@unpublished {ComplexityExplained,
	title = {Complexity Explained: A Grassroot Collaborative Initiative to Create a Set of Essential Concepts of Complex Systems.},
	year = {2019},
	note = {https://complexityexplained.github.io},
	abstract = {Complexity science, also called complex systems science, studies how a large collection of components {\textendash} locally interacting with each other at small scales {\textendash} can spontaneously self-organize to exhibit non-trivial global structures and behaviors at larger scales, often without external intervention, central authorities or leaders. The properties of the collection may not be understood or predicted from the full knowledge of its constituents alone. Such a collection is called a complex system and it requires new mathematical frameworks and scientific methodologies for its investigation.},
	doi = {10.17605/OSF.IO/TQGNW},
	url = {https://complexityexplained.github.io},
	author = {Manlio De Domenico and Chico Camargo and Carlos Gershenson and Daniel Goldsmith and Sabine Jeschonnek and Lorren Kay and Stefano Nichele and Jos{\'e} Nicol{\'a}s and Thomas Schmickl and Massimo Stella and Josh Brandoff and {\'A}ngel Jos{\'e} Mart{\'\i}nez Salinas and Hiroki Sayama}
}
@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}
}
@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}
}
@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}
}
@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}
}
@conference {RodriguezEtAl2007,
	title = {Smartocracy: Social Networks for Collective Decision Making},
	booktitle = {Hawaii International Conference on Systems Science (HICSS)},
	year = {2007},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	abstract = {Smartocracy 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{\textquoteright}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.},
	doi = {10.1109/HICSS.2007.484},
	url = {http://tinyurl.com/ybojp8},
	author = {Rodriguez, Marko A. and Steinbock, Daniel J. and Watkins, Jennifer H. and Gershenson, Carlos and Bollen, Johan and Grey, Victor and deGraf, Brad}
}
@conference {GershensonEtAl2003a,
	title = {Contextual Random {Boolean} Networks},
	booktitle = {Advances in Artificial Life, 7th European Conference, {ECAL} 2003 {LNAI} 2801},
	year = {2003},
	pages = {615{\textendash}624},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	abstract = {We propose the use of Deterministic Generalized Asynchronous Random Boolean Networks (Gershenson, 2002) as models of contextual deterministic discrete dynamical systems. We show that changes in the context have drastic effects on the global properties of the same networks, namely the average number of attractors and the average percentage of states in attractors. We introduce the situation where we lack knowledge on the context as a more realistic model for contextual dynamical systems. We notice that this makes the network non-deterministic in a specific way, namely introducing a non-Kolmogorovian quantum-like structure for the modelling of the network (Aerts 1986). In this case, for example, a state of the network has the potentiality (probability) of collapsing into different attractors, depending on the specific form of lack of knowledge on the context.},
	url = {http://uk.arxiv.org/abs/nlin.AO/0303021},
	author = {Carlos Gershenson and Jan Broekaert and Diederik Aerts},
	editor = {Banzhaf, W and T. Christaller and P. Dittrich and J. T. Kim and J. Ziegler}
}
@conference {GershensonHeylighen2003a,
	title = {When Can We Call a System Self-Organizing?},
	booktitle = {Advances in Artificial Life, 7th European Conference, {ECAL} 2003 {LNAI} 2801},
	year = {2003},
	pages = {606{\textendash}614},
	publisher = {Springer},
	organization = {Springer},
	address = {Berlin},
	abstract = {We do not attempt to provide yet another definition of self-organizing systems, nor review previous definitions. We explore the conditions necessary to describe self-organizing systems, inspired on decades of their study, in order to understand them better. These involve the dynamics of the system, and the purpose, boundaries, and description level chosen by an observer. We show how, changing the level or {\textquoteleft}{\textquoteleft}graining{\textquoteright}{\textquoteright} of description, the same system can be self-organizing or not. We also discuss common problems we face when studying self-organizing systems. We analyse when building, designing, and controlling artificial self-organizing systems is useful. We state that self-organization is a way of observing systems, not a class of systems.},
	url = {http://arxiv.org/abs/nlin.AO/0303020},
	author = {Carlos Gershenson and Francis Heylighen},
	editor = {Banzhaf, W and T. Christaller and P. Dittrich and J. T. Kim and J. Ziegler}
}
@conference {Gershenson2002c,
	title = {Behaviour-Based Knowledge Systems: An Epigenetic Path from Behaviour to Knowledge},
	booktitle = {Proceedings of the 2nd Workshop on Epigenetic Robotics},
	volume = {94},
	year = {2002},
	pages = {35{\textendash}41},
	publisher = {Lund University Cognitive Studies},
	organization = {Lund University Cognitive Studies},
	address = {Edinburgh, Scotland},
	abstract = {In this paper we expose the theoretical background underlying our current research. This consists in the development of behaviour-based knowledge systems, for closing the gaps between behaviour-based and knowledge-based systems, and also between the understandings of the phenomena they model. We expose the requirements and stages for developing behaviour-based knowledge systems and discuss their limits. We believe that these are necessary conditions for the development of higher order cognitive capacities, in artificial and natural cognitive systems.},
	url = {http://www.lucs.lu.se/ftp/pub/LUCS\%5FStudies/LUCS94/Gershenson.pdf},
	author = {Carlos Gershenson},
	editor = {Christopher G. Prince and Yiannis Demiris and Yuval Marom and Hideki Kozima and Christian Balkenius}
}
@conference {DasEtAl2002,
	title = {Neural Net Model for Featured Word Extraction},
	booktitle = {InterJournal of Complex Systems},
	number = {539},
	year = {2002},
	abstract = {Search engines perform the task of retrieving information related to the user-supplied query words. This task has two parts; one is finding {\textquoteright}featured words{\textquoteright} which describe an article best and the other is finding a match among these words to user-defined search terms. There are two main independent approaches to achieve this task. The first one, using the concepts of semantics, has been implemented partially. For more details see another paper of Marko et al., 2002. The second approach is reported in this paper. It is a theoretical model based on using Neural Network (NN). Instead of using keywords or reading from the first few lines from papers/articles, the present model gives emphasis on extracting {\textquoteright}featured words{\textquoteright} from an article. Obviously we propose to exclude prepositions, articles and so on, that is , English words like "of, the, are, so, therefore, " etc. from such a list. A neural model is taken with its nodes pre-assigned energies. Whenever a match is found with featured words and user-defined search words, the node is fired and jumps to a higher energy. This firing continues until the model attains a steady energy level and total energy is now calculated. Clearly, higher match will generate higher energy; so on the basis of total energy, a ranking is done to the article indicating degree of relevance to the user{\textquoteright}s interest. Another important feature of the proposed model is incorporating a semantic module to refine the search words; like finding association among search words, etc. In this manner, information retrieval can be improved markedly.},
	url = {http://uk.arxiv.org/abs/cs.NE/0206001},
	author = {Atin Das and M. Marko and A. Probst and M. A. Porter and C. Gershenson}
}
@article {MarkoEtAl2002,
	title = {Transforming the World Wide Web Into a Complexity-Based Semantic Network},
	journal = {InterJournal of Complex Systems},
	number = {588},
	year = {2002},
	url = {http://uk.arxiv.org/abs/cs.NI/0205080},
	author = {Matus Marko and M. A. Porter and A. Probst and C. Gershenson and A. Das}
}
