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proyectos:tfg:bibliografia:albantakis2017 [2017/10/27 09:01] 127.0.0.1 editor externo |
proyectos:tfg:bibliografia:albantakis2017 [2017/11/15 09:48] (actual) Joaquín Herrero Pintado |
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- | ====== Albantakis, L. Automata and Animats: From Dynamics to Cause–Effect Structures ====== | + | ====== Albantakis, L. Automata and Animats: From Dynamics to Cause–Effect Structures (2017) ====== |
en Imari Walker, S. (ed), 2017, From Matter to Life – Information and Causality | en Imari Walker, S. (ed), 2017, From Matter to Life – Information and Causality | ||
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+ | LARISSA ALBANTAKIS is a postdoctoral researcher with Giulio Tononi at the | ||
+ | Department of Psychiatry at University of Wisconsin–Madison. She | ||
+ | received her degree in physics with distinction at the LudwigMaximilians | ||
+ | Universitt, Munich, followed by a Ph.D. in computational | ||
+ | neuroscience at the Universitat Pompeu Fabra, Barcelona, under the | ||
+ | supervision of Gustavo Deco. Her research focuses on the theoretical | ||
+ | formulation of the integrated information theory of consciousness and its | ||
+ | implications for evolutionary adaptation, emergence, and meaning. | ||
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+ | In Chapter 14 by Albantakis and | ||
+ | Tononi, who consider the distinction between ‘being’ and ‘happening’, | ||
+ | utilising cellular automata (CA) as a case study. Most prior work on | ||
+ | dynamical systems, including CA, focuses on what is ‘happening’ – the | ||
+ | dynamical trajectory of the system through its state space – that is, they take | ||
+ | an extrinsic perspective on what is observed. Often, complexity is | ||
+ | characterised using statistical methods and information theory. In a shift of | ||
+ | focus to that of causal architecture, Albantakis and Tononi consider what the | ||
+ | system ‘is’ from its own intrinsic perspective, utilising the machinery of | ||
+ | integrated information theory (IIT), and demonstrate that intrinsic (causal) | ||
+ | complexity (as quantified by integrated information Φ in IIT) correlates well | ||
+ | with dynamical (statistical) complexity in the examples discussed. These and | ||
+ | similar approaches could provide a path forward for a deeper understanding of the connection between causation and information as hinted at in the | ||
+ | beginning of this chapter. | ||
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