@article {10.7717/peerj.8533,
	title = {Ecosystem antifragility: beyond integrity and resilience},
	journal = {PeerJ},
	volume = {8},
	year = {2020},
	pages = {e8533},
	abstract = {We review the concept of ecosystem resilience in its relation to ecosystem integrity from an information theory approach. We summarize the literature on the subject identifying three main narratives: ecosystem properties that enable them to be more resilient; ecosystem response to perturbations; and complexity. We also include original ideas with theoretical and quantitative developments with application examples. The main contribution is a new way to rethink resilience, that is mathematically formal and easy to evaluate heuristically in real-world applications: ecosystem antifragility. An ecosystem is antifragile if it benefits from environmental variability. Antifragility therefore goes beyond robustness or resilience because while resilient/robust systems are merely perturbation-resistant, antifragile structures not only withstand stress but also benefit from it.},
	keywords = {Antifragility, Complexity, Ecosystem integrity, Resilience},
	issn = {2167-8359},
	doi = {10.7717/peerj.8533},
	url = {https://doi.org/10.7717/peerj.8533},
	author = {Equihua, Miguel and Espinosa Aldama, Mariana and Gershenson, Carlos and L{\'o}pez-Corona, Oliver and Mungu{\'\i}a, Mariana and P{\'e}rez-Maqueo, Octavio and Ram{\'\i}rez-Carrillo, Elvia}
}
@conference {184,
	title = {Coupled Dynamical Systems and Defense-Attack Networks: Representation of Soccer Players Interactions},
	booktitle = {Conference on Complex Systems},
	year = {2018},
	address = {Thessaloniki, Greece},
	author = {Nelson Fern{\'a}ndez and V{\'\i}ctor Rivera and Yesid Madrid and Guillermo Restrepo and Wilmer Leal and Carlos Gershenson}
}
@article {Pina-Garcia2018,
	title = {From neuroscience to computer science: a topical approach on Twitter},
	journal = {Journal of Computational Social Science},
	volume = {1},
	number = {1},
	year = {2018},
	pages = {187{\textendash}208},
	abstract = {Twitter is perhaps the most influential microblogging service, with 271 million regular users producing approximately 500 million tweets per day. Previous studies of tweets discussing scientific topics are limited to local surveys or may not be representative geographically. This indicates a need to harvest and analyse tweets with the aim of understanding the level of dissemination of science related topics worldwide. In this study, we use Twitter as case of study and explore the hypothesis of science popularization via the social stream. We present and discuss tweets related to popular science around the world using eleven keywords. We analyze a sample of 306,163 tweets posted by 91,557 users with the aim of identifying tweets and those categories formed around temporally similar topics. We systematically examined the data to track and analyze the online activity around users tweeting about popular science. In addition, we identify locations of high Twitter activity that occur in several places around the world. We also examine which sources (mobile devices, apps, and other social networks) are used to share popular science related links. Furthermore, this study provides insights into the geographic density of popular science tweets worldwide. We show that emergent topics related to popular science are important because they could help to explore how science becomes of public interest. The study also offers some important insights for studying the type of scientific content that users are more likely to tweet.},
	isbn = {2432-2725},
	doi = {10.1007/s42001-017-0002-9},
	url = {https://doi.org/10.1007/s42001-017-0002-9},
	author = {Pi{\~n}a-Garc{\'\i}a, C. A. and Siqueiros-Garc{\'\i}a, J. Mario and Robles-Belmont, E. and Carre{\'o}n, Gustavo and Gershenson, Carlos and L{\'o}pez, Julio Amador D{\'\i}az}
}
@conference {183,
	title = {Modeling Systems with Coupled Dynamics (SCDs): A Multi-Agent, Networks, and Game Theory-based Approach},
	booktitle = {Conference on Complex Systems},
	year = {2018},
	address = {Thessaloniki, Greece},
	author = {Nelson Fern{\'a}ndez and Osman Ortega and Yesid Madrid and Guillermo Restrepo and Wilmer Leal and Carlos Gershenson}
}
@inbook {LugoGershensonComplex2012,
	title = {Decoding Road Networks into Ancient Routes: The Case of the Aztec Empire in Mexico},
	booktitle = {Proceedings of the Second International Conference on Complex Sciences: Theory and Applications {(COMPLEX 2012)}},
	series = {LNICST},
	volume = {126},
	year = {2014},
	pages = {228{\textendash}233},
	publisher = {Springer},
	organization = {Springer},
	address = {Berlin, Germany},
	doi = {10.1007/978-3-319-03473-7_20},
	url = {http://dx.doi.org/10.1007/978-3-319-03473-7_20},
	author = {Igor Lugo and Carlos Gershenson},
	editor = {Kristin Glass}
}
@article {Gershenson2013The-Past-Presen,
	title = {The Past, Present and Future of Cybernetics and Systems Research},
	journal = {systema: connecting matter, life, culture and technology},
	volume = {1},
	number = {3},
	year = {2014},
	pages = {4{\textendash}13},
	abstract = {Cybernetics 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.},
	url = {http://arxiv.org/abs/1308.6317},
	author = {Carlos Gershenson and Peter Csermely and Peter Erdi and Helena Knyazeva and Alexander Laszlo}
}
@inbook {CortesIWSOS2013,
	title = {Self-organization Promotes the Evolution of Cooperation with Cultural Propagation},
	booktitle = {Self-Organizing Systems},
	series = {Lecture Notes in Computer Science},
	volume = {8221},
	year = {2014},
	pages = {145-150},
	publisher = {Springer},
	organization = {Springer},
	address = {Berlin Heidelberg},
	abstract = {In 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{\textquoteright}s adapting processes is also discussed.},
	isbn = {978-3-642-54139-1},
	doi = {10.1007/978-3-642-54140-7_13},
	url = {http://dx.doi.org/10.1007/978-3-642-54140-7_13},
	author = {Cort{\'e}s-Berrueco, LuisEnrique and Gershenson, Carlos and Stephens, ChristopherR.},
	editor = {Elmenreich, Wilfried and Dressler, Falko and Loreto, Vittorio}
}
@inbook {Edmonds:2012,
	title = {Learning, Social Intelligence and the {Turing} Test - why an {\textquoteleft}{\textquoteleft}out-of-the-box" {Turing} Machine will not pass the {Turing} Test.},
	booktitle = {How the world computes : Turing Centenary Conference and 8th Conference on Computability in Europe, CiE 2012, Cambridge, UK, June 18-23, 2012. Proceedings},
	series = {Lecture Notes in Computer Science},
	volume = {7318/2012},
	year = {2012},
	pages = {182{\textendash}192},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Berlin Heidelberg},
	abstract = {The 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{\textquoteright}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 {\textquoteright}compile{\textquoteright} 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.},
	doi = {10.1007/978-3-642-30870-3_18},
	url = {http://arxiv.org/abs/1203.3376},
	author = {Bruce Edmonds and Carlos Gershenson},
	editor = {S. Barry Cooper and Anuj Dawar and Benedikt L{\"o}we}
}
@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}
}
@article {GershensonLenaerts2008,
	title = {Evolution of Complexity},
	journal = {Artificial Life},
	volume = {14},
	number = {3},
	year = {2008},
	note = {Special Issue on the Evolution of Complexity},
	month = {Summer},
	pages = {1{\textendash}3},
	doi = {10.1162/artl.2008.14.3.14300},
	url = {http://dx.doi.org/10.1162/artl.2008.14.3.14300},
	author = {Carlos Gershenson and Tom Lenaerts}
}
@conference {GershensonLenaerts2006,
	title = {Evolution of Complexity: Introduction to the Workshop},
	booktitle = {{ALife X} Workshop Proceedings},
	year = {2006},
	pages = {71{\textendash}72},
	url = {http://uk.arxiv.org/abs/nlin.AO/0604069},
	author = {Carlos Gershenson and Tom Lenaerts}
}
@conference {GonzalezEtAl2001,
	title = {Integration of Computational Techniques for the Modelling of Signal Transduction},
	booktitle = {Advances in Systems Science: Measurement, Circuits and Control},
	year = {2001},
	publisher = {WSES Press},
	organization = {WSES Press},
	abstract = {A cell can be seen as an adaptive autonomous agent or as a society of adaptive autonomous agents, where each can exhibit a particular behaviour depending on its cognitive capabilities. We present an intracellular signalling model obtained by integrating several computational techniques into an agent-based paradigm. Cellulat, the model, takes into account two essential aspects of the intracellular signalling networks: cognitive capacities and a spatial organization. Exemplifying the functionality of the system by modelling the EGFR signalling pathway, we discuss the methodology as well as the purposes of an intracellular signalling virtual laboratory, presently under development.},
	url = {http://uk.arxiv.org/abs/cs.MA/0211030},
	author = {P. P. Gonz{\'a}lez and M. C{\'a}rdenas and C. Gershenson and J. Lagunez},
	editor = {N.E. Mastorakis and L.A. Pecorelli-Peres}
}
@conference {GonzalezEtAl2000b,
	title = {Modelling Intracellular Signalling Networks Using Behaviour-Based Systems and the Blackboard Architecture},
	booktitle = {Proceedings of the International Conference: Mathematics and Computers in Biology and Chemistry {(MCBC} 2000)},
	year = {2000},
	address = {Montego Bay, Jamaica},
	abstract = {This paper proposes to model the intracellular signalling networks using a fusion of behaviour-based systems and the blackboard architecture. In virtue of this fusion, the model developed by us, which has been named Cellulat, allows to take account two essential aspects of the intracellular signalling networks: (1) the cognitive capabilities of certain types of networks{\textquoteright} components and (2) the high level of spatial organization of these networks. A simple example of modelling of Ca2+ signalling pathways using Cellulat is presented here. An intracellular signalling virtual laboratory is being developed from Cellulat.},
	url = {http://uk.arxiv.org/abs/cs.MA/0211029},
	author = {P. P. Gonz{\'a}lez and C. Gershenson and M. C{\'a}rdenas and J. Lagunez}
}
