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Optimal design of experiments on connected units with application to social networks
Author(s) -
Parker Ben M.,
Gilmour Steven G.,
Schormans John
Publication year - 2017
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12170
Subject(s) - crossover , computer science , design of experiments , network planning and design , range (aeronautics) , variance (accounting) , social network (sociolinguistics) , mathematical optimization , econometrics , operations research , artificial intelligence , mathematics , statistics , economics , engineering , computer network , accounting , world wide web , social media , aerospace engineering
Summary When experiments are performed on social networks, it is difficult to justify the usual assumption of treatment–unit additivity, owing to the connections between actors in the network. We investigate how connections between experimental units affect the design of experiments on those experimental units. Specifically, where we have unstructured treatments, whose effects propagate according to a linear network effects model which we introduce, we show that optimal designs are no longer necessarily balanced; we further demonstrate how experiments which do not take a network effect into account can lead to much higher variance than necessary and/or a large bias. We show the use of this methodology in a very wide range of experiments in agricultural trials, and crossover trials, as well as experiments on connected individuals in a social network.