The middleware doughnut

Where has the "middle" of middleware gone?

This week I attended a couple of the workshops at the Middleware conference, the main venue for the research community working on systems integration middleware. In particular I was an invited speaker at the Future of Middleware (FoME) workshop, which attracted a great bunch of people with views on where the field is going.

Listening to all the talks, one of the things that jumped out was the diversity of concerns people were expressing. Those working at enterprise level were concerned with scaling-out systems to millions of users using cloud computing; at the other extreme were sensor networks people like me, worried about programming  low-powered sensor motes with at least a semblance of programming language support and software engineering process. That a group of people with this spread of interests can find useful things to talk about together says a lot about how broad a term middleware actually is.

But if we compare this to years past, it was also clear that the concerns  asymmetrically affected the different groups. There were very few issues that really commanded broad interest. This led me to wonder: where has the "middle" gone from "middleware"?

In the 1990s, middleware people (including me) were working with CORBA and the like. These systems were intended for broad application in integrating client and server components into single systems. In CORBA's case this involved designing object-oriented domain models that could then be implemented using any chosen programming language and support interactions seamlessly, regardless of  implementation or distribution. CORBA provided (and indeed provides) a lot of supporting infrastructure, including dedicated wire protocols, a shared type system and object model, higher-level services, binding, call models and other groovy toys. It achieved enormous penetration into markets that value long-term interoperability and evolution, such as financial services. It also influenced a whole range of subsequent developments, including web services and service-oriented architectures that are, to some extent, defined by their similarity to, or differences with, CORBA. (For a discussion of these differences see Baker and Dobson. Comparing service-oriented and distributed object architectures. LNCS 3760. 2005.)

As is pretty much apparent from this, CORBA sits resolutely in the middle of middleware. It is intended to integrate disparate systems and allow them to work together, and evolve somewhat separately, while managing the complexity of the overall architecture. It tries to avoid the problem, identified by Bertrand Meyer, that large systems exhibit non-linear behaviour in which the cost of making any change to them is proportional, not to the size of the change being made, but to the size of the system being changed. It doesn't completely succeed in this, of course -- no system does -- but it provides a common centre around which to organise largely independent components.

Another way of looking at CORBA is less flattering: that it was inherently compromised by conflicting, and in large part irreconcilable, goals. It was reasonably performant, but by no stretch of the imagination high-performance given the overheads of a complex and general-purpose wire format. It was reasonably portable, as long as one accepted the limits imposed by the single type system: no first-class functions or mobile code, for example. It was reasonably easy to port and to support new languages, but every new language binding did require considerable ingenuity both in terms of technical design and standardisation of interfaces.

Sitting in the middle, in other words, was tricky and uncomfortable.

The causes of, and justifications for, these compromises aren't hard to find: what else can one do if one is trying to support the whole range of applications? Each piece of middleware sits at a particular point in the design space, trading-off performance against generality for example, or advanced typing against making bindings awkward or impossible for some languages.

And it's this generality that seemed to be missing from discussions of the future of middleware: no-one intends to support this range any more. Instead we're seeing a division of the design space in which different application communities focus on one or two design dimensions that are undoubtedly the most important -- and largely forget the rest. For Twitter, for example, the main design goal is lightweight interaction at clients so that Twitter clients are easy to writ. They have no concern whatever with typing or reliability: if tweets are lost, who cares? For the web services community -- perhaps closest to the "spirit of the middle" -- the issues are extensibility and use of standards, with no concern for protocols, performance or end-point complexity. It's fairly easy to see that these design issues are simply too diverse to be spanned by a single middleware platform.

I don't think this necessarily spells the death of integrating middleware -- and that's just as well, given that we still have to integrate these systems despite their increasing heterogeneity. What it does do, though, is change the locus of innovation away from ever-larger, more complex and more general platforms towards more specialised platforms that can integrate as far as needed -- and no further, so as not to over-burden applications or developers. Several speakers talked about using component-based approaches to build platforms as well as applications. In our talk we discussed similar ideas, removing the a priori assumptions underlying middleware platforms and focusing instead on how to optimise what hits the metal. (Dearle and Dobson. Mission-oriented middleware for sensor-driven scientific systems. Journal of Internet Services and Applications. 2011.) This will give rise to a whole range of further challenges -- how do we guarantee the right features are available? How do we add (and remove) features on the fly? How do we find the features we need? --  that are radically different from those encountered for CORBA and similar systems. But the result will (hopefully) be to improve our ability to create, manage and evolve ever more sophisticated combinations of applications and  services, and make it easier to roll-out and scale-out the next generation of applications and scientific experiments.

Inaugural lecture

I will be giving my inaugural lecture on the topic of "The computer is the new microscope."

It's traditional when appointed a full professor in the UK (strictly speaking, when appointed full prof for the first time) to give an open lecture on a topic of your choice. Because the lecture is open to anyone, they have to be very accessible (well, as accessible as the academic can make it, which often diverges from the normal meaning of "accessible"). My choice of topic follows on from a subject I've been thinking about for a while, that computers change both the way we do science and the science we do, and that this needs to be more fully understood within both communities.

Date: 7 December, 2011 Time: 1715 Venue: Lecture theatre, Medical and Biological Science Building, University of St Andrews, North Haugh, St Andrews, Fife

Followed by a reception in the School of Computer Science next door.

[UPDATE 16Dec2011: the slides are now available.]

Funded research studentships available in St Andrews

We are looking for world-class students to come and work with us. This is open to individuals of any nationality, not just UK or EU citizens. I'm especially interested in people wanting to work on programming languages, but I'd welcome inquiries in any research areas that align with mine.

Funded PhD Research Studentships

University of St Andrews - School of Computer Science

The School of Computer Science at the University of St Andrews has funding for students to undertake PhD research in any of the general research areas in the school: We are looking for highly motivated research students with an interest in these exciting research areas. Our only requirements are that the proposed research would be good, we have staff to supervise it, and that you would be good at doing it. We have up to 6 funded studentships available for students interested in working towards a PhD. The studentships offers costs of fees and an annual tax-free maintenance stipend of about £13,590 per year for 3.5 years. Exceptionally well qualified and able students may be awarded an enhanced stipend of an additional £2,000 per year. Students should normally have or expect at least an upper-2nd class Honours degree or Masters degree in Computer Science or a related discipline. For further information on how to apply, see our postgraduate web pages ( The closing date for applications is March 1st 2012 and we will make decisions on studentship allocation by May 1st 2012. (Applications after March 1st may be considered, at our discretion.) Informal enquiries can be directed to or to potential supervisors.

Studentships in wireless sensor networks in Leuven

A number of PHD positions are available with Danny Hughes in the Netherlands.

Wireless Sensor Networks are expected to play a key role in future Internet architectures and the Internet of Things. The DistriNet research group of the Katholieke Universiteit Leuven wishes to expand its Wireless Sensor Network (WSN) research team. You will play a key role in a highly integrated team, which has a strong and growing research profile in the area of middleware and software development support for WSN. Three positions are available, starting immediately.

Possible Research Topics: • System-level support for realizing WSN applications that are reliable and reconfigurable from the lowest-levels of the network stack to the application layer. • Development-time support for building autonomic and reconfigurable applications for networked embedded systems and sensor networks. • Distributed component based software engineering approaches for networked heterogeneous networked embedded systems such as sensors, smart phones and web pads.

Profile & Skills: • The student must demonstrate significant enthusiasm for, and knowledge of the subject area based upon their previous education, work and research experience. • A master degree in Computer Science or Informatics with previous experience in: Distributed Systems, Middleware, Embedded Systems or related areas. • A team player with the capability to work in an international research team. • Proficiency in English and excellent communication skills, both oral and written.

About DistriNet:

DistriNet is an international research group with extensive expertise in secure and distributed software – obviously including middleware . Research on software engineering techniques is performed in both domains on a wide range of topics such as middleware for cloud computing, internet architectures, network and software security, embedded systems and multi-agent systems. Embedded in the department of Computer Science of the K.U.Leuven, DistriNet has a headcount of over 70 researchers of which 9 professors and 15 Post-Docs.

DistriNet has a tradition of application driven research often in close collaboration with industry partners. Currently DistriNet is actively involved in about 35 national and international research projects, ranging from fundamental over strategic-basic to applied research. More information on projects and publications can be found at the IBBT-DistriNet web pages:

The three open positions are situated within a team that has been established to provide software development support for Wireless Sensor Networks.

Key words: Internet of Things (IoT), Networked Embedded Systems, Wireless Sensor Networks (WSN), Middleware, Component Based Softwa

Latest application date: 2011-12-31

Financing: available

Type of Position: scholarship

Source of Funding: IWT

Duration of the Project : 4 years


Research group: Department of Computer Science

Remarks: To apply you should send your: • Curriculum Vitae • Motivation • Relevant research experience • Study curriculum with rankings • English proficiency (for foreign students) • Pdf of diploma and transcripts (translation if necessary) • Names (and e-mail) of 2 reference persons and the nature of contact with them By e-mail to Danny Hughes: at

Informal inquiries: Danny Hughes ( at

Start date Between 2011-11-01 and 2012-31-04

A convert to tablet computing

I'm definitely a convert to tablet computing.

Many computer scientists can't see the point of tablet computers, and indeed I was one of them. They're too large to be truly personal devices (like smartphones); too small to be usable as "real" computers (like laptops); and don't really support many of the functions and activities people like me spend a lot of their time doing (like coding and writing). Given all that, what's the point?

Having taken the plunge with a Samsung Galaxy 10.1, I can safely say that all the above are true -- and that that's not the point at all. What tablets are great for is consuming, be that papers, photos, videos, news, web pages, and any and all other information. In fact they're so good for it that I can't see me using a laptop for these sorts of activities again.

Home screen with fish

It's hard to explain why tablets seem so much better: it's probably the naturalness of the interface. That may not sound too surprising, but for a computer scientist a keyboard is basically as natural as breathing, and I've never felt that a keyboard is a barrier -- indeed, I often find keyboards more natural and powerful than GUIs. So I'm surprised that I find a touch screen so much better than a mouse.

It's interesting to compare the Galaxy with the Kindle (the ordinary one, not the new tablet). Kindles are fantastic for reading books, especially outdoors, and have the other great feature that they isolate you from the internet and other distractions while you're reading. The e-ink display is very restful, too. What they're not as good for is reading PDF files (and annotating them), as the screen resolution and size are both "off" for most paper formats: if the document you're reading doesn't re-flow, forget it. That makes Kindles less useful for academics than they could be, given we spend so much time consuming papers. I have to say that I also like the ability to jump out of a book and onto the web, or into linked content. Despite the arguments over the impact of linking, it's hard to argue that restricting access to information improves understanding.

I always made do with reading off my MacBook Air, but for some reason that's never been very effective. So I was surprised to discover that reading PDFs on a tablet is actually extremely easy and restful: more so than the laptop, even though the screen resolutions and pixel densities are comparable. My only suggestion for why this is so is that a tablet can be moved more easily, like a book: you constantly change the angle at which you view it, to conform to your reading position, how alert you feel, and so on, in a way that you can't with a laptop. (If this hypothesis is correct, it's a really good example of the subtleties involved in designing mobile and pervasive and their interfaces.)

Given that I've had the tablet for less than a week I certainly haven't found all the apps I want to, but here are my favourite so far:

  • TaptuNews: Taptu. The tablet comes with Pulse, but I think Taptu is better: it seems to be more aggressive about downloading the articles it lists, which means it's more effective to read on the train.
  • PDF: Adobe PDF Reader. Good display capabilities but no annotation: apparently ezPDF allows annotation, so might be worth buying.
  • Books: Kindle Reader. The only choice, really.
  • A sample Zinio magazineMagazines: Zinio. Subscribe to or buy single issues of a wide range of glossy magazines covering virtually all topics (from Maxim to National Geographic to Shutterbug).
  • Game: Air Attack HD Part 1. Basically a clone of the old console/arcade game "1942" -- blow up everything in sight with no sign of strategy. Way better graphics, of course, and I don't think the Nazis actually used airships or had big electric-spark-generating towers for destroying marauding aircraft, but great fun!!! (Fruit Ninja comes a close second.)

What the tablet doesn't do well is anything to do with typing. Soft keyboards have no "give" and no tactile feedback, so typing on one is a lot like drumming your fingers on the table. This isn't something I could do for any length of time, so writing anything longer than a quick email is going to be problematic. (Before you ask, I'm writing this on the Air.) But this limitation is massively outweighed by the advantages.

Even the little things are challenging to the status quo. A tablet is pretty much always-on (since it sleeps by default rather than shutting down), and instant boot. Why can't "real" computers do that? Of course, they can -- and indeed did, 30 years ago -- but we've accepted a different approach as normal. There are reasons for this -- it maximises flexibility to load everything off disc through a flexible boot process -- but once you see an instant boot it's hard to make the argument that it shouldn't be replicated on all devices.

I feel about tablets the same way I feel about the first laptop I encountered: it's game-changing. But in fact they're more game-changing than laptops ever were, because they open information up to an enormous new user population who'd never use a traditional computer effectively. Using one for even a few days sparks off all sorts of ideas about what should be the future of books, the future of programming languages, rich information linkage, the ways we educate and the ways we make information available. Tablets are easily the most exciting new piece of kit I've seen in the past decade, and I can't wait to see where we can take them.

Research fellowship in sensor networks

We have a three-year postdoc available immediately to work on programming languages and platforms for sensor networks.

Research Fellow – CD1060

School of Computer Science, £32,751 - £35,788 per annum. Start: As soon as possible, Fixed Term 3 years

We seek a Research Fellow to design and implement an integrated software platform based on mission specifications and evolution operators. The work will be evaluated based on case study deployments in the context of real-world large-scale WSANs.  You will specifically focus on generative programming techniques to integrate the overall design, and will work with Professors Dearle and Dobson.

The project involves re-architecting WSAN systems so that system-wide behaviour is defined using explicit mission specifications. These allow top-level constraints and trade-offs to be captured directly and used to inform software deployment and evolution in a well-founded manner. We compile mission-level components to collections of node-level components connected using network overlays. We maintain both mission constraints and management interfaces through to run-time where they can be manipulated by evolution and recomposition operators.

You should have a good honours degree in Computer Science or a related discipline, and preferably have, or be about to obtain, a PhD in Computer Science. You will have strong software development/OS/programming language skills. Experience in generative programming, compilers, operating systems, component deployment and/or sensor networks would be advantageous.  You should be a highly motivated individual and be able to lead the day-to-day work.

This is a fixed-term post for 3 years, starting as soon as possible.

More information on the university's job page. You can also email Al or myself for more information.

An issue for smart grids

Unbeknown to her -- until she reads this, anyway -- the other day my mother trashed an idea that's been a cornerstone of a lot of research on smart grids.

In the UK the fire services often send people around to check people's smoke alarms and the like. Not usually firemen per se, but information providers who might reasonably be described as the propaganda department of the fire service, intent on giving advice on how not to burn to death. They also change batteries. Pretty useful public service, all told.

Anyway, my mother lives in Cheshire, and recently had a visit from two such anti-fire propagandists.They did the usual useful things, but also got talking about the various risk factors one can avoid beyond the usual ones of having a smoke alarm and not searching for gas leaks with a cigarette lighter. The conversation turned to the subject of appliances, and they revealed that the most dangerous appliances from a fire-causing perspective are washing machines. In fact, they said, the Cheshire fire service gets called to more washing-machine fires than any other kind of domestic fire. (I don't know if that includes hoaxes, which are a major problem.) Since they have in common (a) lots of current and (b) water, I would guess that dishwashers are a similar problem.

So their advice was never to run a washing machine or dishwasher overnight or when in bed, as the chances of a fire are relatively high. "Relatively high" probably still means low on any meaningful scale, but it makes sense to minimise even small hazards when the costs are potentially to catastrophic.

Mum related this to me to encourage me also not to run appliances at night. But of course this has research consequences as well.

Smart grids are the application of information technology to the provision and management of electricity and (to a lesser extent) gas. The idea is that the application of data science can provide better models of how people use their power, and can allow the grid operators and power generators to schedule and provision their supplies more accurately. It usually involves more detailed monitoring of electricity usage, for example using an internet-connected smart meter to log and return the power usage profile instead of just aggregated power usage for billing.

The idea is getting more and more common because of the rise in renewable energy. Most countries have feed-in tariffs for the grid that power generators have to pay. The scheme is usually some variant of the following: at every accounting period (say three hours), each generator  has to present an estimate of the power it will generate in the next several accounting periods (say three).  So using these numbers, every three hours an electricity generator has to say how much power it will inject into the grid in the next nine hours. There's a complementary tariff scheme for aggregate consumers (not individuals), and taken together these allow the grid operators to balance supply and demand. The important point is that this exercise has real and quantifiable financial costs: generators are charged if they over- or under-supply by more than an agreed margin of error.

Now this is fine if you run a gas-, oil- or nuclear-powered power station. However, if you run a wind farm or a tidal barrage, it's rather more tricky, since you don't know with any accuracy how much power you'll generate: it depends on circumstances outwith your control. (I did some work for a company making control systems for wind farms, and one of their major issues was power prediction.) The tariffs can be a show-stopper, and can cause a lot of renewable-energy generators to run significantly below capacity just to hedge their tariff risk.

The other side of smart grids is to manage demand. It's well-known that demand is spiky, for example leaping a half-time in a popular televised football match as everybody puts the kettle on. A major goal of smart grids is to smooth-out demand, and one of the ways to do this is to identify power loads than can be time-shifted: they are relatively insensitive to when they occur, and so can be moved so that they occur at times when the aggregate power demand is less. In a domestic setting, some kinds of storage heating work like this and can create and store heat during off-peak hours (overnight). Lights and television can't be time-shifted as they're needed at particular times. So what are the major power loads, other than storage heating, in domestic settings that can apparently be time-shifted?

Washing machines and dishwashers.

Except we now know that time-shifting them to overnight running runs exactly counter to fire service advice as it increases the dangers of domestic fires. So one of the major strategies for smart grid demand management would, if widely deployed, potentially cause significant losses, of property and even lives. Reducing energy bills will (in time) increase the insurance premiums for anyone allowing time-shifting of their main appliances. In other words, while these risks exist, its a non-starter.

In some ways this is a good thing: good to learn about now, anyway, before too much investment. There are a lot of things that could be done to ameliorate the risks, for example designing machines explicitly designed for time-shifted operation.

But I think a more pertinent observation is the holistic nature of this kind of pervasive computing system. You can't treat any one element in isolation, as they all interact with each other. It's as though pervasive computing breaks the normal way we think of computing systems as being built from independent components. In pervasive computing the composition operators are non-linear: two independently-correct components or solutions do not always compose to form one that is also correct. This has major implications for design and analysis, as well as for engineering.

Thanks, mum!

Why do people go to university?

Changes to admissions systems in the UK and Ireland simply tinker with the existing approach. They don't address the more fundamental changes in the relationship between university education, economic and social life.

We're in the middle of a lot of changes in academia, with the fees increase for students in England eliciting a response in Scotland, and a report in Ireland suggesting changes to the "points" system of admissions to introduce more targeting. Despite the upsets these changes cause to academics -- they heighten the arbitrary and distressing discontinuity in costs between Scottish and English students in St Andrews, for example -- they're actually quite superficial in the sense of not massively changing the degrees on offer or their structure. However, these would seem to be the areas in which change could be most fruitful, in response to changing patterns of life, patterns of access to information and the like. It's hard to generate enthusiasm for this amid the problems of managing the financial structures. In other words, the fees débâcle may be masking changes that we would otherwise be well advised to make.

What are these changes? First of all we need to understand what we believe about the lives and futures of the people who might attend university. For me this includes the following:

People don't know what they're passionate about. Passionate enough to want to spend a substantial portion of their lives on it, that is. There's a tendency to follow an "approved" path into a stable career, and this in may in some cases lead people to do the "wrong" degree as they worry about their job prospects. But if you're going into a career in something, you have to accept that you'll spend about half your waking life on it. It makes sense to be sure you'll enjoy it. So we need to focus on allowing people to find their passions, which argues against too-early specialisation and for a broader course of study.

My first postdoc supervisor, Chris Wadsworth, told me about 20 years ago that "it takes you 10 years to decide what you're actually interested in." In your mid-20s you tend to disregard statements like this and assume you know what your research interests are, but on reflection he was right: it did take me about 10 years to work out what I wanted to spend my career researching, and it wasn't really what I was doing back then: related, yes, but definitely off to one side. I've also become interested in a whole range of other things that were of no interest to me back then, not least because most of them didn't exist. If that's true of an academic, it's just as true of an 18-year-old undergraduate. You can have an idea what you like and what interests you, but not much more than that.

We can't teach enough. It used to be simpler: go to university, learn all the things you'll need, then go and practice those skills with marginally upgrading for the rest of your career. I can't think of many topics like that any more.

This changes the emphasis of education: it's not the stuff we teach that's important, it's the ability to upskill effectively. For that you need foundations, and you need to know the important concepts, and you need to be able to figure out the details for yourself. It's not that these details aren't important -- in computing, for example, they're critical -- but they're also changing so fast that there's no way we could keep up. And in fact, if we did, we'd be doing the students a dis-service by suggesting that this isn't a process of constant change.

The jobs that people will want to do in 20 years' time don't exist now. Fancy a career as a web designer? Didn't exist 20 years ago; 10 years ago it was a recognised and growing profession; lately it's become part and parcel of graphic design. The world changes very rapidly. Even if the job you love still exists, there's a good chance you'll want to change to another one mid-career. Again, the ability to learn new skills becomes essential. I suspect a lot of people -- including politicians -- haven't appreciated just how fast the world is changing, and that the pace of change is accelerating. You don't have to believe in the Sigularity to believe that this has profound implications for how economies and careers work.

We don't know where the future value comes from. In a time of increased financial stress, governments fall back on supporting courses that "obviously" support economic growth: the STEM subjects in science, engineering, technology and medicine. The only problem with this as an argument is that it's wrong. Most of the value in the digital age hasn't come from these areas. The profits at Apple and Google pale into insignificance behind the aggregate profits of the companies (often much smaller) making content to add value to the devices and services these companies provide. I've argued before that this value chain is best supported by humanities graduates, not scientists and engineers. (If you want a supporting example, consider this as a proposition: the whole argument about network neutrality is essentially an argument about whether ISPs should be allowed to tax the producers and consumers of the content they transmit in terms of its end-user value, rather than in terms of its transmission cost. That's where the value is.)

Does the above have anything to suggest about changes in admissions or the structure of degrees. To me it suggests a number of things. Have broad introductory years in which students are encouraged to explore their wider interests before specialising. (The Scottish broad curriculum attempts to do this, but in sciences we don't do it particularly well.) Focus teaching on core principles and on how these affect the evolution of tools and techniques. Also focus on students learning the tools and techniques themselves, and showing how they relate back to the taught core. Generate a set of smaller degree-lets that people can take over their career with less commitment: at a distance, in the evening, in compressed blocks, over a long period -- whatever, just suitable for people to do alongside working at something else. Above all, don't assume we (either the universities or the State) can pick winners in terms of subjects. We're definitely in a post-industrial world, that means new intellectual territory and that what worked before won't always work in the future. I hope computer science will continue to change the world, but I'm also completely confident that a history graduate will come up with one of the Next Big Things.

On not funding the arts and humanities

If we don't adequately fund the arts, where will all the digital content come from?

Recent noises from within the UK's funding structures suggest that the future for arts and humanities education is somewhat threatened. In a time of restricted resources (the argument goes) the available funding needs to be focused on topics that make a clear, traceable contribution to the national economy. This essentially means supporting the STEM subjects -- science, technology, engineering and medicine -- at the expense of the arts and humanities.

As a computer scientist I might be expected to be loosely in favour of such a move: after all, it protects my discipline's funding (at least partially). But this is to mis-understand the interconnected nature of knowledge, of scholarship, and of the modern world as a whole.

We need first to think about how people use their degrees. Contrary to popular belief (even amongst students), degrees don't generally lead to jobs -- and nor should they. It's true that we teach a lot of information and skills in a degree: how to program, how to analyse algorithms and understand different technologies, in the case of computer science. But this isn't the reason to get a degree.

What we try to teach are the critical skills needed to understand the world, contribute to it and change it. Computer science is a great example of this. Three years ago there were no tablet computers and no cloud computing: the field changes radically even on the timescales of a typical degree programme. So there's not really much point in focusing on particular technologies or languages. What we teach instead is how to learn new languages and technologies, and how to assess how they fit into the changing pattern of computer science. Put another way, we have to turn them into people who can learn and assimilate complex technological ideas throughout their lives, and create new ones

Education is what survives when what has been learnt has been forgotten

B.F. Skinner

This is even more true in humanities. Most people who study geography do not become geographers (or polar explorers, for that matter): they go into fields that require critical minds who can come to grips with complex ideas. But they bring to these jobs an appreciation of a complex and layered subject, an ability to deal with multiple simultaneous constraints and demands upon shared resources, the interaction of people with the natural world, and so forth. This is much more valuable than the specific knowledge they may have acquired: they have the ability to acquire specific knowledge whenever they need it, and to fit it into the wider scheme of their understanding.

But even if we accept in part the narrower view of education as a direct feeder for the economy -- and realistically we have to accept it at least to some extent -- reducing humanities graduates seems shortsighted. If we also accept that the future is of a digital and knowledge economy, then the technologies underlying this economy are only one part of it -- and probably only a small part. The rest, the higher-value services, come from content and applications, not directly from the technology.

Consider how much value has been created from building computers. Now consider how much value is created from selling things that use computers. Computer scientists didn't create much of the latter; nor did physicists, mathematicians, materials scientists or electronic engineers. Humanities people did.

So even aside from the reduction in quality of life that would come from reducing the contributions of people who've studied history and literature, there's a direct economic effect in play. Without such people, there'll be no-one to create the digital content and services on which the knowledge economy depends. (It's called knowledge economy, remember, not science economy.) Increasing the proportion of knowledgeable, educated people is valuable per se for the creativity those people unleash. The fact that we perhaps can't directly trace the route from university student places to value to society doesn't make that contribution unreal: it just means we're not measuring it.

When we went about inventing computers and the internet we had specific goals in mind, to do with scientific analysis and communication. But it turned out that the most socially significant impacts of these technologies didn't come from these areas at all: they came from people who thought differently about the technology and came up with applications that no computer scientist would ever have thought of. It still amazes me that no professional computer scientist -- including me -- ever dreamed of social network sites like Facebook, even though we happily talked about concepts like "social network" and of using computers to examine the ways people interacted at least five years before it debuted. We don't have the mindset to come up with these sorts of applications: we're too close to the technology. Scientists can happily develop services for science: it needs people who are closer to the humanities to develop services for humanity.

Metrics and the Research Evaluation Framework

Is the success of the UK and US in global university research rankings any more than a consequence of the choice of particular metrics? And does this explain the design of the funding and evaluation methodologies?

We're currently at the start of the UK's periodic Research Evaluation Framework (REF) process, during which every School and Department in every university in the country is evaluated against every other, and against international benchmarks, to rate the quality of our research. As one might imagine, this isn't the most popular process with the academics involved (especially those like me who are directors of research and so have to manage the return of our School's submission).

It certainly absorbs a lot of intellectual capacity. In principle it shouldn't: the REF is intended to generate a fair reflection of a School's behaviour, is supposed to measure those activities that academics working at the cutting edge would do anyway, and to encourage people to adopt best practices by rewarding them in the assessment methodology. In reality of course the stakes are so high that the process can distort behaviour, as people (for example) target their published work at venues that will be good in the REF (to get a better REF result) and not necessarily those that would give the work the best exposure in the appropriate community. In principle this sort of arbitrage is impossible as the REF should value most highly those venues that are "best" -- a sort of academic version of the efficient markets hypothesis. In reality there's often a difference, or at least a perceived difference. In computer science, for example, we often use conferences in preference to journals for really new results because they get the word out faster into the community. Other sciences don't do this, so we worry that conferences (which in other fields are quite peripheral to the publications process) won't be judged as seriously even though they might be scientifically more valuable.

Managing our engagement with the REF process has made me think more widely about what the REF is trying to achieve, and how it's achieving it. In particular, are the outcomes the REF encourages actually the ones we should be encouraging?

If we look at universities across the world, one thing that stands out is the way that, in science and technology at any rate,  the UK comes a close second to the US (and way above all other countries) in terms of publications, citations, patents and other concrete outputs of research -- despite the smaller population and even smaller proportional science budget. Despite the perpetual worries about a brain-drain of the best scientists to better-funded US Schools, the top UK institutions continue to do amazingly well.

The US and the UK also have strikingly similar funding models for universities. A small number of funding agencies, mostly national, disburse funding to Schools in particular fields according to priorities that are set at least notionally by the researchers (known in the UK as the Haldane Principle) but also and increasingly with regard to nationally-defined "strategic priorities" (usually in defence or security in the US). The agencies are constantly pressured about the value they return for taxpayers' money -- disproportionately pressured, given the relatively small sums involved -- and so tend to adopt risk-reduction strategies such as following their money and increasing the funding of individuals and groups who have had previous funding and have not made a mess of it. This has three consequences: it becomes harder for junior staff to make their names on their own; it concentrates funding in a few institutions that are able to exploit their "famous names" to acquire follow-on funding; and, over time, it subtly encourages ambitious and talented people to gravitate to these well-funded institutions. The medium-term result is the creation of larger research-intensive Schools that absorb a relatively large proportion of the available funding (and to a lesser extent talent).

One can argue about whether this is a good outcome or not. Proponents would claim that it generates a "critical mass" of researchers who can work together to do better work. Critics would argue that larger Schools can rapidly become too large for comfortable interaction, and that the focus needed to grow can come at the expense of cross-disciplinary research can discourage people from undertaking risky projects, since these might damage (or at least not advance) the School's collective reputation.

Do these two processes -- high impact and winner-takes-most funding -- run together? Is one causative of the other? It seems to be that they're actually both a consequence of a more basic decision: the choice of metrics for evaluation. And this in turn tells us where the REF fits in.

The UK and US systems have chosen specific, measurable outcomes of research, in terms of a broadly-defined international "impact". Given this choice, the funding model makes perfect sense: find groups that perform well under these metrics, fund them more (to get more good results) and help them to grow (to create more people who do well under the metrics). The system then feeds-back on itself to improve the results the country gets under it's chosen framework for evaluation.

This all seems so logical that it's worth pointing out that there's nothing inherently "right" about this choice of metrics, that there are alternatives, and that the current system is in conflict with other parts of universities' missions.

A noticeable feature of the US and UK system is that they are increasingly two-tier, with research-led institutions and more general teaching institutions doing little or no research -- even in fields that don't actually benefit as much from economies of scale or critical masses of researchers (like mathematics). This means that students at the teaching institutions don't get exposure to the research leaders, which is one of the main benefits of attending a research-led university. This is bound to have an impact on student satisfaction and achievement -- and is explicitly excluded from the REF and similar metrics. It's interesting to note that, in the university rankings that include student-centred metrics such as those performed in the UK by the Sunday Times and the Guardian, the sets of  institutions at the top are shuffled compared to the research-only rankings. (St Andrews, for example, does enormously better in surveys that take account of the student experience over those that focus purely on research. We would argue this is a good thing.)

If we accept the choice of metrics as given, then experience seems to suggest that the UK's evaluation and funding structures are optimised to deliver success against them. This is hardly surprising. However, one could also choose different sets of equally defensible metrics and get radically different results. If one takes the view, for example, that the desirable metric is to expose as many students as possible to a range of world-class researchers, one could set up a system which eschews critical mass and instead distributes researchers evenly around a country's institutions. This is in fact roughly what happens in France and Italy, and while I can't say that this is a direct consequence of a deliberate choice of metrics, it's certainly consistent with one.

There is nothing so useless as doing efficiently that which should not be done at all.

Peter Drucker

Like many things, this is actually about economics: not in the narrow sense of money, but in the broad sense of how people respond to incentives. Rather than ask how well we do under REF metrics, we should perhaps ask what behaviours the REF metrics incentivise in academics and institutions, and whether these are the behaviours that are of best overall social benefit to the country. Certainly scientific excellence and impact are of vital importance: but one can also argue that the broadest possible exposure to excellence, and the motivational effect this can have on a wider class of students, is of equal or greater importance to the success and competitiveness of the country. The narrow, research-only metrics may simply be too narrow, and it'd be a mistake to optimise against them if in doing so -- and achieving "a good result" according to them -- we destroyed something of greater value that simply wasn't being measured.