BEING EMERGENCE VS. PATTERN EMERGENCE Complexity, Control and Goal-Directedness in Biological Systems
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What Is a Complex Adaptive System?
PROJECT GUTS What is a Complex Adaptive System? Introduction During the last three decades a leap has been made from the application of computing to help scientists ‘do’ science to the integration of computer science concepts, tools and theorems into the very fabric of science. The modeling of complex adaptive systems (CAS) is an example of such an integration of computer science into the very fabric of science; models of complex systems are used to understand, predict and prevent the most daunting problems we face today; issues such as climate change, loss of biodiversity, energy consumption and virulent disease affect us all. The study of complex adaptive systems, has come to be seen as a scientific frontier, and an increasing ability to interact systematically with highly complex systems that transcend separate disciplines will have a profound affect on future science, engineering and industry as well as in the management of our planet’s resources (Emmott et al., 2006). The name itself, “complex adaptive systems” conjures up images of complicated ideas that might be too difficult for a novice to understand. Instead, the study of CAS does exactly the opposite; it creates a unified method of studying disparate systems that elucidates the processes by which they operate. A complex system is simply a system in which many independent elements or agents interact, leading to emergent outcomes that are often difficult (or impossible) to predict simply by looking at the individual interactions. The “complex” part of CAS refers in fact to the vast interconnectedness of these systems. Using the principles of CAS to study these topics as related disciplines that can be better understood through the application of models, rather than a disparate collection of facts can strengthen learners’ understanding of these topics and prepare them to understand other systems by applying similar methods of analysis (Emmott et al., 2006). -
Self-Organization Principles of Intracellular Pattern Formation
Self-organization principles of intracellular pattern formation J. Halatek. F. Brauns, and E. Frey∗ Arnold{Sommerfeld{Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig-Maximilians-Universit¨atM¨unchen, Theresienstraße 37, D-80333 M¨unchen,Germany (Dated: February 21, 2018) Abstract Dynamic patterning of specific proteins is essential for the spatiotemporal regulation of many important intracellular processes in procaryotes, eucaryotes, and multicellular organisms. The emergence of patterns generated by interactions of diffusing proteins is a paradigmatic example for self-organization. In this article we review quantitative models for intracellular Min protein patterns in E. coli, Cdc42 polarization in S. cerevisiae, and the bipolar PAR protein patterns found in C. elegans. By analyzing the molecular processes driving these systems we derive a theoretical perspective on general principles underlying self-organized pattern formation. We argue that intracellular pattern formation is not captured by concepts such as \activators", \inhibitors", or \substrate-depletion". Instead, intracellular pattern formation is based on the redistribution of proteins by cytosolic diffusion, and the cycling of proteins between distinct conformational states. Therefore, mass-conserving reaction-diffusion equations provide the most appropriate framework to study intracellular pattern formation. We conclude that directed transport, e.g. cytosolic diffusion along an actively maintained cytosolic gradient, is the key process underlying pattern formation. Thus the basic principle of self-organization is the establishment and maintenance of directed transport by intracellular protein dynamics. Keywords: self-organization; pattern formation; intracellular patterns; reaction-diffusion; cell polarity; arXiv:1802.07169v1 [physics.bio-ph] 20 Feb 2018 NTPases 1 INTRODUCTION In biological systems self-organization refers to the emergence of spatial and temporal structure. -
Organization, Self-Organization, Autonomy and Emergence: Status and Challenges
Organization, Self-Organization, Autonomy and Emergence: Status and Challenges Sven Brueckner 1 Hans Czap 2 1 New Vectors LLC 3520 Green Court, Suite 250, Ann Arbor, MI 48105-1579, USA Email: [email protected] http://www.altarum.net/~sbrueckner 2 University of Trier, FB IV Business Information Systems I D-54286 Trier, Germany Email: [email protected] http://www.wi.uni-trier.de/ Abstract: Development of IT-systems in application react and adapt autonomously to changing requirements. domains is facing an ever-growing complexity resulting from Therefore, approaches that rely on the fundamental principles a continuous increase in dynamics of processes, applications- of self-organization and autonomy are growing in acceptance. and run-time environments and scaling. The impact of this Following such approaches, software system functionality trend is amplified by the lack of central control structures. As is no longer explicitly designed into its component processes a consequence, controlling this complexity and dynamics is but emerges from lower-level interactions that are one of the most challenging requirements of today’s system purposefully unaware of the system-wide behavior. engineers. Furthermore, organization, the meaningful progression of The lack of a central control instance immediately raises local sensing, processing and action, is achieved by the the need for software systems which can react autonomously system components themselves at runtime and in response to to changing environmental requirements and conditions. the current state of the environment and the problem that is to Therefore, a new paradigm is necessary how to build be solved, rather than being enforced from the “outside” software systems changing radically the way one is used to through design or external control. -
Guiding Self-Organized Pattern Formation in Cell Polarity Establishment
ARTICLES https://doi.org/10.1038/s41567-018-0358-7 Guiding self-organized pattern formation in cell polarity establishment Peter Gross1,2,3,8, K. Vijay Kumar" "3,4,8, Nathan W. Goehring" "5,6, Justin S. Bois" "7, Carsten Hoege2, Frank Jülicher3 and Stephan W. Grill" "1,2,3* Spontaneous pattern formation in Turing systems relies on feedback. But patterns in cells and tissues seldom form spontane- ously—instead they are controlled by regulatory biochemical interactions that provide molecular guiding cues. The relationship between these guiding cues and feedback in controlled biological pattern formation remains unclear. Here, we explore this relationship during cell-polarity establishment in the one-cell-stage Caenorhabditis elegans embryo. We quantify the strength of two feedback systems that operate during polarity establishment: feedback between polarity proteins and the actomyosin cortex, and mutual antagonism among polarity proteins. We characterize how these feedback systems are modulated by guid- ing cues from the centrosome, an organelle regulating cell cycle progression. By coupling a mass-conserved Turing-like reac- tion–diffusion system for polarity proteins to an active-gel description of the actomyosin cortex, we reveal a transition point beyond which feedback ensures self-organized polarization, even when cues are removed. Notably, the system switches from a guide-dominated to a feedback-dominated regime well beyond this transition point, which ensures robustness. Together, these results reveal a general criterion for controlling biological pattern-forming systems: feedback remains subcritical to avoid unstable behaviour, and molecular guiding cues drive the system beyond a transition point for pattern formation. ell polarity establishment in Caenorhabditis elegans zygotes phase where myosin appears to reach a steady state (Supplementary is a prototypical example for cellular pattern formation that Fig. -
1 Emergence of Universal Grammar in Foreign Word Adaptations* Shigeko
1 Emergence of Universal Grammar in foreign word adaptations* Shigeko Shinohara, UPRESA 7018 University of Paris III/CNRS 1. Introduction There has been a renewal of interest in the study of loanword phonology since the recent development of constraint-based theories. Such theories readily express target structures and modifications that foreign inputs are subject to (e.g. Paradis and Lebel 1994, Itô and Mester 1995a,b). Depending on how the foreign sounds are modified, we may be able to make inferences about aspects of the speaker's grammar for which the study of the native vocabulary is either inconclusive or uninformative. At the very least we expect foreign words to be modified in accordance with productive phonological processes and constraints (Silverman 1992, Paradis and Lebel 1994). It therefore comes as some surprise when patterns of systematic modification arise for which the rules and constraints of the native system have nothing to say or even worse contradict. I report a number of such “emergent” patterns that appear in our study of the adaptations of French words by speakers of Japanese (Shinohara 1997a,b, 2000). I claim that they pose a learnability problem. My working hypothesis is that these emergent patterns are reflections of Universal Grammar (UG). This is suggested by the fact that the emergent patterns typically correspond to well-established crosslinguistic markedness preferences that are overtly and robustly attested in the synchronic phonologies of numerous other languages. It is therefore natural to suppose that these emergent patterns follow from the default parameter settings or constraint rankings inherited from the initial stages of language acquisition that remain latent in the mature grammar. -
Clunio Populations to Different Tidal Conditions
Genetic adaptation in emergence time of Clunio populations to different tidal conditions DIETRICH NEUMANN Zoologisches Institut der Universitiit Wiirzburg, Wiirzburg KURZFASSUNG: Genetische Adaptation der Schliipfzeiten yon Clunio-Populationen an verschiedene Gezeitenbedingungen. Die Schliipfzeiten der in der Gezeitenzone Iebenden Clunio-Arten (Diptera, Chironomidae) sind mit bestimmten Wasserstandsbedingungen syn- chronisiert, und zwar derart, dat~ die nnmittelbar anschlief~ende Fortpflanzung der kurz- lebigen Imagines auf dem trockengefallenen Habitat stattfinden kann. Wenn das Habitat einer Clunio-Art in der mittleren Gezeitenzone liegt und parallel zu dem halbt~igigen Gezeiten- zyklus (T = 12,4 h) zweimal t~iglich auftaucht, dann kann sich eine 12,4stiindige Schliipf- periodik einstellen (Beispiel: Clunio takahashii). Wenn das Habitat in der unteren Gezeiten- zone liegt und nur um die Zeit der Springtiden auftaucht, dann ist eine 15t~igige (semilunare) SchRipfperiodik zu erwarten (Beispiele: Clunio marinus und C. mecliterraneus). Diese 15t~igige Schliipfperiodik ist synchronisiert mit bestimmten Niedrigwasserbedingungen, die an einem Kiistenort alle 15 Tage jewqils um die gleiche Tageszeit auftreten. Sie wird daher dutch zwei Daten eindeutig gekennzeichnet: (1) die lunaren Schliipftage (wenige aufeinanderfolgende Tage um Voll- und Neumond) und (2) die t~igliche Schliipfzeit. Wie experimentelle Untersuchungen iiber die Steuerung der Schliipfperiodik zeigten, kiSnnen die Tiere beide Daten richtig voraus- bestimmen. Die einzelnen Kiistenpopulationen -
Ophthalmology and the Emergence of Artificial Intelligence
Perspectives Ophthalmology and the emergence of artificial intelligence Rapid advances in AI in ophthalmology are a harbinger of things to come for other fields of medicine he autonomous detection and triage of eye for retinopathy using retinal photography. This disease, or even accurate estimations of gender, vast demand for diabetic eye screening services Tage, and blood pressure from a simple retinal has stimulated the development of AI algorithms to photo, may sound like the realms of science fiction, identify sight-threatening disease. Several algorithms but advances in artificial intelligence (AI) have already have achieved performance that meets or exceeds that made this a reality.1 Ophthalmology is at the vanguard of human experts.4,5 Accordingly, in 2018, the United of the development and clinical application of AI. States Food and Drug Administration approved an AI Advances in the field may provide useful insights into system to detect referable diabetic retinopathy from the application of this technology in health care more retinal photographs, the first autonomous diagnostic broadly. system to be approved in any field of medicine.6 Advances in deep learning have extended to other Artificial intelligence imaging modalities that are commonly used in ophthalmology. Ocular coherence tomography is an Once described as the capacity of intelligent machines imaging technology that produces highly detailed, to imitate human intelligence and behaviour, AI now depth-resolved images of the retina. A recent describes many theories and practices used to achieve collaboration between researchers and clinicians computer intelligence (Box 1).2 Machine learning is at Google DeepMind, Moorfields Eye Hospital an application of AI that uses algorithms or statistical and University College London culminated in the models to make decisions or predictions. -
The Emergence of Artificial Intelligence in the Home: Products, Services, and Broader
Western Oregon University Digital Commons@WOU Student Theses, Papers and Projects (Computer Department of Computer Science Science) 3-8-2017 The meE rgence of Artificial Intelligence in the Home: Products, Services, and Broader Developments of Consumer Oriented AI Bingqing Tang [email protected] Follow this and additional works at: https://digitalcommons.wou.edu/ computerscience_studentpubs Part of the Management Information Systems Commons Recommended Citation Tang, Bingqing, "The meE rgence of Artificial Intelligence in the Home: Products, Services, and Broader Developments of Consumer Oriented AI" (2017). Student Theses, Papers and Projects (Computer Science). 6. https://digitalcommons.wou.edu/computerscience_studentpubs/6 This Professional Project is brought to you for free and open access by the Department of Computer Science at Digital Commons@WOU. It has been accepted for inclusion in Student Theses, Papers and Projects (Computer Science) by an authorized administrator of Digital Commons@WOU. For more information, please contact [email protected]. The Emergence of Artificial Intelligence in the Home: Products, Services, and Broader Developments of Consumer Oriented AI Bingqing Tang IS642 MIS Graduate Project Dr. David Olson, Dr. John Leadley & Dr. Scot Morse March 15, 2017 Tang 1 Abstract Current home automation system merges a family's lifestyle with the latest technology & energy management tools to simplify people's lives. It allows users to easily manipulate a variety of home systems, including appliances, security systems, and environmental systems. Setting up a home automation system confuses many consumers. Multiple product lines and platforms make choosing the best system difficult. Basic requirements of setting up a home automations system and the comparison between different platforms are explained. -
Artificial Intelligence: How Does It Work, Why Does It Matter, and What Can We Do About It?
Artificial intelligence: How does it work, why does it matter, and what can we do about it? STUDY Panel for the Future of Science and Technology EPRS | European Parliamentary Research Service Author: Philip Boucher Scientific Foresight Unit (STOA) PE 641.547 – June 2020 EN Artificial intelligence: How does it work, why does it matter, and what can we do about it? Artificial intelligence (AI) is probably the defining technology of the last decade, and perhaps also the next. The aim of this study is to support meaningful reflection and productive debate about AI by providing accessible information about the full range of current and speculative techniques and their associated impacts, and setting out a wide range of regulatory, technological and societal measures that could be mobilised in response. AUTHOR Philip Boucher, Scientific Foresight Unit (STOA), This study has been drawn up by the Scientific Foresight Unit (STOA), within the Directorate-General for Parliamentary Research Services (EPRS) of the Secretariat of the European Parliament. To contact the publisher, please e-mail [email protected] LINGUISTIC VERSION Original: EN Manuscript completed in June 2020. DISCLAIMER AND COPYRIGHT This document is prepared for, and addressed to, the Members and staff of the European Parliament as background material to assist them in their parliamentary work. The content of the document is the sole responsibility of its author(s) and any opinions expressed herein should not be taken to represent an official position of the Parliament. Reproduction and translation for non-commercial purposes are authorised, provided the source is acknowledged and the European Parliament is given prior notice and sent a copy. -
Pattern Formation, Social Forces, and Diffusion Instability in Games
EPJ manuscript No. (will be inserted by the editor) Pattern Formation, Social Forces, and Diffusion Instability in Games with Success-Driven Motion Dirk Helbing ETH Zurich, UNO D11, Universit¨atstr.41, 8092 Zurich, Switzerland Received: date / Revised version: date Abstract. A local agglomeration of cooperators can support the survival or spreading of cooperation, even when cooperation is predicted to die out according to the replicator equation, which is often used in evolutionary game theory to study the spreading and disappearance of strategies. In this paper, it is shown that success-driven motion can trigger such local agglomeration and may, therefore, be used to supplement other mechanisms supporting cooperation, like reputation or punishment. Success-driven motion is formulated here as a function of the game-theoretical payoffs. It can change the outcome and dynamics of spatial games dramatically, in particular as it causes attractive or repulsive interaction forces. These forces act when the spatial distributions of strategies are inhomogeneous. However, even when starting with homogeneous initial conditions, small perturbations can trigger large inhomogeneities by a pattern-formation instability, when certain conditions are fulfilled. Here, these instability conditions are studied for the prisoner's dilemma and the snowdrift game. Furthermore, it is demonstrated that asymmetrical diffusion can drive social, economic, and biological systems into the unstable regime, if these would be stable without diffusion. PACS. 02.50.Le Decision theory and game theory { 87.23.Ge Dynamics of social systems { 82.40.Ck Pattern formation in reactions with diffusion, flow and heat transfer { 87.23.Cc Population dynamics and ecological pattern formation 1 Introduction tions [17,18], which agree with replicator equations for the fitness-dependent reproduction of individuals in biology Game theory is a well-established theory of individual [19,20,21]. -
Emergent Complex Systems
Futures 1994 26(6) 568-582 EMERGENT COMPLEX SYSTEMS Silvio Funtowicz and Jerome R. Ravetz Complex systems are becoming the focus of important innovative research and application in many areas, reflecting the progressive displacement of classical physics and the emergence of a new and creative role for mathematics. This article makes a distinction between ordinary and emergent complexity and argues that a full analysis requires dialectical thinking. In so doing the authors aim to provide a philosophical foundation for post-normal science. The exploratory analysis developed here is complementary to those conducted with a more formal, mathematical approach, and begins to articulate what lies on the other side of that somewhat indistinct divide, the conceptual space called emergent complexity. In response to the new leading problems for science, in which the traditional reductionist approach is patently inadequate, complex systems are becoming the focus of important innovative research and application in many areas.’ This development reflects the progressive displacement of classical physics as the exemplar science of our time, and the emergence of a new and creative role for mathematics. Now, formalisms and computations are no longer taken to represent the core of immutable truth and certainty in a world of flux; but they are used with respect for the variability and uncertainty of the world of experience. The distinction has already been made between simple and complex systems;’ we find it useful to further refine ‘complexity’ into ordinary and emergent. These types are characterized by two different patterns of structure and relationships. In ordinary complexity, the most common pattern is a complementarity of competition and cooperation, with a diversity of elements and subsystems. -
Selforganization, Emergence, and Constraint in Complex Natural
1 SelfOrganization, Emergence, and Constraint in Complex Natural Systems Abstract: Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraintbased interpretation of emergence and arguing that such accounts recover many of the features of more traditional accounts. We show that the constraintbased account of emergence also leads naturally into a meaningful definition of selforganization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper. Keywords: Complexity Emergence Selforganization Spontaneous order Dynamical systems Corresponding Author: Jonathan Lawhead, PhD University of Southern California Philosophy & Earth Sciences 3651 Trousdale Parkway Zumberge Hall of Science, 223D Los Angeles, CA 900890740 [email protected] 775.287.8005 2 0. Introduction There’s a growing body of multidisciplinary research exploring complexity theory and related ideas. This field has not yet really settled yet, and so there’s plenty of terminological confusion out there. Different people use the same terms to mean different things (witness the constellation of definitions of ‘complexity’ itself). A good understanding of how central concepts in complexity theory fit together will help in applying those concepts to realworld social and scientific problems.