Towards Autopoietic Computing

A key challenge in modern computing is to develop systems that address complex, dynamic problems in a scalable and efficient way, because the increasing complexity of software makes designing and maintaining efficient and flexible systems increasingly difficult. Biological systems are thought to possess robust, scalable processing paradigms that can automatically manage complex, dynamic problem spaces, possessing several properties that may be useful in computer systems. The biological properties of self-organisation, self-replication, self-management, and scalability are addressed in an interesting way by autopoiesis, a descriptive theory of the cell founded on the concept of a system's circular organisation to define its boundary with its environment. In this paper, therefore, we review the main concepts of autopoiesis and then discuss how they could be related to fundamental concepts and theories of computation. The paper is conceptual in nature and the emphasis is on the review of other people's work in this area as part of a longer-term strategy to develop a formal theory of autopoietic computing.Comment: 10 Pages, 3 figure

Performance measurement : challenges for tomorrow

This paper demonstrates that the context within which performance measurement is used is changing. The key questions posed are: Is performance measurement ready for the emerging context? What are the gaps in our knowledge? and Which lines of enquiry do we need to pursue? A literature synthesis conducted by a team of multidisciplinary researchers charts the evolution of the performance-measurement literature and identifies that the literature largely follows the emerging business and global trends. The ensuing discussion introduces the currently emerging and predicted future trends and explores how current knowledge on performance measurement may deal with the emerging context. This results in identification of specific challenges for performance measurement within a holistic systems-based framework. The principle limitation of the paper is that it covers a broad literature base without in-depth analysis of a particular aspect of performance measurement. However, this weakness is also the strength of the paper. What is perhaps most significant is that there is a need for rethinking how we research the field of performance measurement by taking a holistic systems-based approach, recognizing the integrated and concurrent nature of challenges that the practitioners, and consequently the field, face

Science as systems learning. Some reflections on the cognitive and communicational aspects of science

This paper undertakes a theoretical investigation of the 'learning' aspect of science as opposed to the 'knowledge' aspect. The practical background of the paper is in agricultural systems research – an area of science that can be characterised as 'systemic' because it is involved in the development of its own subject area, agriculture. And the practical purpose of the theoretical investigation is to contribute to a more adequate understanding of science in such areas, which can form a basis for developing and evaluating systemic research methods, and for determining appropriate criteria of scientific quality. Two main perspectives on science as a learning process are explored: research as the learning process of a cognitive system, and science as a social, communicational system. A simple model of a cognitive system is suggested, which integrates both semiotic and cybernetic aspects, as well as a model of selfreflective learning in research, which entails moving from an inside 'actor' stance to an outside 'observer' stance, and back. This leads to a view of scientific knowledge as inherently contextual and to the suggestion of reflexive objectivity and relevance as two related key criteria of good science

Combining Luhmann and Actor-Network Theory to see Farm Enterprises as Self-organizing Systems

From a rural, sociological point of view no social theories have so far been able to grasp the ontological complexity and special character of a farm enterprise as an entity in a really satisfying way. The contention of this paper is that a combination of Luhmann’s theory of social systems and actor-network theory (ANT) of Latour, Callon, and Law offers a new and radical framework for understanding a farm as a self-organizing, heterogeneous system. Luhmann’s theory offers an approach to understand a farm as a self-organizing system (operating in meaning) that must produce and reproduce itself through demarcation from the surrounding world by selection of meaning. The meaning of the system is expressed through the goals, values, and the logic of the farming processes. His theory, however, is less useful when studying the heterogeneous character of a farm as a mixture of biology, sociology, technology, and economy. ANT offers an approach to focus on the heterogeneous network of interactions of human and non-human actors such as knowledge, technology, money, farmland, animals, plants, etc., and as to how these interactions depend on both the quality of the actors and the network context of interaction, but the theory is weak when it comes to explaining the self-organizing character of a farm enterprise

What is Autonomy?

A system is autonomous if it uses its own information to modify itself and its environment to enhance its survival, responding to both environmental and internal stimuli to modify its basic functions to increase its viability. Autonomy is the foundation of functionality, intentionality and meaning. Autonomous systems accommodate the unexpected through self-organizing processes, together with some constraints that maintain autonomy. Early versions of autonomy, such as autopoiesis and closure to efficient cause, made autonomous systems dynamically closed to information. This contrasts with recent work on open systems and information dynamics. On our account, autonomy is a matter of degree depending on the relative organization of the system and system environment interactions. A choice between third person openness and first person closure is not required

Combining Luhmann and Actor-Network Theory to see Farm Enterprises as Self-organizing Systems

From a rural, sociological point of view no social theories have so far been able to grasp the ontological complexity and special character of a farm enterprise as an entity in a really satisfying way. The contention of this paper is that a combination of Luhmann’s theory of social systems and the actor-network theory (ANT) of Latour, Callon, and Law offers a new and radical framework for understanding a farm as a self-organizing, heterogeneous system. Luhmann’s theory offers an approach to understand a farm as a self-organizing system (operating in meaning) that must produce and reproduce itself through demarcation from the surrounding world by selection of meaning. The meaning of the system is expressed through the goals, values, and logic of the farming processes. This theory is, however, less useful when studying the heterogeneous character of a farm as a mixture of biology, sociology, technology, and economy. ANT offers an approach to focus on the heterogeneous network of interactions of human and non-human actors, such as knowledge, technology, money, farmland, animals, plants, etc., and how these interactions depend on both the quality of the actors and the network context of interaction. But the theory is weak when it comes to explaining the self-organizing character of a farm enterprise. Using Peirce’s general semiotics as a platform, the two theories in combination open a new and radical framework for multidisciplinary studies of farm enterprises that may serve as a platform for communication between the different disciplines and approaches

Is defining life pointless? Operational definitions at the frontiers of Biology

Despite numerous and increasing attempts to define what life is, there is no consensus on necessary and sufficient conditions for life. Accordingly, some scholars have questioned the value of definitions of life and encouraged scientists and philosophers alike to discard the project. As an alternative to this pessimistic conclusion, we argue that critically rethinking the nature and uses of definitions can provide new insights into the epistemic roles of definitions of life for different research practices. This paper examines the possible contributions of definitions of life in scientific domains where such definitions are used most (e.g., Synthetic Biology, Origins of Life, Alife, and Astrobiology). Rather than as classificatory tools for demarcation of natural kinds, we highlight the pragmatic utility of what we call operational definitions that serve as theoretical and epistemic tools in scientific practice. In particular, we examine contexts where definitions integrate criteria for life into theoretical models that involve or enable observable operations. We show how these definitions of life play important roles in influencing research agendas and evaluating results, and we argue that to discard the project of defining life is neither sufficiently motivated, nor possible without dismissing important theoretical and practical research

Chemical communication between synthetic and natural cells: a possible experimental design

The bottom-up construction of synthetic cells is one of the most intriguing and interesting research arenas in synthetic biology. Synthetic cells are built by encapsulating biomolecules inside lipid vesicles (liposomes), allowing the synthesis of one or more functional proteins. Thanks to the in situ synthesized proteins, synthetic cells become able to perform several biomolecular functions, which can be exploited for a large variety of applications. This paves the way to several advanced uses of synthetic cells in basic science and biotechnology, thanks to their versatility, modularity, biocompatibility, and programmability. In the previous WIVACE (2012) we presented the state-of-the-art of semi-synthetic minimal cell (SSMC) technology and introduced, for the first time, the idea of chemical communication between synthetic cells and natural cells. The development of a proper synthetic communication protocol should be seen as a tool for the nascent field of bio/chemical-based Information and Communication Technologies (bio-chem-ICTs) and ultimately aimed at building soft-wet-micro-robots. In this contribution (WIVACE, 2013) we present a blueprint for realizing this project, and show some preliminary experimental results. We firstly discuss how our research goal (based on the natural capabilities of biological systems to manipulate chemical signals) finds a proper place in the current scientific and technological contexts. Then, we shortly comment on the experimental approaches from the viewpoints of (i) synthetic cell construction, and (ii) bioengineering of microorganisms, providing up-to-date results from our laboratory. Finally, we shortly discuss how autopoiesis can be used as a theoretical framework for defining synthetic minimal life, minimal cognition, and as bridge between synthetic biology and artificial intelligence.Comment: In Proceedings Wivace 2013, arXiv:1309.712