September 30, 2014 weblog

# Mathematical model tackles 'Game of Thrones' predictions

Take events from the past, build a statistical model, and tell the future. Why not apply the formula to novels? Can contents in future books be predicted based only on data from existing ones? Richard Vale at the University of Canterbury in New Zealand, said The Physics arXiv Blog, has taken on the challenge in predicting content of as yet unpublished novels in the "A Song of Ice and Fire" series by George R R Martin. The novels are the basis of the television series, "Game of Thrones." The series has five books and two more are awaited. Before proceeding, it should be emphasized that the paper comes with a *spoiler alert*, so avoid linking to Vale's study if you have not read the first five books.

As The Physics arXiv Blog explained, "Each chapter in the existing books is told from the point of view of one of the characters. So far, 24 characters have starred in this way. The approach that Vale has taken is to use the distribution of characters in chapters in the first five books to predict the distribution in the forthcoming novels."

After creating a model, Vale runs a computer program to find the parameters in the model that best fit the data. He uses the model to find the probability distributions of the number of chapters that each character will star in, in book 6 and book 7. What kinds of predictions result? They include predictions about certain characters unlikely to star in any chapters and if one particular character is likely to be dead. Vale's paper submitted to arXiv is "Bayesian Prediction for *The Winds of Winter*." As Vale described it, "Predictions are made for the number of chapters told from the point of view of each character in the next two novels in George R. R. Martin's emph{A Song of Ice and Fire} series by fitting a random effects model to a matrix of point-of-view chapters in the earlier novels using Bayesian methods." There is also a "Spoiler Warning" that readers who have not read all five existing novels in the series should not read further "as major plot points will be spoiled."

The blog commented on how this is a "fascinating exercise in statistical modeling that will do more to introduce the process to a wider range of people than any number of textbooks or Wikipedia entries."

Vale is a Lecturer in the Statistics Department at the University of Canterbury. He has a PhD in mathematics from the University of Glasgow and was an HC Wang Assistant Professor at Cornell University. Vale acknowledged several shortcomings in his model— such as not dealing with the possibility of new characters being introduced and a model resting on a relatively small amount of data. Robin Kawakami of The Wall Street Journal, writing in the Speakeasy blog, quoted Vale saying "Game of Thrones" cannot be predicted using statistics alone. He said his project can be viewed as "fun data analysis." He said in an e-mail to Speakeasy, just as many people make fan art by drawing favorite characters from books, "As a mathematician/statistician, this mathematical model is my version of fan art."

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**More information:**Bayesian Prediction for The Winds of Winter, arXiv:1409.5830 [stat.AP] http://arxiv.org/abs/1409.5830 (PDF)

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**Citation**: Mathematical model tackles 'Game of Thrones' predictions (2014, September 30) retrieved 20 September 2019 from https://phys.org/news/2014-09-mathematical-tackles-game-thrones.html

Maybe 2/3 the score might be based on a computer algorithm which counts each interaction between the two characters, and each character's positive, negative, or neutral mention of the other character, and so forth.

About 1/3 of the score would come from the reader interpreting "intangibles" such as false leads, present and past persistent injuries, and so forth, and rating the interaction from -5 to 5.

After finding these values for all characters, the algorithm would total all positive and negative mentions and positive and negative conditions of a character and assign a probability that they have been killed or mortally wounded.