Quantifying synergistic information
Data(s) |
2014
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Resumo |
Within the microcosm of information theory, I explore what it means for a system to be functionally irreducible. This is operationalized as quantifying the extent to which cooperative or “synergistic” effects enable random variables X<sub>1</sub>, ... , X<sub>n</sub> to predict (have mutual information about) a single target random variable Y . In Chapter 1, we introduce the problem with some emblematic examples. In Chapter 2, we show how six different measures from the existing literature fail to quantify this notion of synergistic mutual information. In Chapter 3 we take a step towards a measure of synergy which yields the first nontrivial lowerbound on synergistic mutual information. In Chapter 4, we find that synergy is but the weakest notion of a broader concept of irreducibility. In Chapter 5, we apply our results from Chapters 3 and 4 towards grounding Giulio Tononi’s ambitious φ measure which attempts to quantify the magnitude of consciousness experience. |
Formato |
application/pdf application/pdf |
Identificador |
http://thesis.library.caltech.edu/8041/16/griffith_virgil_2014_thesis.pdf http://thesis.library.caltech.edu/8041/22/262705_pdf_E9E63120-8AC8-11E3-8615-FB1EEF8616FA.pdf Griffith, Virgil (2014) Quantifying synergistic information. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:12132013-161604752 <http://resolver.caltech.edu/CaltechTHESIS:12132013-161604752> |
Relação |
http://resolver.caltech.edu/CaltechTHESIS:12132013-161604752 http://thesis.library.caltech.edu/8041/ |
Tipo |
Thesis NonPeerReviewed |