Complex Insight - Understanding our world

Good column by Bradley Voytek at O'Reilly Radar on the reality behind for Big Data stories. In the article he breaks down the importance of deep data analysis: what can be learnt from the statistical outliers and asking, "What's different here?  If you want to understand some of the Big Data techniques and good data analysis practises - worth reading.

This paper illustrates the use of the nonparametric Wald-Wolfowitz test to detect stationarity and ergodicity in agent-based models. A nonparametric test is needed due to the practical impossibility to understand how the random component influences the emergent properties of the model in many agent-based models. Nonparametric tests on real data often lack power and this problem is addressed by applying the Wald-Wolfowitz test to the simulated data. The performance of the tests is evaluated using Monte Carlo simulations of a stochastic process with known properties. It is shown that with appropriate settings the tests can detect non-stationarity and non-ergodicity. Knowing whether a model is ergodic and stationary is essential in order to understand its behavior and the real system it is intended to represent; quantitative analysis of the artificial data helps to acquire such knowledge.