Functional Information: Towards Synthesis of Biosemiotics and Cybernetics

Sharov, A.A., 2010. Functional Information: Towards Synthesis of Biosemiotics and Cybernetics. Entropy 12, 1050–1070. doi:10.3390/e12051050

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Understanding of the informational nature of life has led to the emergence of two new disciplines, cybernetics and biosemiotics, which approached the same problem from different angles.

Cybernetics was conceived to study communication and control in machines and living organisms in the light of the information theory. However, because living organisms are too complex and we still have very limited means to control them, cybernetics has much stronger links with technology (i.e., communication industry, computers, and robots) than with biology.

In contrast, biosemiotics is focused on studying living organisms and their unique ability to generate and interpret meaningful information. Thus, biosemiotics and cybernetics appeared to be separated by the boundary between life and non-life.

But both disciplines can benefit from their integration in our development of technology. As artificial agents become more complex, they resemble living organisms and require novel principles for their design and analysis that go beyond traditional cybernetic approaches (e.g., goal oriented feedback, attractors, and computation). In particular, to achieve their goals, complex agents need efficient internal communication (between autonomous subagents, and with their future states) as well as external communication with other agents. Communication includes production and interpretation of signs, therefore analysis of complex agents requires semiotic approaches.

As one reason for his construction of a cybersemiotics Brier argues that information (as it is understood in cybernetics) is not enough to explain the phenomena of experience, communication, and knowledge. He suggested to complement it with the semiotic theory and philosophy of Charles Peirce, which is based on the distinction between three basic categories: Firstness (chaos, potentiality, pure feeling and potential qualia), Secondness (force, will), and Thirdness (habits, symbols).

However, many obstacles remain on the path of integration. The notion that natural and artificial agents should be analyzed within the same logical framework is still not fully accepted in biosemiotics. The idea that life and semiosis are coextensive is often interpreted literally, so that artificial agents appear non-semiotic.

Another challenge comes from molecular biology which studies complex molecular machines involved in DNA replication, transcription, and protein synthesis. Molecular machines have no learning capacity, and in this respect they are similar to human machines.

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However, it is not logical to exclude them from the consideration of biosemiotics because they participate in cellular semiosis.

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All agents are externally programmed (e.g., by genome, family, culture); and these external programs that carry historical and evolutionary roots appear more important for agents than the presence of individually acquired programs. Learning often requires longer times than the life span of individual agents, thus it should be viewed as an optional rather than necessary feature of agents.

Finally, analysis of agents requires an update for the philosophy of science. Agents are subjective beings and therefore we need to find a place for subjectivity within the scientific worldview.

Based on Peirce’s semiotic philosophy Brier considers that the universe has some very basic elements of consciousness such as pure feelings, wills, and a tendency to take habits. However, his approach is not compatible with biology as a science. Development of habits in living organisms requires heredity or long-term memory which has not been found in the physical universe, and feelings require complex heritable sensors which are also not found outside of life and its products.

In this paper I follow a functional-evolutionary approach to agents in general, which are defined as systems with goal-directed programmed behavior. Agents are either living organisms or their products because only these systems are known to pursue their goals. Agents are interconnected horizontally, hierarchically, and genealogically; they often include subagents and are always produced by other agents of comparable or higher complexity. Agents can be well individuated or diffused (“swarm agents”), autopoietic, autotrophic, with or without learning capacity. What unites them, is their ability to perform functions for the purpose of reaching certain goals.

Functions of agents are encoded and controlled by a set of signs which I call functional information. These include stable memory signs, transient messengers, and natural signs. Agents always receive some of their functional information from parental/recruiter agents and often follow parental/recruiter goals. This induced semiosis is common for living organisms and artificial devices. In contrast to the cybernetic understanding of agents with its emphasis on control, feedback loops, and attractors, my approach is focused on the origin, evolution, functionality, and communication of agents.

The origin of life is seen as the emergence of autocatalytic molecules that can encode properties of a larger host system in a way that enhances autocatalysis. Such a system is an agent because it can be described in terms of encoded goal-directed actions, although an alternative mechanistic description is also possible. But mechanistic models cannot fully capture the dynamics of complex agents; instead they can be applied only to their simple components. Agents represent a new cross-disciplinary ontological entity because we can describe them in terms of actions, signs, goals, and benefits, which do not belong to the vocabulary of physics.

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Because most artificial devices are not yet capable of learning and evolution, their functional information has human origin. However, future artificial agents may have increased abilities to generate their own functional information, and some of them (synthetic organisms) may be capable of autonomous adaptive evolution.

Contemporary biology is based mostly on the material understanding of life: cells and organisms are described in terms of parts (e.g., proteins, lipids, nucleic acids, carbohydrates) and their interactions. An alternative “relational” approach, is to define living systems on the basis of their functions rather than composition.

If an artificial device performs the same (or similar) functions as a living organism, then, according to relational biology, it is alive. However, it would be confusing to apply the term ―living organism to artificial devices. Instead, it is better to use the term “agent” which equally fits to living organisms and artificial devices.

Here I consider an agent as a system with spontaneous activity whose actions are programmed for reaching certain goals. Goals are considered in a broad sense, including both achievable events (e.g., capturing a resource or producing an offspring), and sustained values (e.g., survival, energy balance, and attention).

The following three criteria can help us to distinguish agents: (1) agents select specific actions out of multiple options, (2) these selected actions are useful in a sense that they help agents to reach their goals, and (3) agents do not emerge by chance, they are produced only by other agents of comparable or higher level of functional complexity. These criteria, however, are difficult to apply.

Agents may remain dormant for a long time; thus, actions are not instantly detectable. Actions may occur at the molecular level and therefore go unnoticed if we don’t use molecular sensors. Evaluation of benefits is also not trivial because we do not know the goals of agents. We usually assume that goals of living organisms include survival and reproduction, a notion that comes from the theory of genetic selection. However, an organism may follow a goal of a larger agent (e.g., family or population) and sacrifice its life for the benefits of the super-agent.

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I suggest following a conservative scientific approach and apply the term “agent” only to those systems that are well proven to have reproducible // goal-directed activities. In particular, I do not consider the existence of non-material agents (gods) and potential agency in immortal systems (e.g., in the universe).

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Mortality implies that agents are either self-reproducing systems or products of other self-reproducing systems. This is a testable necessary condition which helps to narrow down the set of systems that can be agents.

Goals may emerge within the agent, or alternatively can be set by parental agents or higher-level agents. Primitive organisms mostly follow genetically inherited goals, thus, their individual contribution to these goals is relatively small. In contrast, higher animals are capable to develop novel individual goals, especially those that are related to short-term needs (e.g., fast response, ability to find resources and avoid enemies). In human populations, goals are communicated and propagated through the cultures.

Goals of artificial agents are usually set by their designers; however, advanced robots can learn and develop new lower-level goals that help to reach externally supplied goal of the higher level. Learning capacity is optional for agents, but non-learning agents always originate from learning agents and are supplied with previously developed tools and programs that are necessary for performing their functions.

Agents often have a hierarchical structure and contain subagents. For example, multicellular organisms are made of cells and products of their activity, and cells are made of smaller subagents like organelles and chromosomes.

Because agents do not emerge by chance, they persist in the world only via continuous production of other agents. All agents are artifacts because they are manufactured by other agents, which matches the notion that “life is artifact making”. Living agents are capable of self-repair and self-reproduction, a property known as autopoiesis.

But autopoietic agents may still depend on the environment and available resources. For example, predators need prey to survive, parasites need host organisms, and computer viruses propagate only within a network of interconnected computers.

Worker bees and mules are examples of semi-autopoietic agents; they can repair their cells and tissues but they cannot reproduce.

In contrast, germ cells (gametes and their progenitors) are fully autopoietic and potentially immortal.

Functional complexity of new agents is always comparable or lower than the complexity of parental agents because new functions appear only by modifications of already existing functions. Thus, the complexity can increase in lineages of agents only slowly by gradual modification of already existing functions.

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Because living systems are substantially more complex than non-living natural systems, the statement “life from life” follows from the principle of gradualism. In particular, complex systems cannot originate by pure chance, and life gradually emerged from very simple primordial systems. The statement “life from life” can be further generalized as “agents from agents” because the principle of gradualism works for all kinds of agents. Because living organisms appeared before human-made agents, all agents originate from life. Thus, the world of all agents, which I propose to call pragmasphere, is an extension of the biosphere. The distinction between natural and artificial appears less important than the distinction between agents and non-agents.

It should be recognized that any agent is more than a physical body but a link between its parental agents and potential future products.

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Programmed artificial devices may be not enough smart and lack learning abilities, but they are manufactured by humans and produce useful things. Thus, they are components of human functional cycles and evolve together with human knowledge. To facilitate the synthesis of biosemiotics and cybernetics we need to apply principles of biosemiotics to all agents and not to restrict them to living organisms.

[Discussion of functions]

Functions at the cellular level include resource capturing, growth, metabolism, modification of the cytoskeleton, and surface properties, sensing of external conditions, cell cycle, and control of major internal processes. Each of these functions requires thousands and millions of molecular interactions. Multicellular organisms have even more complex sets of functions related to differentiation of cells.

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Finally, there are functions at the level of super-organisms (e.g., families, populations, species, and ecosystems). Functions of a colony include communication (bee dances), // construction of nests, and defense (soldier ants).

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Ecosystems also can be viewed as agents, but their integrity is rather weak because many populations can easily migrate in and out. Examples of ecosystem functions are carbon circulation and recycling of dead organisms.

Agents often outsource their functions to server agents, which can be either manufactured or recruited. Sever agents have induced semiosis because their goals and functional information (defined in the next section) are modified or reset by master agents. The set of all server agents represents a functional envelope for master agents. Human functional envelope includes manufactured machines as well as recruited living organisms (cultivated plants and domesticated animals).

Making functional envelopes is not specific to humans, it is rather a standard strategy for all agents. Animal body (somatic cells) is a mortal functional envelope for immortal germ cells. Similarly, a bacterial cell can be viewed as a functional envelope for the DNA molecule. Production of worker bees by a bee queen is another example of a functional envelope.

Development of functional envelopes is more than just increasing the number of agents; it creates super-agents that belongs to a new level of agent hierarchy.

Any function of an agent has to be reproducible, which means that agents should be able to repeat corresponding actions with a certain fidelity to ensure the same beneficial result. The only way to ensure this reproducibility over long evolutionary times is to manufacture, preserve, and replicate signs that control actions; thus, every function is encoded and controlled by signs. Storing signs in memory (e.g., genetic, epigenetic, or neural) can be interpreted as self-communication, because memory is a message sent by an agent to its own future state.

Because I am interested in the role of signs in supporting the functions of agents, I prefer to emphasizes [sic] the pragmatic aspect of signs, i.e., their ability to specify or modify actions of agents in a beneficial way. In other words, agents use signs to organize their activity. Activity of agents may result in the production of other signs (e.g., mRNA is synthesized using DNA as a template), or in the production of agent components (e.g., resources, structural elements, or sub-agents).

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Theoretically, any material object can be a sign for some agent who can interpret it in functional terms. However, most important are those signs that are produced by agents for communication purposes (including self-communication).

[…] I will follow the definition of Bateson that “information” the elementary unit of information “is a difference which makes a difference”. This understanding of “information” brings it close to the notion of “sign”, however these terms are still not synonymous. Besides other differences in their meanings, I would like to emphasize the aggregative nature of information which unites many signs used together by an agent.

In human semiotics, the notion of “sign” is often applied to individual words or sentences, for which the meaning can be clearly specified, but not to a dictionary, library, or database. In contrast, the term “information” is relevant for large and heterogeneous sets of signs. Thus, I consider information as a sign or set of signs used together by agents.

To emphasize the functional role of information I proposed the term “functional information” which is a set of signs that encode the functions of the organism. In this paper I want to widen the meaning of this term and include also those signs that control the functions. Functional information of organisms includes their genome, epigenome, internal messengers (e.g., mRNA, miRNA, transcription factors, kinases, and phosphatases), external messengers (e.g., pheromones), and natural signs (e.g., temperature and salinity of water).

The notion of “functional information” can be easily extended from organisms to artificial agents. Then, semiosis can be defined as a set of processes by which functional information is interpreted, duplicated, modified, and disseminated by agents for their own benefits.

Functional information evolved in parallel with living organisms starting from the origin of life, and primitive agents differ substantially from advanced agents in the level of complexity of their semiotic processes. Thus it is important to delineate threshold zones that separate primitive levels of semiosis from advanced levels.

[Discussion of attempts to correlate Peircean terminology with models of semiosis]

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[Discussion of DNA and autocatalysis]

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As the number of perceived signals increased in evolution, agents learned how to integrate them into meaningful categories representing various objects and situations (e.g., food items, partner agents, and enemies) and predict events using models. These classifications and models represent the knowledge of an agent about itself and its environment. Following the terminology of Uexküll, this knowledge is the Innenwelt and Umwelt of the organism. Although Kull assumes that Umwelt may exist even at the vegetative level of semiosis, I prefer to limit the use of this term to the higher levels of semiosis.

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At the vegetative level, signs are mere prescriptions of actions and do not carry knowledge. In contrast, signs at the animal and social levels of semiosis are linked with ideal // representations of objects, situations, and actions.

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However, higher levels of semiosis are grounded at the vegetative level because all activities of an organism (e.g., sensing, movement, and interpretation) are apparently supported by certain molecular functions, although we still know very little about molecular mechanisms of neural processes. Information processed by animal and social semiosis does not necessary induce physical actions, however it still can be called “functional information” because: (1) it involves mental functions (e.g., accumulation of knowledge) and (2) may affect future physical actions.

Animal semiosis include icons (e.g., visual images and sound patterns), and indexes, which are associations between classes of objects, as well as between classes of objects and actions. The knowledge about objects, their associations, and possible modifications (Umwelt) is stored in the individual memory but it is not transmitted to other animals because animals do not have the language capacity. Only humans fully crossed the transition zone from animal to social semiosis and developed symbolic languages for efficient horizontal communication. Birds and mammals can use a few symbols, but these seem to be limited to a small number of biological functions (e.g., danger warnings and sexual courtship).

Individual memory is not heritable, and thus, the Umwelt cannot be transferred genetically to the next generation. However, development of Umwelt can be strongly constraint by heritable features of the body.

In addition to effectors (legs, tails, mouth), sense organs are highly important as constraints for the developing Umwelt. The structure, sensitivity, and resolution of senses determines what patterns an animal will be able to learn in its individual life. The size of the body, life span, and movement speed also contribute to the perception and interpretation of the world. Thus, animals of different sizes (e.g., a cow and ant) perceive the same environment (meadow) in entirely different ways.

In contrast to Umwelt, the Innenwelt has a substantial heritable component in organisms. Organisms need robust methods for producing body parts, organs, and tissues, and these methods should remain functional in variable environments as well as in variable conditions within the body over long evolutionary times. Adaptive evolutionary changes in one organ should not affect the methods for generating other organs, because otherwise the species may lose its adaptability.

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Considering levels of vegetative, animal, and social semiosis, where can we place the semiosis of programmed artificial devices? As components of human semiosis, they belong to the social symbolic level. However, if taken alone artificial devices can operate only at vegetative and animal level. Because most machines cannot learn and evolve, their functional information has human origin. Even computer viruses, which is the only class of autopoietic artificial systems, are not capable of adaptive evolution. Machines can process icons and indexes supplied by humans, but most of them do not develop their own icons and indexes.

However, there are trainable computers which can learn to classify objects or optimize actions for reaching certain goals. They can find non-trivial solutions and adapt to situations unforeseen by designers.

If such machines are supplied with both sensors and effectors then, theoretically, they can start developing new sequences of actions for reaching certain goals. However humans have not made any artificial agents capable of autonomous adaptive evolution. Possibly, this kind of agents will be created by imitating living organisms, a strategy known as synthetic biology.

Cybernetics and biosemiotics deviate substantially from the old paradigm of science, which is based on objective observations and experiments. Agents are subjective beings, and it is not possible to understand them well in pure objective notions.

Thus, if we want to include agents into the sphere of science we need to update the philosophy of science.

An alternative approach to the traditional objectivist epistemology was developed within theories of pragmatism and constructivism.

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However, both pragmatism and constructivism have a serious flaw: they underestimate or totally ignore the role of logic as a creative force. Pragmatism has reduced truth to usefulness, and radical constructivism does not view truth as a useful concept.

This prompted Brier to return to the objective idealism of Charles Peirce which included “synechism, an evolutionary perspective, and a pragmatic(istic) epistemology”. Chance (potentiality) and laws (habits of the universe) are assumed to be objective components of the world (its Firstness and Secondness, respectively), and new laws emerge as the universe evolves. Finally, the world includes observers/agents who integrate chances and laws into useful representations and habits (Thirdness).

[Discussion of problems with aligning the objective idealism of Peirce with a philosophy of science]

An alternative way of combining constructivism and pragmatism with logic is to view logic as a useful tool that facilitates creative evolution of communicating agents. In organisms, logic is used to derive novel behaviors that are more likely to be successful than behaviors selected by chance.

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The usefulness of logic belongs to the meta-level compared to the usefulness of actions (i.e., it represents adaptability rather than adaptation), and it is selected at the time scales of macroevolution. In particular, rules of self-organization and eigenbehavior are subject to selection at the level of lineages rather than individual organisms because some of these rules yield higher adaptability and broader diversification within certain lineages. he usefulness of logic is more related to its internal organization rather than to its interface with actions. Thus, humans often use aesthetic criteria to select logical systems based on their universality, simplicity, and richness.

The world has certain regularities, however each kind of agents perceives different sets of these regularities. Two communication systems may have partially overlapping ontologies, and this overlap allows them to establish limited communications. But it is hardly possible to develop a universal ontology that covers all regularities of the world because such ontology would be infinite, and hence, not operational (because of infinite processing time).

The synthesis of biosemiotics and cybernetics is seen as integration of their efforts in the study of agents, a new cross-disciplinary ontological entity. First, the focus of biosemiotics should be shifted from living organisms to agents in general, which all belong to the pragmasphere or functional universe.

Second, agents need to be considered in the context of their hierarchy and origin because their semiosis can be inherited from parental agents or induced by higher-level agents. In particular, the absence of learning in isolated individual agents does not mean that they are not semiotic.

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Third, the cybernetics needs to shift from the computational paradigm to the functional paradigm. I believe that the notion of functional information, which is a set of signs used by agents to encode and control their // functions, can be a starting point for this transition.

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And fourth, following the evolutionary principles, multiple levels of semiosis should be distinguished to classify molecular agents, cells, plants, animals, humans, and machines. The lowest vegetative levels of semiosis is the most intriguing, as it provides clues for the origin of life, and also serves as a basis for the emergence of animal, and social levels of semiosis.

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