The presentation proposes to complement an existing development on meaning generation for animals, humans and artificial agents by looking at what could have existed at pre-biotic times and what could be a post-human meaning generation. The core of the approach is based on an existing model for meaning generation: the Meaning Generator System (MGS). The MGS is part of an agent submitted to an internal constraint. The MGS generates a meaning when it receives an information that has a connection with (...) the constraint. The generated meaning is used by the agent to implement an action (physical, biological or mental) aimed at satisfying the constraint. The action can be in or out the agent. The purpose of the presentation is to widen the MGS approach in order to reach a coverage for information, constraint and meaning from a pre-biotic level to a possible post-human one. We present the MGS for animals, humans and artificial agents with the corresponding constraints. We then look at what could have been a constraint at a pre-biotic far from thermodynamic equilibrium level. At the other end of the spectrum we look at a possible post-human status with an evolution of a ‘limit anxiety’ human constraint and also with AAs submitted to animal or human type constraints. Such approach links information science with physics, evolution, anthropology, semiotics and human mind. Continuations are proposed. (shrink)

Biosemiotics deal with the study of signs and meanings in living entities. Constructivism considers human knowledge as internally constructed by sense making rather than passively reflecting a pre-existing reality. Consequently, a constructivist perspective on biosemiotics leads to look at an internal active construction of meaning in living entities from basic life to humans. That subject is addressed with an existing tool: the Meaning Generator System (MGS) which is a system submitted to an internal constraint related to the nature of the (...) agent containing it (biological or artificial). Simple organisms generate meanings to satisfy a “stay alive” constraint. More complex living entities manage meaningful representations with more elaborated constraints. The generated meanings are used by the agents to implement actions aimed at satisfying the constraints. The actions can be physical, biological or mental and take place in the agent or in its environment. The case of human agency is introduced with meaningful representations that may have allowed our ancestors to become self-conscious by representing themselves as existing entities. This paper proposes to use the MGS as a thread to address the above items linking biosemiotics to constructivism with relations to normativity, agency and autonomy. Possible continuations are introduced. (shrink)

One of the mains challenges of biosemiotics is ‘to attempt to naturalize biological meaning’ [Sharov & all 2015]. That challenge brings to look at a possible evolutionary thread for biosemiotics based on meaning generation for internal constraint satisfaction, starting with a pre-biotic entity emerging from a material universe. Such perspective complements and extends previous works that used a model of meaning generation for internal constraint satisfaction (the Meaning Generator System) [Menant 2003a, b; 2011]. We propose to look at such an (...) evolutionary thread for biosemiotics in three steps. The first step presents the proposed emergence of a pre-biotic entity as a far from thermodynamic equilibrium volume constrained to maintains its status [Menant 2015]. Such constraint dependence introduces natural links with teleology and with meaning generation. It also introduces perspectives for evolutionary origins of agency, self, and autonomy, coming in addition to other biosemiotic perspectives [Tønnessen, 2015]. The next step recalls the MGS as being a system approach linking the agent containing it to its environment and bringing to the agent a control from within. We apply the MGS to animal life. Relations with the Umwelt, with constructivism and with the Peircean triadic approach are highlighted. The last step of the thread brings the evolution of life up to humans where specificities related to human mind have to be taken into account. Among them is self-consciousness for which an existing evolutionary scenario introduces anxiety management as a foundational human constraint [Menant, 2014]. We link that scenario to the evolutionary thread because it introduces specific human constraints and is based on the evolution of meaningful representations. A conclusion summarizes the steps of the proposed evolutionary thread. More work is needed on that subject. Possible continuations are introduced. (shrink)

The management of meaningful information by biological entities is at the core of biosemiotics [Hoffmeyer 2010]. Intentionality, the ‘aboutness’ of mental states, is a key driver in philosophy of mind. Philosophers have been reluctant to use intentionality for non human animals. Some biologists have been in favor of it. J. Hoffmeyer has been using evolutionary intentionality and Peircean semiotics to discuss a biosemiotic approach to the problem of intentionality [Hoffmeyer 1996, 2012]. Also, recent philosophical studies are bringing new openings on (...) the subject of biological intentionality.[Asma 2014]. What we propose here is to use an existing system approach to meaning generation to introduce a link between biosemiotics and bio-intentionality at basic life level in an evolutionary perspective. Meanings do not exist by themselves. They have to be generated for a given reason by a defined entity. A system approach to meaning generation based on constraint satisfaction has been developped to that end: the Meaning Generator System (MGS) [Menant 2003a]. It has been used for biosemiotics in an evolutionary perspective [Menant 2003b, 2011]. To look at relating biosemiotics to intentionality through meaning genaration we recall the system structure of the MGS with the agent that contains it. Meaning generation and agent interfacing with environment make available components for the groundings of the generated meaning in terms of data, data processing, interfacing and constraint [Menant, 2011]. These groundings of the meaning can be in or out the agent containing the MGS. They display what the generated meaning is about. For basic life the ‘aboutness’ of the generated meaning relies on a ‘stay alive’ constraint that has to be satisfied (others constraints, like ‘live group life’, are to be introduced through the evolution of life). Such ‘aboutness’ of a generated meaning within basic life can be associated to an elementary biological intentionality, to a ‘bio-intentionality’. As biosemiotics deals with meaning management by biological entities, the relations introduced by the MGS between meaning generation and bio-intentionality introduce a link between biosemiotics and bio-intentionality for basic life. We present and develop that link. Besides making available a model usable for bio-intentionality, the proposed approach may also provide an entry point to the concept of intentionality without having to take into account human specificities like self-consciousness. It should also be noted that the approach takes life as a given and that the‘stay alive’ constraint brings in a teleological component. Such presentation of bio-intentionality calls for other developments and continuations. Some will be introduced. (shrink)

The Turing Test (TT), the Chinese Room Argument (CRA), and the Symbol Grounding Problem (SGP) are about the question “can machines think?” We propose to look at these approaches to Artificial Intelligence (AI) by showing that they all address the possibility for Artificial Agents (AAs) to generate meaningful information (meanings) as we humans do. The initial question about thinking machines is then reformulated into “can AAs generate meanings like humans do?” We correspondingly present the TT, the CRA and the SGP (...) as being about generation of human-like meanings. We model and address such possibility by using the Meaning Generator System (MGS) where a system submitted to an internal constraint generates a meaning in order to satisfy the constraint. The system approach of the MGS allows comparing meaning generations in animals, humans and AAs. The comparison shows that in order to have AAs capable of generating human-like meanings, we need the AAs to carry human constraints. And transferring human constraints to AAs raises concerns coming from the unknown natures of life and human mind which are at the root of human constraints. Implications for the TT, the CRA and the SGP are highlighted. It is shown that designing AAs capable of thinking like humans needs an understanding about the natures of life and human mind that we do not have today. Following an evolutionary approach, we propose as a first entry point an investigation about the possibility for extending a “stay alive” constraint into AAs. Ethical concerns are raised from the relations between human constraints and human values. Continuations are proposed. (This paper is an extended version of the proceedings of an AISB/IACAP 2012 presentation). (shrink)

It is pretty obvious that language and human consciousness entertain tight relations. We could not really be conscious of ourselves without the possibility to say “I” or “me”. And language is a key contributor in our capability to identify ourselves as conscious entities existing in the environment. But the relations linking language and consciousness are complex and difficult to analyze. Evolutionary origins of language are unknown as no fossil traces have been left by our ancestors. Sciences of consciousness however begin (...) to make available some possible evolutionary scenarios about the nature of human consciousness. We want here to propose a link between language and consciousness by using such an evolutionary scenario and also introduce the usage of a systemic approach to meaning generation. In the first part of the presentation we will use an existing scenario about the evolutionary nature of self-consciousness where the development of language has a role (1). We will highlight this role in order to identify language and self-consciousness as inter-dependant in their nature through a possible common evolutionary origin. Self-consciousness and language could then be considered as tightly inter-dependant through a common build up of human nature during evolution. The scenario presents an evolutionary nature of self-consciousness as resulting of the capability for pre-human primates to identify with their conspecifics (1, 2). The conspecifics are represented as existing in the environment, and such identification brought our pre-human ancestors to consider themselves as also existing in the environment. The scenario takes this event as being a first step for a conscious self-representation within pre-human primates, which progressively evolved toward our today human consciousness (3). But such identification with conspecifics was not for free at times of survival of the fittest. Identifying with conspecifics meant for our pre-human ancestor to also identify with their sufferings or encountered dangers. These came in addition to the dangers or sufferings naturally encountered and created a significant anxiety increase (1). The resulting level of anxiety had to be limited. One possibility for that was to develop psychological or physical tools that could have reduced the risks of occurrences and developments of such dangers and sufferings. Among these tools is the performance of language which can induce significant evolutionary advantages. We will propose a first scheme about how these evolutionary advantages could have reduced the dangers and sufferings encountered by our pre-human ancestors. We will also show how the development of language produced by itself a positive feedback on the development of inter-subjectivity in the evolutionary scenario, and so participated directly to the development of human consciousness. Other evolutionary advantages have existed like the development of imitation and synergy through experience (4). Language played a role there also, and has to be taken into account. The second part of the presentation will propose the usage of an existing systemic approach to meaning generation in terms of constraints satisfaction (5, 6). Constraints for pre-human primates, ranging from a basic “stay alive” to highly elaborated “limit anxiety”, were source of multiple meaning generations in which language has played (and still plays) a key role. Several continuations will be proposed linked to the here above thread on a co-evolutions of language and human consciousness, as based on the evolutionary scenario. (shrink)

The notions of information, representation and enaction entertain historical and complex relations with cognition. Historical relations because representational structures belong to the central hypothesis of cognitive sciences. Complex relations because cognitive sciences apply the notion of representation to animals, humans and robots, and also because the enactive approach tends to disregard the GOFAI type of representations. In this wide horizon of relations, we propose to look at a systemic approach that could bring up a common denominator for information and representations (...) in the build up of cognition, and also keep a link with the enactive approach. Our purpose is to show that systems submitted to constraints can generate meaningful information to maintain their natures, and consequently build up meaningful representations that have some compatibility with the enactive approach. Such a systemic approach to the notion of meaningful information could then make available a link between enaction and meaningful representations. The first part of the presentation is about reminding that cognition does not exist per se, but is related to the system that builds it. We look at cognition as constituted by dynamic meaningful representations built up by systems that have constraints to satisfy in their environments. Cognition is considered here at the level of the system that builds it and uses it in order to maintain its nature in its environment. Such a systemic approach fits with evolution. Organisms build representations to cope with survival constraints (frogs build representations of moving black dots in order to satisfy food constraints). Humans build representations and cognition to satisfy constraints that are conscious and unconscious. Artificial systems can use representations and cognition to run activities related to constraints implemented by the designers or coming from the environment (a goal to reach being considered as a constraint to satisfy). In the second part of the presentation we define what are a meaningful information and a representation for a system submitted to a constraint in its environment, and we link these to the enactive approach. We define a meaningful information (a meaning) as an information generated by a system submitted to a constraint when it receives an external information that has a connection with the constraint. The meaning is precisely that connection. The meaning belongs to the interactions that link the system to its environment. The function of the meaning is to participate to the determination of an action that will be implemented in order to satisfy the constraint. (Menant, 2003). The satisfaction of the constraint goes with maintaining the nature of the system in its environment. A Meaning Generator System (MGS) is defined correspondingly. It is a building block for higher level systems. We present some characteristics of the MGS (groundings of a meaning, domain of efficiency and transfer of meanings, networking of meanings, evolutionary usage). The MGS approach is close to a simplified version of the Peircean triadic theory of signs (Menant, 2003, 2005 ). We define the representation of an item for a system as being the dynamic set of meaningful information corresponding to the item for the system in its environments (an elementary representation being made of a single meaningful information). These representations link the system to its environment by their meaningful components related to the nature of the system. These representations are different from the GOFAI ones. The possibilities for linking these notions of meaning and representation with the enactive approach come from the structure of the MGS: the need for an action is the cause of the meaning generation by and for the system. The action on the environment is for the system to maintain its nature (its identity). The MGS links together the generation of meaningful representations, the nature of the system, and the interactions with the environment. This can be considered as close to enacting a world by meaning generation (Di Paolo and all 2007), and to the enactive concept of sense making (De Jaegher, Di Paolo 2007). We propose that basing the definition of a representation on the notion of meaningful information generated by a system submitted to a constraint can open a way for making the notion of representation compatible with the enactive approach. In the third part of the presentation, we consider some cases of meaningful information and representations for organisms and for robots. Regarding organisms, the MGS can be used in an evolutionary context by looking at the evolution of the systems and of the constraints. Purpose is to modelize the generation of meanings and of representations in order to make available a tool usable for different levels of evolution, as evolution has a place in cognitive sciences (Proust, 2007). Constraints for basic life are survival constraints (individual and species). Group life constraints are also to be considered. Reaching the level of humans in evolution brings in new constraints that cannot be clearly identified as they have to take into account human consciousness which is today a mystery (the “hard problem”). On an evolutionary standpoint, human constraints come in addition to the ones existing for non human organisms. We can make some hypothesis on the nature of human constraints (Maslow pyramid based constraints, anxiety limitation…). For robots, the MGS is initially based on the design of the robot. The meaning generated within a robot is initially derived from the constraints implemented by the designer and from the environment. But some non calculable or non predictable evolutions of the robot can introduce meanings that look proper to the robot. This last point can be linked to the notion of autonomy in robots. In such examples, the dynamic management of meanings thru the MGSs in their environments keeps the link with the enactive approach. We finish the presentation by summarising the points addressed and by proposing several continuations. (shrink)

The notion of representation is at the foundation of cognitive sciences and is used in theories of mind and consciousness. Other notions like ‘embodiment’, 'intentionality‘, 'guidance theory' or ‘biosemantics’ have been associated to the notion of representation to introduce its functional aspect. We would like to propose here that a conception of 'usage related' representation eases its positioning in an evolutionary context, and opens new areas of investigation toward self-representation and self-consciousness. The subject is presented in five parts:Following an overall (...) presentation, the first part introduces a usage related representation as being an information managed by a system submitted to a constraint that has to be satisfied. We consider that such a system can generate a meaningful information by comparing its constraint to a received information (Menant 2003). We define a representation as being made of the received information and of the meaningful information. Such approach allows groundings in and out for the representation relatively to the system. The second part introduces the two types of representations we want to focus on for living organisms: representations of conspecifics and auto-representation, the latter being defined without using a notion of self-representation. Both types of representations have existed for our pre-human ancestors which can be compared to today great apes.In the third part, we use the performance of intersubjectivity as identified in group life with the presence of mirror neurons in the organisms. Mirror neurons have been discovered in the 90‘s (Rizzolatti & al.1996, Gallese & al.1996). The level of intersubjectivity that can be attributed to non human primates as related to mirror neurons is currently a subject of debate (Decety 2003). We consider that a limited intersubjectivity between pre-human primates made possible a merger of both types of representations. The fourth part proposes that such a merger of representations feeds the auto-representation with the meanings associated to the representations of conspecifics, namely the meanings associated to an entity perceived as existing in the environment. We propose that auto-representation carrying these new meanings makes up the first elements of self-representation. Intersubjectivity has allowed auto-representation to evolve into self-representation, avoiding the homunculus risk. The fifth part is a continuation to other presentations (Menant 2004, 2005) about possible evolution of self-representation into self-consciousness. We propose that identification with suffering or endangered conspecifics has increased anxiety, and that the tools used to limit this anxiety (development of empathy, imitation, language and group life) have provided a positive feedback on intersubjectivity and created an evolutionary engine for the organism. Other outcomes have also been possible. Such approach roots consciousness in emotions. The evolutionary scenario proposed here does not introduce explicitly the question of phenomenal consciousness (Block 1995). This question is to be addressed later with the help of this scenario.The conclusion lists the points introduced here with their possible continuations. (shrink)

Information and meaning exist around us and within ourselves, and the same information can correspond to different meanings. This is true for humans and animals, and is becoming true for robots. We propose here an overview of this subject by using a systemic tool related to meaning generation that has already been published (C. Menant, Entropy 2003). The Meaning Generator System (MGS) is a system submitted to a constraint that generates a meaningful information when it receives an incident information that (...) has a relation with the constraint. The content of the meaningful information is explicited, and its function is to trigger an action that will be used to satisfy the constraint of the system. The MGS has been introduced in the case of basic life submitted to a "stay alive" constraint. We propose here to see how the usage of the MGS can be extended to more complex living systems, to humans and to robots by introducing new types of constraints, and integrating the MGS into higher level systems. The application of the MGS to humans is partly based on a scenario relative to the evolution of body self-awareness toward self-consciousness that has already been presented (C. Menant, Biosemiotics 2003, and TSC 2004). The application of the MGS to robots is based on the definition of the MGS applied to robots functionality, taking into account the origins of the constraints. We conclude with a summary of this overview and with themes that can be linked to this systemic approach on meaning generation. (shrink)

The relations between life and cogntion have been addressed through different perspectives [Stewart 1996, Boden 2001, Bourgine and Stewart 2004, van Duijn & all 2006, Di Paolo 2009]. We would like here to address that subject by relating life to cognition through a process of meaning generation. Life emerged on earth as a far from thermodynamic equilibrium performance that had to maintain herself. Life is charactertized by a ‘stay alive’ constraint that has to be satisfied (such constraint can be included (...) in the constraint of being able to maintain far from equilibrium thermodynamic conditions [Bickhard 2011]). The local ‘stay alive‘ constraint has to be satisfied in an environment containing elements potentially supportive or harmfull. A key activity for the living entity is to characterize these elements in terms of meaningfulness relatively to the ‘stay alive’ constraint. This process can be modeled with an existing tool where a system submitted to an internal constraint generates meaningful information characterizing elements of the environment: the Meaning Generator System (MGS) [Menant, 2003, 2014 a]. In a few words: when a system submitted to an internal constraint receives from the environment an information that has a connection with the constraint it generates a meaning usable for the implementation of an action satisfying the constraint.The generated meaning is the connection existing between the received information and the constraint. The MGS models this process and interfaces to the action implementation for constraint satisfaction The meaning is generated by and for the system. The MGS grounds meaning generation in constraint satisfaction and links the living entity to her environment in a relational process. A simple example is with a paramecium close to a drop of acid. The paramecium which is submitted to a ‘stay alive’ constraint will move away from the acid area. The received information ‘presence of acid’ generates the meaning ‘acid not compatible with the ‘stay alive’ constraint‘ which triggers the moving away action (that example is close to Varela’s bacteria swimming up a sugar gradient. What the MGS brings in addition is a modeling of the significance of the chemical gradient for the organism). The action implemented to satisfy the constraint modifies the environment and the received information, establishing an interactive process linking the living entity to her environment. During its evolution animal life has elaborated new constraints (like ‘live group life’) and new functions enriching meaning generation and action scenarios. As a result the build up of meaningful representations has improved the constraint satisfaction processes of animals, embedding them in their environments in relational and interactive terms [Menant, 2011]. Cognition can be defined by proposing that ‘a system is cognitive if and only if sensory inputs serve to trigger actions in a specific way, so as to satisfy a viability constraint’ [Bourgine, Stewart 2004]. Cognition can also be considered as exemplifying a ‘vital criterion of responsiveness’ [Boden, 2001 ]. Consequently the MGS can be positioned as an elementary and generic version of animal cognition. For animal life, meaning generation for internal constraint satisfaction links life and cognition in a relational and interactive process. Cognition for human life is more complex as new performances have to be taken into account like self-consciousness and free-will. Meaning generation at human level is a challenging subject as human constraints are not clearly understood [Menant, 2011]. Many research activities are in process looking for some understanding of human mind [Philpapers]. One area of investigation is an evolutionary approach to self-consciousness using meaning generation where anxiety limitation comes up as a generic human constraint [Menant, 2014 b]. Assuming that we can have clear enough an understanding of some human constraints, we can look at the MGS for partly extending to humans the link between life and cognition that has been established for animals. So overall, we can consider that the MGS approach makes available an evolutionary link between life and cognition for animals, and partly for humans. A characteristic of the proposed system approach to meaning generation is the possibility to use it for any type of agent, be it organic (with intrinsic constraints ) or artificial (with derived constraints). Such characterization of agents through meaning generation can be used to discriminate artificial itelligence from human intelligence (see the MGS usage to support Searle’s chinese room argument [Menant, 2013]). On a more general basis, the proposed system approach can positions the MGS as a simple model for an internal source of normativity. It’s usage as a simple building block allows a bottom-up modeling for normativity in the sensorimotor approach. The ‘stay alive’ constraint could also be taken as a starting point for an evolutionary grounding of sensorimotor norms ‘in the biological normativity of the agent as a whole’ [Di Paolo & all 2014]. The proposed presentation will develop the points summarized here above and position them relatively to the autopoietic and enactive approaches. Several possible continuations will also be highlighted. (shrink)

Humans are cognitive entities. Our ongoing interactions with the environment are threaded with creations and usages of meaningful information. Animal life is also populated with meaningful information related to survival constraints. Information managed by artificial agents can also be considered as having meanings, as derived from the designer. Such perspective brings us to propose an evolutionary approach to cognition based on meaningful information management. We use a systemic tool, the Meaning Generator System (MGS), and apply it consecutively to animals, humans (...) and artificial agents [1, 2]. The MGS receives information from its environment and compares it with its constraint. The generated meaning is the connection existing between the received information and the constraint. It triggers an action aimed at satisfying the constraint. The action modifies the environment and the generated meaning. Meaning generation links agents to their environments. The MGS is a system: a set of elements linked by a set of relations. Any system submitted to a constraint and capable of receiving information can lead to a MGS. Animals, humans and robots are agents containing MGSs dealing with different constraints. Similar MGSs carrying different constraints will generate different meanings. Cognition is system dependent. Contrary to approaches on meaning generation based on psychology or linguistics, the MGS approach is not based on human mind. We want to avoid the circularity of taking human mind as a starting point. Free will and self-consciousness participate to the management of human meanings. They do not exist for animals or robots. Staying alive is a constraint that we share with animals. Robots ignore that constraint. We first use the MGS for animals with “stay alive” and “group life” constraints. The analysis of meaning and cognition in animals is however limited by our un-complete understanding of the nature of life (the question of final causes). Extending the analysis of meaning generation and cognition to humans is complex and has some true limitations as the nature of human mind is a mystery for today science and philosophy. The natures of our feelings, free will or self-consciousness are unknown. Approaches to identify human constraints are however possible, where the MGS can highlight some openings [3, 4]. Modeling meaning management in artificial agents is rather straightforward with the MGS. We, the designers, know the agents and the constraints. The derived nature of constraints, meaning and cognition is however to be highlighted. We define a meaningful representation of an item for an agent as being the networks of meanings relative to the item for the agent, with the action scenarios involving the item. Such meaningful representations embed the agents in their environments and are far from the GOFAI type of representations. Cognition, meanings and representations exist by and for the agents. We finish by summarizing the points presented here and highlight possible continuations . [1] “Information and Meaning” [2] “Introduction to a systemic theory of meaning” [3] “Computation on Information, Meaning and Representations. An Evolutionary Approach” [4] “Proposal for a shared evolutionary nature of language and consciousness”. (shrink)

More than one researcher is currently proposing that the notion of information become an important element for defining living systems as well as for explaining conditions that make their origins possible. During the pre-biotic era, the type of compounds encountered would mainly have been very simple in nature and might have been immersed in the natural dynamic of the physical world and in processes of self-organization. It is furthermore quite possible that they formed a relationship between and among certain types (...) of processes that here we are specifically proposing as central for the emergence of cell organization. Consequently, an important initial step towards constructing a theory of biological information is to ask ourselves the question: how do biological systems process information? In this way, we will be contributing to the proposals of this paper where we seek to identify general principles that govern biological computing and that deal with biosemiotic approaches as they are defended in naturalistic normative terms. (shrink)

Despite an impressive number of investigations and indirect evidence, the mechanisms that link patterns and processes across the landscape remain a debated point. A new definition of landscape as a semiotic interface between resources and organisms opens up a new perspective to a better understanding of such mechanisms. If the landscape is considered a source of signals converted by animal cognition into signs, it follows that spatial configurations, extension, shape and contagion are not only landscape patterns but categories of identifiable (...) signals. The eco-field hypothesis, by which cognitive templates are used to identify spatial configurations as carriers of meaning according to an active function, are combined with the sign theory to create an eco-semiotic model of landscape representation. Signs from landscape change in efficacy according to mechanisms of degradation, and metric sign categories have to be considered. An interdisciplinary coalescence is expected by using the theoretical approach in different fields of conservation and resource management and planning. (shrink)

In hybrid nature that results from a random mix of technological infrastructures and natural ecosystems, environmental fundamentals are modified producing dramatic effects on the semiosis of several species. Human intrusion in ecosystems creates new spatial configurations that have a reduced ecological complexity when compared with systems less affected by human manipulation. This causes cascading effects on other environmental fundamentals. F.i., systems that face a low complexity, are more exposed to changes that in turn can reduce the performances of individual species (...) due to a higher level of uncertainty. The flux of information could be apparently maintained in hybrid nature when some natural patterns and processes are replaced by human artifacts and associated dynamics, but the process of signification could be strongly modified. The strict contact between people and wild organisms in hybrid nature requires new types of strategies to guarantee the continuation of the natural dynamic of populations and communities. Rural sanctuaries are proposed as one of the possible models on which to invest in order to harmonize the technological and natural processes with the goal to assure a multiscale sustainability. (shrink)

The physical and relational structure of the biologic continuum (both internal and external to the organism) creates the information signature that is the basis for the origination of meaning in the living system. A meaning metric can be grounded in the significance of that information to the stability of the system during the process of adaptive reconciliation of divergences from the steady state condition. From this perspective, an information-theoretic formulation of the process for translating incident information into adaptive action is (...) proposed that can be practically used in defining and quantifying the meaning of that information for the living system. (shrink)