OLD QUESTIONS, NEW TOOLBOX:
UNDERSTANDING COMPLEXITY AND COLLAPSE WITH BIG DATA AND COMPUTATIONAL MODELS
Over the past 10,000 years, human societies have evolved from small-scale egalitarian groups to complex, large-scale societies characterized by great differentials in wealth and power, extensive division of labor, and elaborate governance structures. There are many theories attempting to explain this major evolutionary transition. Which are correct?
Another challenge for researchers is that social complexity has not increased in a steady, gradual fashion. Instead, complex human societies—including our own—periodically experience social dysfunction and turbulence, which often results in state breakdown, political fragmentation, simplification of the economy, population declines, and loss of institutions and other accumulated cultural knowledge. Again, past thinkers and modern social scientists have put forward myriad theories explaining social collapse, but these theories have not yet been systematically tested with massive amounts of data.
At the Hub we are using a massive accumulation of empirical historical data to test theoretical predictions about the evolution of social complexity and the causes of collapse. We translate various theories, proposed to explain social complexity and collapse, into mathematical models, and build large databases to empirically test model predictions. For example, we quantify the importance of various structural pressures underlying societal breakdown: popular immiseration, growing inequality, intra-elite competition and conflict, state fiscal distress, unraveling of social norms, external military pressure, and environmental factors such as droughts and pandemics. Our aim is to merge the computational and statistical methods of complexity science with the new discipline of Cultural Evolution and more traditional theories in social sciences, while fully deploying data on past societies from history and archaeology.