Politics and numbers

I’m thoroughly enjoying William Deringer’s Calculated Values: Finance, Politics and the Quantitative Age – almost finished. The book asks, why from the early 18th century did argument about numbers, statistics, come to have a special weight in political debate? Often, the growing role of statistics (as in numbers relevant to the state) is seen as part of the general Enlightenment spread of scientific discourse and rationality. Deringer argues that in fact the deployment of numerical calculation about the state of the nation was one of the weapons of choice in the bitter political division between Whigs and Tories. The sphere of public debate was, as he puts it, “uncivil, impolite, sometimes irrational”.

The book presents plentiful evidence of this use of numbers. For instance, pamphlets proliferated about hot issues such as the payment to be made to Scotland for its Union with England, or the balance of trade – and were packed with errors. Printers, and presumably readers, “did not seem to care about getting the numbers right.” They were only there to slug against somebody else’s numbers. Deringer writes (in the context of the fierce debate about the ‘Equivalent’ England should pay Scotland on their Union], “Critics of quantification in the modern era argue that efforts to address political questions through quantitative means often have the effect of foreclosing political controversy by translating substantive political questions into ‘technical’ ones. This was not the case in 1706; if anything, the opposite was true. The Equivalent calculation brought new (technical) questions into public view and revealed their political stakes.”

The book therefore argues that the emergence of partisan politics after the 1688 Revolution was a major reason for the emergence of the political life of numbers. Starting with a more or less blank slate, a good part of the contest was epistemological: what were the right categories and classifications to even begin to measure? What was the right methodology? For example, one innovation was the introduction of discounting future values, something many contemporaries found both hard to understand and unsettling: “It placed almotst no value on anything that happened beyond one human lifetime. This peculaira claim clashed violently with many Britons’ intuition about what the future was worth to them.” It probably still clashes with intuition, to the extent the typical Briton thinks about it.

Of course, one can’t read this history and not think about the notoriously misleading £350m claim on the Brexit bus, or the claim and counter-claim about the likely Brexit effect on UK trade. It isn’t that both sides are equally true or false – far from it – but the political clash is putting statistics centre stage. David Hume was sceptical about the usefulness of the quantitative debate for exactly this reason: “Every man who has ever reasoned on this subject, has always proved his theory, whatever it was, by facts and calculations.” I’m with Deringer in concluding that nevertheless the conversation about numbers is essential.

As Calculated Values concludes, “modern quantitative culture is fundamentally two-sided.” People think numerical evidence has a special, trustworthy status; and at the same time that it’s especially easy to lie with statistics. These are two sides of the same coin. The adversarial debate reveals how useful the numbers and calculations can be to interrogate and test the opposing claims.

Attitudes have shifted over time. In the 19th century, the partisan contest abated. Dickens for one saw numbers as a dry, soulless window on society when he portrayed Mr Gradgrind in Hard Times. The bureacratisation of statistics, and development of official agencies, made statistics seem just a tiny bit dull. Needless to say, they are centre stage again now for reasons of both political conflict and epistemological uncertainty. Once again, some politicians wield numbers without any great concern about their accuracy or meaningfulness; the victory in debate is all that matters. Once again, given the profound changes in the structure of the economy, we can’t be sure what categories and methods will give us the understanding we would like. This is a terrific book for reflecting on contested and uncertain statistical terrain.

Price: £32.42
Was: £32.95
Share

Morals and economics

Every time I read something about Karl Polanyi’s The Great Transformation – and it’s in vogue now – I go back to my copy and confirm how much it annoys me. It’s the over-statement or pomposity that does it, rather than the broad outlines: markets mean inevitable cataclysm. “Our thesis is that the idea of a self-adjusting market implied a stark utopia. Such an institution could not exist for any length of time without annihilating the human and natural substance of society; it would have physically destroyed man and transformed his surroundings into a wilderness.” He goes on to argue that the social crises and conflicts of the early 20th century were caused by the disruption to the market and economy caused by the reactions to the market forces leading to social annihilation.

Similar arguments have been made by many others, from Daniel Bell (The Cultural Contradictions of Capitalism) to any number of left-leaning authors. The Moral Economists by Tim Rogan puts Polanyi in the context of a succession of critics of capitalism, preceded by R.H.Tawney and succeeded by E.P.Thompson, whose common territory was a rejection of utilitarianism: “The moral economists argued that the solidarities they found in Lancashire, Red Vienna and Yorkshire harbored unique promise: here social interaction was more meaningful than utilitarian analyses allowed, without becoming regimented in the way of so many contemporary social experiments.” They shared a more rounded sense of human personality than homo economicus in the utilitarian analyses, as well as a conviction about the role of social interaction and solidarity in economic outcomes. Tawney, for instance, opposed Fabian socialism because of its dry utilitarianism.

Rogan gives Polanyi a sympathetic reading, noting that he regarded Adam Smith as a moral economist, with the decline into ‘economism’ coming later – this is a reading of Smith, and emphasis on The Moral Sentiments, that has become more prominent in the past decade or so. The Moral Economists argues that the Tawney/Polanyi intellectual agenda was stymied, however, by the postwar turn away from religion in particular and traditional moralism in general. For this reason, it argues, E.P.Thompson was unable to reinvigorate the moral critique of capitalism. However, Rogan asks, surely the critics of contemporary capitalism need to restore a role for morality or virtue in a secular world?

The book ends with a section on the inadequacy of modern welfare economics based on the Pareto optimality idea, and is sympathetic to Sen’s approach. I agree about this. Rogan ends: “Politics pervades commercial societies, frustrating technocratic visionaries of the 21st century [Bell would agree about this too] just as it confounded the goat-and-greyhound utilitarians of the 19th century. … In an age of extremes, the moral economists discovered in their midst the elements of humane, solidaristic, low-key and non-authoritarian politics of reform.” Can we do the same in today’s context of extremes and the all-too-apparent flaws of the current version of capitalism?

It’s an interesting book, and I agreed with much of the argument about putting virtue back into economics, although I find ‘capitalism’ (without further explanation) an unhelpful abstraction looking across such a long and eventful timespan.

(But I’m still not going to change my mind about Polanyi.)

Share

Still grumpy about compulsory happiness

Richard Layard has been a powerful advocate for the use of well-being or happiness as the aim of government policy for many years now. The new book he has co-authored with other happiness researchers, The Origins Of Happiness: The Science of Well-Being Over the Life Course, is a useful overview of the now-large body of empirical work exploring the links between measures of happiness (I’m going to use the book’s shorthand) and potential explanatory factors. It looks at both adult outcomes and child development. There is a substantial bibliography and excellent index. And, while not a completely easy read for the general audience – as technical jargon does slip in – it’s also very accessible.

My ‘but’ is not about the book specifically but about the advocacy of a well-being policy target in general. The applied work linking potential causal explanators to individual happiness is persuasive, and it’d be hard to argue with the kind of policy conclusions one might draw: keep employment high and stable; fund mental health care far more generously; aim to have a high trust society. Some conclusions are equally persuasive without having obvious policy implications: children need a stable and loving family more than they need a high income family; family conflict is bad for children’s well-being.

I was less familiar with the work reported in the book on education, and am not sure what to make of these sections. The empirical claim is that additional years of education contribute relatively modestly to individual happiness, and this is almost outweighed by the negative effect of comparing oneself to others: “Extra education brings considerable benefits (direct and mediated [via higher income]) to the individual. But these are substantially offset by the negative effect of one person’s education on others in the peer group.” In contrast to the received wisdom, in other words, education has negative rather than positive externalities. Maybe, this chapter concludes, it’s ok to continue with higher education because there will perhaps be some civic benefits. The unwritten coda is that the authors might like to see the education arms race halted – just as they want to policies to end the income arms race, for their conclusion is that people in the main care about their relative status, and the only way to stop this making them unhappy is to stop them trying.

Less jawdropping is a chapter on the influence of schools on children’s well-being. Its conclusion is that there is great variation between schools, and a good school has a far better impact on children’s well-being, attainment and behaviour than a similar amount of money spent by the individual family. The result doesn’t seem to be due to individual teachers, so the cause lies in the institutional context. This seems interesting and well worth further exploration, especially given the wide variation between schools.

The book’s main message is the same as the original Easterlin paper: rising incomes do not translate into rising happiness, and we could all be happier if we stop pouring our energies into positional outcomes such as income, where we compare ourselves with other people. Anyway, the argument goes, the psychological mechanism of adaptation or habituation moderates the benefit of rising incomes. Therefore policymakers should aim to increase happiness, not incomes. This seems illogical to me: if we are all going to return to a happiness set-point after a period, why bother with policies trying to increase happiness? (And never mind the point that happiness data by construction range from 0 to 10, with the great majority of people in the top half of that range; while income measured by real GDP is an analytical construct that can in theory rise without limit. Am I missing something here? It rarely gets mentioned in the discussions of the happiness lit.)

In sum, I’m in full agreement with the authors about policies to improve some of the factors that clearly affect people’s well-being. But I still don’t want well-meaning economists and psychologists (all with PhDs) trying to make people happy, utilitarian engineers of souls – still less politicians.

I also just read Animals Strike Curious Poses by Elena Passarello and was hugely disappointed. It had rave reviews, and is about the relationship between humans and animals. Although she obviously knows a lot – and some sections of the book were very interesting – it’s massively over-written and veering into the kind of creative writing task you set school children: “Write about how it feels to be a woolly mammoth being hunted.”

Price: £10.04
Was: £12.99

 

Share

Civilisation: primeval slime to Mars

I finished Daniel Dennett’s From Bacteria to Bach and Back, and personally have no problem with his view that human consciousness is an evolved characteristic built over time from the ground up, and that human culture evolves too, starting with language and continues through memes. In other words, it’s all cranes, not skyhooks. Some people are obviously troubled by this argument. I lack the technical knowledge to evaluate all the detail here. It fundamentally seems far more plausible to me than the alternative.

As a matter of logic, this requires me – and Dennett – to take seriously the argument that computers/AI could evolve minds and consciousness. He puts some weight on the importance of embodiment – but that might be possible although we’re not there yet. Computer vision would be different from ours, but then so is flies’ vision or cephalopods’.

More of an issue, it seems to me, is that computers/AI are very energy-hungry compared to our brains: at the moment, Dennett writes, computer intelligence is parasitical, depending on humans to feed them a lot of energy and otherwise maintain them. What’s more, computers don’t have to struggle or compete: “Down in the hardware, the electric power is doled out evenhandedly and abundantly; no circuit risks starving. At the software level, a benevolent scheduler doles out machine cycles to whatever process has the highest priority, and although there may be a bidding mechanism … this is an orderly queue, not a struggle for life.”

So for now, I’ll stick to thinking Singularity-talk is mystical hype; but will try to keep an open mind on this question.

I’ve always had a soft spot for memes. Dennett uses words as the paradigmitic examples. It reminded me of a jokey line I read once about libraries being the dominant life form on Earth because they are so good at finding new hosts who will start to accumulate books.

There’s a nice section on the importance of social trust at the end of Bacteria/Bach, citing Paul Seabright’s wonderful Company of Strangers. Trust is the invisible glue of human societies, Dennett writes, and much too recent to be a hard-wired natural instinct. “We have bootstrapped ourselves into the heady altitudes of modern civilisation, and our natural emotions and other instinctual responses do not always serve our new circumstances. Civilisation is a work in progress and we abandon our attempt to understand it at our peril.”

Looking at the news these days, that isn’t a very optimistic note on which to end. And yet yesterday brought the amazing launch of Space X’s Falcon Heavy. Astonishing. Perhaps we’ll end up on Mars while the computers colonise Earth.

 

Share

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.

Share