Finished on Wed, 02 Apr 2014 13:35:10 -0700. Rating 3/5.
Finished on Wed, 02 Apr 2014 13:35:10 -0700. Rating 3/5.
A fantastic exploration of the purpose and dangers of economics and its effects on society.
(Full disclosure: like Philip Roscoe, I work at the University of St Andrews, and we know each other slightly.)
Roscoe's main observation is of a fallacy, that economics is a descriptive endeavour. Instead, he argues that it becomes normative: it causes people to enact the behaviour that it thinks it simply describes. Introducing an economic model into a situation causes those concerned to act in accordance with the model, rather than (as we often think) the model simply exposing behaviours and motivations that were there already. Roascoe supports this hypothesis with a fabulous range of examples, from Norwegian fishermen to prostitution and internet dating.
In many ways this argument is reminiscent of that made by Adam Curtis in The Trap. Mathematical and scientific models are necessarily abstractions of the agents and phenomena they describe, but when these models are used to design systems, we can find that – rather than the systems failing because the models are incomplete – the people involved respond to the systems' incentives by becoming more like the simplified abstractions. What game theory is for Curtis, economics is for Roscoe: simplifying assumptions lead to a reduction in human richness and behaviour through the power of incentives.
One can read this book as a call to arms to reclaim economics (and society) by embracing these characteristics about systems and people, and to design systems with this power in mind: systems that enrich and encourage people rather than simply following the dictates of money and its tendency to further enrich the already wealthy at the expense of the poor. That would be a noble outcome for what is a fascinating piece of work
Finished on Sat, 29 Mar 2014 12:53:26 -0700. Rating 5/5.
A tour through the history of media, beginning with Cicero's speeches and letters and continuing through to the familiar Facebook and Twitter.
The author asks a simple question: are social media, and the questions they raise about people's interaction styles, really new phenomena? He asked similar questions about the internet as a whole in The Victorian Internet, his (excellent) history of the telegraph system, and the answer he arrive at here is similar. Unlikely though it may seem at first glance, the majority of the history of media has actually been of social media, in the sense of information flowing informally along the relationships between individuals. The recent prominence of newspapers, radio, and television have blinded us to the fact that these broadcast media are actually historical anomalies, expressing a centralising and one-way tendency that is singularly unusual in the annals of human communication.
For me, the most interesting observation came in the epilogue. Through fretting about the impact of social media, families sometimes adopt strategies like "Unplugged Sunday" where internet-connected devices are banned in favour of more communal pursuits – and these pursuits may involve watching television together, making use of a technology that was previously targeted as the destroyer of family time. (The same might be said of reading novels, targeted in their turn in an earlier age.) Yesterday's radical, disruptive technology becomes today's comfort blanket sanctified by time and familiarity with startling regularity.
Finished on Sat, 22 Mar 2014 09:56:10 -0700. Rating 4/5.
An excellent discussion of ecology and ecological services. Essentially the author is putting the case for measuring the value associated with various elements of nature and their interactions, as a way to make more compelling arguments for conservation and environmental protection. This is an approach I agree with very strongly: while the emotional arguments are of course very strong, backing them up with numbers and prices can only be helpful.
The author doesn't make the mistake of so many books of this kind, of painting a picture of ecological damage that's irredeemable in any realistic sense. One can argue whether or not this is actually the case, but it's certainly true that encouraging a state of learned helplessness amongst the citizens of the developed world isn't going to be helpful.
The book covers a huge range, and everyone will find a new perspective on some familiar aspect of nature, from the evolution of pollinators to the demise of the oyster beds off the New England coast and their possible effects on the behaviours of hurricanes.
In terms of economics, the author makes a couple of points, one familiar and one less so. The point of including the "externalities" of natural services into prices and company accounts is still strong despite its familiarity: the problem remains coming up with good pricing structures. The less familiar point, though, is that companies treat the services they receive from nature as dividends that are renewed rather than as capital being spent, although many ecosystems have now passed the point of un-managed self-recovery, and so their degradation should be costed in. Having long-term investors think like this would, it is argued, have a significant effect on company behaviour in encouraging them to behave more sustainably. While a lot of the degradation is coming from the other end of the economic spectrum -- subsistence farmers trying to make a living in competition with more efficient large-scale actors -- there's still a lot to be said for this approach.
Finished on Mon, 17 Mar 2014 12:19:50 -0700. Rating 4/5.
With all the hype about big data it's sometimes hard to realise that it's about more than just data. In fact, it's real interest doesn't come from big-ness at all.
The term big data is deceptively hard to parse. It's a relative term, for a start: when you get down to it, all it really means is data that's large relative to the available storage and processing capacity. From that perspective, big data has always been with us -- and always will be. It's also curiously technology-specific for something that's garnering such broad interest. A volume of data may be "big" for one platform (a laptop, for example) and not for others (a computing cluster with a large network-attached store).
Getting away from the data, what researchers typically mean by big data is a set of computational techniques that can be applied broadly to data somewhat independent of its origins and subject. I'm not a fan of "data science", but the term does at least focus on techniques and not on data size alone. This is much more interesting, and poses a set of challenges to disciplines -- and to computer science -- to identify and expand the set of techniques and tools researchers can make use of.
What often frightens people about big data (or makes it an attractive career niche, depending on your point of view) is that there is a step-change in how you interact with data beyond a certain size -- and it's this change in tooling requirements that I think is a more significant indicator of a big-data problem than simply the data size.
Suppose you collect a dataset consisting of a few hundred samples, each with maybe ten variables -- something that could come from a small survey, for example. This size of data can easily be turned into a spreadsheet model, and a large class of researchers have become completely comfortable with building such models. (This didn't used to be the case: I had a summer job in the late 1980's building spreadsheet models for a group that didn't have the necessary experience. Research skills evolve.) Even a survey with thousands of rows would be possible.
But what if the survey has several million rows? -- for example because it came from an online survey, or because it was sensed automatically in some way. No spreadsheet program can ingest that much data.
A few million rows does not constitute what many people would regard as "big data": it'll be at most several megabytes. But that's not the point: rather, the point is that, in order to deal with it, the researchers have to change tools -- and change them quite radically. Rather than using a spreadsheet, they have to become programmers; not just that, they have to become programmers who are familiar with languages like Python (to get the libraries), and Hadoop and cloud computing (to be able to scale-out the solutions). They could employ programmers, of course, but that removes them from the immediate and intimate contact with their data that many researchers find extremely valuable, and that personal computers have given us. To many people, hiring a programmer and running calculations in the cloud is suspiciously like a return to the mainframe computing we thought was rendered obsolete decades ago.
It's not the terabytes, it's the tooling. Big data starts when you cross the threshold from your familiar world of interactive tools and into a more specialised, programmers world.
This transition is becoming common in humanities and social sciences, as well as in science and medicine, and to my mind it's the major challenge of big data. The basic problem is simple: changing tools takes time, expertise, and mental effort, that is taken away from what's a researcher's actual research interest. A further disincentive is that the effort may not be rewarded: after all, if this really is research, one is often not sure whether there actually is anything valuable on the other side of a data analysis. In fields where competition is really competitive, like medicine, this feels like a lot of risk for an uncertain reward. There's evidence to suggest -- and I can't prove this contention -- that people are steering clear of doing the experiments they know they should do because they know they'll generate data volumes they're uncomfortable with.
This, then, is where the big data challenge actually is: minimising the cost of transition from tools that are familiar to tools that are effective on the larger data volumes that are now becoming commonplace. This is a programming and user interface challenge, to make the complex infrastructure appear easy and straightforward to people who want to accomplish tasks on it. A large challenge, but not an unprecedented one: I'm writing this just after the 25th birthday of the World Wide Web that took a complex infrastructure (the internet) and made it usable by everyone. Let's just not get too hung-up on the idea that data needs to be really big to be interesting in this new data-driven world.
"Data science" and "data scientist" are not good terms -- for anything.
The recent revolution in our ability to monitor more and more human (and indeed non-human) activity has spawned a whole new field of study. Known variously as "big data", "data science", and "digital humanities", the idea is that -- by studying the data collected -- we can perform feats of prediction and customisation that defeat other approaches.
This is not all hype. There's no doubt that deriving algorithms from data -- known as "data-driven design" -- can often work better than a priori design. The best-known example of this is Google Translate, whose translation approach is driven almost entirely by applying statistical learning to a large corpus of documents with for which exact translations exist across languages, and using the correlations found as the basis for translation. (The documents in question were actually the core agreements governing the operation of the EU, the acquis communautaire, which explains why Google Translate works best on bureaucratese and worst on poetry.) It turns out that data-driven learning works better in most cases than grammar-directed parsing.
The data-driven approach rests on several pillars. Chief among them is applied machine learning as mentioned above, allowing algorithms to look through bodies of data and learn the correlations that exist between events. (We use similar techniques to analyse sensor data and perform situation recognition. See Ye, Dobson and McKeever. Situation identification techniques in pervasive computing: a review. Pervasive and Mobile Computing 8(1), pp. 36--66. 2012.) Various other statistical techniques can also be applied, ranging up from simple mean and variance calculations. One can usually augment the basic techniques using more structural methods: if you know the structure of the links in a web site, for example, you can learn more about how people navigate because you know that some routes are more probable (by clicking hyperlinks) than others. The results of these analyses then need to be presented as analytics for consumption by managers and decision-makers, and distilling large volumes of information into visually compelling and comprehensible form is a skill in itself.
Going a stage further, one may conduct experiments directly. If you see people searching for the same term, present the results to different people in different ways and see how that influences the links they click. Google again are at the forefront.
The excitement of these techniques has spilled over into the wider science and humanities landscapes. Just within St Andrews this week I've talked about projects for analysing DNA data using machine learning, improving clinical trials, building a concept map for two centuries-worth of literature pertaining to Edinburgh, detecting intruders in computer systems, and mapping the births, deaths, and marriages of Scotland from parish records -- and it's only Wednesday. All these activities are inherently data-driven: they only become possible because we can ingest and analyse large data volumes at high-enough speeds.
However, none of this implies that there is a subject called "data science", and I'm starting to worry that a false premise is being established.
The term "data science" is a tricky one to parse. How many sciences do you know that aren't based in data? "Data-driven" is the same. That's not to say that they're meaningless, exactly: they identify a sub-set of techniques that are qualitatively different to the more theory-driven approaches, and even differentiate themselves from experimentally-driven approaches by their attempt to correlate across datasets rather than being based on a single homogeneous sampling (however large). But that's a nuanced reading, and a general reader would be forgiven for believing that "data science" was somehow a separate discipline from "ordinary" science, instead of it denoting a set of computational techniques with general applicability across the range of (non-)traditional sciences.
But it's "data scientist" that really troubles me. This seems to suggest that one can find scientific meaning in the data and the data alone. It seems to suggest that there's a short-cut to scientific insight through the data (and by implication, computer science) rather than through traditional scientific training. And this simply isn't true.
The problem is the age-old different between correlation and causality. Correlation is when things happen together: you leave a cup of a tea and a biscuit on a table for long enough, the tea goes cold and the biscuit gets stale. Causality is when one thing happening triggers another thing happening: the tea got cold, and that made the biscuit become stale. Mistaking one for the other leads to all sorts of interesting possibilities: if we put the tea in an insulated cup, the biscuit will stay fresh longer.
Now, the final tea-and-biscuit example is clearly meaningless, but ask yourself this: how did you recognise it as meaningless? Because you understand that the two effects are happening independently because of the passage of time, not because of each other: they are correlated but not causative. You understand this because you have insight into the processes of the world.
And it's here that the problems start for data scientists. In order to interpret the machine learning, statistics, analytics, or other results, you have to have an understanding of the underlying processes. It doesn't happen in the data at all. That's fine for tea and biscuits, and is also probably fairly fine for sales of consumer goods on mass-market web sites, where we have a good intuitive understanding of the processes involved, but will drop-off rapidly as we approach areas that are more complex, more noisy, less intuitive, and less well-understood. How can you differentiate correlation from causality if you don't understand what's possible in the underlying domain? How, in fact, can you determine anything from the data in and of itself?
This suggests to me that data scientist isn't a job description, or even an aspiration: it's a misnomer that should really be read as "a trained scientist working with lots of rich empirical data". Not as sexy, but probably more useful and less likely to disappoint everyone involved.
Whatever your view on Eastern philosophy, this is a book worth reading for its poetry alone. This particular edition, with careful translations and wonderfully atmospheric accompanying photography, is a complete joy and a perfect encapsulation of the words.
Finished on Sun, 09 Mar 2014 14:33:54 -0700. Rating 5/5.
I came to this book at the recommendation of a friend, and it's one of the best I've read recently.
The book tells the story of the meeting of psychiatrist Douglas Kelley with Reichsmarschall Hermann Goering in the Nuremberg gaol where the latter was on trial. It's mainly a book about Kelley, Goering's personality having been dissected enough in other works (not least Kelley's own 22 cells in nuremberg, which is itself well worth reading for eyewitness value). It follows Kelley from his childhood as the driven and over-achieving child of a famous, eccentric, Californian family; through his committed treatment of shell-shocked soldiers and his appointment to oversee the mental health of the Nuremberg inmates; to his later career and eventual suicide. The fascination of his suicide is that Kelley chooses the same method as Goering himself -- cyanide -- in a dramatic and public gesture that seems to lack any real motive.
Kelley's commitment to traumatised soldiers notwithstanding, he was not an attractive personality. His psychiatric certainty is alarming, especially given his reliance on Rorschach blot tests and truth drugs that would not nowadays be well thought of. The author does a good job of highlighting the controversy that Rorschach interpretation engendered, even though the consensus now favours Kelley's view that there was no "Nazi personality" or particular criminal characteristic that set the Nuremberg prisoners apart.
This really is an excellent read, and the only reason for giving it only four stars instead of five is this (which may be an unfair criticism anyway): the author never really nails the interaction between Kelley and Goering, in the sense of how the experience affected Kelley's personality. The doctor comes across as self-absorbed, opinionated, and controlling. He steps into the limelight whenever possible, offering his opinions with a quite hair-raising certainty, and one can't quite escape the suspicion that his certainly could easily have abetted miscarriages of justice in his later career. He tries to shape his eldest son in a particular image, is withdrawn and self-absorbed, and kills himself in what almost seems like a fit of pique.
But despite the book's title, the core question remains unanswered: to what extent was Kelley shaped by Nuremberg, and by Goering in particular? Did the experience of the prison change him from confident to arrogant, or was that transition inevitable and perhaps just slightly reinforced? He clearly always had a yearning for fame, and yet received his most famous assignment largely by chance. Was the experience formative for him (as wartime experiences were for so many), or did it simply provide a lever by which to accomplish pre-existing goals? I suspect Nuremberg affected Kelley less than one might imagine, his self-absorption protecting him while allowing him to function. In that sense he was the perfect choice for Nuremberg psychiatrist.
Finished on Fri, 07 Mar 2014 01:49:39 -0800. Rating 4/5.
Finished on Thu, 06 Mar 2014 12:45:03 -0800. Rating 5/5.
A short exploration of the state of modern internet and social experience. In some ways the book is mis-named, in that it isn't in any way prescriptive or suggestive of how one should thrive, but rather illustrates some of the issues one should consider in order to: such issues as privacy, time away, imaginary vs real experience, and the like. Definitely worth a read, and with an excellent bibliography pointing to further information.
Finished on Tue, 25 Feb 2014 00:50:33 -0800. Rating 3/5.