Transcendental Information Cascade
A Transcendental Information Cascade is a phenomenon described in complex systems, network theory and philosophy in which any system is looked at as a sequence of events that can be captured as information tokens without any assumption of causality.[1] The phenomenon can therefore be assumed to be acausal in principle. That does not mean it's meaningless or random, but that for the particular notion of meaning under consideration, causality is not a required assumption.
The approach has been applied to Wikipedia edit logs to find hidden temporal link structures,[2] to the Zooniverse citizen science platform to capture the emergent coordination of contributing volunteers,[3] to historic literature such as the books of Charles Dickens and Jane Austen to map the evolution of character interactions in literary works and to understand how human personality was constructed by different authors,[4][5] and most recently to genomic data from the COVID-19 outbreak to uncover global patterns of viral spread.[6]
Transcendental information cascades have been introduced as a new method that sits alongside established network approaches to complex systems such as recurrence networks or horizontal visibility graphs,[7][8] and should not be confused with the phenomenon of information cascades, which is studied in behavioural economics and social network analysis, for example, to understand how behaviour or information diffuses through an existing network.
Basic approach[edit]
The basic approach to construct a transcendental information cascade from source data works as follows:
- Select (one or more) method(s) to tokenise the source data sequence.
- Consider every element of the source data sequence a vertex in a network (vertices are chronologically ordered according to the order of the source data sequence).
- Extract information tokens from source data sequence using the selected tokenisation method(s).
- Store the tokens extracted from the elements of the source data sequence as the token sets of their respective vertices.
- Create an edge between any two vertices if and only if they share a common token as part of their token sets that does not occur at any sequence step between the two vertices.
References[edit]
- ↑ Luczak-Roesch, Markus; O'Hara, Kieron; Dinneen, Jesse D.; Tinati, Ramine (September 2018). "What an entangled Web we weave: An information-centric approach to time-evolving socio-technical systems". Minds & Machines. 28 (4): 709–733. doi:10.1007/s11023-018-9478-1.
- ↑ Tinati, R.; Luczak-Roesch, M.; Hall, W. (May 2015). "Finding Structure in Wikipedia Edit Activity: An Information Cascade Approach". doi:10.1145/2872518.2891110.
- ↑ Luczak-Roesch, M.; Tinati, R.; Shadbolt, N. (May 2015). "When Resources Collide: Towards a Theory of Coincidence in Information Spaces". doi:10.1145/2740908.2743973.
- ↑ Luczak-Roesch, M.; Grener, A.; Fenton, E. (28 February 2018). "Not-so-distant reading: A dynamic network approach to literature". it - Information Technology. 60 (1). doi:10.1515/itit-2017-0023.
- ↑ Fischer, R.; Karl, J.; Luczak-Roesch, M.; Fetvadjiev, V.; Grener, A. (June 2020). "Tracing Personality Structure in Narratives: A Computational Bottom‐Up Approach to Unpack Writers, Characters, and Personality in Historical Context". European Journal of Personality. doi:10.1002/per.2270.
- ↑ Luczak-Roesch, Markus (17 February 2020). "Networks of information token recurrences derived from genomic sequences may reveal hidden patterns in epidemic outbreaks: A case study of the 2019-nCoV coronavirus". medrxiv.org. doi:10.1101/2020.02.07.20021139. Unknown parameter
|url-status=ignored (help) - ↑ Donner, R.; Zou, Y.; Donges, J.; Marwan, N.; Kurths, J. (March 2010). "Recurrence networks—a novel paradigm for nonlinear time series analysis". New Journal of Physics. 12. doi:10.1088/1367-2630/12/3/033025.
- ↑ Nuñez, A.; Lacasa, L.; Valero, E.; Gómez, J. P.; Luque, B. (March 2011). "Detecting series periodicity with horizontal visibility graphs". International Journal of Bifurcation and Chaos. 22 (7). doi:10.1142/S021812741250160X.
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