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Algorithms to Live By - Brian Christian and Tom Griffiths *****

I was captivated by much of this book. It's the perfect antidote to the argument you often hear from young maths students - 'What's the point? I'll never use this in real life!' This often comes up with algebra (which often is useful), but reflects the way that we rarely cover the most applicable bits of maths to everyday life at high school. Although this book is subtitled 'the computer science of human decisions', it's really about the maths of human decision making (which is often supported by computers) - I suspect the 'computer science' label is to make it more sexy than boring old mathematics.

 If there is any danger that the 'M' word would turn you off, the book tends to skip over the mathematical workings, concentrating on the outcomes and how they're relevant to the kind of decisions we make in everyday life - and it's that application side that makes it particularly interesting (helped by a good, readable style from the co-authors). So, for instance, one of the earliest areas covered is the kind of decision where you are selecting between a number of options that arrive sequentially and where you have to make a decision on which is best for you part way through the sequence, even though there may be better options in the future. The classic examples for this are some kinds of job interviews, house buying and finding a partner for life.

 It might seem there can be no sensible advice, but mathematically it's very clear. You wait until you've got through 37% of the choices, then pick the next one that's better than any you've seen before. It's not that this will necessarily deliver your best of all possible worlds. More often than not it won't. But it will give you a better result than any other mechanism for deciding when to go for a particular option. Of course it's not always easy to apply. For example, unless it's something like an interview with closed applications, how do you know when you are 37% of the way through the available options? Luckily, the authors point out that there are approximations to get around this, which include that the approach can also apply to the amount of time available for the process.

 And that's just the start. Along the way you will discover the best way to sort the books on your shelves into alphabetic order (something I confess I did last year, using a sub-optimal mechanism), how to balance exploration (for example, trying out new restaurants) with exploitation (for example, returning to tried and tested restaurants), how the concept of caching can revolutionise your filing system (and make that pile of papers on your desk that everyone mocks the sensible approach), why Bayes theorem is so important and much more. I absolutely revelled in this book.

 The content only fades a bit when the applications aren't about real world decisions. So, for instance, there's some material about how the internet works that is very interesting if you like that kind of thing (I do), but hasn't got the same feeling of personal utility to it, so lacks some of the bite of the other chapters. This is even more obvious in the section on randomness. I would also have liked to see more acknowledgement that most of the content was really from the area of study called operational (operations in the US) research, a discipline that happens to make use of computers, rather than true computer science - but that's a specialist moan.

 Realistically speaking, I don't think much of the content of this book will truly change how any of us do things. Interestingly, the authors reveal than an expert in the field pretty much consciously ignored the mathematical approach in a particular case, opting for more of a 'feels right' choice. But that doesn't stop the whole business, whether it's the relative simplicity of the 37% rule or the mind twisting possibilities of game theory, from being both potentially practical and highly enjoyable as presented here. Recommended.
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Author interview
Review by Brian Clegg
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