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Referee Report: https://doi.org/10.5256/f1000research.10181.r15871, Neil McRoberts, Department of Plant Pathology, University of California, Davis, Davis, CA, USA, 25 Oct 2016, version 1, 1 approved, 1 approved with reservations

Referee Comment: <b>Steven Frank</b>; <i>Posted: 27 Oct 2016</i>; Neil McRoberts’ summary provides an excellent overview of the broader subject and the place of my article within that wider context. &nbsp; McRoberts mentions the duality between the understanding of probability patterns and the complementary problems of inference. I originally came to this subject through that connection, particularly through my study of Jaynes pioneering work (see references to my earlier work in my article). However, I was not aware of several of the explicit connections mentioned by McRoberts, which I appreciate learning about from his review. &nbsp; McRoberts suggests that I provide “a more comprehensive section by section guide in the introduction.” I often provide such a guide in my longer articles. In this case, I had that kind of guide in my early drafts. However, the technical underlying nature of the work made the overview into what seemed like more of an obstacle than an invitation to the article. So I dropped it, allowing me to move the article very quickly into the example of tree size that I use throughout to help connect the underlying abstractions to real-world problems. Perhaps it would be possible to write a helpful introductory guide, but I have not yet found the right expression. &nbsp; The main difficulty with the current structure is that some readers may mistakenly focus on the tree size problem as the central message of the article. It is not. The main message is that we can understand almost all common probability patterns by a few simple underlying invariances. That understanding provides great insight into many aspects of commonly observed patterns, including patterns such as tree size. &nbsp; One advantage of the F1000Research format is that I can submit a revised version at any time. For now, I will keep the current structure, while I continue to think about how to improve the presentation. I welcome comments from readers.

Referee Report: https://doi.org/10.5256/f1000research.10817.r17664, Scott E. Page, Department of Political Science, Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI, USA, 16 Nov 2016, version 2, indexed

Referee Comment: <b>Steven Frank</b>; <i>Posted: 17 Nov 2016</i>; I have revised the article to make the suggested change, noting an equivalence of the normal and lognormal distributions and pointing to the extended discussion of the lognormal distribution in the Appendix. I am grateful to Scott Page for pushing on this issue, which has significantly enhanced the scope and reduced the chance of misunderstanding.

Grant Information: National Science Foundation grant DEB–1251035 supports my research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.