@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}
}
@article {Escobar2019,
	title = {Effects of Antimodularity and Multiscale Influence in Random Boolean Networks},
	journal = {Complexity},
	volume = {2019},
	year = {2019},
	pages = {14},
	type = {10.1155/2019/8209146},
	url = {https://doi.org/10.1155/2019/8209146},
	author = {Escobar, Luis A. and Kim, Hyobin and Gershenson, Carlos}
}
@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 {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}
}
@book {GershensonEtAl-PnC,
	title = {Philosophy and Complexity},
	series = {Worldviews, Science and Us},
	year = {2007},
	publisher = {World Scientific},
	organization = {World Scientific},
	address = {Singapore},
	abstract = {Scientific, 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.},
	url = {http://www.worldscibooks.com/chaos/6372.html},
	editor = {Carlos Gershenson and Diederik Aerts and Bruce Edmonds}
}
