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Anticipatory Functions, Digital-Analog Forms and Biosemiotics: Integrating the Tools to Model Information and Normativity in Autonomous Biological Agents

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Abstract

We argue that living systems process information such that functionality emerges in them on a continuous basis. We then provide a framework that can explain and model the normativity of biological functionality. In addition we offer an explanation of the anticipatory nature of functionality within our overall approach. We adopt a Peircean approach to Biosemiotics, and a dynamical approach to Digital-Analog relations and to the interplay between different levels of functionality in autonomous systems, taking an integrative approach. We then apply the underlying biosemiotic logic to a particular biological system, giving a model of the B-Cell Receptor signaling system, in order to demonstrate how biosemiotic concepts can be used to build an account of biological information and functionality. Next we show how this framework can be used to explain and model more complex aspects of biological normativity, for example, how cross-talk between different signaling pathways can be avoided. Overall, we describe an integrated theoretical framework for the emergence of normative functions and, consequently, for the way information is transduced across several interconnected organizational levels in an autonomous system, and we demonstrate how this can be applied in real biological phenomena. Our aim is to open the way towards realistic tools for the modeling of information and normativity in autonomous biological agents.

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Notes

  1. We would like to make clear at this point that we do not discuss meaning-formation (or the emergence of meaning) in a full-blown cognitive agent. What we mean by meaning-formation is the emergence of new functionality either through the emergence of new functions or of new roles for existing functions in a biological system, so that the latter will be maintained and continue to interact with its environment (maintain system autonomy). Likewise, we use normativity in this more general way, hence we use meaning and normativity cognately.

  2. Shannon’s communication theory (Shannon 1948) and algorithmic information theory (Kolmogorov 1965; Chaitin 1990) approaches are the two leading mathematical information theories.

  3. Brier (1992) provides an interesting analysis of the limitations of defining information as negentropy, which we feel is in accordance to the analysis of physical information systems we provide in this section and in general with the perspective adopted in this paper.

  4. It is noteworthy that Shannon (1948) pointed out that a constraint is equivalent to information.

  5. Collier (1990a) borrowed this term from engineering usage. Lyla Gatlin (1972) called this information stored information, but this name is somewhat misleading, as it does not reflect either the dynamical and often active nature that expressed information allows, or its hierarchical nature.

  6. Standard semiotics (those pertaining merely to the actions of cognitive agents) typically deals with this level. There may be a need for an additional social level.

  7. The symmetry problem has been pointed out in one form or another by Sarkar (2000), Sterelny (2000) and Winnie (2000). Sterelny and Godfrey-Smith (2000) are also concerned with symmetries between genes and their contexts. Collier (2003, 2008) argued that the contexts serve the role of channels. Of course, there might be regulatory genes that serve a regulation function and code for channel construction. These issues are complex, and need to be untangled, but they do not seem to present any special difficulties if we have a suitable account of function that breaks the symmetries.

  8. The notion of representation in agency, and especially, in biological systems is highly controversial. Nevertheless, we think that what is of interest here is that any indication (any predication about the environment) that the environment is appropriate for an interaction, constitutes a primitive form of representation with emergent truth value and content provided by the conditions of the dynamic presuppositions of the indication. Now, given that these indications are realized in the system as anticipatory functions, we may consider that the form of anticipation implied in this framework is the locus of the emergence of normative representational content, and as such of representation in general. This is the context in which we use the notion of representation in this paper. As Bickhard (2001) states, “higher forms of such anticipation are involved in the subsequent macro-evolutionary sequence of learning, emotions, and reflexive consciousness” (ibid, 459). Accordingly we suggest that the normative basis of these higher forms of anticipatory function is provided by the type and level of functionality concerning lower-level autonomous biological agents, as we discuss and model in this paper (see also the discussion on emergent interpretant and Digital-Analog consensus in DAC-logic and the integration of emerging normativity section). However, in this paper we choose to emphasize more the aspect of function and its normative and anticipatory nature as we are more interested in the effects of the transduction of information in biological systems.

  9. Similar examples have been introduced in Tommi Vekhaavara (2003). At any rate, we do not claim that our example is a complete one, but it shows the proof of concept regarding the emergence of normativity in autonomous agents, by suggesting a model that aims at the unification of the modality of ‘interaction’, ‘perception’ and ‘action’ with the smallest possible number of normative primitives.

  10. As we will see in DAC-logic and the integration of emerging normativity section, these category of scaffold or adaptor proteins play a crucial role in the necessary coincidence detection for assuring proper categorical sensing and interpretation of an ambiguous signal, a process that will be characterized as a “digital-analogical consensus”.

  11. We are following here El-Hani et al. (2006, 2009) account of what is information.

  12. In this context a ‘logical product’ is understood as the result of an operation which is not exclusively or necessarily and additive operation, or a summation. For example, the logical product of two propositions p, q, is their conjunction, p & q. The logical product of two sets is their intersection. In a material-mechanical understanding of biochemical-metabolic-ecological processes there is no place for logical products to contribute to causality. Everything is considered to be additive and subtractive budgets of matter and energy; there is no possibility for “an absence” to contribute to causality. On the other hand, pattern (information) can combine presences and absences into logical products. The causal role of patterns and information is not proportional to the quantity of matter that constitutes them. Bateson (1972) used the expression “logical product” to explain that “synaptic summations” where not really summations but the formation of logical products, therefore in (A + B), A and B can have equal levels of causal influence.

  13. This is in a direct accordance with the model of action-selection based on dynamic anticipation with emergent representational content, presented in The normative nature of anticipatory function section.

  14. A way to model and implement hierarchical categorical sensing is through the concept of differentiations as these are explained in The normative nature of anticipatory function section.

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Acknowledgments

Authors wish to thank the editors for valuable comments and suggestions during the reviewing process. Argyris Arnellos holds a Marie Curie Research Fellowship (IEF-273635).

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  1. IAS-Research Centre for Life, Mind, and Society - Department of Logic and Philosophy of Science, University of the Basque Country, Donostia - San Sebastián, Spain

    Argyris Arnellos

  2. Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark

    Luis Emilio Bruni

  3. Institute of Biology, Federal University of Bahia, Bahia, Brazil

    Charbel Niño El-Hani

  4. School of Religion, Philosophy and Classics, University of KwaZulu-Natal, Durban, South Africa

    John Collier

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Arnellos, A., Bruni, L.E., El-Hani, C.N. et al. Anticipatory Functions, Digital-Analog Forms and Biosemiotics: Integrating the Tools to Model Information and Normativity in Autonomous Biological Agents. Biosemiotics 5, 331–367 (2012). https://doi.org/10.1007/s12304-012-9146-4

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