00610nas a2200193   4500008004100000245002600041210002600067100003400093700002300127700002500150700002600175700002600201700002700227700003400254700002200288700003300310700003100343856004200374       2019                        eng d00aComplejidad Explicada0 aComplejidad Explicada1 aHolguín, Valerie, C. Valerio1 aGershenson, Carlos1 aHerrera, José, Luis1 aMartínez, Johann, H.1 aSantos, Manuel, Rueda1 aCorona, Oliver, López1 aJáuregui, Guillermo, de Anda1 aIñiguez, Gerardo1 aGuzmán, Alfredo, J. Morales1 aCarlock, José, R. Nicolá  uhttps://complexityexplained.github.io01394nas a2200241   4500008004100000245012100041210006900162520060000231100002400831700001900855700002300874700002200897700002300919700001600942700002100958700002000979700002100999700002001020700001901040700003201059700001901091856004201110       2019                        eng d00aComplexity Explained: A Grassroot Collaborative Initiative to Create a Set of Essential Concepts of Complex Systems.0 aComplexity Explained A Grassroot Collaborative Initiative to Cre3 aComplexity 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.1 aDe Domenico, Manlio1 aCamargo, Chico1 aGershenson, Carlos1 aGoldsmith, Daniel1 aJeschonnek, Sabine1 aKay, Lorren1 aNichele, Stefano1 aNicolás, José1 aSchmickl, Thomas1 aStella, Massimo1 aBrandoff, Josh1 aSalinas, Ángel, José Mart1 aSayama, Hiroki  uhttps://complexityexplained.github.io00571nas a2200157   4500008004100000245010500041210006900146260002500215100002300240700002000263700001800283700002400301700001700325700002300342856004800365       2018                        eng d00aCoupled Dynamical Systems and Defense-Attack Networks: Representation of Soccer Players Interactions0 aCoupled Dynamical Systems and DefenseAttack Networks Representat  aThessaloniki, Greece1 aFernández, Nelson1 aRivera, Víctor1 aMadrid, Yesid1 aRestrepo, Guillermo1 aLeal, Wilmer1 aGershenson, Carlos  uhttp://turing.iimas.unam.mx/sos/?q=node/18403133nas a2200253   4500008004100000020001400041245005000055210005000105260000600155300001100161490000700172520244900179653001602628653001802644653001402662653001602676653002302692653002202715100002302737700001902760700002602779700002302805856005102828       2017                        eng d  a1476-945X00aComplexity of lakes in a latitudinal gradient0 aComplexity of lakes in a latitudinal gradient  c9  a1–200 v313 aMeasuring complexity is fast becoming a key instrument to compare different ecosystems at various scales in ecology. To date there has been little agreement on how to properly describe complexity in terms of ecology. In this regard, this manuscript assesses the significance of using a set of proposed measures based on information theory. These measures are as follows: emergence, self-organization, complexity, homeostasis and autopoiesis. A combination of quantitative and qualitative approaches was used in the data analysis with the aim to apply these proposed measures. This study systematically reviews the data previously collected and generated by a model carried out on four aquatic ecosystems located between the Arctic region and the tropical zone. Thus, this research discusses the case of exploring a high level of self-organization in terms of movement, distribution, and quality of water between the northern temperate zone and the tropics. Moreover, it was assessed the significance of the presence of a complex variable (pH) in the middle of the latitudinal transect. Similarly, this study explores the relationship between self-organization and limiting nutrients (nitrogen, phosphorus and silicates). Furthermore, the importance of how a biomass subsystem is affected by seasonal variations is highlighted in this manuscript. This case study seeks to examine the changing nature of how seasonality affects the complexity dynamics of photosynthetic taxa (lakes located in northern temperate zone) at high latitudes, and it also investigates how a high level of self-organization at the tropical zone can lead to increase the amount of planktonic and benthic fish which determines the dynamics of complexity. This research also compares the emerging role of how a biomass subsystem has a highest temporal dynamics compared to he limiting nutrients' subsystem. In the same way, the results associated to autopoiesis reflect a moderate degree of autonomy of photosynthetic biomass. It is also discussed the case of how complexity values change in the middle of the latitudinal gradient for all components. Finally, a comparison with Tsallis information was carried out in order to determine that these proposed measures are more suitable due to they are independent of any other parameter. Thus, this approach considers some elements closely related to information theory which determine and better describe ecological dynamics.10aAutopoiesis10aBiocomplexity10aEmergence10aHomeostasis10aInformation theory10aSelf-organization1 aFernández, Nelson1 aAguilar, José1 aPiña-García, C., A.1 aGershenson, Carlos  uhttp://dx.doi.org/10.1016/j.ecocom.2017.02.00200365nam a2200109   4500008004100000245005600041210005600097260001900153100002300172700002300195856003700218       2017                        eng d00aConference on Complex Systems 2017 Abstract Booklet0 aConference on Complex Systems 2017 Abstract Booklet  aCancun, Mexico1 aGershenson, Carlos1 aMateos, Jose, Luis  uhttp://ccs17.unam.mx/booklet.pdf00890nas a2200133   4500008004100000245006400041210006300105300001400168520046200182100001800644700002300662700002300685856004800708       2016                        eng d00aComplexity and Structural Properties in Scale-free Networks0 aComplexity and Structural Properties in Scalefree Networks  a730–7313 aWe 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.1 aMadrid, Yesid1 aGershenson, Carlos1 aFernández, Nelson  uhttp://turing.iimas.unam.mx/sos/?q=node/16900420nas a2200121   4500008004100000245004000041210003900081260001000120300001000130490000800140100002300148856012700171       2015                        eng d00aComplejidad, Tecnología y Sociedad0 aComplejidad Tecnología y Sociedad  cEnero  a48-540 v4601 aGershenson, Carlos  uhttp://www.investigacionyciencia.es/revistas/investigacion-y-ciencia/numeros/2015/1/complejidad-tecnologa-y-sociedad-1273200418nas a2200121   4500008004100000245005900041210005800100260002200158300001400180100002300194700003100217856004800248       2015                        eng d00aComplejidad y medicina: perspectivas para el siglo XXI0 aComplejidad y medicina perspectivas para el siglo XXI  bCONACYT, AMC, CCC  a101–1111 aGershenson, Carlos1 aCarmona, Mario, César Sal  uhttp://www.ccciencias.mx/libroshdvcm/14.pdf01302nas a2200157   4500008004100000245006300041210006300104260001600167300001200183490000700195520084700202100002001049700001701069700002301086856003501109       2015                        eng d00aComplexity measurement of natural and artificial languages0 aComplexity measurement of natural and artificial languages  cJuly/August  a25–480 v203 aWe 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.1 aFebres, Gerardo1 aJaffe, Klaus1 aGershenson, Carlos  uhttp://arxiv.org/abs/1311.542701866nas a2200157   4500008004100000245014000041210006900181260003400250300001200284490000600296520128800302100001601590700002301606700002601629856005301655       2014                        eng d00aCan Government Be Self-Organized? A Mathematical Model of the Collective Social Organization of Ancient {Teotihuacan}, Central {Mexico}0 aCan Government Be SelfOrganized A Mathematical Model of the Coll  bPublic Library of Sciencec10  ae1099660 v93 a<p>Teotihuacan was the first urban civilization of Mesoamerica and one of the largest of the ancient world. Following a tradition in archaeology to equate social complexity with centralized hierarchy, it is widely believed that the city's origin and growth was controlled by a lineage of powerful individuals. However, much data is indicative of a government of co-rulers, and artistic traditions expressed an egalitarian ideology. Yet this alternative keeps being marginalized because the problems of collective action make it difficult to conceive how such a coalition could have functioned in principle. We therefore devised a mathematical model of the city's hypothetical network of representatives as a formal proof of concept that widespread cooperation was realizable in a fully distributed manner. In the model, decisions become self-organized into globally optimal configurations even though local representatives behave and modify their relations in a rational and selfish manner. This self-optimization crucially depends on occasional communal interruptions of normal activity, and it is impeded when sections of the network are too independent. We relate these insights to theories about community-wide rituals at Teotihuacan and the city's eventual disintegration.</p>
1 aFroese, Tom1 aGershenson, Carlos1 aManzanilla, Linda, R.  uhttp://dx.doi.org/10.1371%2Fjournal.pone.010996601543nas a2200121   4500008004100000245003700041210003200078300001200110490000700122520121000129100002301339856005901362       2013                        eng d00a?`{Cómo} hablar de complejidad?0 aCómo hablar de complejidad  a14–190 v113 aResum 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, emergence1 aGershenson, Carlos  uhttp://revistes.ub.edu/index.php/LSC/article/view/568200984nas a2200121   4500008004100000245001500041210001500056260001600071520069900087100002300786700001800809856003500827       2013                        eng d00aComplexity0 aComplexity  bSAGEcApril3 aThe 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.1 aGershenson, Carlos1 aKaldis, Byron  uhttp://arxiv.org/abs/1109.021401262nas a2200133   4500008004100000245010700041210006900148300001000217490000700227520080700234100002301041700002301064856004101087       2012                        eng d00aComplexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales0 aComplexity and Information Measuring Emergence Selforganization   a29-440 v183 aConcepts 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.1 aGershenson, Carlos1 aFernández, Nelson  uhttp://dx.doi.org/10.1002/cplx.2142400436nas a2200145   4500008004100000245002100041210002100062260002000083300001400103490000700117520008400124100002300208700002400231856003500255       2011                        eng d00aComplex Networks0 aComplex Networks  bMIT PresscFall  a259–2610 v173 aIntroduction to the Special Issue on Complex Networks, Artificial Life journal.1 aGershenson, Carlos1 aProkopenko, Mikhail  uhttp://arxiv.org/abs/1104.553801214nas a2200121   4500008004100000245008400041210006900125300001200194490000600206520080900212100002301021856004801044       2010                        eng d00aComputing Networks: A General Framework to Contrast Neural and Swarm Cognitions0 aComputing Networks A General Framework to Contrast Neural and Sw  a147-1530 v13 aThis 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.1 aGershenson, Carlos  uhttp://dx.doi.org/10.2478/s13230-010-0015-z00300nam a2200109   4500008004100000020001500041245002800056210002700084260002600111100002300137856003000160       2008                        eng d  a879213013500aComplexity: 5 Questions0 aComplexity 5 Questions  bAutomatic Peess / VIP1 aGershenson, Carlos  uhttp://tinyurl.com/ovg3jn00440nas a2200157   4500008004100000245003000041210003000071260003300101300001200134100002300146700001900169700002300188700001400211700001800225856003900243       2007                        eng d00aComplexity and Philosophy0 aComplexity and Philosophy  aOxfordbRadcliffe Publishing  a117-1341 aHeylighen, Francis1 aCilliers, Paul1 aGershenson, Carlos1 aBogg, Jan1 aGeyer, Robert  uhttp://arxiv.org/abs/cs.CC/060407201436nas a2200133   4500008004100000245004800041210004600089260000900135300001400144490000600158520106400164100002301228856005101251       2004                        eng d00aCognitive Paradigms: Which One is the Best?0 aCognitive Paradigms Which One is the Best  cJune  a135–1560 v53 aI 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.1 aGershenson, Carlos  uhttp://dx.doi.org/10.1016/j.cogsys.2003.10.00200799nas a2200121   4500008004100000245007000041210006900111260002000180300001600200520039100216100002300607856004700630       2003                        eng d00aComparing Different Cognitive Paradigms with a Virtual Laboratory0 aComparing Different Cognitive Paradigms with a Virtual Laborator  bMorgan Kaufmann  a1635–16363 aA 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.1 aGershenson, Carlos  uhttp://turing.iimas.unam.mx/sos/?q=node/6201425nas a2200205   4500008004100000245004100041210003900082260002000121300001400141520087400155100002301029700001901052700002001071700001501091700002001106700001701126700001601143700001601159856004401175       2003                        eng d00aContextual Random {Boolean} Networks0 aContextual Random Boolean Networks  bSpringer-Verlag  a615–6243 aWe 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.1 aGershenson, Carlos1 aBroekaert, Jan1 aAerts, Diederik1 aBanzhaf, W1 aChristaller, T.1 aDittrich, P.1 aKim, J., T.1 aZiegler, J.  uhttp://uk.arxiv.org/abs/nlin.AO/030302101283nas a2200157   4500008004100000245004800041210004600089260003400135300001000169520082900179100002301008700002101031700001801052700001901070856003601089       2002                        eng d00aClassification of Random {Boolean} Networks0 aClassification of Random Boolean Networks  aCambridge, MA, USAbMIT Press  a1–83 aWe 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.1 aGershenson, Carlos1 aStandish, R., K.1 aBedau, M., A.1 aAbbass, H., A.  uhttp://arxiv.org/abs/cs/020800101631nas a2200109   4500008004100000245014100041210006900182260006900251520110500320100002301425856007301448       2002                        eng d00aA Comparison of Different Cognitive Paradigms Using Simple Animats in a Virtual Laboratory, with Implications to the Notion of Cognition0 aComparison of Different Cognitive Paradigms Using Simple Animats  bSchool of Cognitive and Computing Sciences, University of Sussex3 aIn 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.1 aGershenson, Carlos  uhttp://www.cogs.susx.ac.uk/easy/Publications/Online/MSc2002/cg26.pdf01106nas a2200109   4500008004100000245002300041210002300064260002000087520082200107100002300929856004400952       2002                        eng d00aComplex Philosophy0 aComplex Philosophy  aLa Habana, Cuba3 aWe 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.1 aGershenson, Carlos  uhttp://uk.arXiv.org/abs/nlin.AO/010900101689nas a2200097   4500008004100000245009800041210006900139520132900208100002301537856003101560       2002                        eng d00aContextuality: A Philosophical Paradigm, with Applications to Philosophy of Cognitive Science0 aContextuality A Philosophical Paradigm with Applications to Phil3 aWe 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.1 aGershenson, Carlos  uhttp://cogprints.org/2621/01384nas a2200133   4500008004100000245002800041210002800069260004900097300001400146520099600160100002301156700002701179856004401206       2001                        eng d00aComments to Neutrosophy0 aComments to Neutrosophy  aUniversity of New Mexico, Gallup, NMbXiquan  a139–1463 aAny 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.1 aGershenson, Carlos1 aSmarandache, Florentin  uhttp://uk.arxiv.org/abs/math.GM/011123700998nas a2200109   4500008004100000245004500041210004200086260002000128520068700148100002300835856003000858       1998                        eng d00aControl de Tráfico con Agentes: {CRASH}0 aControl de Tráfico con Agentes CRASH  aXalapa, México3 aEl 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.1 aGershenson, Carlos  uhttp://tinyurl.com/ybgwk8