Entropy 2013, 15(6), 2246-2276; https://doi.org/10.3390/e15062246
Bootstrap Methods for the Empirical Study of Decision-Making and Information Flows in Social Systems
1
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
2
Cognitive Science Program, 819 Eigenmann, 1900 E. 10th St., Indiana University, Bloomington, IN 47406, USA
3
Department of History, University of Hertfordshire, Hatfield, Hertfordshire, AL10 9AB, UK
*
Author to whom correspondence should be addressed.
Received: 15 March 2013 / Revised: 21 May 2013 / Accepted: 30 May 2013 / Published: 5 June 2013
(This article belongs to the Special Issue Estimating Information-Theoretic Quantities from Data)
Abstract
We characterize the statistical bootstrap for the estimation of informationtheoretic 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. View Full-TextKeywords:
biological systems; cognition; social systems; information theory; statistical estimation; bootstrap; Bayesian estimation
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DeDeo, S.; Hawkins, R.X.D.; Klingenstein, S.; Hitchcock, T. Bootstrap Methods for the Empirical Study of Decision-Making and Information Flows in Social Systems. Entropy 2013, 15, 2246-2276.