view of info-computationalism, a mechanism behind this virtual machine hierarchy is computational self-organization of information, i.e. morphological computing, see (Dodig-Crnkovic, 2012b) and references therein. In his new research programme, Sloman goes a step further studying meta-morphogenesis which is morphogenesis of morphogenesis, (Sloman, 2013) – a way of thinking in the spirit of second order cybernetics. 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 embodied 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.
Knowledge, on the other hand, 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. 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
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 world1 as specific informational structures of an
1 Maturana was the first to suggest that knowledge is a biological phenomenon. He and Varela argued that life should be understood as a process of cognition which enables an organism to adapt and survive in the changing environment. agent. The origin of knowledge in first living agents is not well researched, as the idea still prevails 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. Harms proved a theorem showing that natural selection will always lead a population to accumulate information, and so to 'learn' about its environment. (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. ” 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.
That may be seen not as a drawback of the theory but as 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. There is fresh indication 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. From bio-computing we learn that in living organism biological structure (hardware) is at the same time a program (software) which controls the behaviour of that hardware. (Kampis, 1991) 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.
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). Obviously evolution shows that organisms change their organization through the interactions with the environment. In a sense organisms preserve their organisation, but that organisation is dynamic. 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 longer time scale they evolve and thus change their organization.
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 applying ideas of Maturana and Varela on social autopoetic systems proposes adapted triple autopoietic model of the biological, psychic and socio-communicative systems. (Brier, 2013)
In short, 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 organization (structures) of living beings in a sense of meta-morphogenesis.
Through autopoetic processes with structural coupling (interactions with the environment) (Maturana & Varela, 1992) (Maturana & Varela, 1980) biological system changes its structures and thus the information processing patterns in a self-reflective, recursive manner. 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 evolve) 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.
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.” (Maturana, 2002) 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 statistical laws.
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.
Reality as Simulation in a Living (Cognizing) Agent
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 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. The brain creates a picture of reality that we experience as (and mistake for) 'the actual thing' ” (Ballard, 2002) Already Kant argued that “phenomena” or things as they appear 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. Belief that we ever can experience the world 'directly as it is' is an illusion (Nørretranders, 1999).
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 maps 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 seems to be 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 and by understanding their relationship we can gain insights into limitations and power of models and simulations as knowledge generators. (Foerster, 2003)
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 indirectly impact what can be known.
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.
1. Conclusions
The info-computational framework as a conceptual system builds on the two fundamental theories – informational structural realism (world as an informational structure for an agent) and natural computationalism (computation is information processing) and proposes morphological computation as 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) it is becoming increasingly visible how cognizing agents construct their
1. Computational Constructive Realism. Reality as Simulation
2. Information: Observer-dependence of knowledge production
3. Computation: Natural/morphological computing
4. Self-organization and Autopoiesis. System vs. Environment: Open vs. Closed
This article argues that computational self-organization and autopoiesis as a special case of self- generative processes in living organism) 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 difference in another physical system.
To conclude: Info-computationalism is not a final theory of everything, but a tool for investigations, and it should be judged by its fruitfulness in producing new knowledge. (Dodig-Crnkovic & Mueller, 2009)
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After thinking about this for a number of months I came up with a tentative definition. My definition is that an autonomous agent is something that can both reproduce itself and do at least one thermodynamic work cycle. It turns out that this is true of all free-living cells, excepting weird special cases. They all do work cycles, just like the bacterium spinning its flagellum as it swims up the glucose gradient. The cells in your body are busy doing work cycles all the time. Stuart Kauffman (2003) In: "The Adjacent Possible: A Talk with Stuart Kauffman" at edge.org, March 11, 2003
“
I think an agent is something that can reproduce molecularly and has to do at least one thermodynamic work cycle.
Kauffman
Criticism (typical):
I think that this account of ‘agency’ is absurd. Agency requires the capacity to act on beliefs and desires. An agent is doing something when it is acting to fulfill its desires, given its beliefs. A particular state has meaning or value for an agent to the degree that propositions that are the objects of its more and stronger desires are true in that state. There is a purpose when there is a proposition P that is the object of a desire to be made true.
Bacteria do not have desires.”
However, in this concept of agency an answer may be found to the question whether life can be reduced to physics. To that question there are several possible reactions:
1. If “reduced to physics means derived from physical laws and physical objects” then it is more like being synthesized from physics than reduced to physics.
2. With the shifting of the focus of most advanced sciences toward life sciences, physics is also undergoing changes – physics of living being is one of the fields where life is modeled as physical phenomenon. Biophysics is another.
3. Along with the development of branches of physics involved in modelling life, there is also a process of reformulating physics in terms of information, that is expected to facilitate modelling of life processes including complex phenomena such as mind.