@conference {183,
	title = {Modeling Systems with Coupled Dynamics (SCDs): A Multi-Agent, Networks, and Game Theory-based Approach},
	booktitle = {Conference on Complex Systems},
	year = {2018},
	address = {Thessaloniki, Greece},
	author = {Nelson Fern{\'a}ndez and Osman Ortega and Yesid Madrid and Guillermo Restrepo and Wilmer Leal and Carlos Gershenson}
}
@article {Karbovskii2016,
	title = {Multimodel agent-based simulation environment for mass-gatherings and pedestrian dynamics},
	journal = {Future Generation Computer Systems},
	volume = {79},
	number = {1},
	year = {2018},
	month = {February},
	pages = {155{\textendash}165},
	abstract = {Abstract 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{\textendash}-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.},
	issn = {0167-739X},
	doi = {10.1016/j.future.2016.10.002},
	url = {http://dx.doi.org/10.1016/j.future.2016.10.002},
	author = {Vladislav Karbovskii and Daniil Voloshin and Andrey Karsakov and Alexey Bezgodov and Carlos Gershenson}
}
@article {Karbovskii2016,
	title = {Multimodel agent-based simulation environment for mass-gatherings and pedestrian dynamics},
	journal = {Future Generation Computer Systems},
	year = {2016},
	pages = {-},
	abstract = {Abstract 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{\textendash}-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.},
	keywords = {Urgent computing},
	issn = {0167-739X},
	doi = {10.1016/j.future.2016.10.002},
	url = {http://dx.doi.org/10.1016/j.future.2016.10.002},
	author = {Vladislav Karbovskii and Daniil Voloshin and Andrey Karsakov and Alexey Bezgodov and Carlos Gershenson}
}
@inbook {156,
	title = {Modelling Complexity for Policy: Opportunities and Challenges},
	booktitle = {Handobook on Complexity and Public Policy},
	year = {2015},
	pages = {205-220},
	publisher = {Edward Elgar},
	organization = {Edward Elgar},
	chapter = {13},
	author = {Bruce Edmonds and Carlos Gershenson},
	editor = {Robert Geyer and Paul Cairney}
}
@article {Zubillaga2014Measuring-the-C,
	title = {Measuring the Complexity of Self-organizing Traffic Lights},
	journal = {Entropy},
	volume = {16},
	number = {5},
	year = {2014},
	pages = {2384{\textendash}2407},
	abstract = {We apply measures of complexity, emergence, and self-organization to an urban traffic model for comparing a traditional traffic-light coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only is traffic a non-stationary problem, requiring controllers to adapt constantly; controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures and extending Ashby{\textquoteright}s law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system.},
	doi = {10.3390/e16052384},
	url = {http://dx.doi.org/10.3390/e16052384},
	author = {Dar{\'\i}o Zubillaga and Geovany Cruz and Luis Daniel Aguilar and Jorge Zapot{\'e}catl and Nelson Fern{\'a}ndez and Jos{\'e} Aguilar and David A. Rosenblueth and Carlos Gershenson}
}
@unpublished {Amoretti:2012,
	title = {Measuring the Complexity of Ultra-Large-Scale Evolutionary Systems},
	year = {2013},
	note = {Submitted to Computer Networks},
	abstract = {Ultra-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.},
	url = {http://arxiv.org/abs/1207.6656},
	author = {Michele Amoretti and Carlos Gershenson}
}
@article {RosenbluethGershenson:2010,
	title = {A model of city traffic based on elementary cellular automata},
	journal = {Complex Systems},
	volume = {19},
	number = {4},
	year = {2011},
	pages = {305-322},
	abstract = {There 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.},
	url = {http://www.complex-systems.com/pdf/19-4-1.pdf},
	author = {David A. Rosenblueth and Carlos Gershenson}
}
@article {BalpoGershenson:2011,
	title = {Modular Random {Boolean} Networks},
	journal = {Artificial Life},
	volume = {17},
	number = {4},
	year = {2011},
	pages = {331{\textendash}351},
	publisher = {MIT Press},
	abstract = {Random 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.},
	doi = {10.1162/artl_a_00042},
	url = {http://arxiv.org/abs/1101.1893},
	author = {Rodrigo {Poblanno-Balp} and Carlos Gershenson}
}
@inbook {BalpoGershenson:2010,
	title = {Modular Random {Boolean} Networks},
	booktitle = {{Artificial Life XII} Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems},
	year = {2010},
	pages = {303-304},
	publisher = {MIT Press},
	organization = {MIT Press},
	address = {Odense, Denmark},
	url = {http://mitpress.mit.edu/books/chapters/0262290758chap56.pdf},
	author = {Rodrigo {Poblanno-Balp} and Carlos Gershenson},
	editor = {Harold Fellermann and Mark D{\"o}rr and Martin M. Hanczyc and Lone Ladegaard Laursen and Sarah Maurer and Daniel Merkle and Pierre-Alain Monnard and Kasper St${\o}$y and Steen Rasmussen}
}
@unpublished {GershensonRosenblueth2009,
	title = {Modeling self-organizing traffic lights with elementary cellular automata},
	year = {2009},
	note = {Submitted},
	abstract = {There 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.},
	url = {http://arxiv.org/abs/0907.1925},
	author = {Carlos Gershenson and David A. Rosenblueth}
}
@article {HeylighenGershenson2003,
	title = {The Meaning of Self-Organization in Computing},
	journal = {IEEE Intelligent Systems},
	year = {2003},
	month = {July/August},
	pages = {72{\textendash}75},
	url = {http://pcp.vub.ac.be/Papers/IEEE.Self-organization.pdf},
	author = {Francis Heylighen and Carlos Gershenson}
}
@conference {Gershenson1999,
	title = {Modelling Emotions with Multidimensional Logic},
	booktitle = {Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society {(NAFIPS} {\textquoteright}99)},
	year = {1999},
	pages = {42{\textendash}46},
	publisher = {IEEE Press},
	organization = {IEEE Press},
	address = {New York City, NY},
	abstract = {One of the objectives of Artificial Intelligence has been the modelling of "human" characteristics, such as emotions, behaviour, conscience, etc. But in such characteristics we might find certain degree of contradiction. Previous work on modelling emotions and its problems are reviewed. A model for emotions is proposed using multidimensional logic, which handles the degree of contradiction that emotions might have. The model is oriented to simulate emotions in artificial societies. The proposed solution is also generalized for actions which might overcome contradiction (conflictive goals in agents, for example).},
	url = {http://tinyurl.com/yek3ms},
	author = {Carlos Gershenson}
}
