Can the Burnham-Anderson book on multimodel inference be recommended? As motivated by the recent change of the default model selection statistic in 
the R's forecast package from AIC to AICc, I am curious whether the latter is 
indeed applicable wherever the former is. I have a series of questions with this 
respect and here is the first one. 
I know that to replace AIC with AICc everywhere is what the well-known book in (1) by 
Burnham and Anderson (non-statisticians), as summarized here, recommends. The book is sometimes uncritically referred to by younger statisticians, see e.g. comments to this blog post by Rob Hyndman, but the statistician Brian Ripley advised in a radically different way:
“Burnham and Anderson (2002) is a book I would recommend people NOT read until 
they have read the primary literature. I see no evidence that the authors have 
actually read Akaike’s papers." [quoted from [AIC MYTHS AND MISUNDERSTANDINGS][4] by
Burnham-Anderson]

It does follow from what Ripley writes on the AIC and related theory that the warning should be taken seriously. I have both a good collection of Akaike's own papers and the Burnham-Anderson book. I will eventually have my own opinion on the quality of the book, but it will also help to know what the community of statisticians, both young and old, think on that. In particular, are there professors of statistics (or other good students of statistics) who explicitly recommended the book as a useful summary of knowledge on using AIC for model selection? 
Reference:
(1) Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach Springer, 2002
PS. In reply to the recent "answer" stating that "Dr.Burnham is a Ph.D. statistician" I'd like to add this clarification. Yes, by himself he is a statistician, a Fellow of the ASA and the recipient of numerous professional awards, including Distinguished Achievement Medal from the ASA. But who says he is not? All I have said above is that as a pair of authors they are not statisticians and the book reflects this fact.  
 A: The OP appears to be seeking a high-quality survey of high-quality statisticians to help assess whether one particular book is of high quality particularly with regards to the AIC versus AICc debate.  This site is not particularly geared towards systematic surveys.  Instead I'll try to address the underlying question directly.
The AIC and AICc both score models according to a heuristic tradeoff between model fit (in terms of the likelihood) and overfit (in terms of the number of parameters).  In this tradeoff, the AICc gives slightly greater penalty on the number of parameters.  Thus, the AICc always recommends in favor of models that are of complexity less-than-or-equal to the complexity of the best AIC model.  In this sense the relationship between the two is very simple, despite the horribly complicated arguments underlying their derivations.
The AIC and AICc are only two out of a large field of candidate information criteria, with the BIC and DIC being perhaps the leading alternatives.  The BIC is far more conservative (penalizing large numbers of model parameters) than either of the AIC or AICc in most cases.  The question of which criterion is the best is truly problem specific.  One could legitimately prefer an extremely conservative criterion in cases where robust out-of-sample prediction is needed.
FWIW, I found the conservatism level of the AICc to be typically preferable over the AIC in extensive simulation studies on the prediction error in capture-recapture models.
