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In this talk I introduce Transcendental Information Cascades, a method to understand the temporal dynamics of naturally occurring complex systems through the lens of a specific kind of spatio-temporal network that represents information token recurrence. The method suits very well for exploratory data analysis when it is unknown what is going on at the very low levels of a system's dynamics, and allows switching between looking at individual events or accumulated event sequences. I will present one application of this method in a digital humanities project where we analysed the entire corpus of Charles Dickens's novels and discuss the potential it has for other application areas such as the analysis of brain wave recordings, patent mining or the study of online communities for example. These are the slides of a talk at the RWTH Aachen University, Chair for Computational Social Sciences and Humanities (Germany, date of the talk 06-03-2018) {"references": ["Popper, K., 2013. Knowledge and the Body-Mind Problem: In defence of interaction. Routledge.", "Berners-Lee, Tim; Mark Fischetti (1999). Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by its inventor. Britain: Orion Business. ISBN 0-7528-2090-7.", "Luczak-Roesch, M., Tinati, R., Simperl, E., Van Kleek, M., Shadbolt, N., & Simpson, R. (2014). Why won't aliens talk to us? Content and community dynamics in online citizen science. Proceedings of the Eighth AAAI Conference on Weblogs and Social Media, {ICWSM} 2014, Ann Arbor, Michigan, USA, June 1-4, 2014.", "Bikhchandani, Sushil, David Hirshleifer, and Ivo Welch. \"A theory of fads, fashion, custom, and cultural change as informational cascades.\" Journal of political Economy (1992): 992-1026.", "Cheng, Justin, et al. \"Can cascades be predicted?.\" Proceedings of the 23rd international conference on World wide web. International World Wide Web Conferences Steering Committee, 2014.", "Markus Luczak-Roesch, Ramine Tinati, and Nigel Shadbolt. 2015. When Resources Collide: Towards ...

AbstractResearch on the gap between science and policymaking and the barriers to utilizing science in policy remains controversial, since previous research methods have dominated data collection with perceived participant responses. This approach makes research findings applicable to particular contexts, particular participants, and particular times but cannot be generalized. We used a computational model to analyse linguistic text data from two communities and compared the results with self-reported research findings. The outcomes support that scientists and policymakers have their own linguistic characteristics, with scientists in the medical field preferring jargon and policymakers' language containing public satisfaction pressures. Language representation also validates the existence of barriers in the use of science.