When Big Data Fails! Relative success of adaptive agents using coarse-grained information to compete for limited resources

TL;DR

This paper demonstrates that in complex adaptive systems, having more or finer data does not always lead to better performance, as the effectiveness of information depends on the population dynamics and resource competition.

Contribution

It reveals the nuanced relationship between data granularity and agent success, challenging the assumption that more data always confers an advantage.

Findings

More data can lead to worse performance in some scenarios.

Performance depends on population composition and resource competition.

Information asymmetry impacts individual payoffs in complex ways.

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 can 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.