The Data Science of Play: from String Theory, Lyric Poetry, and Fan Fiction to Wikipedia and the French Revolution
Speaker: Simon DeDeo (Carnegie Mellon University and Santa Fe Institute, USA)
Date: 03 August 2017
Time: 16:00 17:00
Venue: The Alan Turing Institute
Email: Turing Events to register your place.
Some of our most meaningful activities are conducted in the absence of externally-imposed goals. We believe that releasing investigators from the need to solve immediate problems will drive long-term scientific and cultural evolution through the creation of unexpected ideas, new needs to satisfy, and further questions to answer. Yet we lack a common language to discuss these activities, which cover a vast range of timescales and population sizes, from the speculative after-dinner walk with friends to the global scientific Enlightenment. This makes it hard to see the similarities that tie these processes together, the commonalities between the problems they face, or the ways in which we might intervene to assist their flourishing. To help remedy this, I present a new framework for the quantitative study of play, and apply it to an analysis of twenty thousand papers in high-energy physics from the arXiv preprint server. I compare the results of this analysis to a second population-level study, of eighty years of poetry from a major American poetry magazine, and to a large-scale database of “fan fiction”, works written by non-elites that extend fictional worlds such as Star Trek, Harry Potter, and Sherlock Holmes in unusual ways. At the end of the talk, I draw on recent research into conflict patterns on Wikipedia and a “big history” study of the French Revolution to show how playful innovation can be both suppressed and enhanced by conflict. New information-theoretic methods allow us to see how these different activities confront, and solve, the challenges of perpetual innovation. And they show us how these forms of play lie on a continuum with, and can help inform, more directed and incentivized tasks designed to attack a well-defined problem.