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

Entropy

Simon DeDeo¹  Robert X. D. Hawkins¹  Sara Klingenstein¹ 

[1] Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA; E-Mails:

关键词: biological systems;    cognition;    social systems;    information theory;    statistical estimation;    bootstrap;    Bayesian estimation;   

DOI  :  10.3390/e15062246

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.