How has a story as complex as "Game of Thrones" enthralled the world?
The unravelling of "A Song of Ice and Fire," the books on which the TV series is based.
A research paper has just been published in the Proceedings of the National Academy of Sciences by a team of physicists, mathematicians and psychologists from Coventry, Warwick, Limerick, Cambridge and Oxford universities have used data science and network theory to analyse the acclaimed book series by George R.R. Martin.
The characters are arranged is similar to how humans maintain relationships and interact in the real world. Moreover, although important characters are famously killed off at random as the story is told, the underlying chronology is not at all so unpredictable.
Interactions between characters, average to have only 150 others to keep track of. This is the same number that the average human brain has evolved to deal with.
The chronological sequence is reconstructed the deaths are not random: rather, they reflect how common events are spread out for non-violent human activities in the real world.
'Game of Thrones' has invited all sorts of comparison to history and myth and the marriage of science and humanities. For example, that it is more akin to the Icelandic sagas than to mythological stories such as England's Beowulf or Ireland's Táin Bó Cúailnge. The secret in Game of Thrones, it seems, is to mix realism and unpredictability in a cognitively engaging manner.
Professor Robin Dunbar, from the University of Oxford, observed: "This study offers convincing evidence that good writers work very carefully within the psychological limits of the reader."
Dr Pádraig MacCarron, from University of Limerick commented: "These books are known for unexpected twists, often in terms of the death of a major character, it is interesting to see how the author arranges the chapters in an order that makes this appear even more random than it would be if told chronologically."
Dr Joseph Yose, from Coventry University said: "I am excited to see the use of network analysis grow in the future, and hopefully, combined with machine learning, we will be able to predict what an upcoming series may look like."
A research paper has just been published in the Proceedings of the National Academy of Sciences by a team of physicists, mathematicians and psychologists from Coventry, Warwick, Limerick, Cambridge and Oxford universities have used data science and network theory to analyse the acclaimed book series by George R.R. Martin.
The characters are arranged is similar to how humans maintain relationships and interact in the real world. Moreover, although important characters are famously killed off at random as the story is told, the underlying chronology is not at all so unpredictable.
Interactions between characters, average to have only 150 others to keep track of. This is the same number that the average human brain has evolved to deal with.
The chronological sequence is reconstructed the deaths are not random: rather, they reflect how common events are spread out for non-violent human activities in the real world.
'Game of Thrones' has invited all sorts of comparison to history and myth and the marriage of science and humanities. For example, that it is more akin to the Icelandic sagas than to mythological stories such as England's Beowulf or Ireland's Táin Bó Cúailnge. The secret in Game of Thrones, it seems, is to mix realism and unpredictability in a cognitively engaging manner.
Professor Robin Dunbar, from the University of Oxford, observed: "This study offers convincing evidence that good writers work very carefully within the psychological limits of the reader."
Dr Pádraig MacCarron, from University of Limerick commented: "These books are known for unexpected twists, often in terms of the death of a major character, it is interesting to see how the author arranges the chapters in an order that makes this appear even more random than it would be if told chronologically."
Dr Joseph Yose, from Coventry University said: "I am excited to see the use of network analysis grow in the future, and hopefully, combined with machine learning, we will be able to predict what an upcoming series may look like."