@article {Gershenson:2013,
	title = {Living in Living Cities},
	journal = {Artificial Life},
	volume = {In Press},
	year = {2013},
	abstract = {<p>This paper presents and overview of current and potential applications of living technology to urban problems. Living technology can be described as technology that exhibits the core features of living systems. These features can be useful to solve dynamic problems. In particular, urban problems concerning mobility, logistics, telecommunications, governance, safety, sustainability, and society and culture are presented, while solutions involving living technology are reviewed. A methodology for developing living technology is mentioned, while self-organizing traffic lights are used as a case study of the benefits of urban living technology. Finally, the usefulness of describing cities as living systems is discussed.</p>
},
	keywords = {cities, Self-organization, traffic, transport},
	url = {http://arxiv.org/abs/1111.3659},
	author = {Carlos Gershenson}
}
@article {Farnsworth2013Living-is-Infor,
	title = {Living is Information Processing: From Molecules to Global Systems},
	journal = {Acta Biotheoretica},
	volume = {61},
	number = {2},
	year = {2013},
	month = {June},
	pages = {203-222},
	abstract = {We extend the concept that life is an informational phenomenon, at every level of organisation, from molecules to the global ecological system. According to this thesis: (a) living is information processing, in which memory is maintained by both molecular states and ecological states as well as the more obvious nucleic acid coding; (b) this information processing has one overall function{\textendash}-to perpetuate itself; and (c) the processing method is filtration (cognition) of, and synthesis of, information at lower levels to appear at higher levels in complex systems (emergence). We show how information patterns, are united by the creation of mutual context, generating persistent consequences, to result in {\textquoteleft}functional information{\textquoteright}. This constructive process forms arbitrarily large complexes of information, the combined effects of which include the functions of life. Molecules and simple organisms have already been measured in terms of functional information content; we show how quantification may be extended to each level of organisation up to the ecological. In terms of a computer analogy, life is both the data and the program and its biochemical structure is the way the information is embodied. This idea supports the seamless integration of life at all scales with the physical universe. The innovation reported here is essentially to integrate these ideas, basing information on the {\textquoteleft}general definition{\textquoteright} of information, rather than simply the statistics of information, thereby explaining how functional information operates throughout life.},
	doi = {10.1007/s10441-013-9179-3},
	url = {http://arxiv.org/abs/1210.5908},
	author = {Farnsworth, Keith D. and Nelson, John and Gershenson, Carlos}
}
@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 {Zenil:2012,
	title = {Life as Thermodynamic Evidence of Algorithmic Structure in Natural Environments},
	journal = {Entropy},
	volume = {14},
	number = {11},
	year = {2012},
	pages = {2173{\textendash}2191},
	abstract = {In evolutionary biology, attention to the relationship between stochastic organisms and their stochastic environments has leaned towards the adaptability and learning capabilities of the organisms rather than toward the properties of the environment. This article is devoted to the algorithmic aspects of the environment and its interaction with living organisms. We ask whether one may use the fact of the existence of life to establish how far nature is removed from algorithmic randomness. The paper uses a novel approach to behavioral evolutionary questions, using tools drawn from information theory, algorithmic complexity and the thermodynamics of computation to support an intuitive assumption about the near optimal structure of a physical environment that would prove conducive to the evolution and survival of organisms, and sketches the potential of these tools, at present alien to biology, that could be used in the future to address different and deeper questions. We contribute to the discussion of the algorithmic structure of natural environments and provide statistical and computational arguments for the intuitive claim that living systems would not be able to survive in completely unpredictable environments, even if adaptable and equipped with storage and learning capabilities by natural selection (brain memory or DNA).},
	issn = {1099-4300},
	doi = {10.3390/e14112173},
	url = {http://www.mdpi.com/1099-4300/14/11/2173},
	author = {Zenil, Hector and Gershenson, Carlos and Marshall, James A. R. and Rosenblueth, David A.}
}
@article {doi:10.1162/artl.2007.13.1.91,
	title = {Live Evolving: Molecules, Mind, and Meaning. Christian De Duve. (2003, Oxford University Press.) Hardback, {\textsterling}25, $39. 358 pages},
	journal = {Artificial Life},
	volume = {13},
	number = {1},
	year = {2007},
	pages = {91-92},
	doi = {10.1162/artl.2007.13.1.91},
	url = {http://www.mitpressjournals.org/doi/abs/10.1162/artl.2007.13.1.91},
	author = {Gershenson, Carlos}
}
@conference {Gershenson1998a,
	title = {L{\'o}gica Multidimensional: Un Modelo de L{\'o}gica Paraconsistente},
	booktitle = {Memorias {XI} Congreso Nacional {ANIEI}},
	year = {1998},
	pages = {132{\textendash}141},
	address = {Xalapa, M{\'e}xico},
	abstract = {La l{\'o}gica multidimensional es un nuevo sistema de l{\'o}gica propuesto para modelar l{\'o}gica paraconsistente. Una breve definici{\'o}n de l{\'o}gica paraconsistente y ejemplos de cuando es usada son dados. Se definen los principios y propiedades de la l{\'o}gica multidimensional, tales como las variables l{\'o}gicas multidimensionales. Los operadores l{\'o}gicos Y, O, NO, SI... ENTONCES y SI Y S{\'O}LO SI son definidos y explicados para la l{\'o}gica multidimensional. Adem{\'a}s, se definen equivalencia, grado de contradicci{\'o}n, y la proyecci{\'o}n de la l{\'o}gica multidimensional en la difusa. Esto incluye un peque{\~n}o programa que},
	url = {http://tinyurl.com/y9hb4e},
	author = {Carlos Gershenson}
}
