Optimizing bacteria & the value of information

One of the most exciting moments in my World Economic Forum experience was meeting Daniel Dennett, who was the first person to turn up to hear me talk about measuring the economy – he’s the distinguished Father Christmas-bearded chap on the right.

Daniel Dennett listening to meIt’s only quite recently that I read his Intuition Pumps and Other Tools for Thinking. Now I’m half way through the more recent From Bacteria to Bach and Back.

It’s interesting in part because of the read-over to economics. For example, the book talks about the reverse engineering approach to evolution – how come this creature has this feature? “Reverse engineering is methodoloigcally committed to optimality considerations,” Dennett writes. (NB this does not mean simplistic ‘just so’ stories.) The control circuits of an elevator have been top-down designed for good reasons, while the control proteins of bacteria have been bottom-up ‘designed’ (that is, evolved in their environment) for good reasons too. In economics optimisation is presented as an assumption about top-down choices, but maybe it’s more useful to consider it as a bottom-up description of how people decide to act, given their environment.

When the book turns to information – ‘a distinction that makes a difference ‘ – Dennett makes the link with economics explicit. Like other biological information, economic information is semantic information of some us to us. How useful depends on individual context – the value of information lies in the receiver. This means it can’t be measured in a non-arbitrary way (although its usefulness can often be empirically confirmed). Dennett also argues that semantic information does not need to be encoded to be useful, obvioulsy a claim of relevance to the debate about AI. (He spoke about this in Davos.)

This is interesting as it was an issue discussed in the recent ESCoE seminar by Richard Heys (on a paper I co-authored) about the price of telecommunication services. As a sort of thought experiment, we calculated a unit value index for these services – revenues divided by the volume of data used in bytes. This index declined 90% in the five years to 2015. But, one strand of the discussion went, not all units of data are equal; some are far more valuable than others. Of course. But the value is in the eye (or bank account) of the user, and what other signal of value could we select other than the decision to use?

This reminds me that I’ve been meaning to write, since I read it in October, about Jason Smith’s interesting e-book A Random Physicist Takes On Economics. I find it very hard to write about books in the Kindle app as I can’t page through them (though it seems there’s now a paperback); so all I will say is that it’s worth reading for its observations on the methodology of economics. I share some of its reservations – above all, the trouble macro has with aggregation. It also made me think about the role of context or environment, and why this might be more influential than individual choice processes in determining economic outcomes. Smith alludes to the literature on biological market theory, pointing out, though, that this does not rest at all on the utility of biological agents, be they pigeons or fungi.

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An impulse buy

I was just passing through the Blackwell’s on campus here when I spotted Lorenz by Captain Jerry Roberts. I’m a sucker for books about the code breaking efforts at Bletchley Park. I loved the wider and gripping account of intelligence efforts in the UK during the war, Most Secret War by R.V Jones.

This book is about breaking the Lorenz (rather than the Enigma) code. Captain Roberts (whom my husband met) died in 2014 but the book was just published last year. War is often the crucible of innovation but we often think of material technologies (canned foods for Napoleon’s armies, Teflon in the Cold War/space race). The immaterial technology of codebreaking and computing was surely by far the most significant, though?

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Learning about (machine) learning

Last week I trotted off for my first Davos experience with four books in my bag and managed to read only one – no doubt old hands could have warned me what a full-on (and rather weird) experience it is. The one (and that read mainly on the journeys) was The Master Algorithm by Pedro Domingos. I was impressed when I heard him speak last year & have been meaning to read it ever since.

The book is a very useful overview of how machine learning algorithms work, and if you’ve been wondering, I highly recommend it. On the whole it stays non-technical, although with lapses – and I could have done without the lame jokes, no doubt inserted for accessibility. The book also has an argument to make: that there is an ultimate ‘master algorithm’, a sort of Grand Unified Theory of learning. This was a bit of a distraction, especially as there’s an early chapter doing the advocacy before the later chapters explaining what Domingos hopes will eventually be unified.

However, the flaws are minor. I learned a lot about both the history of the field and its recent practice, along with some insights as to how quickly it’s progressing in different domains and therefore what we might expect to be possible soon. Successive chapters set out the currently competing algorithmic approaches (the book identifies five), explains their history within the discipline and how they relate to each other, how they work and what they are used for. There is an early section on the importance of data.

As a by the by, I agree wholeheartedly with this observation: “To make progress, every field of science needs to have data commensurate with the complexity of the phenomena it studies.” This in my view is why macroeconomics is in such a weak state compared to applied microeconomics: the latter has large data sets, and ever more of them, but macro data is sparse. It doesn’t need more theories but more data. Nate Silver made a simliar point in his book The Signal and the Noise – he pointed out that weather forecasts improved by gathering much more data, in contrast to macro forecasting.

Another interesting point Domingos makes en passant is how much more energy machines need than do brains: “Your brain uses only as much power as a small lightbulb.” As the bitcoin environmental disaster makes plain, energy consumption may be the achilles heel of the next phase of the digital revolution.

I don’t know whether or not one day all the algorithmic approaches will be combined into one master algorithm – I couldn’t work out why unification was a better option than horses for courses. But never mind. This is a terrific book to learn about machine learning.

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Moral sentiments and economics

Having read it some years ago, I’ve been looking through Economic Analysis, Moral Philosophy and Public Policy by Daniel Hausman and Michael McPherson while revising a paper for publication. It reminded me – if ever I absorbed it properly in the first place – that economists use the word ‘normative’ in a specific sense. It means, to us, the opposite of positive, something involving ethics or value judgements, and therefore outside the scope of what economists aim to do. We concentrate on ‘positive’ questions, following the distinction famously made by Milton Friedman, and Lionel Robbins before him.

However, as Hausman and McPherson argue, while economists claim ethical issues are a sphere apart, they/we often assume rationality – and that itself implies a moral stance. “Like morality, rationality is normative. One ought to be rational. One is wicked if not moral and foolish if not rational.” The book goes on to argue the case that the “moral principles implicit in standard views of rationality are implausible.” As a card carrying economist, I don’t agree with all they say here, but do wholeheartedly agree that economics and ‘moral sentiments’ cannot be separated. The impartiality economics aims for in trying to insist on being a ‘positive’ science is laudable, and economics should continue to distinguish the technical and value-laden elements of analysis. But we should not continue to shy away from the ethical debate. Doing so has not served the reputation of economics at all well.

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What are top people reading?

It’s always interesting to hear what books people refer to at a conference. At the end of last week I attended one with people in the UK and French business and policy worlds. These were the titles I noted being mentioned:

Life 3.0 by Max Tegmark

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WTF by Robert Peston

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I, Robot by Isaac Asimov

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Homo Deus by Noal Yuval Harari

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Les trentes glorieuses by Jean Fourastié

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Devil’s Bargain by Joshua Green

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This week I’m going (for the 2nd time – the 1st was in 2001) to the World Economic Forum in Davos – feeling a bit like one of the entertainers on board a cruise liner, as I’ll be talking about measuring the economy. I’ll report back on the reading materials of the global elite. I’ll miss Donald Trump’s speech on Friday but doubt he’ll be citing many books, so that’s ok.