02377nas a2200133 4500008004100000245015200041210007100193260002100264520184400285100002302129700002002152700002302172856004802195 2019 eng d00aEl Síndrome de los Datos Ricos e Información Pobre en Deportes de Competición: Perspectiva desde las Ciencias Computacionales y Ciencia de Datos0 aEl Síndrome de los Datos Ricos e Información Pobre en Deportes d aPachuca, México3 aLa gran capacidad existente de capturar datos, conlleva la subsecuente responsabilidad de producir información confiable, verificable y auditable para la toma de decisiones. En el futbol, la existencia de compañías y plataformas con capacidad de medir un sinnúmero de variables de desempeño, ha generado una explosión de datos de difícil interpretación. En este sentido, las dificultades relativas al análisis y visualización de estos datos, ha derivado en el “Síndrome de los datos ricos e información pobre”. En este contexto, esta plática se centra en evaluar las lecciones aprendidas y las perspectivas futuras en el manejo de datos en el futbol, desde una perspectiva computacional y de ciencia de datos. Nuestro enfoque metodológico, parte de la evaluación de los formatos en que se produce los datos y los tipos de reportes generados para distintos tipos de usuarios. Planteamos una forma adecuada de manejar e interpretar múltiples variables con soporte en técnicas de aprendizaje automático, con técnicas de ordenación y clasificación para discriminar los factores y variables que tienen mayor contribución en el juego. Finalmente, brindamos información sobre perspectivas novedosas para el modelado de los eventos espacio-temporales, que tienen lugar en los partidos, como la aplicación desde la ciencia de redes, redes de latencia y modelos de gravitación para el modelado. Nuestra perspectiva computacional y de ciencia de datos brinda la posibilidad de mejores visualizaciones, con el propósito de simplificar el gran número de dimensiones y categorías que se inspeccionan en el futbol. De esta forma, nos enfocamos en las interacciones relevantes del juego, que darían soporte a una mejor toma de decisiones por parte de distintos tipos de usuarios, como jugadores, entrenadores y directivos.1 aFernández, Nelson1 aZumaya, Martín1 aGershenson, Carlos uhttp://turing.iimas.unam.mx/sos/?q=node/21100496nas a2200121 4500008004100000245012800041210007000169260002100239100002300260700002000283700002300303856004800326 2019 eng d00aSistemas con Dinámica Acoplada y Redes de Defensa y Ataque: Representación de las Interacciones en Juegos de Competición0 aSistemas con Dinámica Acoplada y Redes de Defensa y Ataque Repre aPachuca, México1 aFernández, Nelson1 aRivera, Víctor1 aGershenson, Carlos uhttp://turing.iimas.unam.mx/sos/?q=node/21200571nas 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/18400571nas a2200157 4500008004100000245010700041210006900148260002500217100002300242700001800265700001800283700002400301700001700325700002300342856004800365 2018 eng d00aModeling Systems with Coupled Dynamics (SCDs): A Multi-Agent, Networks, and Game Theory-based Approach0 aModeling Systems with Coupled Dynamics SCDs A MultiAgent Network aThessaloniki, Greece1 aFernández, Nelson1 aOrtega, Osman1 aMadrid, Yesid1 aRestrepo, Guillermo1 aLeal, Wilmer1 aGershenson, Carlos uhttp://turing.iimas.unam.mx/sos/?q=node/18300890nas 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/16901277nas a2200157 4500008004100000245009900041210006900140260001300209300001000222520076000232100002300992700002201015700002301037700002401060856003501084 2014 eng d00aInformation Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis0 aInformation Measures of Complexity Emergence Selforganization Ho bSpringer a19-513 a
This chapter reviews measures of emergence, self-organization, complexity, homeostasis, and autopoiesis based on information theory. These measures are derived from proposed axioms and tested in two case studies: random Boolean networks and an Arctic lake ecosystem. Emergence is defined as the information produced by a system or process. Self-organization is defined as the opposite of emergence, while complexity is defined as the balance between emergence and self-organization. Homeostasis reflects the stability of a system. Autopoiesis is defined as the ratio between the complexity of a system and the complexity of its environment. The proposed measures can be applied at different scales, which can be studied with multi-scale profiles.
1 aFernández, Nelson1 aMaldonado, Carlos1 aGershenson, Carlos1 aProkopenko, Mikhail uhttp://arxiv.org/abs/1304.184201436nas a2200205 4500008004100000245006300041210006200104300001600166490000700182520082000189100002201009700001801031700002601049700002301075700002301098700001901121700002701140700002301167856004001190 2014 eng d00aMeasuring the Complexity of Self-organizing Traffic Lights0 aMeasuring the Complexity of Selforganizing Traffic Lights a2384–24070 v163 aWe apply measures of complexity, emergence, and self-organization to an urban traffic model for comparing a traditional traffic-light coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only is traffic a non-stationary problem, requiring controllers to adapt constantly; controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures and extending Ashby's law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system.1 aZubillaga, Darío1 aCruz, Geovany1 aAguilar, Luis, Daniel1 aZapotécatl, Jorge1 aFernández, Nelson1 aAguilar, José1 aRosenblueth, David, A.1 aGershenson, Carlos uhttp://dx.doi.org/10.3390/e1605238401262nas 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.2142400527nas a2200133 4500008004100000245011300041210007000154260003700224100002300261700001900284700002300303700002000326856004700346 2012 eng d00aSistemas Dinámicos como Redes Computacionales de Agentes para la evaluación de sus Propiedades Emergentes.0 aSistemas Dinámicos como Redes Computacionales de Agentes para la aUniversidad Central de Venezuela1 aFernández, Nelson1 aAguilar, José1 aGershenson, Carlos1 aTerán, Oswaldo uhttp://turing.iimas.unam.mx/sos/?q=node/15