Keywords: storytelling, b-privy, quantitative horizon scanning, technology surprise, literature-based discovery
If one believes that future breakthroughs will stem from the fusion of existing knowledge from disparate fields, then the next natural step is to attempt to mitigate technology surprise (MTS) through quantitative horizon scanning (QHS). The advancement of technology cannot be stopped but being surprised by new technologies can be deterred by systematically exploring the scientific literature. A methodology exists for QHS for MTS, b-privy builds upon two guiding principles (1) technological surprise often involves the fusion of existing ideas, and (2) science is a social effort, where in this case the social aspect is co-authorship of scientific papers. One output of b-privy analytics shows where one country has a statistically significant likelihood of merging information from two disparate fields over another country. The higher likelihood stems from a particular country having a more tightly linked co-authorship network over another country.
Automated storytelling attempts to explain the relationship of a set of bookend documents by finding chains of pertinent documents to explain the relationship of a beginning document to an ending document. The story should flow well from beginning to end highlighting useful tidbits of information and should be coherent, i.e. provide the user with enough information to understand why the next article was chosen but jump far enough that the chain is concise in length.
Once b-privy analytics has deemed a set of clusters to be interesting based on an increased likelihood that one country can surprise another country by merging the information of two clusters into a new field of research, maybe automated storytelling could provide a chain of documents to explain to the analyst how such a surprise might actually come into existence. This presentation explores the use of automated storytelling as a methodology to yield plausible explanations for MTS through QHS.