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Christmas books for 2024
My rate of book reading has picked up significantly this year. The following are the really interesting books I read, as is usually the case, most were not published in this year.
I have enjoyed Grayson Perry’s TV programs on the art world, so I bought his book “Playing to the Gallery: Helping Contemporary Art in its Struggle to Be Understood“. It’s a fun, mischievous look at the art world by somebody working as a traditional artist, in the sense of creating work that they believe means/says something, rather than works that are only considered art because they are displayed in an art gallery.
“The Computer from Pascal to von Neumann” by H. H. Goldstine. This history of computing from the mid-1600s (the time of Blaise Pascal) to the mid-1900s (von Neumann died in 1957) told by a mathematician who was first involved in calculating artillery firing tables during World War II, and then worked with early computers and von Neumann. This book is full of insights that only a technical person could provide and is a joy to read.
I saw a poster advertising a guided tour of the trees in my local park, organized by Trees for Cities. It was a very interesting lunchtime; I had not appreciated how many different trees were growing there, including three different kinds of Oak tree. Trees for Cities run events all over the UK, and abroad. Of course, I had to buy some books to improve my tree recognition skills. I found “Collins tree guide” by O. Johnson and D. More to be the most useful and full of information. Various organizations have created maps of trees in cities around the world. The London Tree Map shows the location and species information for over 880,000 of trees growing on streets (not parks), New York also has a map. For a general analysis of patterns of tree growth, see “How to Read a Tree” by T. Gooley.
“Medieval Horizons: Why the Middle Ages Matter” by I. Mortimer. This book takes the reader through the social, cultural and economic changes that happened in England during the Middle Ages, which the author specifies as the period 1000 to 1600. I knew that many people were surfs, but did not know that slaves accounted for around 10% of the population, dropping to zero percent during this period. Changes, at least for the well-off, included moving from living in longhouses to living in what we would call a house, art works moved from two-dimensional representations to life-like images (e.g., renaissance quality), printing enables an explosion of books, non-poor people travelled more, ate better, and individualism started to take-off.
Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications by N. N. Taleb is a mathematically dense book (while the pdf is in color, I was disappointed that the printed version is black/white; this is the one I read while travelling). This book tells you a lot more than you need to know about the consequences of fat tail distributions. Why might you be interested in the problems of fat tails? Taleb starts by showing how little noise it takes for the comforting assumptions implied by the Normal/Gaussian distribution to fly out the window. The primary comforting assumptions are that the mean and variance of a small sample are representative of the larger population. A world of fat tail distributions is one where the unexpected is to be expected, where a single event can wipe out an organization or industry (banks are said to have lost more in the 2008 financial crisis than they had made in the previous many decades). This book is hard going, and I kept at it to get a feel for the answers to some of the objections to the bad news conveyed. There are a couple of places where I should have been more circumspect in my Evidence-based software engineering book.
I have previously reviewed General Relativity: The Theoretical Minimum by Susskind and Cabannes.
“Embracing Defeat: Japan in the Wake of World War II” by John W. Dower describes in harrowing detail the dire circumstances of the population of Japan immediately after World War II and what they had to endure to survive.
For more detailed book reviews, see: Mr. and Mrs. Psmith’s Bookshelf with some excellent and insightful long book reviews, and the annual Astral Codex Ten book review contest usually has a few excellent reviews/books.
For those of you who think that civilization is about to collapse, or at least like talking about the possibility, a reading list. At the practical level, I think sword fighting and archery skills are more likely to be useful in the longer term.
Patches for the code of Peter Turchin’s Attrition Warfare Model
The paper Empirically Testing Predictions of an Attrition on Warfare Model for the War in Ukraine, by Peter Turchin, recently showed up during one of my regular searches for software engineering data. A quick scan of the paper founded that it is very empirical, and that the analysis coding was done in R; I could not resist checking out the source code.
One of my first jobs was helping academics fix coding issues with the programs they had written to solve scientific/engineering problems, and this R code reminded me of several habits of scientists who code: the single letter variables used in equations are directly mapped to identifier names, and there is no structure to the code. The code is so short (86 lines) that the lack of structure is a minor inconvenience; a few thousand lines, and it becomes a major headache. The code for Imperial’s COVID model was ten times larger.
Two mistakes in the code/paper jumped out at me, leading to this post. First, some background.
The empirical predictions in the paper are intended to provide insight into who is likely to win the ongoing Ukraine/Russia war. Fighting requires soldiers and these are killed/wounded over time. The country that does not have enough soldiers to at least keep the opposition at bay, looses.
Turchin has proposed what he calls the Attrition War model, based on Lanchester’s laws (various attempts to validate Lanchester’s models, lots of maths to shake a stick at), and the paper solves this model’s set of eight differential equations (each country has the same set of four equations; the connection between the two sets is that one country’s casualty rate and Army size is influenced by the opposing country’s stock of war matériel). The four quantities modelled are casualties, army size, stock of warfare matériel, and production capacity.
Getting predictions out of differential equations requires being able to find a solution to the equations and feeding in numeric values for the various parameters.
Solving the equations is a maths problem, i.e., no knowledge of military matters required. Selecting the equations to solve and the numeric values to feed into the solution is what requires military knowledge. I don’t know anything about military matters; the following analysis is purely related to writing code to solve a set of differential equations, using the equations plus numeric values in Turchin’s November 2023 paper.
For obvious reasons, countries involved in the war do not publish information on the quantities modelled by these equations (which are also likely to be time-dependent). Turchin addresses the changeable nature of the numeric values by introducing various random components into his Attrition model.
From the perspective of solving the eight equations and presenting the results, the following are the two mistakes that jumped out at me (both involving the implementation of the random component):
- When a model contains a random component, there will be a huge/infinite number of possible solutions. The takeaway plots in the paper show a single solution (for each of the four variables/two countries), with the width of the plotted lines and their fluctuating appearance suggesting that they contain multiple solutions. The plot below left shows the solution for artillery shell production over time, as it appears in the paper, while the plot below right shows 100 solutions (each line is a different solution; code):
The wedge of lines shows the range of possible solutions (each line drawn overwrites anything previously drawn, and plotting with transparent colors would show the density of solution at a given point; I decided to keep the code simple).
- All the random components are assumed to have a Gaussian distribution. When distribution information is not available, this is usually a safe choice. However, two of the random components must always have non-negative values (i.e., casualties and matériel used can never be negative). The Poisson distribution is the obvious candidate, and a simple search turned up an empirical paper agreeing with this choice (at least for casualties).
The plot below left shows one solution for the number of casualties over time, using the original code, while the plot below right shows 100 solutions using a Poisson distribution for the random component (code):
With a Poisson random component, the solutions don’t meander as much, and the variance is smaller than when a Gaussian is used. Technically, it is a more accurate model (if more variance is to be expected a Negative Binomial distribution could be used; see commented out code)
The latest (November) UK government estimate of Russian casualties is 300K, roughly three times larger than predicted by Turchin’s model. Changing the value for the ‘conversion rate of expended matériel to casualties’ from
to 
brings the casualty prediction inline with current estimates (we have been hearing a lot about the accuracy of the Ukrainian targetting; see code for details).
I have also reworked the code to add some structure, e.g., separating out solving the equations and setting the initial conditions.
Turchin used the traditional approach to solving differential equations, the one we are taught at school. Before seeing the code, I was half expecting to see a System dynamics approach. The advantage of a systems dynamic approach is flexibility (i.e., easier to add more components) and visualization (i.e., a chart showing what feeds into/out of what); an example. There is an R-based book: System Dynamics Modelling with R.
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