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The Signal and the Noise: Why So Many Predictions Fail-but Some Don't

Silver, Nate
Penguin: London, 2012
ISBN 1594204111 (hb)

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Reviewed by Bruce Edmonds
Manchester Metropolitan University

Cover of book Before I started to read Nate Silver's book, I had formed expectations as to its style and content. I expected it to be a "gung-ho" manifesto for statistical methods, big data and prediction. However I was pleasantly surprised, this is a cautious, self-depreciating and wise book. Of course, the book is shaped by the author's expertise, which is prediction, but it does take quite a wide approach to this topic covering much that will be of interest to social simulators.

The main point that this book makes (from our point of view) is that social prediction is possible. The author correctly predicted the Obama election results in all 50 states weeks before the election (who would probably win each state) and, what is more important, has established an impressive track record at this kind of prediction. However, what is much more interesting is what he says about doing prediction. I summarise these points and briefly discuss their relevance for social simulation.

He briefly touches upon some agent-based models and is broadly sympathetic to the approach, seeing how expressive they are and the range of evidence they can address. However he values them for the increased understanding they can bring and not as predictive vehicles. He rightly points out their brittleness and the amount of data that they need, makes then unsuited to prediction.

My only difference in emphasis I would make is to switch attention from the accuracy of predictions for a single target towards finding out the scope of model applicability - the conditions under which a model is reliable. However this is understandable as he does expend a lot of thought as to choosing his targets (and then wishes to simply compete on that target). Achieving generality in his predictive models is not his aim, indeed I suspect he would not claim any beyond each specific target.

All in all this is a revealing and highly readable book. A lot of it will be familiar to those who know Bayesian statistics, but a lot of the examples and lessons he draws from these are highly instructive. If you are even thinking of going into the business of predicting social phenomena, this is a must-read, and even if you are not this suggests lots of useful lessons.

* Notes

1The key distinction between risk and uncertainty was made by Knight (1921).

2 Although I have argued that simplicity is no guide to truth in modeling (Edmonds 2007), and in particular criticized the process of elaborating unsuccessful models starting from simple ones (Edmonds 2000), pruning complex models when one has a good reason to do so (e.g. it can be shown they are not helpful for a particular purpose) is entirely sensible.

* References

EDMONDS, B. (2000). Complexity and Scientific Modelling. Foundations of Science, 5, pp. 379-390.

EDMONDS, B. (2007). "Simplicity is Not Truth-Indicative." In Gershenson, C., Diederik, A. and Edmonds, B. (Eds.). Worldviews, Science and Us. Philosophy and Complexity. Singapore: World Scientific Publishing, pp. 65-80.

GILBERT, N. and Troitsch, K.G. (2005). Simulation for the Social Scientist (2nd Edition). Open University Press.

KNIGHT, F. H. (1921). Risk, Uncertainty and Profit. Boston: Houghton Mifflin. (Republished 2009, Signalman Publishing).


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