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.io02377nas 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/21100539nas a2200157 4500008004100000245004000041210003900081100002300120700002200143700001900165700001900184700001900203700001700222700002000239856012200259 2019 eng d00aEvasión en IVA: Análisis de redes0 aEvasión en IVA Análisis de redes1 aGershenson, Carlos1 aIñiguez, Gerardo1 aPineda, Carlos1 aGuerrero, Rita1 aIslas, Eduardo1 aPineda, Omar1 aZumaya, Martín uhttp://omawww.sat.gob.mx/gobmxtransparencia/Paginas/documentos/estudio_opiniones/Evasion_en_IVA_Analisis_de_Redes.pdf01720nas a2200241 4500008004100000022001400041245009300055210006900148300001100217490000800228520098300236653002901219653001401248653002001262653001801282100002001300700002901320700002101349700001801370700001901388700002301407856004801430 2019 eng d a0378-437100aRank-frequency distribution of natural languages: A difference of probabilities approach0 aRankfrequency distribution of natural languages A difference of a1217950 v5323 aIn this paper we investigate the time variation of the rank k of words for six Indo-European languages using the Google Books N-gram Dataset. Based on numerical evidence, we regard k as a random variable whose dynamics may be described by a Fokker–Planck equation which we solve analytically. For low ranks the distinct languages behave differently, maybe due to the syntax rules, whereas for k>50 the law of large numbers predominates. We analyze the frequency distribution of words using the data and their adjustment in terms of time-dependent probability density distributions. We find small differences between the data and the fits due to conflicting dynamic mechanisms, but the data show a consistent behavior with our general approach. For the lower ranks the behavior of the data changes among languages presumably, again, due to distinct dynamic mechanisms. We discuss a possible origin of these differences and assess the novel features and limitations of our work.10aFokker–Planck equation10aLanguages10aMaster equation10aRank dynamics1 aCocho, Germinal1 aRodríguez, Rosalío, F.1 aSánchez, Sergio1 aFlores, Jorge1 aPineda, Carlos1 aGershenson, Carlos uhttps://doi.org/10.1016/j.physa.2019.12179500496nas 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/18401049nas a2200109 4500008004100000245009000041210006900131260001300200520065500213100002300868856004800891 2018 eng d00aInformation in Science and Buddhist Philosophy: Towards a Non-Materialistic Worldview0 aInformation in Science and Buddhist Philosophy Towards a NonMate cNovember3 aInformation theory has been developed for seventy years with technological applications that have transformed our societies. The increasing ability to store, transmit, and process information is having a revolutionary impact in most disciplines. The goal of this work is to compare the formal approach to information with Buddhist philosophy. Considering both approaches as compatible and complementary, I argue that information theory can improve our understanding of Buddhist philosophy and vice versa. The resulting synthesis leads to a worldview based on information that overcomes limitations of the currently dominating physics-based worldview.1 aGershenson, Carlos uhttp://turing.iimas.unam.mx/sos/?q=node/20100571nas 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/18301843nas a2200193 4500008004100000022001400041245009400055210006900149260001300218300001400231490000700245520123600252100002401488700002101512700002101533700002101554700002301575856005101598 2018 eng d a0167-739X00aMultimodel agent-based simulation environment for mass-gatherings and pedestrian dynamics0 aMultimodel agentbased simulation environment for massgatherings cFebruary a155–1650 v793 aAbstract The increasing interest in complex phenomena, especially in crowd and pedestrian dynamics, has conditioned the demand not only for more sophisticated autonomous models but also for mechanisms that would bring these models together. This paper presents a multimodel agent-based simulation technique based on the incorporation of multiple modules. Two key principles are presented to guide this integration: a common abstract space where entities of different models interact, and commonly controlled agents–-abstract actors operating in the common space, which can be handled by different agent-based models. In order to test the proposed methodology, we run a set of simulations of cinema building evacuation using the general-purpose {PULSE} simulation environment. In this paper we utilize crowd pressure as a metric to estimate the capacity of different emergent conditions to traumatically affect pedestrians in the crowd. The proposed approach is evaluated through a series of experiments simulating the emergency evacuation from a cinema building to the city streets, where building and street levels are reproduced in heterogeneous models. This approach paves the way for modeling realistic city-wide evacuations.1 aKarbovsk, Vladislav1 aVoloshin, Daniil1 aKarsakov, Andrey1 aBezgodov, Alexey1 aGershenson, Carlos uhttp://dx.doi.org/10.1016/j.future.2016.10.00200564nam a2200145 4500008004100000245011200041210006900153260003300222100002500255700002300280700001500303700001900318700002000337856006100357 2018 eng d00aUnifying Themes in Complex Systems IX: Proceedings of the Ninth International Conference on Complex Systems0 aUnifying Themes in Complex Systems IX Proceedings of the Ninth I aCambridge, MA, USAbSpringer1 aMorales, Alfredo, J.1 aGershenson, Carlos1 aBraha, Dan1 aMinai, Ali, A.1 aBar-Yam, Yaneer uhttps://link.springer.com/book/10.1007/978-3-319-96661-800365nam 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.pdf00462nas a2200145 4500008004100000245006600041210006100107260001300168300001200181490000600193100002600199700002000225700002300245856004800268 2017 eng d00aImproving ``tail'' computations in a BOINC-based Desktop Grid0 aImproving tail computations in a BOINCbased Desktop Grid cDecember a371-3780 v71 aKolokoltsev, Yevgeniy1 aIvashko, Evgeny1 aGershenson, Carlos uhttp://turing.iimas.unam.mx/sos/?q=node/20900370nas a2200109 4500008004100000245006300041210006200104100002300166700001700189700001700206856003700223 2016 eng d00aAdaptive Cities: A Cybernetic Perspective on Urban Systems0 aAdaptive Cities A Cybernetic Perspective on Urban Systems1 aGershenson, Carlos1 aSanti, Paolo1 aRatti, Carlo uhttps://arxiv.org/abs/1609.0200000890nas 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/16900593nas a2200157 4500008004100000245007000041210006900111300001200180490000800192100002600200700001700226700002300243700002900266700002300295856011700318 2016 eng d00aExploring Dynamic Environments Using Stochastic Search Strategies0 aExploring Dynamic Environments Using Stochastic Search Strategie a43–570 v1211 aPiña-García, C., A.1 aGu, Dongbing1 aGershenson, Carlos1 aSiqueiros-García, Mario1 aRobles-Belmont, E. uhttp://rcs.cic.ipn.mx/2016_121/Exploring%20Dynamic%20Environments%20Using%20Stochastic%20Search%20Strategies.pdf00398nas a2200109 4500008004100000245006100041210006100102260004000163300001400203100002300217856004800240 2016 eng d00aImproving Urban Mobility by Understanding Its Complexity0 aImproving Urban Mobility by Understanding Its Complexity aMexico City, Mexicob{Buró–Buró a149–1511 aGershenson, Carlos uhttp://turing.iimas.unam.mx/sos/?q=node/17600416nas a2200157 4500008004100000245001700041210001700058300001000075100001600085700002100101700001900122700002700141700001900168700002300187856004800210 2016 eng d00aIntroduction0 aIntroduction a3–91 aFroese, Tom1 aSiqueiros, Mario1 aAguilar, Wendy1 aIzquierdo, Eduardo, J.1 aSayama, Hiroki1 aGershenson, Carlos uhttp://turing.iimas.unam.mx/sos/?q=node/17001826nas a2200181 4500008004100000022001400041245009400055210006900149300000600218520123800224653002101462100002401483700002101507700002101528700002101549700002301570856005101593 2016 eng d a0167-739X00aMultimodel agent-based simulation environment for mass-gatherings and pedestrian dynamics0 aMultimodel agentbased simulation environment for massgatherings a-3 aAbstract The increasing interest in complex phenomena, especially in crowd and pedestrian dynamics, has conditioned the demand not only for more sophisticated autonomous models but also for mechanisms that would bring these models together. This paper presents a multimodel agent-based simulation technique based on the incorporation of multiple modules. Two key principles are presented to guide this integration: a common abstract space where entities of different models interact, and commonly controlled agents–-abstract actors operating in the common space, which can be handled by different agent-based models. In order to test the proposed methodology, we run a set of simulations of cinema building evacuation using the general-purpose \{PULSE\} simulation environment. In this paper we utilize crowd pressure as a metric to estimate the capacity of different emergent conditions to traumatically affect pedestrians in the crowd. The proposed approach is evaluated through a series of experiments simulating the emergency evacuation from a cinema building to the city streets, where building and street levels are reproduced in heterogeneous models. This approach paves the way for modeling realistic city-wide evacuations.10aUrgent computing1 aKarbovsk, Vladislav1 aVoloshin, Daniil1 aKarsakov, Andrey1 aBezgodov, Alexey1 aGershenson, Carlos uhttp://dx.doi.org/10.1016/j.future.2016.10.00200440nas a2200121 4500008004100000245006700041210006700108300001400175100002600189700003200215700002300247856004800270 2016 eng d00aPerformance Metrics of Collective Coordinated Motion in Flocks0 aPerformance Metrics of Collective Coordinated Motion in Flocks a322–3291 aZapotecatl, Jorge, L.1 aMuñoz-Meléndez, Angélica1 aGershenson, Carlos uhttp://turing.iimas.unam.mx/sos/?q=node/17101157nam a2200181 4500008004100000020001800041245005500059210005500114260004000169520055800209100002300767700001600790700002500806700001900831700002700850700001900877856007900896 2016 eng d a978026233936000aProceedings of the Artificial Life Conference 20160 aProceedings of the Artificial Life Conference 2016 aCambridge, MA, USAbMIT PresscJuly3 aThe ALife conferences are the major meeting of the artificial life research community since 1987. For its 15th edition in 2016, it was held in Latin America for the first time, in the Mayan Riviera, Mexico, from July 4 -8. The special them of the conference: How can the synthetic study of living systems contribute to societies: scientifically, technically, and culturally? The goal of the conference theme is to better understand societies with the purpose of using this understanding for a more efficient management and development of social systems.1 aGershenson, Carlos1 aFroese, Tom1 aSiqueiros, Jesus, M.1 aAguilar, Wendy1 aIzquierdo, Eduardo, J.1 aSayama, Hiroki uhttps://mitpress.mit.edu/books/proceedings-artificial-life-conference-201600479nas a2200145 4500008004100000245005700041210005600098260003100154300001600185100001900201700001700220700002300237700002500260856004800285 2016 eng d00aSelf-organized UAV Traffic in Realistic Environments0 aSelforganized UAV Traffic in Realistic Environments aDaejeon, South KoreabIEEE a1645–16521 aVirágh, Csaba1 aNagy, Máté1 aGershenson, Carlos1 aVásárhelyi, Gábor uhttp://turing.iimas.unam.mx/sos/?q=node/17900489nas a2200133 4500008004100000245010700041210006900148300000600217490000600223100002600229700002300255700002900278856004800307 2016 eng d00aTowards a Standard Sampling Methodology on Online Social Networks: Collecting Global Trends on Twitter0 aTowards a Standard Sampling Methodology on Online Social Network a30 v11 aPiña-García, C., A.1 aGershenson, Carlos1 aSiqueiros-García, Mario uhttp://dx.doi.org/10.1007/s41109-016-0004-100420nas 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/1311.542701284nas a2200121 4500008004100000245005500041210005300096260004000149300001200189520090300201100002301104856003501127 2015 eng d00aEnfrentando a la Complejidad: Predecir vs. Adaptar0 aEnfrentando a la Complejidad Predecir vs Adaptar aBarcelonabUniversitat de Barcelona a25–383 aUna de las presuposiciones de la ciencia desde los tiempos de Galileo, Newton y Laplace ha sido la previsibilidad del mundo. Esta idea ha influido en los modelos cient{íficos y tecnológicos. Sin embargo, en las últimas décadas, el caos y la complejidad han mostrado que no todos los fenómenos son previsibles, aún siendo éstos deterministas. Si el espacio de un problema es previsible, podemos en teor{ía encontrar una solución por optimización. No obstante, si el espacio de un problema no es previsible, o cambia más rápido de lo que podemos optimizarlo, la optimización probablemente nos dará una solución obsoleta. Esto sucede con frecuencia cuando la solución inmediata afecta el espacio del problema mismo. Una alternativa se encuentra en la adaptación. Si dotamos a un sistema de ésta propiedad, éste mismo podrá encontrar nuevas soluciones para situaciones no previstas.1 aGershenson, Carlos uhttp://arxiv.org/abs/0905.490800477nas a2200133 4500008004100000245005800041210005800099260004200157300001400199100002300213700002900236700003000265856004800295 2015 eng d00aHacia un sistema de salud autoorganizante y emergente0 aHacia un sistema de salud autoorganizante y emergente aMexicobAcademia Nacional de Medicina a245–2541 aGershenson, Carlos1 aBarajas, Enrique, Ruelas1 aCorona, Ricardo, Mansilla uhttp://turing.iimas.unam.mx/sos/?q=node/15200866nas a2200133 4500008004100000245007100041210006900112260001300181300001200194490000700206520046100213100002300674856003500697 2015 eng d00aHarnessing the Complexity of Education with Information Technology0 aHarnessing the Complexity of Education with Information Technolo cMay/June a13–160 v203 aEducation at all levels is facing several challenges in most countries, such as low quality, high costs, lack of educators, and unsatisfied student demand. Traditional approaches are becoming unable to deliver the required education. Several causes for this inefficiency can be identified. I argue that beyond specific causes, the lack of effective education is related to complexity. However, information technology is helping us overcome this complexity.1 aGershenson, Carlos uhttp://arxiv.org/abs/1402.282700473nas a2200145 4500008004100000245006600041210006500107260001700172300001200189100001900201700002300220700001800243700001800261856004800279 2015 eng d00aModelling Complexity for Policy: Opportunities and Challenges0 aModelling Complexity for Policy Opportunities and Challenges bEdward Elgar a205-2201 aEdmonds, Bruce1 aGershenson, Carlos1 aGeyer, Robert1 aCairney, Paul uhttp://turing.iimas.unam.mx/sos/?q=node/15600356nas a2200109 4500008004100000245005800041210005500099300001400154490000700168100002300175856004800198 2015 eng d00aRequisite Variety, Autopoiesis, and Self-organization0 aRequisite Variety Autopoiesis and Selforganization a866–8730 v441 aGershenson, Carlos uhttp://turing.iimas.unam.mx/sos/?q=node/15900503nas a2200145 4500008004100000245008700041210006900128260003000197300001400227490000800241100001500249700002300264700001900287856005100306 2014 eng d00aDecoding Road Networks into Ancient Routes: The Case of the Aztec Empire in Mexico0 aDecoding Road Networks into Ancient Routes The Case of the Aztec aBerlin, GermanybSpringer a228–2330 v1261 aLugo, Igor1 aGershenson, Carlos1 aGlass, Kristin uhttp://dx.doi.org/10.1007/978-3-319-03473-7_2000372nas a2200121 4500008004100000245003400041210003300075260002800108100002300136700001900159700002400178856004800202 2014 eng d00aDolor, placebos y complejidad0 aDolor placebos y complejidad aMexicobEditorial Alfil1 aGershenson, Carlos1 aRosado, Javier1 aBistre-Cohén, Sara uhttp://turing.iimas.unam.mx/sos/?q=node/15300716nas a2200121 4500008004100000245005900041210005700100300001400157490000600171520032400177100002300501856007000524 2014 eng d00aInfo-computationalism or Materialism? Neither and Both0 aInfocomputationalism or Materialism Neither and Both a241–2420 v93 aThe limitations of materialism for studying cognition have motivated alternative epistemologies based on information and computation. I argue that these alternatives are also inherently limited and that these limits can only be overcome by considering materialism, info-computationalism, and cognition at the same time.1 aGershenson, Carlos uhttp://www.univie.ac.at/constructivism/journal/9/2/241.gershenson01277nas 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/e1605238401082nas a2200169 4500008004100000245006900041210006400110300001100174490000600185520058400191100002300775700002000798700001600818700002100834700002200855856003500877 2014 eng d00aThe Past, Present and Future of Cybernetics and Systems Research0 aPast Present and Future of Cybernetics and Systems Research a4–130 v13 aCybernetics and Systems Research (CSR) were developed in the mid-twentieth century, offering the possibility of describing and comparing different phenomena using the same language. The concepts which originated in CSR have spread to practically all disciplines, many now used within the scientific study of complex systems. CSR has the potential to contribute to the solution of relevant problems, but the path towards this goal is not straightforward. This paper summarizes the ideas presented by the authors during a round table in 2012 on the past, present and future of CSR.1 aGershenson, Carlos1 aCsermely, Peter1 aErdi, Peter1 aKnyazeva, Helena1 aLaszlo, Alexander uhttp://arxiv.org/abs/1308.631701543nas 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/1109.021401219nas a2200157 4500008004100000020002200041245004900063210004700112260003200159300000900191520077000200100002300970700001500993700001801008856003501026 2013 eng d a978-3-642-32816-900aFacing Complexity: Prediction vs. Adaptation0 aFacing Complexity Prediction vs Adaptation aBerlin HeidelbergbSpringer a3-143 aOne of the presuppositions of science since the times of Galileo, Newton, Laplace, and Descartes has been the predictability of the world. This idea has strongly influenced scientific and technological models. However, in recent decades, chaos and complexity have shown that not every phenomenon is predictable, even if it is deterministic. If a problem space is predictable, in theory we can find a solution via optimization. Nevertheless, if a problem space is not predictable, or it changes too fast, very probably optimization will offer obsolete solutions. This occurs often when the immediate solution affects the problem itself. An alternative is found in adaptation. An adaptive system will be able to find by itself new solutions for unforeseen situations.1 aGershenson, Carlos1 aMassip, A.1 aBastardas, A. uhttp://arxiv.org/abs/1112.384301084nas a2200109 4500008004100000245006400041210006000105490001500165520073600180100002300916856003500939 2013 eng d00aThe Implications of Interactions for Science and Philosophy0 aImplications of Interactions for Science and Philosophy0 vEarly View3 aReductionism has dominated science and philosophy for centuries. Complexity has recently shown that interactions–-which reductionism neglects–-are relevant for understanding phenomena. When interactions are considered, reductionism becomes limited in several aspects. In this paper, I argue that interactions imply non-reductionism, non-materialism, non-predictability, non-Platonism, and non-nihilism. As alternatives to each of these, holism, informism, adaptation, contextuality, and meaningfulness are put forward, respectively. A worldview that includes interactions not only describes better our world, but can help to solve many open scientific, philosophical, and social problems caused by implications of reductionism.1 aGershenson, Carlos uhttp://arxiv.org/abs/1105.282701124nas a2200157 4500008004100000245002800041210002800069490001300097520073900110653001100849653002200860653001200882653001400894100002300908856003500931 2013 eng d00aLiving in Living Cities0 aLiving in Living Cities0 vIn Press3 aThis paper presents and overview of current and potential applications of living technology to urban problems. Living technology can be described as technology that exhibits the core features of living systems. These features can be useful to solve dynamic problems. In particular, urban problems concerning mobility, logistics, telecommunications, governance, safety, sustainability, and society and culture are presented, while solutions involving living technology are reviewed. A methodology for developing living technology is mentioned, while self-organizing traffic lights are used as a case study of the benefits of urban living technology. Finally, the usefulness of describing cities as living systems is discussed.
10acities10aSelf-organization10atraffic10atransport1 aGershenson, Carlos uhttp://arxiv.org/abs/1111.365901253nas a2200109 4500008004100000245007100041210006900112520088200181100002201063700002301085856003501108 2013 eng d00aMeasuring the Complexity of Ultra-Large-Scale Evolutionary Systems0 aMeasuring the Complexity of UltraLargeScale Evolutionary Systems3 aUltra-large scale (ULS) systems are becoming pervasive. They are inherently complex, which makes their design and control a challenge for traditional methods. Here we propose the design and analysis of ULS systems using measures of complexity, emergence, self-organization, and homeostasis based on information theory. We evaluate the proposal with a ULS computing system provided with genetic adaptation mechanisms. We show the evolution of the system with stable and also changing workload, using different fitness functions. When the adaptive plan forces the system to converge to a predefined performance level, the nodes may result in highly unstable configurations, that correspond to a high variance in time of the measured complexity. Conversely, if the adaptive plan is less "aggressive", the system may be more stable, but the optimal performance may not be achieved.1 aAmoretti, Michele1 aGershenson, Carlos uhttp://arxiv.org/abs/1207.665600396nas a2200121 4500008004100000245007300041210007000114300001000184490000700194100002300201700001900224856003100243 2013 eng d00aPreviniendo enfermedades crónico-degenerativas con vacunas sociales0 aPreviniendo enfermedades crónicodegenerativas con vacunas social a83-840 v811 aGershenson, Carlos1 aWisdom, Thomas uhttp://tinyurl.com/cdswlx501487nas a2200133 4500008004100000245011100041210006900152300001200221490000700233520101500240100002301255700002701278856004801305 2012 eng d00aAdaptive self-organization vs. static optimization: A qualitative comparison in traffic light coordination0 aAdaptive selforganization vs static optimization A qualitative c a386-4030 v413 aUsing a recently proposed model of city traffic based on elementary cellular automata, we compare qualitatively two methods for coordinating traffic lights: a \emph{green-wave} method that tries to optimize phases according to expected flows and a \emph{self-organizing} method that adapts to the current traffic conditions. The \emph{self-organizing} method delivers considerable improvements over the \emph{green-wave} method. Seven dynamical regimes and six phase transitions are identified and analyzed for the \emph{self-organizing} method. For low densities, the \emph{self-organizing} method promotes the formation and coordination of platoons that flow freely in four directions, i.e.\ with a maximum velocity and no stops. For medium densities, the method allows a constant usage of the intersections, exploiting their maximum flux capacity. For high densities, the method prevents gridlocks and promotes the formation and coordination of ``free-spaces" that flow in the opposite direction of traffic.1 aGershenson, Carlos1 aRosenblueth, David, A. uhttp://dx.doi.org/10.1108/0368492121122947901262nas 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.2142401203nas a2200133 4500008004100000245006100041210006000102260001400162300001200176490000800188520081500196100002301011856003501034 2012 eng d00aGuiding the Self-organization of Random Boolean Networks0 aGuiding the Selforganization of Random Boolean Networks cSeptember a181-1910 v1313 aRandom Boolean networks (RBNs) are models of genetic regulatory networks. It is useful to describe RBNs as self-organizing systems to study how changes in the nodes and connections affect the global network dynamics. This article reviews eight different methods for guiding the self-organization of RBNs. In particular, the article is focussed on guiding RBNs towards the critical dynamical regime, which is near the phase transition between the ordered and dynamical phases. The properties and advantages of the critical regime for life, computation, adaptability, evolvability, and robustness are reviewed. The guidance methods of RBNs can be used for engineering systems with the features of the critical regime, as well as for studying how natural selection evolved living systems, which are also critical.1 aGershenson, Carlos uhttp://arxiv.org/abs/1005.573302379nas a2200181 4500008004100000245013300041210006900174260003900243300001400282490001400296520175600310100001902066700002302085700001802108700001602126700002002142856003502162 2012 eng d00aLearning, Social Intelligence and the {Turing} Test - why an ``out-of-the-box" {Turing} Machine will not pass the {Turing} Test.0 aLearning Social Intelligence and the Turing Test why an outofthe aBerlin HeidelbergbSpringer-Verlag a182–1920 v7318/20123 aThe Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the TT. Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.1 aEdmonds, Bruce1 aGershenson, Carlos1 aCooper, Barry1 aDawar, Anuj1 aLöwe, Benedikt uhttp://arxiv.org/abs/1203.337601420nas a2200133 4500008004100000245003600041210003500077300001400112490000700126520100200133100003001135700002301165856009801188 2012 eng d00aSelf-organizing systems on chip0 aSelforganizing systems on chip a182–2010 v163 aSelf-organization in the context of computing systems refers to a technological approach to deal with the increasing complexity associated with the deployment, maintenance, and evolution of such systems. The terms self-organizing and autonomous are often used interchangeably in relation to systems that use organic principles (self-configuration, self-healing, and so on) in their design and operation. In the specific case of system on chip (SoC) design, organic principles are clearly in the solution path for some of the most important challenges in areas like logic organization, data movement, circuits, and software[47]. In this article, we start by providing a definition of the concept of self-organization as it applies to SoCs, explaining what it means and how it may be applied. We then provide a survey of the various recent papers, journal articles, and books on the subject and close by pointing out possible future directions, challenges and opportunities for self-organizing SoCs.1 aDe La Guardia}, Rafael, {1 aGershenson, Carlos uhttp://noggin.intel.com/technology-journal/2012/162/exploring-control-and-autonomic-computing01934nas a2200133 4500008004100000245006800041210006600109300001000175490000700185520151700192100002301709700002701732856004101759 2012 eng d00aSelf-organizing traffic lights at multiple-street intersections0 aSelforganizing traffic lights at multiplestreet intersections a23-390 v173 aThe elementary cellular automaton following rule 184 can mimic particles flowing in one direction at a constant speed. This automaton can therefore model highway traffic. In a recent paper, we have incorporated intersections regulated by traffic lights to this model using exclusively elementary cellular automata. In such a paper, however, we only explored a rectangular grid. We now extend our model to more complex scenarios employing an hexagonal grid. This extension shows first that our model can readily incorporate multiple-way intersections and hence simulate complex scenarios. In addition, the current extension allows us to study and evaluate the behavior of two different kinds of traffic light controller for a grid of six-way streets allowing for either two or three street intersections: a traffic light that tries to adapt to the amount of traffic (which results in self-organizing traffic lights) and a system of synchronized traffic lights with coordinated rigid periods (sometimes called the ``green wave'' method). We observe a tradeoff between system capacity and topological complexity. The green wave method is unable to cope with the complexity of a higher-capacity scenario, while the self-organizing method is scalable, adapting to the complexity of a scenario and exploiting its maximum capacity. Additionally, in this paper we propose a benchmark, independent of methods and models, to measure the performance of a traffic light controller comparing it against a theoretical optimum.1 aGershenson, Carlos1 aRosenblueth, David, A. uhttp://dx.doi.org/10.1002/cplx.2039501555nas a2200169 4500008004100000245004900041210004800090260003200138300001200170520107700182100002301259700002101282700001501303700001801318700001401336856003501350 2012 eng d00aSelf-organizing urban transportation systems0 aSelforganizing urban transportation systems aBerlin HeidelbergbSpringer a269-2793 aUrban transportation is a complex phenomenon. Since many agents are constantly interacting in parallel, it is difficult to predict the future state of a transportation system. Because of this, optimization techniques tend to give obsolete solutions, as the problem changes before it can be optimized. An alternative lies in seeking adaptive solutions. This adaptation can be achieved with self-organization. In a self-organizing transportation system, the elements of the system follow local rules to achieve a global solution. Like this, when the problem changes the system can adapt by itself to the new configuration. In this chapter, I will review recent, current, and future work on self-organizing transportation systems. Self-organizing traffic lights have proven to improve traffic flow considerably over traditional methods. In public transportation systems, simple rules are being explored to prevent the "equal headway instability" phenomenon. The methods we have used can be also applied to other urban transportation systems and their generality is discussed.1 aGershenson, Carlos1 aPortugali, Juval1 aMeyer, Han1 aStolk, Egbert1 aTan, Ekim uhttp://arxiv.org/abs/0912.158800527nas 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/1501212nas a2200169 4500008004100000245003800041210003400079260003200113300001200145490000800157520076500165100002300930700001500953700001500968700002400983856003501007 2012 eng d00aThe World as Evolving Information0 aWorld as Evolving Information aBerlin HeidelbergbSpringer a100-1150 vVII3 aThis paper discusses the benefits of describing the world as information, especially in the study of the evolution of life and cognition. Traditional studies encounter problems because it is difficult to describe life and cognition in terms of matter and energy, since their laws are valid only at the physical scale. However, if matter and energy, as well as life and cognition, are described in terms of information, evolution can be described consistently as information becoming more complex. The paper presents five tentative laws of information, valid at multiple scales, which are generalizations of Darwinian, cybernetic, thermodynamic, and complexity principles. These are further used to discuss the notions of life and cognition and their evolution.1 aGershenson, Carlos1 aMinai, Ali1 aBraha, Dan1 aBar-Yam}, Yaneer, { uhttp://arxiv.org/abs/0704.030400436nas 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/1104.553801269nas a2200121 4500008004100000245005500041210005300096520090700149100002301056700001801079700001501097856003501112 2011 eng d00aEnfrentando a la Complejidad: Predecir vs. Adaptar0 aEnfrentando a la Complejidad Predecir vs Adaptar3 aUna de las presuposiciones de la ciencia desde los tiempos de Galileo, Newton y Laplace ha sido la previsibilidad del mundo. Esta idea ha influido en los modelos cient{\'ıficos y tecnológicos. Sin embargo, en las últimas décadas, el caos y la complejidad han mostrado que no todos los fenómenos son previsibles, aún siendo éstos deterministas. Si el espacio de un problema es previsible, podemos en teor{\'ıa encontrar una solución por optimización. No obstante, si el espacio de un problema no es previsible, o cambia más rápido de lo que podemos optimizarlo, la optimización probablemente nos dará una solución obsoleta. Esto sucede con frecuencia cuando la solución inmediata afecta el espacio del problema mismo. Una alternativa se encuentra en la adaptación. Si dotamos a un sistema de ésta propiedad, éste mismo podrá encontrar nuevas soluciones para situaciones no previstas.1 aGershenson, Carlos1 aMartorell, X.1 aMassip, A. uhttp://arxiv.org/abs/0905.490800308nas a2200109 4500008004100000245004300041210004100084300001200125490000700137100002300144856003100167 2011 eng d00aEpidemiolog{\'ıa y las Redes Sociales0 aEpidemiolog ıa y las Redes Sociales a199-2000 v791 aGershenson, Carlos uhttp://tinyurl.com/7nmt3p900693nas a2200133 4500008004100000245006600041210006400107300001200171490000700183520026900190100002700459700002300486856005000509 2011 eng d00aA model of city traffic based on elementary cellular automata0 amodel of city traffic based on elementary cellular automata a305-3220 v193 aThere have been several highway traffic models proposed based on cellular automata. The simplest one is elementary cellular automaton rule 184. We extend this model to city traffic with cellular automata coupled at intersections using only rules 184, 252, and 136.1 aRosenblueth, David, A.1 aGershenson, Carlos uhttp://www.complex-systems.com/pdf/19-4-1.pdf00958nas a2200145 4500008004100000245003800041210003600079260001400115300001400129490000700143520057300150100003100723700002300754856003500777 2011 eng d00aModular Random {Boolean} Networks0 aModular Random Boolean Networks bMIT Press a331–3510 v173 aRandom Boolean networks (RBNs) have been a popular model of genetic regulatory networks for more than four decades. However, most RBN studies have been made with random topologies, while real regulatory networks have been found to be modular. In this work, we extend classical RBNs to define modular RBNs. Statistical experiments and analytical results show that modularity has a strong effect on the properties of RBNs. In particular, modular RBNs have more attractors and are closer to criticality when chaotic dynamics would be expected, compared to classical RBNs.1 aPoblanno-Balp}, Rodrigo, {1 aGershenson, Carlos uhttp://arxiv.org/abs/1101.189301537nas a2200181 4500008004100000245008900041210006900130260003200199300001200231490000600243520096500249100002301214700002301237700001501260700001501275700002401290856004101314 2011 eng d00aProtocol Requirements for Self-Organizing Artifacts: Towards an Ambient Intelligence0 aProtocol Requirements for SelfOrganizing Artifacts Towards an Am aBerlin HeidelbergbSpringer a136-1430 vV3 aWe discuss which properties common-use artifacts should have to collaborate without human intervention. We conceive how devices, such as mobile phones, PDAs, and home appliances, could be seamlessly integrated to provide an "ambient intelligence" that responds to the users desires without requiring explicit programming or commands. While the hardware and software technology to build such systems already exists, yet there is no protocol to direct and give meaning to their interactions. We propose the first steps in the development of such a protocol, which would need to be adaptive, extensible, and open to the community, while promoting self-organization. We argue that devices, interacting through "game-like" moves, can learn to agree about how to communicate, with whom to cooperate, and how to delegate and coordinate specialized tasks. Like this, they may evolve distributed cognition or collective intelligence able to tackle any complex of tasks.1 aGershenson, Carlos1 aHeylighen, Francis1 aMinai, Ali1 aBraha, Dan1 aBar-Yam}, Yaneer, { uhttp://arxiv.org/abs/nlin.AO/040400401121nas a2200121 4500008004100000245008900041210006900130300001100199490000600210520070900216100002300925856005100948 2011 eng d00aSelf-organization leads to supraoptimal performance in public transportation systems0 aSelforganization leads to supraoptimal performance in public tra ae214690 v63 aThe performance of public transportation systems affects a large part of the population. Current theory assumes that passengers are served optimally when vehicles arrive at stations with regular intervals. In this paper, it is shown that self-organization can improve the performance of public transportation systems beyond the theoretical optimum by responding adaptively to local conditions. This is possible because of a ``slower-is-faster'' effect, where passengers wait more time at stations but total travel times are reduced. The proposed self-organizing method uses ``antipheromones'' to regulate headways, which are inspired by the stigmergy (communication via environment) of some ant colonies.1 aGershenson, Carlos uhttp://dx.doi.org/10.1371/journal.pone.002146901145nam a2200145 4500008004100000020002200041245014200063210006900205260001300274490000900287520060500296100002700901700002300928856004800951 2011 eng d a978-3-642-19166-400aSelf-Organizing Systems 5th International Workshop, IWSOS 2011, Karlsruhe, Germany, February 23-24, 2011, Proceedings. Springer LNCS 65570 aSelfOrganizing Systems 5th International Workshop IWSOS 2011 Kar bSpringer0 v65573 aThis book constitutes the refereed proceedings of the 5th International Workshop on Self-Organizing Systems, IWSOS 2011, held in Karlsruhe, Germany, in February 2011. The 9 revised full papers presented together with 1 invited paper were carefully selected from 25 initial submissions. It was the 5th workshop in a series of multidisciplinary events dedicated to self-organization in networked systems with main focus on communication and computer networks. The papers address theoretical aspects of self-organization as well as applications in communication and computer networks and robot networks.1 aBettstetter, Christian1 aGershenson, Carlos uhttp://dx.doi.org/10.1007/978-3-642-19167-101158nas a2200121 4500008004100000245009600041210006900137300001000206490000700216520075500223100002300978856003501001 2011 eng d00aThe Sigma Profile: A Formal Tool to Study Organization and its Evolution at Multiple Scales0 aSigma Profile A Formal Tool to Study Organization and its Evolut a37-440 v163 aThe σ profile is presented as a tool to analyze the organization of systems at different scales, and how this organization changes in time. Describing structures at different scales as goal-oriented agents, one can define σ ∈ [0,1] (satisfaction) as the degree to which the goals of each agent at each scale have been met. σ reflects the organization degree at that scale. The σ profile of a system shows the satisfaction at different scales, with the possibility to study their dependencies and evolution. It can also be used to extend game theoretic models. The description of a general tendency on the evolution of complexity and cooperation naturally follows from the σ profile. Experiments on a virtual ecosystem are used as illustration.1 aGershenson, Carlos uhttp://arxiv.org/abs/0809.050400366nas a2200121 4500008004100000245005100041210005000092300000800142490000600150520003000156100002300186856003500209 2011 eng d00aWhat does artificial life tell us about death?0 aWhat does artificial life tell us about death a1-50 v23 aShort philosophical essay1 aGershenson, Carlos uhttp://arxiv.org/abs/0906.282401214nas 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-z00702nas a2200229 4500008004100000245003800041210003600079260003100115300001200146100003100158700002300189700002300212700001600235700002400251700002900275700001800304700001900322700002600341700002000367700002100387856006400408 2010 eng d00aModular Random {Boolean} Networks0 aModular Random Boolean Networks aOdense, DenmarkbMIT Press a303-3041 aPoblanno-Balp}, Rodrigo, {1 aGershenson, Carlos1 aFellermann, Harold1 aDörr, Mark1 aHanczyc, Martin, M.1 aLaursen, Lone, Ladegaard1 aMaurer, Sarah1 aMerkle, Daniel1 aMonnard, Pierre-Alain1 aSt$ø$y, Kasper1 aRasmussen, Steen uhttp://mitpress.mit.edu/books/chapters/0262290758chap56.pdf01557nas a2200109 4500008004100000245007800041210006900119520117400188100002301362700002701385856003501412 2009 eng d00aModeling self-organizing traffic lights with elementary cellular automata0 aModeling selforganizing traffic lights with elementary cellular 3 aThere have been several highway traffic models proposed based on cellular automata. The simplest one is elementary cellular automaton rule 184. We extend this model to city traffic with cellular automata coupled at intersections using only rules 184, 252, and 136. The simplicity of the model offers a clear understanding of the main properties of city traffic and its phase transitions. We use the proposed model to compare two methods for coordinating traffic lights: a green-wave method that tries to optimize phases according to expected flows and a self-organizing method that adapts to the current traffic conditions. The self-organizing method delivers considerable improvements over the green-wave method. For low densities, the self-organizing method promotes the formation and coordination of platoons that flow freely in four directions, i.e. with a maximum velocity and no stops. For medium densities, the method allows a constant usage of the intersections, exploiting their maximum flux capacity. For high densities, the method prevents gridlocks and promotes the formation and coordination of "free-spaces" that flow in the opposite direction of traffic.1 aGershenson, Carlos1 aRosenblueth, David, A. uhttp://arxiv.org/abs/0907.192501719nas a2200133 4500008004100000245009700041210006900138300001000207490000600217520126700223100002301490700002101513856005101534 2009 eng d00aWhy does public transport not arrive on time? The pervasiveness of equal headway instability0 aWhy does public transport not arrive on time The pervasiveness o ae72920 v43 aBackground The equal headway instability phenomenon is pervasive in public transport systems. This instability is characterized by an aggregation of vehicles that causes inefficient service. While equal headway instability is common, it has not been studied independently of a particular scenario. However, the phenomenon is apparent in many transport systems and can be modeled and rectified in abstraction. Methodology We present a multi-agent simulation where a default method with no restrictions always leads to unstable headways. We discuss two methods that attempt to achieve equal headways, called minimum and maximum. Since one parameter of the methods depends on the passenger density, adaptive versions–-where the relevant parameter is adjusted automatically–-are also put forward. Our results show that the adaptive maximum method improves significantly over the default method. The model and simulation give insights of the interplay between transport design and passenger behavior. Finally, we provide technological and social suggestions for engineers and passengers to help achieve equal headways and thus reduce delays. Conclusions The equal headway instability phenomenon can be avoided with the suggested technological and social measures.1 aGershenson, Carlos1 aPineda, Luis, A. uhttp://dx.doi.org/10.1371/journal.pone.000729200300nam a2200109 4500008004100000020001500041245002800056210002700084260002600111100002300137856003000160 2008 eng d a879213013500aComplexity: 5 Questions0 aComplexity 5 Questions bAutomatic Peess / VIP1 aGershenson, Carlos uhttp://tinyurl.com/ovg3jn00351nas a2200133 4500008004100000245002800041210002800069260001100097300001000108490000700118100002300125700001800148856005100166 2008 eng d00aEvolution of Complexity0 aEvolution of Complexity cSummer a1–30 v141 aGershenson, Carlos1 aLenaerts, Tom uhttp://dx.doi.org/10.1162/artl.2008.14.3.1430001368nas a2200121 4500008004100000245004200041210004100083300001100124490000900135520102200144100002301166856005701189 2008 eng d00aTowards Self-organizing Bureaucracies0 aTowards Selforganizing Bureaucracies a1–240 v20083 aThe goal of this paper is to contribute to eGovernment efforts, encouraging the use of self-organization as a method to improve the efficiency and adaptability of bureaucracies and similar social systems. Bureaucracies are described as networks of agents, where the main design principle is to reduce local "friction" to increase local and global "satisfaction". Following this principle, solutions are proposed for improving communication within bureaucracies, sensing public satisfaction, dynamic modification of hierarchies, and contextualization of procedures. Each of these reduces friction between agents (internal or external), increasing the efficiency of bureaucracies. Current technologies can be applied for this end. "Random agent networks" (RANs), novel computational models, are introduced to illustrate the benefits of self-organizing bureaucracies. Simulations show that only few changes are required to reach near-optimal performance, potentially adapting quickly and effectively to shifts in demand.1 aGershenson, Carlos uhttp://www.ijpis.net/issues/no1_2008/no1_2008_p1.htm00440nas 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/060407202024nam a2200157 4500008004100000020002200041245005000063210004900113260002600162520156600188653002201754653001201776653002201788100002301810856003301833 2007 eng d a978-0-9831172-3-000aDesign and Control of Self-organizing Systems0 aDesign and Control of Selforganizing Systems aMexicobCopIt Arxives3 aComplex systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex systems. In this book I pro- pose a methodology to aid engineers in the design and control of com- plex systems. This is based on the description of systems as self- organizing. Starting from the agent metaphor, the methodology pro- poses a conceptual framework and a series of steps to follow to find proper mechanisms that will promote elements to find solutions by ac- tively interacting among themselves. The main premise of the method- ology claims that reducing the "friction" of interactions between el- ements of a system will result in a higher "satisfaction" of the system, i.e. better performance. A general introduction to complex thinking is given, since designing self-organizing systems requires a non-classical thought, while prac- tical notions of complexity and self-organization are put forward. To illustrate the methodology, I present three case studies. Self-organizing traffic light controllers are proposed and studied with multi-agent simulations, outperforming traditional methods. Methods for im- proving communication within self-organizing bureaucracies are ad- vanced, introducing a simple computational model to illustrate the benefits of self-organization. In the last case study, requirements for self-organizing artifacts in an ambient intelligence scenario are dis- cussed. Philosophical implications of the conceptual framework are also put forward.10aComplexity Theory10aPhysics10aSelf-organization1 aGershenson, Carlos uhttp://tinyurl.com/DCSOS200701910nas a2200109 4500008004100000245005000041210004900091260005500140520155100195100002301746856003101769 2007 eng d00aDesign and Control of Self-organizing Systems0 aDesign and Control of Selforganizing Systems aBrussels, BelgiumbVrije Universiteit BrusselcMay3 aComplex systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex systems. In this thesis I propose a methodology to aid engineers in the design and control of complex systems. This is based on the description of systems as self-organizing. Starting from the agent metaphor, the methodology proposes a conceptual framework and a series of steps to follow to find proper mechanisms that will promote elements to find solutions by actively interacting among themselves. The main premise of the methodology claims that reducing the ``friction'' of interactions between elements of a system will result in a higher ``satisfaction'' of the system, i.e. better performance. A general introduction to complex thinking is given, since designing self-organizing systems requires a non-classical thought, while practical notions of complexity and self-organization are put forward. To illustrate the methodology, I present three case studies. Self-organizing traffic light controllers are proposed and studied with multi-agent simulations, outperforming traditional methods. Methods for improving communication within self-organizing bureaucracies are advanced, introducing a simple computational model to illustrate the benefits of self-organization. In the last case study, requirements for self-organizing artifacts in an ambient intelligence scenario are discussed. Philosophical implications of the conceptual framework are also put forward.1 aGershenson, Carlos uhttp://cogprints.org/5442/01387nam a2200133 4500008004100000245003000041210003000071260003200101520100900133100002301142700002001165700001901185856004901204 2007 eng d00aPhilosophy and Complexity0 aPhilosophy and Complexity aSingaporebWorld Scientific3 aScientific, technological, and cultural changes have always had an impact upon philosophy. They can force a change in the way we perceive the world, reveal new kinds of phenomena to be understood, and provide new ways of understanding phenomena. Complexity science, immersed in a culture of information, is having a diverse but particularly significant impact upon philosophy. Previous ideas do not necessarily sit comfortably with the new paradigm, resulting in new ideas or new interpretations of old ideas. In this unprecedented interdisciplinary volume, researchers from different backgrounds join efforts to update thinking upon philosophical questions with developments in the scientific study of complex systems. The contributions focus on a wide range of topics, but share the common goal of increasing our understanding and improving our descriptions of our complex world. This revolutionary debate includes contributions from leading experts, as well as young researchers proposing fresh ideas.1 aGershenson, Carlos1 aAerts, Diederik1 aEdmonds, Bruce uhttp://www.worldscibooks.com/chaos/6372.html00941nas a2200157 4500008004100000245005900041210005700100260001300157300001200170520046800182100002200650700002300672700002300695700002400718856004100742 2007 eng d00aSelf-organizing traffic lights: A realistic simulation0 aSelforganizing traffic lights A realistic simulation bSpringer a41–493 aWe have previously shown in an abstract simulation (Gershenson, 2005) that self-organizing traffic lights can improve greatly traffic flow for any density. In this paper, we extend these results to a realistic setting, implementing self-organizing traffic lights in an advanced traffic simulator using real data from a Brussels avenue. On average, for different traffic densities, travel waiting times are reduced by 50% compared to the current green wave method.1 aCools, Seung, Bae1 aGershenson, Carlos1 aD'Hooghe}, Bart, {1 aProkopenko, Mikhail uhttp://arxiv.org/abs/nlin.AO/061004000439nas a2200121 4500008004100000245007200041210006900113260003300182100002300215700001400238700001800252856004700270 2007 eng d00aTowards a General Methodology for Designing Self-Organizing Systems0 aTowards a General Methodology for Designing SelfOrganizing Syste aOxfordbRadcliffe Publishing1 aGershenson, Carlos1 aBogg, Jan1 aGeyer, Robert uhttp://turing.iimas.unam.mx/sos/?q=node/4900363nas a2200109 4500008004100000245005800041210005700099300001200156100002300168700001800191856004400209 2006 eng d00aEvolution of Complexity: Introduction to the Workshop0 aEvolution of Complexity Introduction to the Workshop a71–721 aGershenson, Carlos1 aLenaerts, Tom uhttp://uk.arxiv.org/abs/nlin.AO/060406900340nas a2200097 4500008004100000245006400041210006100105260000900166100002300175856004400198 2006 eng d00aA General Methodology for Designing Self-Organizing Systems0 aGeneral Methodology for Designing SelfOrganizing Systems bECCO1 aGershenson, Carlos uhttp://uk.arxiv.org/abs/nlin.AO/050500901643nas a2200217 4500008004100000245007400041210006800115260001400183300001200197520099000209100002301199700002501222700002101247700001801268700001901286700001801305700001701323700002201340700001901362856004401381 2006 eng d00aThe Role of Redundancy in the Robustness of Random {Boolean} Networks0 aRole of Redundancy in the Robustness of Random Boolean Networks bMIT Press a35–423 aEvolution depends on the possibility of successfully exploring fitness landscapes via mutation and recombination. With these search procedures, exploration is difficult in "rugged" fitness landscapes, where small mutations can drastically change functionalities in an organism. Random Boolean networks (RBNs), being general models, can be used to explore theories of how evolution can take place in rugged landscapes; or even change the landscapes. In this paper, we study the effect that redundant nodes have on the robustness of RBNs. Using computer simulations, we have found that the addition of redundant nodes to RBNs increases their robustness. We conjecture that redundancy is a way of "smoothening" fitness landscapes. Therefore, redundancy can facilitate evolutionary searches. However, too much redundancy could reduce the rate of adaptation of an evolutionary process. Our results also provide supporting evidence in favour of Kauffman's conjecture (Kauffman, 2000, p.195).1 aGershenson, Carlos1 aKauffman, Stuart, A.1 aShmulevich, Ilya1 aRocha, L., M.1 aYaeger, L., S.1 aBedau, M., A.1 aFloreano, D.1 aGoldstone, R., L.1 aVespignani, A. uhttp://uk.arxiv.org/abs/nlin.AO/051101800975nas a2200145 4500008004100000245003400041210003300075260003100108300001200139520056700151100002300718700002300741700002100764856004400785 2005 eng d00aHow Can We Think the Complex?0 aHow Can We Think the Complex bInformation Age Publishing a47–613 aThis chapter does not deal with specific tools and techniques for managing complex systems, but proposes some basic concepts that help us to think and speak about complexity. We review classical thinking and its intrinsic drawbacks when dealing with complexity. We then show how complexity forces us to build models with indeterminacy and unpredictability. However, we can still deal with the problems created in this way by being adaptive, and profiting from a complex system's capability for selforganization, and the distributed intelligence this may produce.1 aGershenson, Carlos1 aHeylighen, Francis1 aRichardson, Kurt uhttp://uk.arxiv.org/abs/nlin.AO/040202301203nas a2200121 4500008004100000245003500041210003400076300001200110490000700122520087900129100002301008856005001031 2005 eng d00aSelf-Organizing Traffic Lights0 aSelfOrganizing Traffic Lights a29–530 v163 aSteering traffic in cities is a very complex task, since improving efficiency involves the coordination of many actors. Traditional approaches attempt to optimize traffic lights for a particular density and configuration of traffic. The disadvantage of this lies in the fact that traffic densities and configurations change constantly. Traffic seems to be an adaptation problem rather than an optimization problem. We propose a simple and feasible alternative, in which traffic lights self-organize to improve traffic flow. We use a multi-agent simulation to study three self-organizing methods, which are able to outperform traditional rigid and adaptive methods. Using simple rules and no direct communication, traffic lights are able to self-organize and adapt to changing traffic conditions, reducing waiting times, number of stopped cars, and increasing average speeds.1 aGershenson, Carlos uhttp://www.complex-systems.com/pdf/16-1-2.pdf01436nas 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.2003.10.00201234nas a2200181 4500008004100000245004600041210004400087260001500131300001400146520075300160100002300913700001400936700001700950700001500967700001400982700001500996856004101011 2004 eng d00aIntroduction to Random {Boolean} Networks0 aIntroduction to Random Boolean Networks aBoston, MA a160–1733 aThe goal of this tutorial is to promote interest in the study of random Boolean networks (RBNs). These can be very interesting models, since one does not have to assume any functionality or particular connectivity of the networks to study their generic properties. Like this, RBNs have been used for exploring the configurations where life could emerge. The fact that RBNs are a generalization of cellular automata makes their research a very important topic. The tutorial, intended for a broad audience, presents the state of the art in RBNs, spanning over several lines of research carried out by different groups. We focus on research done within artificial life, as we cannot exhaust the abundant research done over the decades related to RBNs.1 aGershenson, Carlos1 aBedau, M.1 aHusbands, P.1 aHutton, T.1 aKumar, S.1 aSuzuki, H. uhttp://arxiv.org/abs/nlin.AO/040800601088nas a2200097 4500008004100000245008300041210006900124520073000193100002300923856004400946 2004 eng d00aPhase Transitions in Random {Boolean} Networks with Different Updating Schemes0 aPhase Transitions in Random Boolean Networks with Different Upda3 aIn this paper we study the phase transitions of different types of Random Boolean networks. These differ in their updating scheme: synchronous, semi-synchronous, or asynchronous, and deterministic or non-deterministic. It has been shown that the statistical properties of Random Boolean networks change considerable according to the updating scheme. We study with computer simulations sensitivity to initial conditions as a measure of order/chaos. We find that independently of their updating scheme, all network types have very similar phase transitions, namely when the average number of connections of nodes is between one and three. This critical value depends more on the size of the network than on the updating scheme.1 aGershenson, Carlos uhttp://uk.arxiv.org/abs/nlin.AO/031100801111nas a2200181 4500008004100000245007400041210006900115260001400184300001400198520057100212100002300783700001600806700001400822700001700836700001600853700001900869856004100888 2004 eng d00aUpdating Schemes in Random {Boolean} Networks: Do They Really Matter?0 aUpdating Schemes in Random Boolean Networks Do They Really Matte bMIT Press a238–2433 aIn this paper we try to end the debate concerning the suitability of different updating schemes in random Boolean networks (RBNs). We quantify for the first time loose attractors in asyncrhonous RBNs, which allows us to analyze the complexity reduction related to different updating schemes. We also report that all updating schemes yield very similar critical stability values, meaning that the "edge of chaos" does not depend much on the updating scheme. After discussion, we conclude that synchonous RBNs are justifiable theoretical models of biological networks.1 aGershenson, Carlos1 aPollack, J.1 aBedau, M.1 aHusbands, P.1 aIkegami, T.1 aWatson, R., A. uhttp://arxiv.org/abs/nlin.AO/040200600799nas 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/030302100391nas a2200121 4500008004100000245005000041210004500091260001600136300001200152100002300164700002300187856005900210 2003 eng d00aThe Meaning of Self-Organization in Computing0 aMeaning of SelfOrganization in Computing cJuly/August a72–751 aHeylighen, Francis1 aGershenson, Carlos uhttp://pcp.vub.ac.be/Papers/IEEE.Self-organization.pdf00861nas a2200097 4500008004100000245005100041210004900092520055500141100002300696856004400719 2003 eng d00aSelf-organizing Traffic Control: First Results0 aSelforganizing Traffic Control First Results3 aWe developed a virtual laboratory for traffic control where agents use different strategies in order to self-organize on the road. We present our first results where we compare the performance and behaviour promoted by environmental constrains and five different simple strategies: three inspired in flocking behaviour, one selfish, and one inspired in the minority game. Experiments are presented for comparing the strategies. Different issues are discussed, such as the important role of environmental constrains and the emergence of traffic lanes.1 aGershenson, Carlos uhttp://uk.arxiv.org/abs/nlin.AO/030903901287nas a2200193 4500008004100000245004700041210004500088260002100133300001400154520075400168100002300922700002300945700001500968700002000983700001701003700001601020700001601036856004101052 2003 eng d00aWhen Can We Call a System Self-Organizing?0 aWhen Can We Call a System SelfOrganizing aBerlinbSpringer a606–6143 aWe do not attempt to provide yet another definition of self-organizing systems, nor review previous definitions. We explore the conditions necessary to describe self-organizing systems, inspired on decades of their study, in order to understand them better. These involve the dynamics of the system, and the purpose, boundaries, and description level chosen by an observer. We show how, changing the level or ``graining'' of description, the same system can be self-organizing or not. We also discuss common problems we face when studying self-organizing systems. We analyse when building, designing, and controlling artificial self-organizing systems is useful. We state that self-organization is a way of observing systems, not a class of systems.1 aGershenson, Carlos1 aHeylighen, Francis1 aBanzhaf, W1 aChristaller, T.1 aDittrich, P.1 aKim, J., T.1 aZiegler, J. uhttp://arxiv.org/abs/nlin.AO/030302001031nas a2200097 4500008004100000245010000041210006900141520066100210100002300871856003900894 2002 eng d00aAdaptive Development of Koncepts in Virtual Animats: Insights Into the Development of Knowledge0 aAdaptive Development of Koncepts in Virtual Animats Insights Int3 aAs a part of our effort for studying the evolution and development of cognition, we present results derived from synthetic experimentations in a virtual laboratory where animats develop koncepts adaptively and ground their meaning through action. We introduce the term "koncept" to avoid confusions and ambiguity derived from the wide use of the word "concept". We present the models which our animats use for abstracting koncepts from perceptions, plastically adapt koncepts, and associate koncepts with actions. On a more philosophical vein, we suggest that knowledge is a property of a cognitive system, not an element, and therefore observer-dependent.1 aGershenson, Carlos uhttp://uk.arxiv.org/abs/cs/021102701233nas a2200193 4500008004100000245008600041210006900127260005900196300001200255490000700267520056100274100002300835700002800858700002100886700001700907700001900924700002500943856007100968 2002 eng d00aBehaviour-Based Knowledge Systems: An Epigenetic Path from Behaviour to Knowledge0 aBehaviourBased Knowledge Systems An Epigenetic Path from Behavio aEdinburgh, ScotlandbLund University Cognitive Studies a35–410 v943 aIn this paper we expose the theoretical background underlying our current research. This consists in the development of behaviour-based knowledge systems, for closing the gaps between behaviour-based and knowledge-based systems, and also between the understandings of the phenomena they model. We expose the requirements and stages for developing behaviour-based knowledge systems and discuss their limits. We believe that these are necessary conditions for the development of higher order cognitive capacities, in artificial and natural cognitive systems.1 aGershenson, Carlos1 aPrince, Christopher, G.1 aDemiris, Yiannis1 aMarom, Yuval1 aKozima, Hideki1 aBalkenius, Christian uhttp://www.lucs.lu.se/ftp/pub/LUCS%5FStudies/LUCS94/Gershenson.pdf01283nas 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/2621/01641nas a2200109 4500008004100000245006200041210006200103490000600165520129200171100002301463856004501486 2002 eng d00aPhilosophical Ideas on the Simulation of Social Behaviour0 aPhilosophical Ideas on the Simulation of Social Behaviour0 v53 aIn this study we consider some of the philosophical issues that should be taken into account when simulating social behaviour. Even though the ideas presented here are philosophical, they should be of interest more to researchers simulating social behaviour than to philosophers, since we try to note some problems that researchers might not put much attention to. We give notions of what could be considered a social behaviour, and mention the problems that arise if we attempt to give a sharp definition of social behaviour in a broad context. We also briefly give useful concepts and ideas of complex systems and abstraction levels (Gershenson, 2002a), since any society can be seen as a complex system. We discuss the problems that arise while modelling social behaviour, mentioning the synthetic method as a useful approach for contrasting social theories, because of the complexities of the phenomena they model. In addition, we note the importance of the study of social behaviour for the understanding of cognition. We hope that the ideas presented here motivate the interest and debate of researchers simulating social behaviour in order to pay attention to the problems mentioned in this work, and attempt to provide more suitable solutions to them than the ones proposed here.1 aGershenson, Carlos uhttp://jasss.soc.surrey.ac.uk/5/3/8.html00788nas a2200097 4500008004100000245005200041210004600093520049700139100002300636856003100659 2002 eng d00aWhere is the problem of ``Where is the mind?''?0 aWhere is the problem of Where is the mind3 aWe propose that the discussions about ``where the mind is'' depend directly on the metaphysical preconception and definition of ``mind''. If we see the mind from one perspective (individualist), it will be only in the brain, and if we see it from another (active externalist), it will be embedded in the body and extended into the world. The ``whereabouts'' of the mind depends on our