Economics: reform or revolution?

Digging into economic arguments around austerity has been like accidentally walking in on a pub brawl: honky-tonk piano still playing, people being hit with chairs, others thrown the full length of the bar breaking all the glasses, someone thrown through the first floor balcony etc. Nobody gets this worked up about spatial economics.

I've been doing this digging since this little fantasy post about the idea of making austerity economics more visible (or, rather, making macroeconomics more visible, and so arguments about austerity more transparent). It's been a dream for a long while, probably since first discovering the MONIAC machine, the (currently not working) prototype of which sits in Leeds University's Business School foyer.

I've taken a few baby steps in that direction1 - it may never come to anything, but the process of attempting it has churned up a whole load of economics stuff I haven't thought about in a long time.

I've found myself flummoxed by some of the things I'm seeing in the bar brawl. Exhibit A: Steve Keen's cartoon - taking the CON out of eCONomics! - from which we learn, apparently, that Krugman and Summers "don't know what they're talking about". Blam! Thwack! Keen's mode of operation may be extreme in heterodox circles but it seems to be wildly popular.

I've also just got a copy of James Kwak's 'Economism': so far, this has been a much more measured book with very little I disagree with. It acknowledges the power of economic thinking - but it paints a convincing picture of "economics 101 as ideology", captured and promoted by particular elites, turned into a propaganda tool. On my first skim, it doesn't recommend smashing everything to bits, but instead changing institutions, expanding and re-weighting curricula.

As it points out, the emergence of 'economism' can be seen just in how topics were re-ordered over time in Samuelson's textbook. It quotes a THES article...

"Economics degrees are highly mathematical, adopt a single narrow perspective and put little emphasis on historical context, critical thinking or real-world applications."2

...but notes that, once one gets past this highly standardised economics (largely harmonised with ideological economism), actually, there is a great deal of much more subtle economic thinking going on. It's just that students aren't initially exposed to it.

I don't know how true that is - I suspect economism of the sort pushed by Paul Ryan when he's showing woefully simplistic supply/demand curves to justify dismantling Obamacare (an example from the book) has a messier relationship to taught econ101. But the book's central claim - that there is a version of economics used as an ideological battering ram - is hard to argue with.

Self-indulgent background follows for several paragraphs... I'm in an odd, sort of lucky, sort of rubbish, situation: for the PhD model (finished 5 years ago now, oi...) I found some economic ideas incredibly useful. While I'm still pretty insecure on my views about this (it was just me in a dark room building a model for years and those ideas remain largely untested) I perhaps got a unique perspective, being forced through trial and error into finding how useful economic ideas can be, rather than having to learn and regurgitate the orthodox package.

I got to jump off the rails and go wandering freely. The model itself forced me to think critically and historically - I ended up running about randomly on the economic landscape, solely on the look-out for ideas that could move the model forward.

There were no stakes (besides me finishing the ****ing thing): I just found micro-economics really useful for modelling how people and firms react to cost changes if they're embedded in space, and that agent modellers' claims to better "realism" made no sense in isolation.

When it comes to austerity and macro-economics, obviously the stakes get a lot heavier - and much more ideological. Black-hole gravity wells of ideology can warp even the purest scientific disciplines so it's unsurprising what a mess it makes of economics.

Before the PhD, I started off with the same worldview as many heterodox economists. I'd studied politics at Sheffield and fully took on board the standard, incredulous dismissal of homo economicus, the perfectly rational calculating machine: insultingly reductionist, logically absurd (Friedman's "realism of assumptions don't matter" line, a caricature of his actual argument, would exemplify that idea) and nothing more than a figleaf covering the goolies of power.

I found the strongest confirmation of this in World Bank reports studied in my final year. One of them claimed the goal in developing countries should be to "supplant or amend" (change or die!) all "behavioural norms" that were "market dysfunctional" and replace them with the market-functional versions. It contained an entire "tactical sequencing" plan (divide and conquer!) to overcome state and societal resistance to this supplant/amend plan.

It looked for all the world like a strategy for creating homo economicus, for transforming entire states to achieve this. It's easy to assume reports like this reflect the Sauron-like power of neoliberalism, rather than just whatever the writer happened to think that day (as well as being just confirmation bias). But it does support the 'Economism' idea: global market norms are pushed by powerful organisations. And economics is the theoretical branch of that push.

On the academic side, you can also see how methods become shibboleths. As Krugman says, his geography ideas needed to be expressed as a general equilibrium model to be accepted:

"Mainstream economics isn’t going away: like it or not, the White House has a Council of Economic Advisers, not a Council of Geographical Advisers, the World Bank hires lots of economists and not many geographers."

The World Bank and other organisations have worked to recreate this same structure, including setting up and staffing finance ministries around the globe. (Says 2002 me - 2018 me replies, "well, did that happen and how successful was it?" I don't know. I do know trying to implement such plans always tangles with messy reality in unexpected ways. And that the World Bank has radically changed since then...?)

So while I kept to my little dark room, building spatial economic models, I found basic economic modelling ideas really really useful. But beyond that, in the bar brawl of macroeconomics, things get a lot messier - there really is global power, propaganda, economism to contend with.

Being simplistic, I can imagine two heterodox ways to think about that reality: revolutionaries and reformers. Where Keen might be a revolutionary, Kwak is more of a reformer (he's clear about that in this blog post). The revolutionaries won't be happy until the Great Neoclassical Tower of Barad-dur has come crashing down, having thrown the... err... Ring of Microeconomics? Into the lava of Mount Marginal?

That's not a political mapping: these revolutionaries are not necessarily more leftie. It's just that they want to throw the theoretical baby out with the ideological bathwater. I get the sense my idealistic notion of creating a clearing for transparent discussion of macro method just wouldn't work for some of those folks. (There may be some also who object to quant modelling generally - I'm saving that for another post.)

And the fact that it's wildly popular does worry me. I wonder if these attacks aren't playing the same ideological game, just as awash with shibboleths as their target. It doesn't seem like a way forward to me, if we really want to develop methods and institutions that can actually manage national economies well, or even - crazy idea I know - maintain a functioning biosphere.

Whether the actual visualisations ever happen - definitely still in the realm of fantasy but I am trying little baby steps! - there's a lot else to think about here. Stuff about realism in particular, about how different methods should be valued or discarded, seems essential to me. I still haven't really clearly explained why.

Also, more careful thought needed on how this all connects to economism. I keep on defending that one chapter by Friedman because I found it really useful at a critical moment - yet he's also a central figure in the creation of economism as an ideology, it seems. Can these two halves in my brain be reconciled?

But the fantasy idea is still appealing: arguing the case about the actual economic method in as transparent a way as possible, using visualisation, models with methods all arguable and clear via github, without diminishing the importance of the political economy. Dream on...

  1. I've also been upping the necessary tech skills - it turned out jumping straight into an online macroeconomics sim was bit much so I'm practicing on house prices; unfinished prototype is going well - those numbers are wage multiples, slider is 1997 to 2016. 

  2. James Kwak, Economism, p.188 

Can a Tralfamadorian make predictions?

Tralfamadorians are four-dimensional alien beings able to travel anywhere in time as well as space. Or so Kurt Vonnegut reports, quoting one as saying:

"I am a Tralfamadorian, seeing all time as you might see a stretch of the Rocky Mountains. All time is all time. It does not change. It does not lend itself to warnings or explanations. It simply is. Take it moment by moment, and you will find that we are all bugs in amber."

My question for the day: could a Tralfamadorian make predictions? Short answer: yup, totally. Longer answer -->

Our root for the word 'prediction' wouldn't make sense to a native of Tralfamadore. It literally means pre-show - to "say or estimate that (a specified thing) will happen in the future".

This has always seemed a bit odd to me, only half of how we actually use the term. Looking at things from a Tralfamadorian perspective makes this more obvious: the word 'forecast' has no meaning on Tralfamadore. All past and future events are accessible to them. There's no such thing as a Tralfamadorian weather forecast. They don't bet on horse races. They can't try and game the stock market. They can't actually have a stock market.

But they could still make the kind of predictions we consider among the most important: what Gregor Betz calls an `ontological prediction'.*

Here are three famous ontological predictions (one mentioned by Betz). One: the existence of Neptune deduced from oddities in the orbit of Uranus (I see you snickering...). There was either another planet or Newton was wrong. It turned out there was another planet. Then two of Einstein's: light should appear to bend as it passes through gravity-warped space; and the existence of gravity waves. The first was famously (though not uncontroversially; see also this) confirmed by Eddington during an eclipse.** Confirmation of gravity waves is brand spanking new. Immediately, they are cosmically awesome, able to dig deep into the universe to solve the riddle of where some of the heaviest elements like gold come from (neutron stars colliding... whoooaaa).

So - those were all predictions, yes? And each did provide statements on the future - but only kind of by default. The future is the only place we can test our theories. On Tralfamadore, that's not true. Tralfamodorian Einstein could come up with his theories, look up a suitable eclipse in the seventeeth century on his four-dimensional road map, pop over to meet Newton for a bit of co-corroboration, maybe nipping to Papua new Guinea in 1698. (Probably best not to over-think it... wouldn't all Tralfamadorian predictions instantly propagate everywhere/when? So everything would by necessity be known instantly leaving nothing to be discov... oh, they're just a fictional device for making a point, OK then. Phew.)

All of which is a slightly belaboured way of saying: ontological predictions are fundamentally different to forecasts. They are timeless (though the realities they seek don't need to have always existed). They are about seeing things that were already there but we didn't know to look for.

The fact we have to test our ontological predictions in the future doesn't change how different this is to a forecast. It's unfortunate our definitions reinforce the idea that `predict' and `forecast' are the same. Of course, the two are dependent on each other: actual forecasts need underlying theory, and discovering that theory is an ontological-prediction job. But there is conceptual clear blue water between the two of them. New forecasts using an existing method don't require extra ontological prediction. You could also, for instance, improve weather forecasting independently of ontological prediction by throwing more powerful computation at it or some novel refactoring, without making any deeper discoveries about the underlying physics.

Why does this matter? Well, this is all a pre-amble to another post: after reading another attack on a cartoon version of Milton Friedman's argument about model assumptions, I feel like having a proper go at exploring why those (seemingly very popular) arguments miss the point, and why that's important. tl;dr: he never said "assumptions are irrelevant". He did say predictions are the ultimate arbiter - but it's hard to get very far without being clear what prediction actually is.

Once you start digging into this, it also ends up saying something about how different disciplines see themselves, how the public sees them, and how we frame the entire research enterprise in applied versus non-applied terms.

It's also a trope used by many people to reassure themselves that the entire edifice of economics is clearly stupid. That's annoying, wrong and that used to be me. But it's also used by people who should know better to bolster their economics-heretic credentials, and that's especially annoying. So, more at some point before 2019, I hope, or possibly before now if I can find a Tralfamadorian to work with. Thoughts gratefully received in the meantime: does this two-part distinction in prediction scan?

Update: oh, it turns out Friedman was quite explicit about including ontological prediction in his definition: "to avoid confusion, it should perhaps be noted explicitly that the "predictions" by which the validity of a hypothesis is tested need not be about phenomena that have not yet occurred, that is, need not be forecasts of future events; they may be about phenomena that have occurred but observations on which have not yet been made or are not known to the person making the prediction. For example, a hypothesis may imply that such and such must have happened in 1906, given some other known circumstances. If a search of the records reveals that such and such did happen, the prediction is confirmed; if it reveals that such and such did not happen, the prediction is contradicted." Source.

* Note Betz also thinks a prediction is a "statement on the future".
** Skipping over gravity having Newtonian effects on light - look, a black hole prediction!) Though if I'm reading that right, it's based on light being a particle with mass.

"Actively seek out new opportunities to feel stupid"

"Laws, like sausages, cease to inspire respect in proportion as we know how they are made." (Misattributed to Bismarck.) This Springs to mind every time I try and write recently, though with "research projects" replacing "laws". Equally, I'm not sure people would lose respect for their sausages if they saw them being made, so much as gain a gag reflex. We don't want that in research either.

But it would be nice to blog. More than nice: I think it's a very useful thing to do, for one's own research progress especially. There are many entirely sterile academic blogs that do little more than promote: how great and wonderful the project is and what fantastic impact and outputs result (though note this entertaining if rather undiplomatic post by a prof in my department... sterile, it is not.) Promotion is necessary, to be sure, but by itself both boring and a tragic waste of the potential of blogging.

Writing is thinking in action. There's a common misconception that it's a two-stage process: staring out into space until an idea arrives, then transcribing that Platonic idea down into word form. Not so. The writing process itself is is a form of thinking.

Anyone who keeps a field diary or a work journal does a lot of that privately, of course – but the process of writing blog entries offers a different kind of thinking. You are, after all, writing for an audience, even if no-one actually comes and reads. They might. You never know. That slight additional pressure enables a blog to help the researcher formulate what the hell it is they're really doing and thinking. That's incredibly useful: having a place that's not just a work journal but that also doesn't impose the kind of austere control required for assembling a full paper.

The problem with blogs is also its advantage, at least for me. I often want to write about stuff I'm trying to work out. It really helps. But that thinking-in-public is a little tricksy. What I'd like to do in the rest of this post is say why I think that's worth pursuing anyway. I'll do that with a couple of ideas. One: blogs are good places for "actively seeking out opportunities to feel stupid". Two: good shit happens in those murky stupid places where you're poking your nose into the darkness beyond the streetlight you're looking under.

Idea one: Martin Schwartz on the importance of stupidity in scientific research. He is talking specifically about the physical sciences, but it applies elsewhere. He tells the story of an incredibly intelligent friend of his who left research because 'it made them feel stupid'. Puzzling over this for a while, he realised:

Science makes me feel stupid too. It's just that I've gotten used to it. So used to it, in fact, that I actively seek out new opportunities to feel stupid. I wouldn't know what to do without that feeling.

Huh. He explains his idea of 'productive stupidity': an 'immersion in the unknown' where it's impossible to know the outcome. As he says, 'if we don't feel stupid it means we're not really trying'. Schwartz is careful to contrast this with the idea of 'relative stupidity' that most people grow up with: learning in a system that ranks people on a scale and where it was always possible to be the least stupid in a group. Research is not like that; Schwartz's friend was too uncomfortable with it to stay.

Nicholas Harberd, in his excellent diary of a working plant scientist, captures what those moments feel like:

Of course science is always like this. There are peaks and troughs. I’ve experienced both. But the problem with being in a trough is that it is a place from which the view is limited. There is the feeling of being trapped with no way out. And always the question of how long the entrapment will last. A self-sustaining state: at the time when new vision is most needed, it is most unlikely to come. [p.6]

So this isn't really stupidity. It can make you feel stupid. That feeling (as Harberd hints at) isn't comfortable or easy. The trick that Schwartz learned – and his friend couldn't – was not to take it personally. This is a lesson that physical libraries can teach as well – something that's easy to forget when it seems like all knowledge is only a google away. Taking a walk through journal stacks instills this feeling in me. It's very easy to imagine being an ant crawling in a vast nest of knowledge, only a tiny sliver of which any single person could ever keep in their own skull – but little ants or not, it's our job to keep that corpus alive and evolving over time.

I'm not saying there aren't times when applying one's existing knowledge to problems isn't valid – of course it is. Geography has many planning- related applications. Planners, not unreasonably, want tried and tested methods, not voyages into darkness. But how are new ideas are discovered? Do we still value that?

Idea two: the streetlight effect. As Kirman explains, it:

corresponds to the behaviour of the person who, having dropped their keys in a dark place, chose to look for them under a streetlight since it was easier to see there (Kirman 1992 p.134).

This has a larger scope than simply "refusing to be stupid" and staying under one's streetlight. All researchers work within disciplines (or possibly Kuhnian paradigms) that shape how they see the world they're investigating. Economics is an instructive example: those outside the discipline often view it is the archetypal methodological utopia, creating "citadels of crystalline mathematical perfection that would shatter if touched by the harsh rays of reality" (Ball 2007 p.647). Many economists, however, openly acknowledge this without rejecting economics outright. Overman describes "the tendency to privilege particular economic forces purely because they are more amenable to the theoretical and empirical tools used by mainstream economists" (Overman 2004 p.504). Summers notes the result: "it is all too easy to confuse what is tractable with what is right" (Summers 1991 p.145). Krugman, as is often the case, puts it best and nicely ties back to the streetlight: "the methodology of economics creates blind spots. We just don’t see what we can’t formalise" (Krugman 2008).

These economists are self-aware; it gives them a humility and caution about the power of economic models (a humility entirely absent in much agent modelling; consider this recent example in the Economist - a topic for another time). But it doesn't actually alter the basic point: economics works under its own streetlight.

I've spent a lot of my time in the past few years asking: what happens to the landscape when costs change? Short answer: no-one exactly knows. There are no existing methods capable of answering the question fully – though of course there are many streetlights to look under. If someone from the transition movement tells you we need to relocalise to adapt to upcoming cost changes, how do you answer? Are they right? How do we know either way?

My current project has its specific goals but, for me, an equally important aim is to ask these questions openly. As the blog's intro post said this is all "good old fashioned location theory question, but 21st century challenges are breathing new life into it." This is true: there has never been a more relevant time for geography of all stripes.

This is important for another reason: the pickle we're in, globally. The kind of innovations we need will not come from being shy about our lack of knowledge or being closed to collaboration. In his 'stupidity' article, Schwartz says "science is made harder by competition for grants and space in top journals". This reality can turn collaboration and openness into little more than empty sentiment: something we'd like to do in theory but that goes too firmly against the grain of academic practice. Academic blogging, however, helps with that. For a start, it achieves that all-important job of staking out what your ideas are publicly. It can also act as a prototyping tool for ideas that may end up in more formal academic outlets.

There's a deeper point also. As Dougald Hine lays out so eloquently in his recent lecture, the world is changing around the university. The kind of knowledge-creating relationships we'll need to in the coming decades may not look like they did in the past.


Schwartz, M.A., 2008. The importance of stupidity in scientific research. J Cell Sci 121, 1771–1771.
Harberd, N., 2006. Seed to Seed: The Secret Life of Plants, First American Edition. ed. Bloomsbury USA.
Kirman, A.P., 1992. Whom or What Does the Representative Individual Represent? Journal of Economic Perspectives, Journal of Economic Perspectives 6, 117–36.
Ball, P., 2007. Social science goes virtual. Nature 448, 647–648.
Overman, H., 2004. Can we learn anything from economic geography proper? Journal of Economic Geography 4, 501–516.
Summers, L.H., 1991. The Scientific Illusion in Empirical Macroeconomics. The Scandinavian Journal of Economics 93, 129–148.
Krugman, P. (2008). ‘How I work’. URL:

GRIT: 'geospatial restructuring of industrial trade'

Our new grant. Intro write-up also up at the Talisman blog. There's a link in there to this interactive viz of the UK's trade flows - a starting point for working on how best to make the spatial economy visible.

Alison Heppenstall, Gordon Mitchell, Malcolm Sawyer (LUBS) and I have been awarded an 18 month grant by the ESRC through their secondary data analysis initiative. Titled 'Geospatial Restructuring of Industrial Trade' (GRIT), the motivation for the grant came from a deceptively simple question: what happens to the spatial economy when the costs of moving goods and people change?

That's a good old fashioned location theory question, but 21st century challenges are breathing new life into it. During the next few decades an energy revolution must take place if we're to stand any chance of avoiding the worst effects of climate change. What price must carbon be to keep within a given global temperature? How long will any switch to new infrastructure take? (Kramer and Haigh 2009; Jefferson 2008) In fact, will peak oil get us before climate change does? (Wilkinson 2008; Bridge 2010) In a time when we're discovering costs may go up as well as down, do we have a good handle on the spatial impact this may have? Can we use new data sources and techniques to answer that, in a way relevant to people and organisations being asked to rapidly adapt?

GRIT will focus on two jobs. First, creating a higher-resolution picture of the current spatial structure of the UK economy. Second, thinking about how possible fuel costs changes could affect it. We'll examine the web of connections between businesses in the UK, looking to identify what sectors and locations may be put under particular pressure if costs change. There is a direct connection with climate change policy: the most carbon-intensive industries (also very water intensive) are also those with the lowest value density, and so most vulnerable to spatial cost changes.

Most economics still works in what Isard called a "wonderland of no dimension" (Isard 1956, 26) where distance plays no role except as another basic input, in principle substitutable for any other. Some economic geographers believe that because energy and fuel are such a small part of total production costs, "it is better to assume that moving goods is essentially costless than to assume [it] is an important component of the production process" (Glaeser and Kohlhase 2004, 199). At the other extreme, social movements like the transition network privilege the cost of distance above all else. They make the intuitive assumption that if the cost of moving goods goes up, they can't be moved as far – so localisation is the only possible outcome. They are making a virtue of what they see as economic necessity imposed by climate change and peak oil. At the extreme, some even argue that "to avoid famine and food conflicts‚ we need to plan to re-localise our food economy".

Reality lies somewhere between those two extremes of ignoring spatial costs altogether or assuming a future of radical relocalisation. GRIT is taking a two-pronged approach to finding out: producing a data-driven model and talking to businesses and others interested in the problem. Our two main data sources both use the 'standard industrial classification' code system, breaking the UK into 110 sectors. First, the national Supply and Use tables contain an input-output matrix of money flows between all of those sectors. (I've created a visualisation of this matrix as a network: click sectors to view the top 5% of its trade links and follow them. Warning: more pretty than useful, but gives a sense of the scale of flows between sectors.) It contains no spatial information, however – we plan to get this from our second source, the 'Business Structure Database' (BSD). As well as location information for individual businesses, each is SIC-coded and also provides fields for turnover and staff number. It also has information on firms' structure: "such as a factory, shop, branch, etc". (There's a PDF presentation here outlining how we're linking them, though I'll write more about that in a later post.)

By linking these two (and adding a dollop of spatial economic theory) we have a chance to create a quite fine-grained picture of the UK's spatial economy. From that base, questions of cost change and restructuring can then be asked. The 'dollop of theory' is obviously central to that; we've tested a synthetic version that produces plausible outputs (see that presentation for more info) but 'plausible' doesn't equal 'genuinely useful or accurate'. I'll save those problems for another post also. This sub-regional picture of the UK economy is a central output from the project in its own right and it is hoped it can be used in other ways – for instance, for thinking about how industrial water demand may change over time.

Even before that, two big challenges come with those datasets. First, BSD data is highly sensitive. It is managed by the Secure Data Service (SDS) and can only be accessed under strict conditions (PDF). Work has to take place on their remote server, and anything produced needs to get through their disclosure vetting before they’ll release it, to make sure no firm’s privacy is threatened. These conditions include things like: "SDS data and unauthorised outputs must not be printed or be seen on the user’s computer screen by unauthorised individuals." So, no-one without authorisation is actually allowed to look at the screen being worked on. Crikey. The main challenge from the BSD, however, is getting any of the geographical information we want through their vetting procedure. The process of working this out is going to be interesting. To their credit, the SDS have so far been very patient and helpful. While genuinely keen to help researchers, they also have to keep to draconian conditions – it can't be an easy tension to manage.

The second challenge is really getting under the skin of the input-output data. On the surface, it appears to very neatly describe trade networks within the UK, but its money flows can't all be translated simply to spatial flows. For a start, as the visualisation clearly shows, the largest UK sector, 'financial services', gets the UK's biggest single money flow from 'imputed rent' – which doesn't actually exist as exchanged goods or services. This comes down to the purpose of the Supply and Use table – a way to measure GDP. Imputed rent is a derived quantity used to account for the value to GDP of owned property. That's only one small example, but it illustrates a point: care is needed when trying to repurpose a dataset to something it wasn't intended for – in this case, to help investigate the structure of the UK's spatial economy. It is hoped that less problems exist for more physical sectors, but that can't be assumed.

The second 'prong' is to talk to businesses and other interested parties to find out how they deal with changing costs and to see if the work of the project makes sense from their point of view. We plan to hold two seminars to dig into the affect of changing spatial costs on businesses. Anecdotal evidence suggests suppliers have been citing fuel costs as a reason for price increases for a while now.

A whole range of other groups are keenly interested in spatial economics, though it might not always be labelled thus. An example already mentioned, the 'transition movement' is taking action at the local level. It has, in recent years, developed strong links with academic researchers. A vibrant knowledge exchange has developed between locally acting groups and researchers, with the aim of making sure that "transition and research form a symbiotic relationship" (Brangwyn 2012). It isn't just about spatial economics: it's imbued with a sense that people can play a part in shaping their own economic destiny. It's hoped that GRIT will be of interest here also.

So that's GRIT in a nutshell. There are clear gaps in the project's current remit. Trade doesn't stop at the UK's borders and any change in costs will have international effects (an issue I've been pestering Anne Owen from Leeds School of Environment about). Many of the costs most essential to business decisions are either hard to quantify or to do with people, not goods. (Think about how much it costs a hairdresser to get a person’s head under the scissors from some distance away, e.g. in the rent they pay; this hints at the reason data appears to show the service sector may be the most vulnerable to fuel cost changes.)

Aside from the technical aspects of the project, there are two other things to write about I'll save for later: the nature of distance costs and the place of modelling in research and society. And on that last point, just a bit of brainfood to finish on from Stan Openshaw (1978). In theory, GRIT wants to tread both of these lines, but that's something far easier said than done. (Hat-tip Andy Turner for lending me the book.)

Without any formal guidance many planners who use models have developed a view of modelling which is the most convenient to their purpose. When judged against academic standards, the results are often misleading, sometimes fraudulent, and occasionally criminal. However, many academic models and perspectives of modelling when assessed against planning realities are often irrelevant. Many of these problems result from widespread, fundamental misunderstandings as to how models are used and should be used in planning. (Openshaw 1978 p.14)



Brangwyn, Ben. 2012. “Researching Transition: Making Sure It Benefits Transitioners.” Transition Network.

Bridge, Gavin. 2010. “Geographies of peak oil: The other carbon problem.” Geoforum 41 (4) (July): 523–530. doi:10.1016/j.geoforum.2010.06.002
Glaeser, EL, and JE Kohlhase. 2004. “Cities, Regions and the Decline of Transport Costs.” Papers in Regional Science 83 (1) (January): 197–228. doi:10.1007/s10110-003-0183-x.

Isard, Walter. 1956. Location and Space-economy: General Theory Relating to Industrial Location, Market Areas, Land Use, Trade and Urban Structure. MIT Press.

Jefferson, M. 2008. “Accelerating the Transition to Sustainable Energy Systems.” Energy Policy 36 (11): 4116–4125.

Kramer, Gert Jan, and Martin Haigh. 2009. “No Quick Switch to Low-carbon Energy.” Nature 462 (7273) (December 3): 568–569. doi:10.1038/462568a.

Openshaw, Stan. 1978. Using Models in Planning: A Practical Guide.

Webber, Michael J. 1984. Explanation, Prediction and Planning. Research in Planning and Design. London: Pion.

Wilkinson, P. 2008. “Peak Oil: Threat, Opportunity or Phantom?” Public Health 122 (7) (July): 664–666; discussion 669–670. doi:10.1016/j.puhe.2008.04.007.

What’s the difference between a boxplot and an x-ray? Visualisation and Processing

A repost of my first blog entry on the Talisman blog, trying harder to understand visualisation and communication.
This article is the first of a few I hope to write about visualisation and Processing, a graphics tool created by Ben Fry and Casey Reas back in 2001. In this one I'll introduce Processing and get into some hard-core navel-gazing about visualisation. Next time I'll look at ways to use the Processing library as part of larger Java projects, where I'll assume some knowledge of Java and the Netbeans IDE. We can then move on to some fruitful combination of coding and chin-stroking. I'll also be attending the Guardian's data visualisation masterclass and will report back.

Communication and visualisation in research is both absolutely essential and mired in misunderstanding. It's essential, of course: all research needs to communicate its findings. Ideally, we want that communication to be effective. In some fields, communication is not only vital but fraught: the science of climate change faces concerted attempts to distort its output, and some wonder whether researchers are the best people to deal with this.

So what works? And, maybe more importantly, what doesn't work - and why not? The misunderstanding is perhaps quite simple: we think all images are equal. They enter the eye the same for everyone, don't they? Well, no. This post looks at some of these complexities by talking about four different static images: an x-ray, a box-plot, a mind-map and a simple graphic from the Guardian.

What are costs, actually? (Another P3 comment...)

Another comment at P3, responding to MT.

Me: "Economics is the study of how people react to cost changes." Hmm, you're right - I need to be more careful with nomenclature myself. I'm sure some of the heterodox econs would say the term is 'essentially contested'. Thinking about it, though, I think my def still works: economics concerns itself with the costs and benefits involved in human choices. I don't see that Marshall's questions contradict that: everything from how we structure institutions, what sectors should be state-owned, the ethics of wealth distribution - is all covered.

"presuming you mean 'cost' in the colloquial sense of money". No, absolutely not. The magic washing machine is a pretty perfect example: what are the costs/benefits of how we use our time? How does technology and societal structure alter that? Rosling very cleverly illustrates precisely this point by pulling books out of the washing machine: it's a machine that produces women's education as well as clean clothes. I see no problem in thinking about time this way while also thinking about how time is socially constructed (see this classic E.P.Thompson article, PDF) and how economic definitions themselves may alter us.

The transition from a mostly agricultural society to what we have today can be thought of in the same way. Two things have happened simultaneously: agri technology has improved, meaning waaay less people can produce massively more (put aside for now 'but it's all just eating fossil fuels'...) The rest of the economy has grown in a feedback process: enabling both increased agri output and freeing up people's time to work elsewhere. As a result, we've also seen a massive morphology change as food processing has moved out of the household/community and into today's sophisticated global industrial networks. A key part of that is how people value their time: we could all be growing food in community gardens and cooking it at home. We have the time to do that. Mostly, we don't. Why not? We prefer to work in paid jobs and access relatively cheaper food, as well as a set of other things we like. Computers, electricity, transport, beer, time to sit and stare at the wall...

Again, there's a whig history danger here: it was meant to be thus, and is natural and good (while forgetting small matters like kicking people off their land when sheep became more profitable, much as we're doing now because car-food is more profitable than people-food [my blog] in many places). But there's also a lot of value in thinking about these changes through the prism of how we value the costs and benefits of our time.

I've found myself looking at my own 'revealed preference' and changing my views. I used to be a lot more fervent about local food growing, until I realised what my shopping habits were telling me: I actually prefer to earn a living in academia and spend time I'd be putting into agriculture on other things, like commenting at P3. If other people feel differently, fine. But I don't think the existence of supermarkets is necessarily a sign of collective moral failure. I also reserve the right to a) reflect on that and change in the future but b) not to have anyone else actually force me to change, unless I've taken part in a democratic process to enforce it ->

Cos maybe supermarket are evil, and individually we're too vulnerable. Marshall lists this in his questions: "what are the proper relations of individual and collective action in a stage of civilization such as ours? How far ought voluntary association in its various forms, old and new, to be left to supply collective action for those purposes for which such action has special advantages?" In the case of supermarkets - and some other market structures - perhaps we should not trust the emergent result of all our collective value-judgements. Instead, maybe we need to get together and decide a set of constraining rules: those we agree are needed, but that we recognise individual actions will tend to corrode over time. That would be democracy. It's also why people who claim that money represents the zenith of democracy [me again] are talking nonsense. If individually we are incapable of making the right carbon choices, collectively we can decide to restrict our choice set.

Whoops, economics

Just caught myself off-guard and spewed on economic modelling at P3. Accidental blog entry!
"Does the absence of a workable model refute a hypothesis?" As usual, Krugman's my favourite on this. Particularly from section 'the evolution of ignorance' where he compares theory-making to early map-making. Early maps were more report-based but, as they slowly became filled in, that heuristic knowledge was lost.

"There was an extended period in which improved technique actually led to some loss in knowledge. Between the 1940s and the 1970s something similar happened to economics. A rise in the standards of rigor and logic led to a much improved level of understanding of some things, but also led for a time to an unwillingness to confront those areas the new technical rigor could not yet reach. Areas of inquiry that had been filled in, however imperfectly, became blanks. Only gradually, over an extended period, did these dark regions get re-explored."

Krugman got his Nobel prize for his work on geographical economics (the 'new economic geography'). Here he is (pdf) reflecting on that. His express purpose in creating his 'simple' core-periphery model was to show economists that geography mattered (as well as that you didn't need comparative advantage to make it work, so some places could become the core through endogenous forces alone). But the bait he used to lure neoclassical economists out of their dimensionless 'wonderland' was a model able to preserve general equilibrium.

Nature's admonitions

Via Paul Krugman via Joe Romm, here's the Economist from 1848:

Suffering and evil are nature’s admonitions—they cannot be got rid of; and the impatient attempts of benevolence to banish them from the world by legislation, before benevolence has learned their object and their end, have always been more productive of evil than good.

What are they complaining about? In the years before the Great Stink of 1858, the Economist was protesting about government attempts to pass housing and sanitation laws; to quote from that wikipedia article:

Part of the problem was due to the introduction of flush toilets, replacing the chamber-pots that most Londoners had used. These dramatically increased the volume of water and waste that was now poured into existing cesspits. These often overflowed into street drains designed originally to cope with rainwater, but now also used to carry outfalls from factories, slaughterhouses and other activities.

Nice. Add to that, no-one knew exactly what caused cholera. Romm got started on this responding to an argument against regulating co2: "it is almost the height of insanity of bureaucracy to have the EPA regulating something that is emitted by all living things."

Romm: what, like with sewage? Krugman: actually, it turns out, yes if the same thinkers had had their way in the nineteenth century. Which reminds me again what an amazing time for ideas nineteenth century Britain was. The nightwatchman state; great quote from Sir Charles Wood, chancellor of the exchequer around the time of the 1845 Irish famine: "the more I see of government interference, the less I am disposed to trust it, and I have no faith in anything but private capital employed under the individual charge."

Great froth of ideas; not so great smell. Anyway, this is about as perfect an example as I could hope for to illustrate an indispensable lesson about Burkean anti-meddling arguments: don't get tangled up in them too much. Life is too short and there's too much sewage in the world.

One man one pound

Mark Littlewood, director general of the Institute of Economic Affairs, talking to Raj Patel on the Today Programme:

The free market operates like a perfect rolling referendum, with the prices representing the outcome of millions of individual decisions.

The Adam Smith Institute said something similar a few years back:

Independent providers are nearer to public demand than public authorities can ever be. Their perpetual search for profitability stimulates them to discover and produce what the consumer wants. In that sense the market sector is more genuinely democratic than the public sector. It involves the decisions of many more individuals at much more frequent intervals.

Climate science and the political compass

In all my banging on about good science yesterday, I realise on one thing I was being unscientific. A couple of links, to Next Left and Crooked Timber, wondered why there seemed to be such an anti-AGW consensus on the right. I speculated it may have something to do with a different assessment of the risks - but this is missing a basic question that could be asked. I'll ask it now, and then suggest that it doesn't matter anyway.

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