00496nas 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/21201311nas a2200229   4500008004100000245005200041210005000093260002800143300001400171520063400185100002300819700001800842700001900860700001900879700002100898700002100919700002000940700001600960700001800976700002100994856006601015       2018                        eng d00aSelf-Organization and Artificial Life: A Review0 aSelfOrganization and Artificial Life A Review  aTokyo, JapanbMIT Press  a510–5173 aSelf-organization has been an important concept within a number of disciplines, which Artificial Life (ALife) also has heavily utilized since its inception. The term and its implications, however, are often confusing or misinterpreted. In this work, we provide a mini-review of self-organization and its relationship with ALife, aiming at initiating discussions on this important topic with the interested audience. We first articulate some fundamental aspects of self-organization, outline its usage, and review its applications to ALife within its soft, hard, and wet domains. We also provide perspectives for further research.1 aGershenson, Carlos1 aTrianni, Vito1 aWerfel, Justin1 aSayama, Hiroki1 aIkegami, Takashi1 aVirgo, Nathaniel1 aWitkowski, Olaf1 aOka, Mizuki1 aSuzuki, Reiji1 aIizuka, Hiroyuki  uhttps://www.mitpressjournals.org/doi/abs/10.1162/isal_a_0009400431nas a2200109   4500008004100000245011300041210006900154100001700223700002100240700002300261856003700284       2017                        eng d00aSelf-Organization in Traffic Lights: Evolution of Signal Control with Advances in Sensors and Communications0 aSelfOrganization in Traffic Lights Evolution of Signal Control w1 aGoel, Sanjay1 aBush, Stephen, F1 aGershenson, Carlos  uhttps://arxiv.org/abs/1708.0718800479nas 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/17901478nas a2200205   4500008004100000020002200041245008600063210006900149260003200218300001200250490000900262520079900271100003401070700002301104700002801127700002501155700002001180700002101200856005101221       2014                        eng d  a978-3-642-54139-100aSelf-organization Promotes the Evolution of Cooperation with Cultural Propagation0 aSelforganization Promotes the Evolution of Cooperation with Cult  aBerlin HeidelbergbSpringer  a145-1500 v82213 aIn this paper three computational models for the study of the evolution of cooperation under cultural propagation are studied: Kin Selection, Direct Reciprocity and Indirect Reciprocity. Two analyzes are reported, one comparing their behavior between them and a second one identifying the impact that different parameters have in the model dynamics. The results of these analyzes illustrate how game transitions may occur depending of some parameters within the models and also explain how agents adapt to these transitions by individually choosing their attachment to a cooperative attitude. These parameters regulate how cooperation can self-organize under different circumstances. The emergence of the evolution of cooperation as a result of the agent's adapting processes is also discussed.1 aCortés-Berrueco, LuisEnrique1 aGershenson, Carlos1 aStephens, ChristopherR.1 aElmenreich, Wilfried1 aDressler, Falko1 aLoreto, Vittorio  uhttp://dx.doi.org/10.1007/978-3-642-54140-7_1301420nas 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/1501121nas 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/0809.050400528nas a2200109   4500008004100000245019500041210006900236300001200305490000700317100002300324856007100347       2008                        eng d00aSelf-Organization and Emergence in Life Sciences. Bernard Feltz, Marc Crommelinck, and Philippe Goujon (Eds.). (2006, Synthese Library Vol. 331, Springer.) Hardcover, €139, $179, 360 pages0 aSelfOrganization and Emergence in Life Sciences Bernard Feltz Ma  a239-2400 v141 aGershenson, Carlos  uhttp://www.mitpressjournals.org/doi/abs/10.1162/artl.2008.14.2.23900941nas 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/061004001369nas a2200181   4500008004100000245006400041210006300105260002600168520081100194100002501005700002601030700002601056700002301082700001801105700001701123700001701140856003001157       2007                        eng d00aSmartocracy: Social Networks for Collective Decision Making0 aSmartocracy Social Networks for Collective Decision Making  bIEEE Computer Society3 aSmartocracy is a social software system for collec- tive decision making. The system is composed of a social network that links individuals to those they trust to make good decisions and a decision network that links individuals to their voted-on solutions. Such networks allow a variety of algorithms to convert the link choices made by individual participants into specific decision outcomes. Simply interpreting the linkages differently (e.g. ignoring trust links, or using them to weight an individual's vote) provides a variety of outcomes fit for different decision making scenarios. This paper will discuss the Smartocracy network data structures, the suite of collective decision making algorithms currently supported, and the results of two collective decisions regarding the design of the system.1 aRodriguez, Marko, A.1 aSteinbock, Daniel, J.1 aWatkins, Jennifer, H.1 aGershenson, Carlos1 aBollen, Johan1 aGrey, Victor1 adeGraf, Brad  uhttp://tinyurl.com/ybojp801203nas 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.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/0309039