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dc.contributor.authorGarrido Merchán, Eduardo Césares-ES
dc.contributor.authorSánchez Cañizares, Javieres-ES
dc.date.accessioned2025-03-20T15:47:41Z
dc.date.available2025-03-20T15:47:41Z
dc.date.issued2024-12-01es_ES
dc.identifier.issn2153-8212es_ES
dc.identifier.urihttp://hdl.handle.net/11531/98159
dc.descriptionArtículos en revistases_ES
dc.description.abstract.es-ES
dc.description.abstractntegrated information theory (IIT) is a theoretical framework that provides a quantitative measure to estimate when a physical system is conscious, its degree of consciousness, and the complexity of the qualia space that the system is experiencing. Formally, IIT rests on the assumption that if a surrogate physical system can fully embed the phenomenological properties of consciousness, then the system properties must be constrained by the properties of the qualia being experienced. Following this assumption, IIT represents the physical system as a network of interconnected elements that can be thought of as a probabilistic causal graph, G, where each node has an input-output function and all the graph is encoded in a transition probability matrix. Consequently, IIT’s quantitative measure of consciousness, Φ, is computed with respect to the transition probability matrix and the present state of the graph. In this paper, we provide a random search algorithm that is able to optimize Φ in order to investigate, as the number of nodes increases, the structure of the graphs that have higher Φ. We also provide arguments that show the difficulties of applying more complex black-box search algorithms, such as Bayesian optimization or metaheuristics, in this particular problem. Additionally, we suggest specific research lines for these techniques to enhance the search algorithm that guarantees maximalen-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoes-ESes_ES
dc.rightses_ES
dc.rights.uries_ES
dc.sourceRevista: Journal of Consciousness Exploration & Research, Periodo: 1, Volumen: 15, Número: 3, Página inicial: 240, Página final: 254es_ES
dc.subject.otherInnovación docente y Analytics (GIIDA)es_ES
dc.titleOptimizing Integrated Information with a Prior Guided Random Search Algorithmes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.holderpolitica editoriales_ES
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses_ES
dc.keywords.es-ES
dc.keywordsConsciousness Qualia Probabilistic causal graph Optimizationen-GB


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