@inbook {Fernandez2013Information-Mea,
	title = {Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis},
	booktitle = {Guided Self-Organization: Inception},
	year = {2014},
	note = {In Press},
	pages = {19-51},
	publisher = {Springer},
	organization = {Springer},
	abstract = {<p>This chapter reviews measures of emergence, self-organization, complexity, homeostasis, and autopoiesis based on information theory. These measures are derived from proposed axioms and tested in two case studies: random Boolean networks and an Arctic lake ecosystem. Emergence is defined as the information produced by a system or process. Self-organization is defined as the opposite of emergence, while complexity is defined as the balance between emergence and self-organization. Homeostasis reflects the stability of a system. Autopoiesis is defined as the ratio between the complexity of a system and the complexity of its environment. The proposed measures can be applied at different scales, which can be studied with multi-scale profiles.</p>
},
	url = {http://arxiv.org/abs/1304.1842},
	author = {Nelson Fern{\'a}ndez and Carlos Maldonado and Carlos Gershenson},
	editor = {Mikhail Prokopenko}
}
@article {GershensonProkopenko:2011,
	title = {Complex Networks},
	journal = {Artificial Life},
	volume = {17},
	number = {4},
	year = {2011},
	month = {Fall},
	pages = {259{\textendash}261},
	publisher = {MIT Press},
	abstract = {Introduction to the Special Issue on Complex Networks, Artificial Life journal.},
	doi = {10.1162/artl_e_00037},
	url = {http://arxiv.org/abs/1104.5538},
	author = {Carlos Gershenson and Mikhail Prokopenko}
}
@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}
}
