academic, writing, gubbins

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 

How to make completely opposing claims using the same survey data (or: how to cherry-pick)

Sheffield Council put out a statement on the BBC recently, during an episode of Countryfile, defending their outsourced Streets Ahead contract against accusations of needlessly felling thousands of mature street trees:

We surveyed 27,000 households. Fewer than 7% said they disagreed with the plans. The contract will bring huge benefits to the city's infrastructure which is why the vast majority of Sheffielders support our plans and why the activists remain out of touch.

Only a small minority ('fewer than 7%') oppose their plans? And 'the vast majority of Sheffielders' are in support? Here's the thing. Using exactly the same data Sheffield Council have used, I could put out the following equally correct statement, in support of the tree protestors:

Fewer than 7% of households said they agreed with Sheffield Council's plans. The vast majority of Sheffielders oppose their plans. The Council remains out of touch.

Wait, what? Only a small minority support the plans? That's entirely the opposite message. How can the same numbers support both of them?

Well, you have to do two misleading things. First, you need to cherry-pick a number. Second, you use a dubious statistical choice that makes it look like a tiny minority oppose the plans, when in reality the data shows an even split of opinion.

Let's go through those two. First, the cherry-pick. The actual survey numbers are as follows. The total number of households they posted letters to is 26677 (round up and you get '27000 households'). 3574 households actually responded - that's 13.4% of total survey invites. Of those 3574, 1774 households opposed the plans and 1800 households supported them.

1774 opposed, 1800 in support? That sounds like something close to an even split of opinion - and indeed, it's not statistically distinguishable from half against, half in support. Not a tiny minority, not a vast majority.

If we cherry-pick just one of those and ignore the other, we're half way to making one of our two opposing statements. The next step: ignore that you should use the number of responses to your survey (3574) to work out the percentages and use the number of letters you posted instead (26677).

By doing that, you can get the 'fewer than 7%' number for both. So we can cherry-pick too: 1800 in support as a proportion of all the letters posted? Fewer than 7%. (1800 over 26677 then multiplied by a hundred to get the percent.)

If exactly the same numbers can be used to produce two completely opposed statements, I hope it's obvious that you're doing something wrong and the numbers are being misused.

The council have defended the statement saying it's factually correct. If you squint, you can just about see how 'we surveyed 27,000 households, fewer than 7% said they disagreed with the plans' is technically true. But I've just shown how the same 'technically true' method can be used to support entirely the opposite message. That's the power of cherry-picking.

And it's not a one-off either. Via the Streets Ahead twitter account, the same data was used to claim only a tiny minority on one street opposed the plans there:

Our household survey results show that of the 54 households on the road, 5% opposed our proposals for street tree replacement.

You won't be surprised to learn: there were only six actual responses on that street, 3 for and 3 against. So again, it's equally correct (but still inappropriate) to say "5% supported our proposals". (It was Rivelin Valley Road, so's you know - again, the numbers are in the document above.)

All of this is ignoring the 'vast majority of Sheffielders in support' statement. In a way, this is the most worrying part. It's just plain wrong, if we're going by this data. But in the context of the 'fewer than 7%' line, I can imagine how one might think, 'well, more than 93% must be in support then'. That's kind of implied, isn't it?

Yet as we've just seen, using the Council's (inappropriate) method, it would actually be 'fewer than 7%' opposed and 'fewer than 7%' in support. They not only omitted to mention this, they have added in a 'vast majority' claim that appears to be completely unfounded. So we're clear, there's nothing in these numbers that even remotely supports a 'vast majority' either for or against. It's an even split.

The ethics of numbers

If your idea of factually correct allows you to make entirely opposed claims with the same numbers, it means you are likely cherry-picking: "pointing to individual cases or data that seem to confirm a particular position, while ignoring a significant portion of related cases or data that may contradict that position". Though here, the cherry-pick wouldn't really work without also mangling how surveys are meant to be used.

I work with numbers in my job: it's a matter of professional ethics to make sure, as much as we can, that our work can be trusted. (Have a read of this code of practice from the UK Statistics Authority - it's a good take on the kind of integrity and honesty we're supposed to aim for.)

We don't know how Sheffield Council created this statement. I can imagine a single over-worked officer under great pressure to get a message out at short notice. But I don't think it's unreasonable to expect the same level of trust from our local councils when they use statistics.

As Ralf Little recently said to Jeremy Hunt (I paraphrase slightly): 'the good news is, now that you know that this statistic is total nonsense, you won’t feel the need to use it again'.

The actual numbers

Let's end on looking at what this survey actually does show - that there's a pretty even split for and against. I should start by saying, we shouldn't really be using the independent tree panel survey1 for this at all. Households were asked their views on trees on their own road. They were not asked, 'do you support or oppose the city-wide Streets Ahead plan for tree management?' They also surveyed households, not individuals. But I guess that's small potatoes compared to the above.

27000 households (rounded up) is the invite number and 3754 is the response number. Trying to maximise response number is central to any survey: the higher the response rate, the more your sample can be relied on to accurately capture what the larger group thinks.

This is hopefully obvious, but let's spell it out to be sure. We don't know what the households who didn't respond think. This is the entire point of surveys: get a sample of views so you can make deductions about everyone else.

So here, the actual split in the response numbers I gave above is 49.6% opposed, 50.4% in support. I may get round to another post explaining why this can't be statistically distinguished from an even 50/50 split - though the intuitive idea is just: how much could that split change as you get more responses? Here, we have a 16% sample - that's pretty big. It's very unlikely to change a lot, but because it's so close to 50%, it could likely shift either side of that 50/50 mark.

At any rate, it is astronomically unlikely that 'fewer than 7%' is the correct percent opposed. For that to be true, all the other households that didn't respond would have to be 100% in favour. The 16% sample would have had to have picked up on every single household opposed. Just... no.

So to end: whether or not the Council knew they were doing this, they have selected numbers to support their own message - as I've shown with a statement claiming exactly the opposite, using exactly the same data and method. This is some way before worrying about sampling rates and confidence intervals. And the 'vast majority' thing... whu?? So let's just end with a tip:

  • Check if you can put out two equally true but mutually exclusive statements using your method. If you can, your method is wrong. Try again.

  1. Sheffield Council surveyed households, one street at a time, to find out if residents wanted an independent tree panel to re-examine decisions about trees on their street. Again, the data is here. It collated all of those single street surveys into one document. 

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.

Don't cling to a mistake just because you have spent a lot of time making it

"The chances of the government admitting that austerity has been a failure are precisely zero. That would mean telling voters that all the sacrifices since 2010 had been in vain." (Larry Elliott)

"Don't cling to a mistake just because you have spent a lot of time making it." (Banksy)

There's a school of thought that says ideas are like Soufflés - if you don't give them just the right care when you're baking them, letting the scaffold form as it should, they collapse in a gooey mess. I used to bake a lot of this kind of thing. I didn't get better, I just stopped trying - too ashamed of all the sad little sticky puddings. But I figure I'm a bit older and, if not wiser, more cautious now. Just throw some stuff out there, poke things a bit and see what happens. Do the thing and all that. Nothing may come of any of it, but then nothing will come of nothing if nothing's all that's done. Profound. So that said...

I've got this notion that it should be possible to show how the economy works in a way that's both robust and accessible. I don't mean accessible just from a three minute glance, infographics-style. But it should be possible - for example - to drag otherwise murky arguments about austerity out into the light where you can test views based on what's actually known, obvious, possible. You'd aim for reducing the range of ambiguity to something much less overwhelming. Expanding the little pool of clarity into something much more vivid.

The reason this draws me in is pretty simple: the 'sacrifices' Larry Elliott talks about - they're almost impossible to grasp. The things that have happened, are happening right now, to people, institutions, all apparently to right a listing economy - there seems to be a very strong case this was all totally unnecessary, literally counter-productive and utterly wrong-headed. And the arguments aren't all that abstruse - it shouldn't be that hard to mark out their boundaries. (Hah - note that for later.)

I'm not naïvely imagining there's some process of alchemy that can transform how the austerity debate is seen (or macro more generally). There's a whole bunch of people that are obviously inaccessible to what I'm talking about, not least those ideologically opposed to the idea of any state action who've seen the crisis as an opportunity. They'll continue to push whatever sophistry furthers that aim, of course. There are also people on both sides who Just Know and nothing could possibly convince them otherwise. (I like to pretend I'm not one of those, though don't we all?) Others won't have anything to do with quant of any kind, especially economics-plus-quant: for them, it's an elite-wielded tool propping up power. That's one to come back to - I have some sympathy for this but it's confusing the tool and the user.

That leaves a whole swathe of people who can meet and converse, given the right space and tools. Have no truck with the convenient lie about post-truth. The global response to Trump pulling out of the Paris Accord shows it's the idea of post-truth that's the danger - something well understood by regimes like Russia. (Paul Mason nails this brilliantly in his stage take on 'why it's kicking off everywhere'.)

I'm not saying there are always right or wrong economic answers, but you should be able to set out what the spread of rightywrongyness looks like. And if I'm talking about a tool, this would mean transparency in how it's built too. Code would need to be accessible, assumptions up-front, well commented and explained. The way models are perceived (even by many modellers) leaves a lot to be desired - I'd see this kind of open process as a chance to talk about that as well. It couldn't be something you went away for years to build - the building process itself would need to be a conversation. It couldn't be - initially at least - some single overarching model (h/t Jon Minton).

But that conversation would need a starting point, which brings me back to the beginning of this post. There's an argument that I should wait until I've got a little working example - I know what the first simple dynamic is I want to look at - but I'm bored of waiting for that. I just want to put something out there to taunt me with past versions of myself who'd annoy friends by trying to drag them into grand visions that I had absolutely no way of ever accomplishing. I think I might have learned how to start small and let things change as they hit reality. We'll see I guess. Hmm, just realised the Banksy quote I meant for austerity applies to me too.

Great recession caused the dirty oil boom? (plus bonus self-indulgent whinge about writer's block)

Here's something (PDF) I didn't know: monetary easing (QE and near-zero or even real-terms-less-than-zero interest rates) might have been responsible for the dirty oil boom and the subsequent price drop. (That's via a little summary of Helen Thompson's book).

It does also make the 'recessions always correlate to oil price hikes' claim you'll see being made by people I might call oil determinists. As she does here, even the recent mortgage credit related crash looks like an oil-triggered thing through this lens. Others, however, see e.g. the 70s oil crisis being made much worse by governments whacking the steering wheel in the wrong direction in reponse to what happened.

But this story about how massively expensive dirty fuel exploitation got going makes sense - and fits with the kind of up-down pattern we can probably expect without anything to counter it. Though I'm trying to picture how that ends and can't - if, for example, renewables continue to undercut fossil fuels, demand for them drops, their price drops... and what's the new equilibrium? How do you eventually see the end of an old energy source, as we have several times before?

Dunno. But I'm going to post this anyway, and try and post anything else interesting I find, as all I've been doing recently is writing abortive chunks of whinge about how I can't write any more. The first thing I need to do to fix that is (a) post little things like this even when this new 'you don't know enough about this' warning light I seem to now have courtesy of academia starts blinking in the cockpit and (b) even when I write horrific sentences like this, still post it because that's better than filling folders full of words that never get posted (well, maybe not for anyone reading...) and (c) work up slowly to the larger topics I keep on trying and failing to find a way to articulate.

I do want to write about what's happened to the writing (and thinking etc) because there's something important there. But it needs working up to and I'd feel better about doing it if I've got the wheel going a little under its own inertia.

The short of it seems to be: I used to love writing but I'm not sure a love of writing can survive in academia. No, correction: not sure my love of writing can. If there was some way for me to find a happy marriage of my own needs and what's required of me... but there, starting to whinge about it, I'll save that for later.

Let's see if it's another year to the next post.

Letter mainly to myself, post-Donald

I think I've probably finished the reading the internet now. Eyelids peeled back, unable to look away, scratching the wallpaper off to get at whatever thoughts or feelings might help shift this... matter through my digestive tract without rupturing something. It's ongoing. In the meantime, as for so many others, dream and reality are in some godawful state of twizzler. Panicked braincells scramble to hide behind each other, desperately straining to avoid the incoming signals.

Which is of course all angel delight to the right-wing maw. As a friend once said after they inflicted a vicious chess defeat that I'd poured everything into: "I wear your pain like a crown."

Well fuck them, obviously. (Not the friend - they're quite nice really.) Paul Ryan's vacant little Mona Lisa smile as he refuses to acknowledge that an appointed 'chief strategist' is clearly Voldemort - that's pretty much all we need to know about these people. "Evil overlord, you say? Hmm. Will this get me more power? Mmmmm."

I've been shocked into doing something. Pretty messed up that it should take this, but there's some comfort knowing exactly the same thing must be happening to thousands, millions of others.

There are a bunch of luxuries we no longer have. We don't have the luxury of much self-doubt. Or rather, it'll always be there - everyone has it - but you're not allowed to let it stop you. Them's the rules now. Do something. Anything from contacting people you haven't in a while, hugging a friend, donating, volunteering, painting, singing, plotting, inventing an entire utopia. Don't make a utopia in your spare room all alone though, and don't be silly and try and do it all on the internet. Come out and play. Bounce that stuff off other people - the one thing we need more now than ever is to connect with others in every way we can.

Sure, your mind may scowl: "you Walter Mitty peabrain, what the hell makes you think you can make any difference to anything? Remember all those things you fucked up? That's the real you. Drink your effing beer, stuff this Netflix into your eyeballs and SHUT UP." But... your mind can fuck off as well. And notice just how useful that bit of your mind is to those aforementioned power-mad eejuts. They want you to do nothing. Fuck them also. I said that already.

Because there are really very sound reasons why you should do the thing and not not do the thing. Jane Jacobs nailed it: anything good that ever happens or gets made is just accident fuelled by intention. It's a lot of people with ideas and bits of stuff they're making and trying, doing the thing - and usually finding out the thing doesn't turn out anything like how they thought. But then this other thing happens - Jacobs realised, in fact, that it happens without fail when we get together to do stuff. It's what humans do. Some magic mix of evolution and artistry: person A goes - hey, that thing you just did? What if I just bodge that in with this other thing? Oh sweet Christ, we've discovered a better way to organise the city! How did that happen?? Accident fuelled by intention, ladies and gentlemen.

But it needs the intention. You need to turn up, do the thing and - this bit is especially important - not not do the thing. Not doing the thing: that's self-doubt's job description. Spotting little green shoots of maybe doing the thing and yanking them up before you have a chance to get out the door.

And you do at least sometimes need to get out the door: do the thing where other people are doing things too, or take the thing out for a visit now and then.

So - for myself - I'm fucked if I'm gonna let some colossal tango-faced bullshit queen, centre of a venn diagram made from a blow-up sex doll and an obese geriatric ginger tomcat, mess with everyone else's amazing work on stabilising carbon output. It should probably have been obvious this couldn't be done without one or two countries going the full man-toddler on us. A puncture on the road is predictable enough - you've just got to roll your eyes, fix the damn thing and press on, even if you can see the little shits who threw the tacks laughing their asses off.

Put aside any eensy niggles about nuclear obliteration - as Nick Fury so wisely said, "until such time as the world ends, we will act as though it intends to spin on". The planet's future is somewhere on a distribution and, like these poor lushes trying to get to the pub across a bridge over a terrifying ravine of certain-ish doom, there is no point at which it's OK just to say: ah sod it, let's just stumble forward unconsciously and hope for the best. The odds are worsening with every year, but there are - for a good while yet - always odds worth taking. Here's Alex Steffan:

It’s a fight for every 1/10th of a degree. If we fail to hold to 2ºC, we have to fight for 2.1º; failing that, we battle on for 2.2º. With millennia of impacts at stake, we never get to give up, even if we end up in 4ºC. For future generations, 4º is still better than 4.1º. "Game over" is neither realistic nor responsible.

And there isn't a shortage of other stuff to get stuck into. At the root of it all, connecting with others is the prime directive. For anyone who believes every person of any sex/gender/skin-colour/age/wealth/size/shape/geolocation/dress-sense deserves our deepest fucking respect and our care, that we all owe that to each other - that act of connection is the most fundamental and sacred thing we can do. Fucking cliché, I know, but you know that cliché about things being clichés for a reason? That.

To summarise:

1. Fuck them. (Obviously.)

2. Do the thing. Do not - and I really want to be clear on this point - not do the thing.

3. Be excellent to each other.

Now get on with it.

p.s. sorry I was rude about you Donald. I'm really cross with you right now.

How to avoid comparing rich and poor

Reading Mark Blaug on Pareto efficiency was a lightbulb moment. As he says, Pareto's idea was a 'watershed moment' in arguments about utility. From a distance, the outcome can seem pretty meaningless but it's an important political fork in the road - and one that shines a light on how the abstractions of economic theory get tangled with power politics. There's a story about Pareto himself to be told, too - I'm not going into that. This is about where his idea went after that.

Benthamite utilitarianism hadn't been going badly. But it was premised on the idea that different people's well-being could be compared - after all, there's no other way of knowing if you're increasing or decreasing the general welfare.

This seemed intuitively straightforward at the time. But, perhaps as the study of utility as an economic concept developed, that began to change. Attempts to actually track down a scientific measurement of people's utility got underway. Folks got upset about the obvious problems in trying to define what utility really was.

Pareto offered a way out of this. I'd known the concept before but not understood its significance until reading Blaug. Pareto efficiency: you've reached an optimal state when it's not possible to make anyone better off without making someone else worse off. Sounds innocuous enough. But notice that it sidesteps comparability. As Blaug says:

"The beauty of Pareto’s definition of a welfare maximum was precisely that it defined the optimum as one which meets with unanimous approval because it does not involve conflicting welfare changes."

It rules out the possibility that one could -

" - evaluate changes in welfare that do make some people better off but also make other people worse off" (Blaug / economic theory in retrospect/ 1997 p.573-4).

So it can say absolutely nothing about inequality. Or rather, it implicitly says that it doesn't matter: you cannot, for example, assess whether taking money from one person and giving it to someone else will improve welfare overall. Bentham schmentham.

Pareto optimality, unsurprisingly, became very popular and is essential to most general equilibrium models. I don't understand those - I'm only familiar with Krugman's spatial GE stuff, which is not the same (they're driven by explicit utility differences across space). But I'm not surprised models that, by default, exclude inter-personal comparisons should form the inner sanctum of modern economics. A model that can, by design, exclude any discussion of redistribution was always going to thrive.

Which is not to say there aren't plenty of approaches that do analyse the differences between rich and poor. But... and I'm not on solid ground with this point at all... the kind of economics that sits in rooms with ruling elites don't generally use those.

I want to make two little points about this. The first comes from having actually used utility as a concept in my modelling work and found it extremely valuable. I spent far too long listening to the siren-calls of agent modellers telling me to go towards 'realism', then in the process of slowly solving my problems, realising I had ended up back at basic micro-economics.

So first: if you're going to use utility at all, you'd better accept it's a silly idea that lets you do useful things. People are not actually utility maximisers, but the concept is a superbly effective way of thinking about how people react to cost changes in certain situations. (This is all very Friedman [pdf].)

So all that pursuit of the actual foundations of utility in our meat-brains is, somewhat, beside the point. Given that, we should use the idea in ways that are useful. Ruling out utility comparisons is just a little bit too convenient a result, politically. There isn't really any reason to, and the angst about utility's epistemological status makes about as much sense as rejecting traffic models because they don't use gravity equations. (Er, at least I think they don't...)

Second, one of the most powerful ideas that utility gives us is diminishing returns. It's easy to forget how much of a puzzle this was - the whole water/diamond problem thing. It should be blatantly obvious to anyone who thinks for a few seconds that money itself has diminishing returns. Say a 7% drop in income forces your family to eat less well and you to have to skip meals sometimes. It shouldn't be beyond our economic theory to see this as more severe than having to compromise on the Land Rover you had your eye on by buying a Mondeo.

This is kind of paragraph that sets the flying monkeys off, though. Particularly since the 2008 crash, particularly in the UK - the story that's been slowly pushed through all media channels is solidifying into political reality: such talk is the politics of envy, rather than - as it actually is - a perfectly sensible way to think about wealth.

These days I generally end up thinking "it's all about the middle way". The same applies here - effective economic comparability could imply deep intrusion in people's lives, the state charged with measuring and judging what forms of spending were more worthy than others, creating a kind of state-sanctioned Maslow hierarchy. But it doesn't need to - if one is capable of accepting the basic premise that severe poverty makes people's valuation of money much higher than for richer folk, it just implies the need for policies that reduce inequality.

And there isn't necessarily anything wrong with Pareto efficiency. The problem here is what happens when powerful abstract ideas interact with powerful political forces. Things get warped to Wizard of Oz proportions. Other perfectly sensible ideas can't get their shoe in the door. But it's foolish to use Pareto efficiency to exclude distribution thinking, just as it would be idiotic to ban its use because it was too right-wing.

I wouldn't want to live in a world where political schools had their own paid-for economic theorists. I do still believe in the pursuit of actual social-scientific truths. But Pareto efficiency is one of those ideas that hammers home just how hard it is to pull economics and politics apart.

The point: as far as possible, your economic/mathematical models shouldn't rule out one particular political way of thinking. The choice of how we balance wealth in society - that's a political issue. There's no easy way to keep an unbreachable line between positive and normative - modelling methods will always interact with our political assumptions and power structures in sometimes very-hard-to-see ways. And I also believe in the power of quant modelling to help us understand which things may not work if pursuing certain political aims. But modelling distribution issues - and using utility to do this - no more makes you a communist than using Pareto efficiency makes you a fascist.

(p.s. googling Pareto inequality reminds me there's a mountain of stuff on this subject I don't know. But if I think like that all the time, I won't get a single blog entry written, let alone seventeen...!)

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New year's earnestness 1/17

Would you trust Uber and Google with your city streets?

Uber-branded taxis are now ubiquitous* in Leeds, having launched last November. I'm back in Leeds for a month - it was immediately striking that pretty much every taxi now has the large white Uber label, at least in the city centre. That's a pretty impressive transformation in just over six months.

It's a classic disruptive firm; one can imagine CEO Travis Kalanick has personal targets for how many local government authorities to annoy. It can certainly be spun as a nimble tech firm zipping around the tree-trunk legs of a geriatric industry. Predictably, there's been trouble. As well as various protests, some Uber drivers are getting organised to fight for a bigger slice of the profits. (If, as that article says, Uber are taking 20-25% per ride, there isn't a lot of head-room for wages to increase - Uber's dirt cheap fares would have to rise.)

Uber have placed themselves between drivers and customers in a way that reminds me of the weirdness of Apple's app store. In a world where anyone can dump code on their blog and anyone can downoad it, Apple have thrived by creating a portal and sitting as gatekeeper. They take around 30% of every single app sale - and, for developers, this has actually worked out great. They get access to a huge market while getting to code on a single, predictable platform. Small niggles about the political implications of that control might buzz about irritatingly but cause no serious discomfort.

Equally, existing tech could - in theory - link customers and taxi drivers without the need for such a powerful intermediary. The possibility of open-standards platforms transitioning us to the next level of transport has excited many people. Harvey Miller's work, for instance (and this great presentation) sees hope for a "transportation polyculture" where smart-city tech opens up a world of collaborative/co-operative transport. In this world, the kind of fluid, efficient city roads that Uber talk about, where ride-sharing is easy and prevalent, come about via open source principles entirely at odds with Uber's - though such a world would perhaps be just as disruptive to existing taxi firms.

Two radically different internet myths are at the heart of this difference. In one founding myth (as the Economist says) the Net is "the spontaneous result of co-operation by growing numbers of people acting outside the control of the governments and big companies" - a "libertarian paradise" promising a level of openness, connectedness and democracy never before possible. That story is still being told.

But then this other story appears.

"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.

Refs:

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: http://web.mit.edu/krugman/www/howiwork.html

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.
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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)

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Refs:

Brangwyn, Ben. 2012. “Researching Transition: Making Sure It Benefits Transitioners.” Transition Network. http://www.transitionnetwork.org/news/2012-03-29/researching-transition-....

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.

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