Historians are turning to data science to mine the vast digital records of the past and potentially predict future events. This involves using machine learning algorithms to analyse historical data sets, a practice known as ‘cliodynamics.’
One of the pioneers of this field, Peter Turchin, uses it to study the rise and fall of societies. His models, based on data from ancient civilisations, have predicted political instability and social upheaval in the modern world. Despite criticism from traditional historians, Turchin argues that cliodynamics provides a scientific approach to understanding societal change.
The potential of this discipline extends beyond academia. Policymakers and businesses are showing interest in using these predictive models. For instance, they could help in anticipating economic trends or social unrest.
However, cliodynamics also raises ethical issues. The use of personal data for such analyses is controversial, and there is a risk of historical events being oversimplified or misinterpreted. Additionally, the predictive power of these models is still uncertain, and their accuracy in forecasting future events remains to be proven.
Despite these challenges, the intersection of history and data science is an emerging field that promises to provide new insights into the patterns of human behaviour over time.
Go to source article: https://www.theguardian.com/technology/2019/nov/12/history-as-a-giant-data-set-how-analysing-the-past-could-help-save-the-future