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We characterize the statistical bootstrap for the estimation ofinformation-theoretic quantities from data, with particular reference to itsuse in the study of large-scale social phenomena. Our methods allow one topreserve, approximately, the underlying axiomatic relationships of informationtheory---in particular, consistency under arbitrary coarse-graining---thatmotivate use of these quantities in the first place, while providingreliability comparable to the state of the art for Bayesian estimators. We showhow information-theoretic quantities allow for rigorous empirical study of thedecision-making capacities of rational agents and the time-asymmetric flows ofinformation in distributed systems. We provide illustrative examples byreference to ongoing collaborative work on the semantic structure of theBritish Criminal Court system and the conflict dynamics of the contemporaryAfghanistan insurgency.

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