@proceedings {212,
	title = {Sistemas con Din{\'a}mica Acoplada y Redes de Defensa y Ataque: Representaci{\'o}n de las Interacciones en Juegos de Competici{\'o}n},
	year = {2019},
	address = {Pachuca, M{\'e}xico},
	author = {Nelson Fern{\'a}ndez and V{\'\i}ctor Rivera and Carlos Gershenson}
}
@inbook {Viragh2016,
	title = {Self-organized UAV Traffic in Realistic Environments},
	booktitle = {Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on},
	year = {2016},
	pages = {1645{\textendash}1652},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Daejeon, South Korea},
	doi = {10.1109/IROS.2016.7759265},
	author = {Csaba Vir{\'a}gh and M{\'a}t{\'e} Nagy and Carlos Gershenson and G{\'a}bor V{\'a}s{\'a}rhelyi}
}
@article {De-La-Guardia:2012,
	title = {Self-organizing systems on chip},
	journal = {Intel Technology Journal},
	volume = {16},
	number = {2},
	year = {2012},
	pages = {182{\textendash}201},
	abstract = {Self-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.},
	url = {http://noggin.intel.com/technology-journal/2012/162/exploring-control-and-autonomic-computing},
	author = {Rafael {De La Guardia} and Carlos Gershenson}
}
@article {GershensonRosenblueth:2011,
	title = {Self-organizing traffic lights at multiple-street intersections},
	journal = {Complexity},
	volume = {17},
	number = {4},
	year = {2012},
	pages = {23-39},
	abstract = {The 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 {\textquoteleft}{\textquoteleft}green wave{\textquoteright}{\textquoteright} 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.},
	doi = {10.1002/cplx.20395},
	url = {http://dx.doi.org/10.1002/cplx.20395},
	author = {Carlos Gershenson and David A. Rosenblueth}
}
@inbook {Gershenson:2011b,
	title = {Self-organizing urban transportation systems},
	booktitle = {Complexity Theories of Cities Have Come of Age: An Overview with Implications to Urban Planning and Design},
	year = {2012},
	pages = {269-279},
	publisher = {Springer},
	organization = {Springer},
	address = {Berlin Heidelberg},
	abstract = {Urban 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.},
	doi = {10.1007/978-3-642-24544-2_15},
	url = {http://arxiv.org/abs/0912.1588},
	author = {Carlos Gershenson},
	editor = {Juval Portugali and Han Meyer and Egbert Stolk and Ekim Tan}
}
@conference {Fernandez:2012,
	title = {Sistemas Din{\'a}micos como Redes Computacionales de Agentes para la evaluaci{\'o}n de sus Propiedades Emergentes.},
	booktitle = {II Simposio Cient{\'{\i}fico y Tecnol{\'o}gico en Computaci{\'o}n SCTC 2012},
	year = {2012},
	address = {Universidad Central de Venezuela},
	author = {Nelson Fern{\'a}ndez and Jos{\'e} Aguilar and Carlos Gershenson and Oswaldo Ter{\'a}n}
}
@article {Gershenson:2011a,
	title = {Self-organization leads to supraoptimal performance in public transportation systems},
	journal = {{PLoS ONE}},
	volume = {6},
	number = {6},
	year = {2011},
	pages = {e21469},
	abstract = {The 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 {\textquoteleft}{\textquoteleft}slower-is-faster{\textquoteright}{\textquoteright} effect, where passengers wait more time at stations but total travel times are reduced. The proposed self-organizing method uses {\textquoteleft}{\textquoteleft}antipheromones{\textquoteright}{\textquoteright} to regulate headways, which are inspired by the stigmergy (communication via environment) of some ant colonies.},
	doi = {10.1371/journal.pone.0021469},
	url = {http://dx.doi.org/10.1371/journal.pone.0021469},
	author = {Carlos Gershenson}
}
@book {IWSOS2011,
	title = {Self-Organizing Systems 5th International Workshop, IWSOS 2011, Karlsruhe, Germany, February 23-24, 2011, Proceedings. Springer LNCS 6557},
	series = {Lecture Notes in Computer Science},
	volume = {6557},
	year = {2011},
	publisher = {Springer},
	organization = {Springer},
	abstract = {This 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.},
	isbn = {978-3-642-19166-4},
	doi = {10.1007/978-3-642-19167-1},
	url = {http://dx.doi.org/10.1007/978-3-642-19167-1},
	editor = {Christian Bettstetter and Carlos Gershenson}
}
@article {Gershenson:2010a,
	title = {The Sigma Profile: A Formal Tool to Study Organization and its Evolution at Multiple Scales},
	journal = {Complexity},
	volume = {16},
	number = {5},
	year = {2011},
	pages = {37-44},
	abstract = {The 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.},
	doi = {10.1002/cplx.20350},
	url = {http://arxiv.org/abs/0809.0504},
	author = {Carlos Gershenson}
}
@inbook {CoolsEtAl2007,
	title = {Self-organizing traffic lights: A realistic simulation},
	booktitle = {Self-Organization: Applied Multi-Agent Systems},
	year = {2007},
	pages = {41{\textendash}49},
	publisher = {Springer},
	organization = {Springer},
	chapter = {3},
	abstract = {We 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.},
	doi = {10.1007/978-1-84628-982-8_3},
	url = {http://arxiv.org/abs/nlin.AO/0610040},
	author = {Seung Bae Cools and Carlos Gershenson and Bart {D{\textquoteright}Hooghe}},
	editor = {Mikhail Prokopenko}
}
@article {Gershenson2005,
	title = {Self-Organizing Traffic Lights},
	journal = {Complex Systems},
	volume = {16},
	number = {1},
	year = {2005},
	pages = {29{\textendash}53},
	abstract = {Steering 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.},
	url = {http://www.complex-systems.com/pdf/16-1-2.pdf},
	author = {Carlos Gershenson}
}
@unpublished {Gershenson2003u,
	title = {Self-organizing Traffic Control: First Results},
	year = {2003},
	note = {Unpublished},
	abstract = {We 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.},
	url = {http://uk.arxiv.org/abs/nlin.AO/0309039},
	author = {Carlos Gershenson}
}
