Geoffrey West (4) (1940–)

This is one of those rare books that says little, but what it does say is well founded. Scale summarizes the work Geoffrey West and various colleagues have done over the year investigating how things scale. The first part of the book focuses on how organisms scale. The second part focuses on how cities scale (with a brief detour into companies).

When I say that this book says very little, what I mean is this: West takes a systems perspective to try to define models which explain some of the show more dominant factors of scaling. These models are, by design, meant to explain the coarse grained behavior of complex adaptive systems and do not provide predictions for specific instances. He also makes clear that he sees the models he and his colleagues have developed to be the starting point of modeling the systems under investigation. Although complex adaptive systems like organisms and cities will never be perfectly predictable, there is still a large gap between where we are and where we likely can be, especially for the scaling of cities.

One of the interesting observations about organisms that has been known for a long time is that many of their properties scale predictably, and they do so sublinearly. A whale lives longer than an elephant lives longer than a dog lives longer than a mouse. However, how much longer is not just a linear extrapolation from the size. Nor, for example, does an animal that is twice the size of another need twice the calories. It needs less than that. These and many other properties show regular scaling properties which are based on quarter powers -- x^(1/4) or x^(3/4) depending on the property.

This alone is a fun party fact but not necessarily useful. What West and his colleagues did is develop of model starting from a couple of key properties that it seemed reasonable to assign to a metabolic system: energy must be delivered to every terminal unit (e.g., cell), terminal units have approximately the same requirements, and the energy used on delivering resources should be minimized. From this, they derived a model of the sort of network structure which would best deliver nutrients and, by looking at the fractal dimension of these networks in relation to volume, provided a model which predicted the quarter power scaling laws seen in practice.

Even such a rough model can be useful. For example, it predicts that the maximum human life span is ~125 years, which seems to match what we see in practice. By understanding at least the most dominant factors for why that is the limit, we can see that we would need to fundamentally change our metabolic properties to significantly increase our life spans. Or, to put it another way, optimizing the system you have might help you live 120 years instead of 80, but it will not get you to 1000. For that, you need to change the system.

The second half of the book discusses cities and how they scale. One interesting aspect of cities is that they do show regular scaling properties. Given the historical contingencies that went into the development of each city and given that cities are young compared to evolutionary timescales (thousands of years compared to hundreds of millions), it is surprising that there is any regularity. Yet while the data is noisier than for various biological properties, there are trends which can be modeled usefully. Since less of this work has been done for cities, the explanatory models are less mature. What has been discovered so far is intriguing.

Physical infrastructure, such as roads, pipes, and power lines, tends to scale sublinearly like organisms. This makes sense since they are problems with a similar shape: within each physical system, the relevant terminal units have fairly constant size (even skyscrapers and tiny cottages have similar sized outlets and faucets), must all be served, and are trying to be optimized relative to physical properties size as distance or, in the case of water, very similar efficient flow models to circulatory systems.

More interesting is that social factors, both good and bad, tend to scale superlinearly. Although, to use a set of examples West mentions, New York, Los Angeles, and Dallas all feel very different, they are also roughly scaled versions of each other. Analyzing the data shows that many socioeconomic such as total wages, the number of professionals, patent production, the amount of crime, number of restaurants, and prevalence of diseases all scale with a factor of approximately 1.15. This means that a larger city doesn't just have more crime, wages, restaurants etc., it has more per capita than a smaller city, and the amount more varies in a predictable way. Note an important caveat here: for any particular property, the scaling relationship holds within a country, not across countries, but the scaling factor itself is fairly comparable. Thus, we cannot use the wages of New York City to estimate wages in Mumbai, but if we know how the wages in Tokyo, we can predict the wages in Osaka.

The model here is not as well developed as for organisms, but it these properties seem to derive from the properties of social networks. In particular, if you assume that humans have a limited capacity for social relationships (à la Dunbar), that people will generally have that capacity saturated, and make various assumptions about how information flow works in a social network (à la Granovetter), then you end up with a model that predicts superlinear scaling of various socioeconomic factors.

One upshot of this is that it is not really accurate to treat properties of cities as if they ought to scale linearly. It is not surprisingly, for example, that LA has more crime per capita than Seattle. It follows predictably from the dominant scaling characteristics. The flip side of this is that we should not expect cities to maintain the same properties as they grow. Some sort of essential character is not the primary determinant of various socioeconomic properties of Seattle. Another upshot of this is that if we want to figure out how to change things, it probably will not help much to look at another city which does that thing better unless it is an outlier after applying the dominant scaling factor. A third upshot is that such consistent scaling laws implies that major changes are unlikely to come from fiddling at the margins and are more likely to come with fundamental paradigm shifts.

Some of West's colleagues have worked on applying similar analyses to companies. The findings there are intriguing but sparse because detailed data on social structure within companies and on details about how companies fare on various factors are not as easy to come by as for organisms and cities. However, what the investigations have shown is that companies tend to be more like organisms in that they seem to scale sublinearly rather than superlinearly like cities. This may be because companies tend to be structured fairly hierarchically, although this is just speculation based on the findings.

The book ends on a note of caution. One obvious question which arises when facing superlinear growth is whether or not it is sustainable. As the failures of Malthus' predictions show, innovations can allow us to buck what would seem to be the natural and catastrophic end to a exponential growth curve. That is one source of hope. On the other hand, the growth of cities seems to be faster than exponential in a way that requires faster and faster innovation cycles to reset the timeline to where resources cannot keep up with growth. Innovation keeps pushing the margin, but every time we push the margin, we use it up even faster than before. We have shown that we can innovate, but it is a separate question to say whether or not we can innovate fast enough.

For a concrete example of faster than exponential growth, consider global population growth. Exponential growth is what it would look like if there was approximately the same amount of time between each population doubling. What we see in practice is that the amount of time it takes the population to double is shrinking. Each time the population doubles, we have less time to figure out how to deal with it. Now, there are indications that this trend may be starting to reverse and that the time between doublings will increase again. Thus, we can hope that even if faster than exponential growth is not sustainable, it does not necessarily need to end in collapse.

However, from a socioeconomic perspective, we have come to expect faster than exponential growth. Thus, it is likely that even without a catastrophic collapse, slowing to "merely" exponential growth will feel like a painful shrinking of resources. Can our increased creative output keep up? It is an interesting challenge. show less

This book is an attempt to communicate to lay people a pattern of relationships intelligible only mathematically - without including any equations. The author tries to replace the equations with graphs - but commits multiple errors of labelling, such that the impression I got was that he was either unforgivably sloppy, or actively attempting to deceive the readers. He also frequently either makes general statements that I happen to know don't apply as broadly as he stated, or gives the one show more example where I'm sure the relationship applies, phrased to make it seem like a stand in for all similar cases.

Sometimes the text is more accurate than the diagram - such as a graph described in the text as showing mammals, mislabelled as showing animals. (AFAIK, the pattern shown applies among mammals, and among birds, but not if you combine the two, and probably doesn't apply to any cold-blooded animals, such as amphibians and reptiles.)

The blurb basically describes the author as the second coming of Einstein, with Newton, Galileo and others thrown in for good measure. He's a theoretical physicist (all bow), so is self-evidently a reliable source on economics, demographics, mammalian biology, and anything else he cares to address (sic).

Needless to say, my snake oil alarm was clanging loudly enough to give me a headache. Yet when I checked for suspicious factors, I didn't find many. The author is not selling things to those convinced by his theories. The book is published by Penguin, not self-published or otherwise dodgy. The author has published some of his work in well regarded, peer-reviewed scientific journals, such as Nature. He has a wikipedia page, which doesn't reek of controversy, except that the careful phrasing in the biography section suggests that he may have failed to finish his doctorate at Stanford. I suspect he failed at theoretical physics, and took his mathematical training into a new and fairly wide open field, where he did good work.

This book would then be an attempt to communicate that work to a non-mathematical audience. I was clearly not the target audience. I still remember basic high school algebra, which is all you'd need to understand his work - though a bit of basic statistics might also help. And I react very badly to people telling me The Truth (TM), without clear explanations of the evidence, while simultaneously loudly blowing their own horn. Why should I believe that this author's statements are any more reliable than those of any other author? Given that I found errors in his work, how can I trust anything else in it? Add to this various other unfortunate writing habits, such as long digressions (fortunately identified as such), and you have a recipe for a Did-Not-Finish.

But some of the ideas are interesting, and might well be true. So persevered until the end.

As for the content: the subtitle more or less accurately describes it. This is all about regularities seen as size changes - in living creatures, cities, and businesses. In general, doubling one factor doesn't result in doubling the dependent factor - it might instead tend to be multiplied by about 1.5 or by about 2.7, or any other number that's not quite 2. But that would be consistent, from smallest, to twice that size, to four times that size, to the very largest known - with a bit of fuzziness around the edges. Not everything shows this sort of regularity - but it shows up in more places than one naively expects. And in some cases, there's a good logical explanation for the pattern - sometimes involving fractal space filling.

The author and his institute has done some of the research in this area - perhaps not as large a proportion as this book seems to suggest. It could be really fascinating - but not as written by an author I find to be both careless and egotistical. Of course YMMV. And I don't know whether there is any other lay-comprehensible writeup available. show less

Another big book with a lot of content. I guess it can be summarised as an attempt to relate a whole lot of real word phenomena on logarithmic scales. Some of this is fairly "old hat" like the number of heartbeats of animals related to their total body mass. (At least I seem to recall seeing this long ago). And, yes, it is fascinating to see that no matter the mass of the animal they basically have about two billion heart beats (whether a hamster or a whale).....it's just that the whale show more lives a lot longer than the hamster if you are using a regular clock and not the pulse to record the time.
I remember trying to improve my scientific results by using log transformations but was always disappointed with the results. The author comes to this field from a background of cosmology so is used to having huge differences in scale and looking for similarities via log transformations. however if you are basically working with monkeys then the deviations between species might be significant and you probably don't need log transformations to see that. In fact, the log transformations just mask the real differences. I guess, that's my main concern about the thrust of this whole book. Sure the big picture is fascinating and can give some insights but log transformations can also mask significant real differences.
For example his Figures 15-18 show the growth curves for various animals (mass vs time) and his smooth curves miss an important feature of such growth curves (it applies also to plants) and that is that immediately after birth there is a "lag phase" where typically the newborn declines in weight....presumably partly shock and partly re-adjusting to the new environment and new feeding systems. I also found myself wondering about his curves showing the accelerating pace of major paradigm shifts. Is it legitimate to include a biological process such as the emergence of eukaryotic cells with the gap between the telephone and the computer. Surely there is a large element of "cherry picking" here where he has chosen events to suit his scale. It looks like nothing of significance happened between the emergence of life and the emergence of the Eukaryotes. In fact, there were huge changes; thermatogales, cyanobacteria, gram positive bacteria, the whole Archaea...including methane-bacteria, and halophiles, show less

A very ambitious study. Could have done with a little more editing.

Are there universal laws of scaling chemistry, biology? Yes.

Do the same universal laws apply to the structures of urbanization and commerce? The jury’s still out on this one.

How important is it to figure out how to reign in the exponential growth our species?

I’d say pretty darned important.

Will technical innovation do it for us? Not bloody likely.

Will we negotiate the political and economic compromises needed with other show more countries to make this happen? Also, not so likely. show less