Going on the cover you might think this was a political polemic - and admittedly there's an element of that - but the reason it's so good is quite different. It shows how behavioural economics and social psychology have led us astray by putting the focus way too much on individuals. A particular target is the concept of nudges which (as described in Brainjacking ) have been hugely over-rated. But overall the key problem ties to another psychological concept: framing. Huge kudos to both Nick Chater and George Loewenstein - a behavioural scientist and an economics and psychology professor - for having the guts to take on the flaws in their own earlier work and that of colleagues, because they make clear just how limited and potentially dangerous is the belief that individuals changing their behaviour can solve large-scale problems. The main thesis of the book is that there are two ways to approach the major problems we face - an 'i-frame' where we focus on the individual ...

Although taking a very new approach, this book by a physicist working in economics made me nostalgic for the business books of the 1980s. More on why in a moment, but Cesar Hidalgo sets out to explain how it is knowledge - how it is developed, how it is managed and forgotten - that makes the difference between success and failure. When I worked for a corporate in the 1980s I was very taken with Tom Peters' business books such of In Search of Excellence (with Robert Waterman), which described what made it possible for some companies to thrive and become huge while others failed. (It's interesting to look back to see a balance amongst the companies Peters thought were excellent, with successes such as Walmart and Intel, and failures such as Wang and Kodak.) In a similar way, Hidalgo uses case studies of successes and failures for both businesses and countries in making effective use of knowledge to drive economic success. When I read a Tom Peters book I was inspired and fired up...

This is a remarkable book, pulling together two key threads - chaos theory and economics. Doyne Farmer has a reputation as someone who breaks the mould: famously, he dropped out of studying physics at graduate level, working with a handful of others to put together a wearable computer (back in the 70s, when such a thing would have seemed pretty much impossible) to enable them to successfully beat the odds at casinos, picking up on the slight biases in roulette wheels. Now, he presents a powerful case for applying chaos theory to economics, modelling economies in a totally different, agent-driven way rather than the traditional approach taken by economists. This combines for me the impact of two books I've read and greatly admired, but in both cases had felt that there needed to be a next step. The first of these was Chaos by James Gleick, which got me all fired up about chaos theory, but proved a bit of a let down as it was great to explain why, for example, it's difficult to ...

There is a lot in the news on misinformation and disinformation - Gary Smith explores the way three factors of this kind can tarnish the public's attitude to science. He suggests that there is rising distrust of science and scientists as a result of: disinformation (telling fibs), data torturing (where data is selectively used, for example choosing the time period that most emphasises the desired result) and data mining (where big data is misused by picking up on the inevitable random correlations that occur in large quantities of data without there being a causal reason for the correlation). Smith makes the important point that in a world where we are presented with interpretations of so much data, a clear understanding of these three factors is essential if we are to make any sense of what we hear and read. While disinformation is often a problem when non-scientists present 'their truth' that is often used to attack science, data torturing and data mining is often underta...

Over the last few years a number of books, notably Sabine Hossenfelder's Lost in Math , David Orrell's Economyths , Cathy O Neil's Weapons of Math Destruction and Tim Palmer's The Primacy of Doubt , have pointed out problems with the mathematical modelling done by businesses, physicists, meteorologists, epidemiologists, economists and more. These are not anti-science polemics, but rather people who know what they're talking about pointing out the dangers of getting too carried away with elegant mathematics and models, often assuming that the models effectively  are reality (and certainly presenting them that way in some of the writing and press releases from the scientists building and using the models). Erica Thompson takes on the problems of mentally inhabiting the mathematical world she describes as 'model land'. As she cogently points out, it's fine to play in model land all that you like - the problem comes with the way that you exit model land a...

At risk of saying what everyone else is saying, this is a really important book, because it's covering something that hardly anyone seems to be thinking about, but that has a huge impact on our future. It's not really a climate change science book - for that you'd be better off with something like Bill McGuire's Hothouse Earth - it's more about the politics and economics of dealing with a huge impact of climate change will have - mass migration. Arguably this means it isn't really a science book at all (anyone who thinks economics is a real science either doesn't know what science is, or doesn't know what economics is). However, because the impending crisis is driven by a scientific issue, and has to respond to scientific forecasts, I think it's worth thinking of it within the popular science canon. What Gaia Vince does very powerfully is show how the changing climate is going to force humanity into large scale migration, with most likely well over ...

Many UK listeners will be familiar with the BBC's excellent  More or Less radio show, hosted by the Financial Times' 'undercover economist' Tim Harford. The programme takes on numbers in the news to explain them and, where necessary, show what's wrong with them, in a light but informative fashion. The only slight problem with the programme is that it does tend towards silly presentation styles (though the last couple of years these have been toned down). On his own, Tim Harford is perhaps less fun and more serious in style, but remains approachable on a subject that most of us ought to understand better. One of the most enjoyable things in the field is to shoot down misuse of stats. It's certainly an important thing to do, but Harford points out that only doing this, while entertaining, is potentially dangerous as it may lead to a total detachment from the usefulness of statistics. Instead, he suggests, we need to get better about thinking about the numbers we...

The news is riddled with numbers that we often taken for granted. Brian Kernighan sets out to give us the tools to test numbers in the headlines and see if they really add up. The fact that they often don't is made clear by the range of examples Kernighan gives where a news source has got a value wrong, whether it's out by a factor of a thousand, using the wrong units or impossibly accurate, perhaps due to a spot of calculator work converting one unit to another. This isn't the first book to take on misleading numbers - as well as the classic How to Lie with Statistics , there was the excellent The Tiger that Isn't . Although Kernighan covers many of the common errors in this slim volume, I didn't get the same sense of fascination here as I did with those earlier titles (particular The Tiger ). Kernighan gives us useful tips on checking numbers, but often the examples felt like hard work for numbers it's hard to care too much about (the US's 60 billion...

This is a very important little book ('little' isn't derogatory - it's just quite short and in a small format) - it gets to the heart of the problem with applying artificial intelligence techniques to large amounts of data and thinking that somehow this will result in wisdom. Gary Smith as an economics professor who teaches statistics, understands numbers and, despite being a self-confessed computer addict, is well aware of the limitations of computer algorithms and big data. What he makes clear here is that we forget at our peril that computers do not understand the data that they process, and as a result are very susceptible to GIGO - garbage in, garbage out. Yet we are increasingly dependent on computer-made decisions coming out of black box algorithms which mine vast quantities of data to find correlations and use these to make predictions. What's wrong with this? We don't know how the algorithms are making their predictions - and the algorithms don...

David Orrell's earlier title  Economyths is one of my favourite popular science books of all time. Or, perhaps, I should say popular non-science, as Orrell shows just how devastatingly traditional economics uses the tools of science without having a scientific basis. I was, therefore, really looking forward to reading Orrell's new book - until I saw the title. As anyone involved with physics can tell you, there's nothing more irritating than the business of sticking the word 'quantum' onto something to give a pseudo-scientific boost to waffle and woo. Was Orrell doing the same thing? Thankfully, his introduction put my fears aside. Orrell, a mathematician with a physics background quickly makes it clear that the way he is using quantum theory is not just employing magic words, but involves making use of strong parallels between the nature of quantum objects and concepts like money (more on money in a moment). Yes, this is to some extent a metaphorical use of ...