24th – 26th May 2016 – Barcelona
SimulPast Workshop. The last decade saw a rapid growth of quantitative and computational methods apt to analyse long-term cultural and biological processes. In particular, the wide diffusion of agent-based simulation platforms and the enhanced accessibility of computer-intensive statistical analyses are offering the possibility to replace explanations based on natural language with formal models. Read more…
26th May 10:35-11:10 EPNet present “Identification of self-organized systems using archaeological proxies” by Xavier Rubio-Campillo – Barcelona Supercomputing Centre
The finding of a power-law distribution in a dataset is typically associated with self-organized criticality. Complexity theory argues that a power-law can be used as the proxy of a system whose evolution can be explained exclusively by internal dynamics. This strong link between empirical datasets and the evolutionary dynamics that caused them explains why much work has been focused on detecting power-law distributions. In particular, several contemporary social phenomena such as city areas, company sizes or income distributions appear to display this distinctive signal.
Despite this ubiquity, it is difficult to detect complete power-laws in archaeological datasets. A possible explanation would be that the generation of power-laws in artefact frequencies requires a critical mass beyond the population densities observed during most of human history. An alternate hypothesis is that they are not present in the archaeological record due to taphonomic processes such as survival bias. The combination of both issues makes identifying self-organization in the past a challenge for traditional statistical methods.
Here we argue that Bayesian model selection would be able to tackle this challenge by evaluating if a power-law distribution is a better match to the empirical record than competing hypotheses. We exemplify the approach by analysing the structure of the market that supplied olive oil to the city of Rome during the Empire.