@proceedings {211,
	title = {El S{\'\i}ndrome de los Datos Ricos e Informaci{\'o}n Pobre en Deportes de Competici{\'o}n: Perspectiva desde las Ciencias Computacionales y Ciencia de Datos},
	year = {2019},
	address = {Pachuca, M{\'e}xico},
	abstract = {La gran capacidad existente de capturar datos, conlleva la subsecuente responsabilidad de producir informaci{\'o}n confiable, verificable y auditable para la toma de decisiones. En el futbol, la existencia de compa{\~n}{\'\i}as y plataformas con capacidad de medir un sinn{\'u}mero de variables de desempe{\~n}o, ha generado una explosi{\'o}n de datos de dif{\'\i}cil interpretaci{\'o}n. En este sentido, las dificultades relativas al an{\'a}lisis y visualizaci{\'o}n de estos datos, ha derivado en el {\textquotedblleft}S{\'\i}ndrome de los datos ricos e informaci{\'o}n pobre{\textquotedblright}. En este contexto, esta pl{\'a}tica 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 metodol{\'o}gico, parte de la evaluaci{\'o}n 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 m{\'u}ltiples variables con soporte en t{\'e}cnicas de aprendizaje autom{\'a}tico, con t{\'e}cnicas de ordenaci{\'o}n y clasificaci{\'o}n para discriminar los factores y variables que tienen mayor contribuci{\'o}n en el juego. Finalmente, brindamos informaci{\'o}n sobre perspectivas novedosas para el modelado de los eventos espacio-temporales, que tienen lugar en los partidos, como la aplicaci{\'o}n desde la ciencia de redes, redes de latencia y modelos de gravitaci{\'o}n para el modelado. Nuestra perspectiva computacional y de ciencia de datos brinda la posibilidad de mejores visualizaciones, con el prop{\'o}sito de simplificar el gran n{\'u}mero de dimensiones y categor{\'\i}as que se inspeccionan en el futbol. De esta forma, nos enfocamos en las interacciones relevantes del juego, que dar{\'\i}an soporte a una mejor toma de decisiones por parte de distintos tipos de usuarios, como jugadores, entrenadores y directivos.},
	author = {Nelson Fern{\'a}ndez and Mart{\'\i}n Zumaya and Carlos Gershenson}
}
@proceedings {212,
	title = {Sistemas con Din{\'a}mica Acoplada y Redes de Defensa y Ataque: Representaci{\'o}n de las Interacciones en Juegos de Competici{\'o}n},
	year = {2019},
	address = {Pachuca, M{\'e}xico},
	author = {Nelson Fern{\'a}ndez and V{\'\i}ctor Rivera and Carlos Gershenson}
}
@conference {184,
	title = {Coupled Dynamical Systems and Defense-Attack Networks: Representation of Soccer Players Interactions},
	booktitle = {Conference on Complex Systems},
	year = {2018},
	address = {Thessaloniki, Greece},
	author = {Nelson Fern{\'a}ndez and V{\'\i}ctor Rivera and Yesid Madrid and Guillermo Restrepo and Wilmer Leal and Carlos Gershenson}
}
@conference {183,
	title = {Modeling Systems with Coupled Dynamics (SCDs): A Multi-Agent, Networks, and Game Theory-based Approach},
	booktitle = {Conference on Complex Systems},
	year = {2018},
	address = {Thessaloniki, Greece},
	author = {Nelson Fern{\'a}ndez and Osman Ortega and Yesid Madrid and Guillermo Restrepo and Wilmer Leal and Carlos Gershenson}
}
@inbook {Madrid2016,
	title = {Complexity and Structural Properties in Scale-free Networks},
	booktitle = {Proceedings of the Artificial Life Conference 2016},
	year = {2016},
	pages = {730{\textendash}731},
	abstract = {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.},
	author = {Yesid Madrid and Carlos Gershenson and Nelson Fern{\'a}ndez}
}
@inbook {Fernandez2013Information-Mea,
	title = {Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis},
	booktitle = {Guided Self-Organization: Inception},
	year = {2014},
	note = {In Press},
	pages = {19-51},
	publisher = {Springer},
	organization = {Springer},
	abstract = {<p>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.</p>
},
	url = {http://arxiv.org/abs/1304.1842},
	author = {Nelson Fern{\'a}ndez and Carlos Maldonado and Carlos Gershenson},
	editor = {Mikhail Prokopenko}
}
@article {Zubillaga2014Measuring-the-C,
	title = {Measuring the Complexity of Self-organizing Traffic Lights},
	journal = {Entropy},
	volume = {16},
	number = {5},
	year = {2014},
	pages = {2384{\textendash}2407},
	abstract = {We 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{\textquoteright}s law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system.},
	doi = {10.3390/e16052384},
	url = {http://dx.doi.org/10.3390/e16052384},
	author = {Dar{\'\i}o Zubillaga and Geovany Cruz and Luis Daniel Aguilar and Jorge Zapot{\'e}catl and Nelson Fern{\'a}ndez and Jos{\'e} Aguilar and David A. Rosenblueth and Carlos Gershenson}
}
@article {GershensonFernandez:2012,
	title = {Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales},
	journal = {Complexity},
	volume = {18},
	number = {2},
	year = {2012},
	pages = {29-44},
	abstract = {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.},
	doi = {10.1002/cplx.21424},
	url = {http://dx.doi.org/10.1002/cplx.21424},
	author = {Carlos Gershenson and Nelson Fern{\'a}ndez}
}
@conference {Fernandez:2012,
	title = {Sistemas Din{\'a}micos como Redes Computacionales de Agentes para la evaluaci{\'o}n de sus Propiedades Emergentes.},
	booktitle = {II Simposio Cient{\'{\i}fico y Tecnol{\'o}gico en Computaci{\'o}n SCTC 2012},
	year = {2012},
	address = {Universidad Central de Venezuela},
	author = {Nelson Fern{\'a}ndez and Jos{\'e} Aguilar and Carlos Gershenson and Oswaldo Ter{\'a}n}
}
