When big data fails: Adaptive agents using coarse-grained information have competitive advantage.

Abstract

The recent trend for acquiring big data assumes that possessing quantitatively more and qualitatively finer data necessarily provides an advantage that may be critical in competitive situations. Using a model complex adaptive system where agents compete for a limited resource using information coarse grained to different levels, we show that agents having access to more and better data perform worse than others in certain situations. The relation between information asymmetry and individual payoffs is seen to be complex, depending on the composition of the population of competing agents.