Philip Ball
Author of Critical Mass: How One Thing Leads to Another
About the Author
Philip Ball is a freelance writer who lives in London. He worked for over twenty years as an editor for Nature, writes regularly in the scientific and popular media, and has authored many books on the interactions of the sciences, the arts, and the wider culture, including, most recently, Serving show more the Reich: The Struggle for the Soul of Physics under Hitler, also published by the University of Chicago Press. show less
Series
Works by Philip Ball
The Devil's Doctor: Paracelsus and the World of Renaissance Magic and Science (2006) 291 copies, 7 reviews
Beyond Weird: Why Everything You Thought You Knew about Quantum Physics Is Different (2018) 222 copies, 10 reviews
The Book of Minds: How to Understand Ourselves and Other Beings, from Animals to AI to Aliens (2022) 84 copies, 1 review
The Modern Myths: Adventures in the Machinery of the Popular Imagination (2021) 61 copies, 6 reviews
Why Society is a Complex Matter: Meeting Twenty-first Century Challenges with a New Kind of Science (2012) 12 copies
Associated Works
Seeing Further: The Story of Science, Discovery, and the Genius of the Royal Society (2010) — Contributor — 1,059 copies, 17 reviews
The New Science of Strong Materials or Why You Don't Fall through the Floor (1968) — Introduction, some editions — 399 copies, 6 reviews
Time unwrapped : kings place : programme 2018 — Contributor — 1 copy
Tagged
Common Knowledge
- Birthdate
- 1962
- Gender
- male
- Nationality
- UK
- Country (for map)
- England, UK
- Places of residence
- London, England, UK
- Education
- University of Oxford
University of Bristol - Occupations
- science writer
- Organizations
- Nature (editor)
Members
Reviews
‘Critical Mass’ is another non-fiction book that I’ve been meaning to read for about a decade. In fact, I read the chapter on traffic behaviour in 2012 to see if it would be relevant to my PhD. It wasn't, not directly at least. Having finally read the whole thing, I think I'd have gained more from the experience in 2009. As it was, I found it rather odd and intermittently frustrating. The central thesis is broad and elastic in the extreme. Ball begins with a potted history of Western political philosophy, then leaps into physics under the broad umbrella of complexity theories. A central idea that repeatedly occurs is that of phase transition: how these occur in substances in water and also, seemingly, in human contexts like road traffic. Ball draws parallels between physical science and social science in a variety of contexts, bringing in a range of theories and methodologies that I’ve comes across or used in the past decade. In some sections he makes very sensible points; in others the content has dated considerably in the 15 years since publication; in others the analysis is reductive. Nonetheless, I found the whole thing readable enough and it does mention a great many topics that interest me: snowflake formation! Economic theory! Traffic behaviour! Utopian literature! Even the French Revolution gets name-checked, albeit in a simplistic fashion that got on my nerves (...it wasn't 'Robespierre's Terror'). On the other hand, in 2004 Ball called out derivatives as wrongly priced and high-risk due to misunderstanding of market fluctuations, which do not follow a Gaussian distribution. So kudos on predicting the financial crisis, there.
I struggled to understand the overall purpose of the book, as it covered so much ground and intersected with such a range of disciplines. Complexity is not, in itself, a specific topic. There is undoubtedly interesting material, however the whole reads now as trying to be a bit ahead of its time. Searching for physical laws in behavioural data is a mainstream idea with the advent of big data, however that data is controlled by the big tech companies and therefore largely inaccessible to researchers. Where it is used to understand behaviour by the firms that control it, the aim is profit maximisation rather than identification of behavioural laws. To generalise, machine learning with big data is a black box filled with layers of correlations. It can be used to make predictions about behaviour, but not to explain why behaviour occurs. And as Ball comments, making predictions about future fluctuations in a market then acting upon them changes the dynamic of the market, requiring further predictions to be made, etc. Oil markets are a classic example here. Fluctuations in the current price of oil occur in part due to changing expectations about the future price of oil. As he puts it, ‘The act of predicting the future (if it is taken at all seriously) is likely to change it’.
Ball certainly provides a good critique of oversimplified neoclassical economic models and Homo Economicus in chapter 9. He also explains power laws neatly in Chapter 10, which was helpful as I teach students about the 80:20 Pareto Rule in business contexts. On the other hand, chapter 14 is titled ‘The Colonisation of Culture’ yet discusses models of cultural spread that are completely ahistorical and ignore colonialism completely. Such models seem not just pointless but insulting. The assumption that a culture adopts a feature of an adjacent culture based on its similarity is a ridiculous way to look at history. In the 18th and 19th centuries, I don’t think Africa, Asia, and Oceania chose to adopt European features because of their similarity to existing cultures! Pretty sure they were aggressively conquered, their indigenous cultures suppressed, and their resources stolen. Models that assume social and cultural change always occurs ‘from the bottom up’ through the interaction of agents in grids do not seem remotely aligned with observable reality, any more than neoclassical economic models that treat technological and environmental change as exogenous (external to the economy). I remain to be convinced of the merits of agent-based modelling more generally, although I have colleagues working on it. While a complex nonlinear system can certainly emerge from a simple rules-based setup, I agree with Ball’s caveat that, ‘Some social scientists remain uneasy, suspecting that any particular agent-based model of a social phenomenon risks coming to conclusions that depend on the underlying assumptions. [...] Such models can hardly be expected to provide a sound basis for policy until we can distinguish what is contingent from what is robust.’ The current approach is to throw in as much data as possible, which can create further problems of data-cleaning, bias, and inaccurate measurement.
Another minor point that got up my nose concerned the tragedy of the commons. This concept is often stretched to infer that human being cannot manage natural resources communally without destroying them. This is obviously not the case, as in pre-capitalist times, and under capitalism in some places, doing so was and still is essential to survival. Equating common grazing land in Medieval Europe with 21st century overfishing is nonsensical. The difference is a powerful profit motive, which is not the immutable natural law that economists say it is. Managing land did not always mean exhausting it for short-term gain and the phrase 'tragedy of the commons' needs to be used with more specificity. George Monbiot has much more to say on this topic in [b:Out of the Wreckage: A New Politics in the Age of Crisis|32171783|Out of the Wreckage A New Politics in the Age of Crisis|George Monbiot|https://i.gr-assets.com/images/S/compressed.photo.goodreads.com/books/1498931739l/32171783._SX50_.jpg|52808178].
The chapters on the internet and social network analysis read in 2019 as endearingly dated. Apparently, ‘it was estimated at the end of 2002 that there were around 3 billion documents then available on the WWW’. Bless. It’s quite impressive that Ball manages to explain the principles of social network analysis (which I’ve come across because a PhD student I supervise is using it) without having social media to reference. Nowadays anyone can get a developer account on twitter, download a huge file of tweets, and use R or similar software to map the network their interconnections represent. At least they could last time I checked. So chapter 16 introduces the theory well, but the application is sadly out of date. I expect there’s plenty of more recent research comparing social media user networks with yeast metabolic networks. Presumably the increasing algorithmic meddling in social media feeds to encourage ‘engagement’ has a measurable impact on this, possibly even changing the nature of such networks. (Thankfully twitter can still be forced to stop this by selecting ‘Latest Tweets’.)
More intriguing was the application of game theory principles to the trenches of the First World War, although I’m wary of simplistic applications of game theory principles. This is nonetheless an interesting point:
The striking thing here is how unusual this situation is in history. The Western European battlelines of WWI hardly moved for years; both sides had very similar weapons and tactics; neither side was fighting in defense of a specific location, for ideology, or for survival. It was thus an even more pointless war than most, which might be what makes it more amenable to interpretation through game theory.
Chapters like this are interesting in isolation, however I remain doubtful of how well the whole book hangs together. If written 5 to 10 years later and infused with use of big data, it would be a lot more cohesive. However I do greatly respect Ball’s distrust of the deterministic social engineering that some of his examples could easily lead to:
Tell that to ‘smart city’ advocates. Remaining skeptical and ambivalent about the social implications of the material he presents is the only sensible choice, but one that then calls the structure of the book into question. Had ‘Critical Mass’ claimed the route to utopia is the application of physical laws to social problems, I would have rejected it out of hand. The book is more subtle than that, which makes it worth reading but does not prevent various flaws. Of the theories and examples Ball presents, though, some are manifestly more credible and useful than others. His initial discussion of the Enlightenment is thoughtful, albeit not original. The explanations of concepts from physics and maths are consistently clear and readable. I’d be wary of recommending it, as certain parts have aged badly and the whole is less relevant 15 years later. I also don’t think it needed to be quite so long.… (more)
I struggled to understand the overall purpose of the book, as it covered so much ground and intersected with such a range of disciplines. Complexity is not, in itself, a specific topic. There is undoubtedly interesting material, however the whole reads now as trying to be a bit ahead of its time. Searching for physical laws in behavioural data is a mainstream idea with the advent of big data, however that data is controlled by the big tech companies and therefore largely inaccessible to researchers. Where it is used to understand behaviour by the firms that control it, the aim is profit maximisation rather than identification of behavioural laws. To generalise, machine learning with big data is a black box filled with layers of correlations. It can be used to make predictions about behaviour, but not to explain why behaviour occurs. And as Ball comments, making predictions about future fluctuations in a market then acting upon them changes the dynamic of the market, requiring further predictions to be made, etc. Oil markets are a classic example here. Fluctuations in the current price of oil occur in part due to changing expectations about the future price of oil. As he puts it, ‘The act of predicting the future (if it is taken at all seriously) is likely to change it’.
Ball certainly provides a good critique of oversimplified neoclassical economic models and Homo Economicus in chapter 9. He also explains power laws neatly in Chapter 10, which was helpful as I teach students about the 80:20 Pareto Rule in business contexts. On the other hand, chapter 14 is titled ‘The Colonisation of Culture’ yet discusses models of cultural spread that are completely ahistorical and ignore colonialism completely. Such models seem not just pointless but insulting. The assumption that a culture adopts a feature of an adjacent culture based on its similarity is a ridiculous way to look at history. In the 18th and 19th centuries, I don’t think Africa, Asia, and Oceania chose to adopt European features because of their similarity to existing cultures! Pretty sure they were aggressively conquered, their indigenous cultures suppressed, and their resources stolen. Models that assume social and cultural change always occurs ‘from the bottom up’ through the interaction of agents in grids do not seem remotely aligned with observable reality, any more than neoclassical economic models that treat technological and environmental change as exogenous (external to the economy). I remain to be convinced of the merits of agent-based modelling more generally, although I have colleagues working on it. While a complex nonlinear system can certainly emerge from a simple rules-based setup, I agree with Ball’s caveat that, ‘Some social scientists remain uneasy, suspecting that any particular agent-based model of a social phenomenon risks coming to conclusions that depend on the underlying assumptions. [...] Such models can hardly be expected to provide a sound basis for policy until we can distinguish what is contingent from what is robust.’ The current approach is to throw in as much data as possible, which can create further problems of data-cleaning, bias, and inaccurate measurement.
Another minor point that got up my nose concerned the tragedy of the commons. This concept is often stretched to infer that human being cannot manage natural resources communally without destroying them. This is obviously not the case, as in pre-capitalist times, and under capitalism in some places, doing so was and still is essential to survival. Equating common grazing land in Medieval Europe with 21st century overfishing is nonsensical. The difference is a powerful profit motive, which is not the immutable natural law that economists say it is. Managing land did not always mean exhausting it for short-term gain and the phrase 'tragedy of the commons' needs to be used with more specificity. George Monbiot has much more to say on this topic in [b:Out of the Wreckage: A New Politics in the Age of Crisis|32171783|Out of the Wreckage A New Politics in the Age of Crisis|George Monbiot|https://i.gr-assets.com/images/S/compressed.photo.goodreads.com/books/1498931739l/32171783._SX50_.jpg|52808178].
The chapters on the internet and social network analysis read in 2019 as endearingly dated. Apparently, ‘it was estimated at the end of 2002 that there were around 3 billion documents then available on the WWW’. Bless. It’s quite impressive that Ball manages to explain the principles of social network analysis (which I’ve come across because a PhD student I supervise is using it) without having social media to reference. Nowadays anyone can get a developer account on twitter, download a huge file of tweets, and use R or similar software to map the network their interconnections represent. At least they could last time I checked. So chapter 16 introduces the theory well, but the application is sadly out of date. I expect there’s plenty of more recent research comparing social media user networks with yeast metabolic networks. Presumably the increasing algorithmic meddling in social media feeds to encourage ‘engagement’ has a measurable impact on this, possibly even changing the nature of such networks. (Thankfully twitter can still be forced to stop this by selecting ‘Latest Tweets’.)
More intriguing was the application of game theory principles to the trenches of the First World War, although I’m wary of simplistic applications of game theory principles. This is nonetheless an interesting point:
So the choice was simple. Either you fought each other constantly, bombarding the enemy trenches with artillery fire and deploying snipers to pick off anyone foolish enough to put their head above the parapet - while enduring the same treatment from the other side. Or you held fire on the tacit understanding that the enemy would do the same - and meanwhile you hoped to be relieved from the front before the next push came. In the one case you endured fear of sudden death at any moment; in the other you had a quiet life and some hope of going home at the end of it. [...] The fighting was, in other words, conducted on a tit-for-tat basis. Such exchanges are a lethal form of communication: they say, “We will do as we are done by.” This is at the same time both a threat and an olive branch, for it also implies that non-aggression will be greeted with the same.
The striking thing here is how unusual this situation is in history. The Western European battlelines of WWI hardly moved for years; both sides had very similar weapons and tactics; neither side was fighting in defense of a specific location, for ideology, or for survival. It was thus an even more pointless war than most, which might be what makes it more amenable to interpretation through game theory.
Chapters like this are interesting in isolation, however I remain doubtful of how well the whole book hangs together. If written 5 to 10 years later and infused with use of big data, it would be a lot more cohesive. However I do greatly respect Ball’s distrust of the deterministic social engineering that some of his examples could easily lead to:
The notion that we could ever construct a scientific ‘utopia theory’ is, then, doomed to absurdity. Certainly, a ‘physics of society’ can provide nothing of the sort. One does not build an ideal world from scientifically based traffic planning, market analysis, criminology, network design, game theory, and the gamut of other ideas discussed in this book.
Tell that to ‘smart city’ advocates. Remaining skeptical and ambivalent about the social implications of the material he presents is the only sensible choice, but one that then calls the structure of the book into question. Had ‘Critical Mass’ claimed the route to utopia is the application of physical laws to social problems, I would have rejected it out of hand. The book is more subtle than that, which makes it worth reading but does not prevent various flaws. Of the theories and examples Ball presents, though, some are manifestly more credible and useful than others. His initial discussion of the Enlightenment is thoughtful, albeit not original. The explanations of concepts from physics and maths are consistently clear and readable. I’d be wary of recommending it, as certain parts have aged badly and the whole is less relevant 15 years later. I also don’t think it needed to be quite so long.… (more)
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Le pitture rupestri delle grotte di Altamira in Spagna ne sono un primo (ma non primitivo) esempio, mentre il primo pigmento di sintesi fu il blu egizio, prodotto 2500 anni fa non per caso ma attraverso un procedimento che richiedeva una tecnologia complessa. Biacca, minio, blu oltremare, oro: la tavolozza del pittore medievale si amplia e con essa l'abilità dell'artista che vuole rendere durevoli e imperiture le sue opere. La necessità di dipingere fedelmente i colori della natura spinge in seguito il pittore a sperimentare nuove tecniche, cercare nuovi materiali che si manifestano sotto forma di giallo limone, arancio cromo, verde smeraldo, blu cobalto, bianco di zinco, solo per citarne alcuni. Colori tanto saturi che diventano essi stessi un mezzo di espressione dell'artista. Chissà se Vincent van Gogh avrebbe utilizzato lo stesso il giallo cromo per i suoi girasoli, sapendo che col tempo questo si sarebbe alterato diventando un ocra scuro.
In questa storia del colore "non ci sono né uova né galline: la scienza e la tecnologia chimiche e l'uso del colore nell'arte sono sempre vissute in una relazione simbiotica che ha determinato il corso di entrambe lungo tutto l'arco della storia".… (more)