: In this paper, we intend to discuss if and in what sense semiosis (meaning process, cf. C. S. Peirce) can be regarded as an "emergent" process in semiotic systems. It is not our problem here to answer when or how semiosis emerged in nature. As a prerequisite for the very formulation of these problems, we are rather interested in discussing the conditions which should be fulfilled for semiosis to be characterized as an emergent process. The first step in this (...) work is to summarize a systematic analysis of the variety of emergence theories and concepts, elaborated by Achim Stephan. Along the summary of this analysis, we pose fundamental questions that have to be answered in order to ascribe a precise meaning to the term "emergence" in the context of an understanding of semiosis. After discussing a model for explaining emergence based on Salthe's hierarchical structuralism, which considers three levels at a time in a semiotic system, we present some tentative answers to those questions. (shrink)

In this paper, we intend to discuss if and in what sense semiosis (meaning process, cf. C. S. Peirce) can be regarded as an "emergent" process in semiotic systems. It is not our problem here to answer when or how semiosis emerged in nature. As a prerequisite for the very formulation of these problems, we are rather interested in discussing the conditions which should be fulfilled for semiosis to be characterized as an emergent process. The first step in this work (...) is to summarize a systematic analysis of the variety of emergence theories and concepts, elaborated by Achim Stephan. Along the summary of this analysis, we pose fundamental questions that have to be answered in order to ascribe a precise meaning to the term "emergence" in the context of an understanding of semiosis. After discussing a model for explaining emergence based on Salthe's hierarchical structuralism, which considers three levels at a time in a semiotic system, we present some tentative answers to those questions. (shrink)

Introduction IT is generally agreed that Charles Sanders Peirce was one of America's greatest philosophers, yet even today there is little agreement as to ...

Any description of the emergence and evolution of different types of meaning processes (semiosis, sensu C.S.Peirce) in living systems must be supported by a theoretical framework which makes it possible to understand the nature and dynamics of such processes. Here we propose that the emergence of semiosis of different kinds can be understood as resulting from fundamental interactions in a triadically-organized hierarchical process. To grasp these interactions, we develop a model grounded on Stanley Salthe's hierarchical structuralism. This model can be (...) applied to establish, in a general sense, a set of theoretical constraints for explaining the instantiation of different kinds of meaning processes (iconic, indexical, symbolic) in semiotic systems. We use it to model a semiotic process in the immune system, namely, B-cell activation, in order to offer insights into the heuristic role it can play in the development of explanations for specific semiotic processes. (shrink)

Cognitive science has been dominated by the computational conception that cognition is computation across representations. To the extent to which cognition as computation across representations is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from their interpretation by human users. Strictly speaking, computers as such — apart (...) from human users — are not only incapable of cognition, but even incapable of computation, properly construed. If we want to understand the nature of thought, then we have to study thinking, not computing, because they are not the same thing. (shrink)

Terms loaded with informational connotations are often employed to refer to genes and their dynamics. Indeed, genes are usually perceived by biologists as basically ‘the carriers of hereditary information.’ Nevertheless, a number of researchers consider such talk as inadequate and ‘just metaphorical,’ thus expressing a skepticism about the use of the term ‘information’ and its derivatives in biology as a natural science. First, because the meaning of that term in biology is not as precise as it is, for instance, in (...) the mathematical theory of communication. Second, because it seems to refer to a purported semantic property of genes without theoretically clarifying if any genuinely intrinsic semantics is involved. Biosemiotics, a field that attempts to analyze biological systems as semiotic systems, makes it possible to advance in the understanding of the concept of information in biology. From the perspective of Peircean biosemiotics, we develop here an account of genes as signs, including a detailed analysis of two fundamental processes in the genetic information system (transcription and protein synthesis) that have not been made so far in this field of research. Furthermore, we propose here an account of information based on Peircean semiotics and apply it to our analysis of transcription and protein synthesis. (shrink)

A comprehensive and systematic reconstruction of the philosophy of Charles S. Peirce, perhaps America's most far-ranging and original philosopher, which reveals the unity of his complex and influential body of thought. We are still in the early stages of understanding the thought of C. S. Peirce (1839-1914). Although much good work has been done in isolated areas, relatively little considers the Peircean system as a whole. Peirce made it his life's work to construct a scientifically sophisticated and logically rigorous philosophical (...) system, culminating in a realist epistemology and a metaphysical theory ("synechism") that postulates the connectedness of all things in a universal evolutionary process. In The Continuity of Peirce's Thought, Kelly Parker shows how the principle of continuity functions in phenomenology and semeiotics, the two most novel and important of Peirce's philosophical sciences, which mediate between mathematics and metaphysics. Parker argues that Peirce's concept of continuity is the central organizing theme of the entire Peircean philosophical corpus. He explains how Peirce's unique conception of the mathematical continuum shapes the broad sweep of his thought, extending from mathematics to metaphysics and in religion. He thus provides a convenient and useful overview of Peirce's philosophical system, situating it within the history of ideas and mapping interconnections among the diverse areas of Peirce's work. This challenging yet helpful book adopts an innovative approach to achieve the ambitious goal of more fully understanding the interrelationship of all the elements in the entire corpus of Peirce's writings. Given Peirce's importance in fields ranging from philosophy to mathematics to literary and cultural studies, this new book should appeal to all who seek a fuller, unified understanding of the career and overarching contributions of Peirce, one of the key figures in the American philosophical tradition. (shrink)

"This book presents recent research efforts in Artificial Intelligence about building artificial systems capable of performing cognitive tasks. A fundamental issue addressed in this book is if these cognitive processes can have any meaningfulness to the artificial system being built"--Provided by publisher.

This book is the first to provide a comprehensive survey of the computational models and methodologies used for studying the evolution and origin of language and communication. Comprising contributions from the most influential figures in the field, it presents and summarises the state-of-the-art in computational approaches to language evolution, and highlights new lines of development. Essential reading for researchers and students in the fields of evolutionary and adaptive systems, language evolution modelling and linguistics, it will also be of interest to (...) researchers working on applications of neural networks to language problems. Furthermore, due to the fact that language evolution models use multi-agent methodologies, it will also be of great interest to computer scientists working on multi-agent systems, robotics and internet agents. (shrink)

Charles Sanders Peirce has been characterized as the greatest American philosophic genius. He is the creator of pragmatism and one of the founders of modern logic. James, Royce, Schroder, and Dewey have acknowledged their great indebtedness to him. A laboratory scientist, he made notable contributions to geodesy, astronomy, psychology, induction, probability, and scientific method. He introduced into modern philosophy the doctrine of scholastic realism, developed the concepts of chance, continuity, and objective law, and showed the philosophical significance of the theory (...) of signs and mathematical logic. The present series is the first published edition of his systematic works. (shrink)

This volume represents an important contribution to Peirce’s work in mathematics and formal logic. An internationally recognized group of scholars explores and extends understandings of Peirce’s most advanced work. The stimulating depth and originality of Peirce’s thought and the continuing relevance of his ideas are brought out by this major book.

Terms loaded with informational connotations are often employed to refer to genes and their dynamics. Indeed, genes are usually perceived by biologists as basically ‘the carriers of hereditary information.’ Nevertheless, a number of researchers consider such talk as inadequate and ‘just metaphorical,’ thus expressing a skepticism about the use of the term ‘information’ and its derivatives in biology as a natural science. First, because the meaning of that term in biology is not as precise as it is, for instance, in (...) the mathematical theory of communication. Second, because it seems to refer to a purported semantic property of genes without theoretically clarifying if any genuinely intrinsic semantics is involved. Biosemiotics, a field that attempts to analyze biological systems as semiotic systems, makes it possible to advance in the understanding of the concept of information in biology. From the perspective of Peircean biosemiotics, we develop here an account of genes as signs, including a detailed analysis of two fundamental processes in the genetic information system (transcription and protein synthesis) that have not been made so far in this field of research. Furthermore, we propose here an account of information based on Peircean semiotics and apply it to our analysis of transcription and protein synthesis. (shrink)

The controversy over the notion of emergence has recently re-emerged But a rigorous debate concerning how it might be explained or defined often lacking Emergence is discussed heir under two strict conditions (I) emergents can be predictable from the knowledge about a system's parts, (11) emergents can be regarded as dependent on, and determined by, the system's micro-structure O’Connor’s definition of an emergent property is taken as a starting-point for a new definition, incorporating Emmeche and colleagues’ analysis of downward causation (...) and Baas' treatment of emergence It is not necessary to assume that das definition might provide die solution to the problem of emergence Rather, theoretical pluralism regarding different pragmatically-workable notions of emergence is welcome The reality of emergents is discussed here from the standpoint of Dennet’s mild realism. (shrink)

Weak emergence has been offered as an explication of the ubiquitous notion of emergence used m complexity science After outlining the problem of emergence and comparing weak emergence with the two other weak objectivist approaches to emergence, the paper explains a version of weak emergence and illustrates at with cellular automata Then it explains the sort of downward causation and explanatory autonomy involved m weak emergence.

This paper describes different types of models for the evolution of communication and language. It uses the distinction between signals, symbols, and words for the analysis of evolutionary models of language. In particular, it show how evolutionary computation techniques, such as artificial life, can be used to study the emergence of syntax and symbols from simple communication signals. Initially, a computational model that evolves repertoires of isolated signals is presented. This study has simulated the emergence of signals for naming foods (...) in a population of foragers. This type of model studies communication systems based on simple signal-object associations. Subsequently, models that study the emergence of grounded symbols are discussed in general, including a detailed description of a work on the evolution of simple syntactic rules. This model focuses on the emergence of symbol-symbol relationships in evolved languages. Finally, computational models of syntax acquisition and evolution are discussed. These different types of computational models provide an operational definition of the signal/symbol/word distinction. The simulation and analysis of these types of models will help to understand the role of symbols and symbol acquisition in the origin of language. (shrink)

The evolutionary emergence of biological processes in organisms with inner, qualitative aspects has not been explained in any sufficient way by neurobiology, nor by the traditional neo-Darwinian paradigm — natural selection would appear to work just as well on insentient zombies (with the right behavioral input-output relations) as on real sentient animals. In consciousness studies one talks about the ‘hard problem’ of qualia. In this paper I sketch a set of principles about sign action, causality and emergent evolution. On the (...) basis of these principles, I characterize a concept of cause that would allow for a naturalistic explanation of the origin of consciousness. The suggested account of causation also turns the ‘hard problem’ of qualia into the easier problem of relating experimental biology to experiential biology. (shrink)

Cognitive science has been dominated by the computational conception that cogniton is computation across representations. To the extent to which cognition is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from ther interpretation by human users. Strictly speaking, computers as such–apart from human users–are not only incapable (...) of cognition but even incapable of computation, properly construed. If we want to understand the nature of thought, we are going to have to study thinking, not computing, because they are not the same thing. (shrink)

Weak emergence has been offered as an explication of the ubiquitous notion of emergence used in complexity science (Bedau 1997). After outlining the problem of emergence and comparing weak emergence with the two other main objectivist approaches to emergence, this paper explains a version of weak emergence and illustrates it with cellular automata. Then it explains the sort of downward causation and explanatory autonomy involved in weak emergence.

The controversy over the notion of emergence has recently re-emerged But a rigorous debate concerning how it might be explained or defined is often lacking Emergence is discussed here under two strict conditions (l) emergents can be predictable from the knowledge about a system's parts, (ll) emergents can be regarded as dependent on, and deternuned by, the system's micro-structure O'Connor's definmon of an emergent property is taken as a starting-point for a new definmon, incorporating Emmeche and colleagues' analysis of dounward (...) causation and Baas' treatment of emergence It is not necessary to assume that this defintaon might provide the solution to the problem of emergence Rather, theoretical pluralism regarding different pragmatically-workable notions of emergence is welcome The reality of emergents is discussed here from the standpoint of Dennet's mild realism. (shrink)

This dissertation examines the functional roles symbols play in biological organisms, scientific models and adaptive learning devices, analyzing the process of how symbols acquire new functions. The semiotic categories of syntactics, semantics, and pragmatics are used to examine the functioning of symbols in organisms, models, and devices. The dissertation explores how we would go about designing self-organizing devices which adaptively construct their own relationships to the physical world . It bears on the frame/feature-generation problem in artificial intelligence, the problem of (...) machine creativity in philosophy, the measurement problem in physics, the problem of generating new observables in science, and the problem of emergent functions in evolutionary biology. Much of the perspective comes out of the work of theoretical biologists Howard Pattee and Robert Rosen. ;An analytical framework is developed for distinguishing between computation, measurement, control, and nonsymbolic functionalities. A taxonomy of adaptive cybernetic devices is proposed in which nonadaptive devices are distinguished from computationally adaptive ones and semantically adaptive ones . Evolutionary devices adaptively construct their own sensors and effectors contingent upon their performance, and are thus qualitatively different from devices now in use. Evolutionary devices are analogous to the biological evolution of new sense and effector organs. Relative capacities and limitations of the device types are outlined. ;Three contemporary conceptions of emergence are distinguished: computational emergence, thermodynamic emergence, and emergence-relative-to-a-model. Levels of adaptivity are connected to types of emergence. ;Throughout, purely computational, logic-driven strategies for generating new functions are criticized as being incapable of generating new primitives. It is argued that computers have completely nonemergent behavior because of their completely specified, digital, symbolic nature, while biological organisms are capable of emergent behavior because they also include analog, nonsymbolic processes. An evolutionary robotics research program is suggested as an alternative to purely computational approaches. (shrink)