%0 Unpublished Work %D 2019 %T Complejidad Explicada %A Valerie C. Valerio Holguín %A Carlos Gershenson %A José Luis Herrera %A Johann H. Martínez %A Manuel Rueda Santos %A Oliver López Corona %A Guillermo de Anda Jáuregui %A Gerardo Iñiguez %A Alfredo J. Morales Guzmán %A José R. Nicolás Carlock %G eng %U https://complexityexplained.github.io %0 Unpublished Work %D 2019 %T Complexity Explained: A Grassroot Collaborative Initiative to Create a Set of Essential Concepts of Complex Systems. %A Manlio De Domenico %A Chico Camargo %A Carlos Gershenson %A Daniel Goldsmith %A Sabine Jeschonnek %A Lorren Kay %A Stefano Nichele %A José Nicolás %A Thomas Schmickl %A Massimo Stella %A Josh Brandoff %A Ángel José Martínez Salinas %A Hiroki Sayama %X Complexity science, also called complex systems science, studies how a large collection of components – locally interacting with each other at small scales – can spontaneously self-organize to exhibit non-trivial global structures and behaviors at larger scales, often without external intervention, central authorities or leaders. The properties of the collection may not be understood or predicted from the full knowledge of its constituents alone. Such a collection is called a complex system and it requires new mathematical frameworks and scientific methodologies for its investigation. %G eng %U https://complexityexplained.github.io %R 10.17605/OSF.IO/TQGNW %0 Conference Paper %B Conference on Complex Systems %D 2018 %T Coupled Dynamical Systems and Defense-Attack Networks: Representation of Soccer Players Interactions %A Nelson Fernández %A Víctor Rivera %A Yesid Madrid %A Guillermo Restrepo %A Wilmer Leal %A Carlos Gershenson %B Conference on Complex Systems %C Thessaloniki, Greece %G eng %0 Book %D 2017 %T Conference on Complex Systems 2017 Abstract Booklet %A Carlos Gershenson %A Jose Luis Mateos %C Cancun, Mexico %G eng %U http://ccs17.unam.mx/booklet.pdf %0 Book Section %B Proceedings of the Artificial Life Conference 2016 %D 2016 %T Complexity and Structural Properties in Scale-free Networks %A Yesid Madrid %A Carlos Gershenson %A Nelson Fernández %X We apply formal information measures of emergence, self-organization and complexity to scale-free random networks, to explore their association with structural indicators of network topology. Results show that the cumulative number of nodes and edges coincides with an increment of the self-organization and relative complexity, and a loss of the emergence and complexity. Our approach shows a complementary way of studying networks in terms of information. %B Proceedings of the Artificial Life Conference 2016 %P 730–731 %G eng %0 Journal Article %J Investigación y Ciencia %D 2015 %T Complejidad, Tecnología y Sociedad %A Carlos Gershenson %B Investigación y Ciencia %V 460 %P 48-54 %8 Enero %G eng %U http://www.investigacionyciencia.es/revistas/investigacion-y-ciencia/numeros/2015/1/complejidad-tecnologa-y-sociedad-12732 %0 Book Section %B Desafíos para la Salud Pública %D 2015 %T Complejidad y medicina: perspectivas para el siglo XXI %A Carlos Gershenson %E Mario César Salinas Carmona %B Desafíos para la Salud Pública %S Hacia dónde va la Ciencia en México %I CONACYT, AMC, CCC %P 101–111 %G eng %U http://www.ccciencias.mx/libroshdvcm/14.pdf %0 Journal Article %J Complexity %D 2015 %T Complexity measurement of natural and artificial languages %A Gerardo Febres %A Klaus Jaffe %A Carlos Gershenson %X We compared entropy for texts written in natural languages (English, Spanish) and artificial languages (computer software) based on a simple expression for the entropy as a function of message length and specific word diversity. Code text written in artificial languages showed higher entropy than text of similar length expressed in natural languages. Spanish texts exhibit more symbolic diversity than English ones. Results showed that algorithms based on complexity measures differentiate artificial from natural languages, and that text analysis based on complexity measures allows the unveiling of important aspects of their nature. We propose specific expressions to examine entropy related aspects of tests and estimate the values of entropy, emergence, self-organization, and complexity based on specific diversity and message length. %B Complexity %V 20 %P 25–48 %8 July/August %G eng %U http://arxiv.org/abs/1311.5427 %R 10.1002/cplx.21529 %0 Journal Article %J {Llengua, Societat i Comunicació %D 2013 %T ?`{Cómo} hablar de complejidad? %A Carlos Gershenson %X Resum En els últims anys s'ha sentit parlar cada cop més de complexitat. Tot i això, com que hi ha una diversitat creixent de discursos sobre aquest tema, en lloc de generar coneixement, estem generant confusió. En aquest article s'ofereix una perspectiva per parlar clarament sobre complexitat des d'un punt de vista epistemològic. Paraules clau: complexitat, epistemologia, context, emergència Resumen En años recientes hemos escuchado hablar más y más sobre complejidad. Pero pareciera que al haber una diversidad creciente de discursos sobre el tema, en lugar de generar conocimiento estamos generando confusión. En este art{ículo se ofrece una perspectiva para hablar claramente sobre la complejidad desde un punto de vista epistemológico.
Palabras clave: complejidad, epistemolog{ía, contexto, emergencia

Abstract In recent years, we have heard more and more about complexity. However, it seems that given the increasing discourse divergence on this topic, instead of generating knowledge we are generating confusion. This paper offers a perspective to speak clearly about complexity from an epistemological point of view.
Keywords: complexity, epistemology, context, emergence %B {Llengua, Societat i Comunicació %V 11 %P 14–19 %G eng %U http://revistes.ub.edu/index.php/LSC/article/view/5682 %0 Book Section %B Encyclopedia of Philosophy and the Social Sciences %D 2013 %T Complexity %A Carlos Gershenson %E Byron Kaldis %X The term complexity derives etymologically from the Latin plexus, which means interwoven. Intuitively, this implies that something complex is composed by elements that are difficult to separate. This difficulty arises from the relevant interactions that take place between components. This lack of separability is at odds with the classical scientific method - which has been used since the times of Galileo, Newton, Descartes, and Laplace - and has also influenced philosophy and engineering. In recent decades, the scientific study of complexity and complex systems has proposed a paradigm shift in science and philosophy, proposing novel methods that take into account relevant interactions. %B Encyclopedia of Philosophy and the Social Sciences %I SAGE %8 April %G eng %U http://arxiv.org/abs/1109.0214 %0 Journal Article %J Complexity %D 2012 %T Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales %A Carlos Gershenson %A Nelson Fernández %X Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to ambiguity and confusion in their meaning. In this paper we use information theory to provide abstract and concise measures of complexity, emergence, self-organization, and homeostasis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focusing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance. Homeostasis can be seen as a measure of the stability of the system. We use computational experiments on random Boolean networks and elementary cellular automata to illustrate our measures at multiple scales. %B Complexity %V 18 %P 29-44 %G eng %U http://dx.doi.org/10.1002/cplx.21424 %R 10.1002/cplx.21424 %0 Journal Article %J Artificial Life %D 2011 %T Complex Networks %A Carlos Gershenson %A Mikhail Prokopenko %X Introduction to the Special Issue on Complex Networks, Artificial Life journal. %B Artificial Life %I MIT Press %V 17 %P 259–261 %8 Fall %G eng %U http://arxiv.org/abs/1104.5538 %R 10.1162/artl_e_00037 %0 Journal Article %J Paladyn, Journal of Behavioral Robotics %D 2010 %T Computing Networks: A General Framework to Contrast Neural and Swarm Cognitions %A Carlos Gershenson %X This paper presents the Computing Networks (CNs) framework. CNs are used to generalize neural and swarm architectures. Artificial neural networks, ant colony optimization, particle swarm optimization, and realistic biological models are used as examples of instantiations of CNs. The description of these architectures as CNs allows their comparison. Their differences and similarities allow the identification of properties that enable neural and swarm architectures to perform complex computations and exhibit complex cognitive abilities. In this context, the most relevant characteristics of CNs are the existence multiple dynamical and functional scales. The relationship between multiple dynamical and functional scales with adaptation, cognition (of brains and swarms) and computation is discussed. %B Paladyn, Journal of Behavioral Robotics %V 1 %P 147-153 %G eng %U http://dx.doi.org/10.2478/s13230-010-0015-z %R 10.2478/s13230-010-0015-z %0 Book %D 2008 %T Complexity: 5 Questions %E Carlos Gershenson %I Automatic Peess / VIP %@ 8792130135 %G eng %U http://tinyurl.com/ovg3jn %0 Book Section %B Complexity, Science and Society %D 2007 %T Complexity and Philosophy %A Francis Heylighen %A Paul Cilliers %A Carlos Gershenson %E Jan Bogg %E Robert Geyer %B Complexity, Science and Society %I Radcliffe Publishing %C Oxford %P 117-134 %G eng %U http://arxiv.org/abs/cs.CC/0604072 %0 Journal Article %J Cognitive Systems Research %D 2004 %T Cognitive Paradigms: Which One is the Best? %A Carlos Gershenson %X I discuss the suitability of different paradigms for studying cognition. I use a virtual laboratory that implements five different representative models for controlling animats: a rule-based system, a behaviour-based system, a concept-based system, a neural network, and a Braitenberg architecture. Through different experiments, I compare the performance of the models and conclude that there is no "best" model, since different models are better for different things in different contexts. Using the results as an empirical philosophical aid, I note that there is no "best" approach for studying cognition, since different paradigms have all advantages and disadvantages, since they study different aspects of cognition from different contexts. This has implications for current debates on "proper" approaches for cognition: all approaches are a bit proper, but none will be "proper enough". I draw remarks on the notion of cognition abstracting from all the approaches used to study it, and propose a simple classification for different types of cognition. %B Cognitive Systems Research %V 5 %P 135–156 %8 June %G eng %U http://dx.doi.org/10.1016/j.cogsys.2003.10.002 %0 Conference Paper %B {IJCAI}-03: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence %D 2003 %T Comparing Different Cognitive Paradigms with a Virtual Laboratory %A Carlos Gershenson %X A public virtual laboratory is presented, where animats are controlled by mechanisms from different cognitive paradigms. A brief description of the characteristics of the laboratory and the uses it has had is given. Mainly, it has been used to contrast philosophical ideas related with the notion of cognition, and to elucidate debates on "proper" paradigms in AI and cognitive science. %B {IJCAI}-03: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence %I Morgan Kaufmann %P 1635–1636 %G eng %0 Conference Paper %B Advances in Artificial Life, 7th European Conference, {ECAL} 2003 {LNAI} 2801 %D 2003 %T Contextual Random {Boolean} Networks %A Carlos Gershenson %A Jan Broekaert %A Diederik Aerts %E Banzhaf, W %E T. Christaller %E P. Dittrich %E J. T. Kim %E J. Ziegler %X We propose the use of Deterministic Generalized Asynchronous Random Boolean Networks (Gershenson, 2002) as models of contextual deterministic discrete dynamical systems. We show that changes in the context have drastic effects on the global properties of the same networks, namely the average number of attractors and the average percentage of states in attractors. We introduce the situation where we lack knowledge on the context as a more realistic model for contextual dynamical systems. We notice that this makes the network non-deterministic in a specific way, namely introducing a non-Kolmogorovian quantum-like structure for the modelling of the network (Aerts 1986). In this case, for example, a state of the network has the potentiality (probability) of collapsing into different attractors, depending on the specific form of lack of knowledge on the context. %B Advances in Artificial Life, 7th European Conference, {ECAL} 2003 {LNAI} 2801 %I Springer-Verlag %P 615–624 %G eng %U http://uk.arxiv.org/abs/nlin.AO/0303021 %0 Conference Paper %B Artificial Life {VIII}: Proceedings of the Eight International Conference on Artificial Life %D 2002 %T Classification of Random {Boolean} Networks %A Carlos Gershenson %E Standish, R. K. %E M. A. Bedau %E H. A. Abbass %X We provide the first classification of different types of Random Boolean Networks (RBNs). We study the differences of RBNs depending on the degree of synchronicity and determinism of their updating scheme. For doing so, we first define three new types of RBNs. We note some similarities and differences between different types of RBNs with the aid of a public software laboratory we developed. Particularly, we find that the point attractors are independent of the updating scheme, and that RBNs are more different depending on their determinism or non-determinism rather than depending on their synchronicity or asynchronicity. We also show a way of mapping non-synchronous deterministic RBNs into synchronous RBNs. Our results are important for justifying the use of specific types of RBNs for modelling natural phenomena. %B Artificial Life {VIII}: Proceedings of the Eight International Conference on Artificial Life %I MIT Press %C Cambridge, MA, USA %P 1–8 %G eng %U http://arxiv.org/abs/cs/0208001 %0 Thesis %D 2002 %T A Comparison of Different Cognitive Paradigms Using Simple Animats in a Virtual Laboratory, with Implications to the Notion of Cognition %A Carlos Gershenson %X In this thesis I present a virtual laboratory which implements five different models for controlling animats: a rule-based system, a behaviour-based system, a concept-based system, a neural network, and a Braitenberg architecture. Through different experiments, I compare the performance of the models and conclude that there is no ``best'' model, since different models are better for different things in different contexts. The models I chose, although quite simple, represent different approaches for studying cognition. Using the results as an empirical philosophical aid, I note that there is no ``best'' approach for studying cognition, since different approaches have all advantages and disadvantages, because they study different aspects of cognition from different contexts. This has implications for current debates on ``proper'' approaches for cognition: all approaches are a bit proper, but none will be ``proper enough''. I draw remarks on the notion of cognition abstracting from all the approaches used to study it, and propose a simple classification for different types of cognition. %I School of Cognitive and Computing Sciences, University of Sussex %G eng %U http://www.cogs.susx.ac.uk/easy/Publications/Online/MSc2002/cg26.pdf %9 masters %0 Conference Paper %B Proceedings of the 1st Biennial Seminar on Philosophical, Methodological $\And$ Epistemological Implications of Complexity Theory %D 2002 %T Complex Philosophy %A Carlos Gershenson %X We present several philosophical ideas emerging from the studies of complex systems. We make a brief introduction to the basic concepts of complex systems, for then defining "abstraction levels". These are useful for representing regularities in nature. We define absolute being (observer independent, infinite) and relative being (observer dependent, finite), and notice the differences between them. We draw issues on relative causality and absolute causality among abstraction levels. We also make reflections on determinism. We reject the search for any absolute truth (because of their infinity), and promote the idea that all comprehensible truths are relative, since they were created in finite contexts. This leads us to suggest to search the less-incompleteness of ideas and contexts instead of their truths. %B Proceedings of the 1st Biennial Seminar on Philosophical, Methodological $\And$ Epistemological Implications of Complexity Theory %C La Habana, Cuba %G eng %U http://uk.arXiv.org/abs/nlin.AO/0109001 %0 Unpublished Work %D 2002 %T Contextuality: A Philosophical Paradigm, with Applications to Philosophy of Cognitive Science %A Carlos Gershenson %X We develop on the idea that everything is related, inside, and therefore determined by a context. This stance, which at first might seem obvious, has several important consequences. This paper first presents ideas on Contextuality, for then applying them to problems in philosophy of cognitive science. Because of space limitations, for the second part we will assume that the reader is familiar with the literature of philosophy of cognitive science, but if this is not the case, it would not be a limitation for understanding the main ideas of this paper. We do not argue that Contextuality is a panaceic answer for explaining everything, but we do argue that everything is inside a context. And because this is always, we sometimes ignore it, but we believe that many problems are dissolved with a contextual approach, noticing things we ignore because of their obviousity. We first give a notion of context. We present the idea that errors are just incongruencies inside a context. We also present previous ideas of absolute being, relative being, and lessincompleteness. We state that all logics, and also truth judgements, are contextdependent, and we develop a ``Context-dependant Logic''. We apply ideas of Contextuality to problems in semantics, the problem of ``where is the mind'', and the study of consciousness. %G eng %U http://cogprints.org/2621/ %0 Conference Paper %B Proceedings of the First International Conference on Neutrosophy, Neutrosophic Logic, Set, Probability and Statistics %D 2001 %T Comments to Neutrosophy %A Carlos Gershenson %E Florentin Smarandache %X Any system based on axioms is incomplete because the axioms cannot be proven from the system, just believed. But one system can be less-incomplete than other. Neutrosophy is less-incomplete than many other systems because it contains them. But this does not mean that it is finished, and it can always be improved. The comments presented here are an attempt to make Neutrosophy even less-incomplete. I argue that less-incomplete ideas are more useful, since we cannot perceive truth or falsity or indeterminacy independently of a context, and are therefore relative. Absolute being and relative being are defined. Also the "silly theorem problem" is posed, and its partial solution described. The issues arising from the incompleteness of our contexts are presented. We also note the relativity and dependance of logic to a context. We propose "metacontextuality" as a paradigm for containing as many contexts as we can, in order to be less-incomplete and discuss some possible consequences. %B Proceedings of the First International Conference on Neutrosophy, Neutrosophic Logic, Set, Probability and Statistics %I Xiquan %C University of New Mexico, Gallup, NM %P 139–146 %G eng %U http://uk.arxiv.org/abs/math.GM/0111237 %0 Conference Paper %B Memorias {XI} Congreso Nacional {ANIEI} %D 1998 %T Control de Tráfico con Agentes: {CRASH} %A Carlos Gershenson %X El simulador CRASH (Car and Road Automated Simulation in Hyperways) usa programación orientada a agentes para modelar el tráfico de una ciudad sin necesidad de semáforos, tratando de demorar los veh\'ıculos el menor tiempo posible (y sin que se impacten). Esto se hace por medio de agentes en cada automóvil y en cada cruce, y un control central. Se hace una breve introducción al modelo de programación orientada a agentes, para después explicar el modelo del simulador. Se describen las clases usadas en la implementación, sus propiedades y sus relaciones, mostrando el diagrama de las clases. Finalmente, se exponen las conclusiones que se llegaron con las simulaciones. %B Memorias {XI} Congreso Nacional {ANIEI} %C Xalapa, México %G eng %U http://tinyurl.com/ybgwk8