Bootstrap Methods for the Empirical Study of Decision-Making and Information Flows in Social Systems

TL;DR

This paper develops bootstrap methods for reliably estimating information-theoretic measures from data, enabling empirical analysis of decision-making and information flows in large social systems.

Contribution

It introduces bootstrap techniques that preserve information-theoretic axioms and offer reliable estimates for studying social phenomena.

Findings

Bootstrap methods maintain axiomatic relationships of information theory.

Application to British Criminal Court system reveals semantic structures.

Analysis of Afghanistan insurgency shows information flow dynamics.

Abstract

We characterize the statistical bootstrap for the estimation of information-theoretic quantities from data, with particular reference to its use in the study of large-scale social phenomena. Our methods allow one to preserve, approximately, the underlying axiomatic relationships of information theory---in particular, consistency under arbitrary coarse-graining---that motivate use of these quantities in the first place, while providing reliability comparable to the state of the art for Bayesian estimators. We show how information-theoretic quantities allow for rigorous empirical study of the decision-making capacities of rational agents and the time-asymmetric flows of information in distributed systems. We provide illustrative examples by reference to ongoing collaborative work on the semantic structure of the British Criminal Court system and the conflict dynamics of the contemporary Afghanistan insurgency.