@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 = {

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.

}, 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} }