Of brains and cities; neuroscience and cultures of decision-making
By: Dan Hill
Tags: Data Science, Neuroscience, Science, Strategic Design, Urban Planning

What parallels between how neuroscientists understand our brains, and how we understand our cities, via leaky metaphors and steady states.

A chilly December night in 2011. I had been invited to take part in an evening event called the “North House Salon”, one of a series of salons organised by Dr Sarah Caddick, neuroscience advisor to Lord David Sainsbury (ex-Minister for Science and Innovation in the UK government) and the Gatsby Foundation, bringing together various “expert groups” with select groups of neuroscientists. It was an absolute privilege to share a conversation with some of the UK’s leading scientists — for a start, it’s always fascinating to see another discipline at work, and we were also fortunate that they were all great communicators as well as great researchers.

This particular event was a collaboration with one of my old bosses at Arup, Dr Chris Luebkeman, Director of Arup’s Global Foresight, and it concerned the potential correlations between our emerging understanding of the brain, and our understanding of cities. (Perhaps we should also say our emerging understanding of cities?) The event was dubbed “The Urban Nervous System”…

As Chris put it in his intro, we do have an increasingly shared vocabulary and way of thinking emerging about the systems of the brain and the systems of cities. This may partly be due to biomimicry shaping design discourse, partly the vogue for “smart cities” strategies, and partly because of recent advances in “brain science” (note: neuroscience is to some extent now seen as part of a continuum including behavioural psychology, behavioural economics, neurology, developmental biology and others. I’ll be using the term “brain science” as short-hand for all that. At one point, we tried to discuss the limits of neuroscience. We didn’t get very far.)

Team Neuroscience (not that we lined up as teams, of course) consisted of Doctors Peter Latham (UCL), Semir Zeki (UCL), Daniel Wolpert (Cambridge University), Troy Margrie (National Institute for Medical Research/UCL), Dmitri Kullmann (UCL), Steve Wilson (UCL) the aforementioned Sarah Caddick, and Geoffrey West (Santa Fe Institute). (Note that Geoffrey is not a neuroscientist but a physicist, and could probably swap sides at half-time, should he want to, at least to some extent; again, boundaries were intriguingly eroding.)

On Team Urbanist we had Mark Bidgood and Duncan Wilson (both Arup), Robin Daniels (Living PlanIT), and me. Actually, the “teams” really were non-existent; the presentations were mixed up, as was the conversation — in a good way. (Unfortunately, as you can see, the gender (im)balance was not good, although that’s partly because a few people couldn’t make it, sadly.)

The format was papers sent beforehand (I sent this, this and this), and on the evening, three-minute presentations from all participants — some slides, some not — followed by drinks reception and talking, followed by dinner and more talking, followed by pub for a few of us.

So how did the conversation start? The presentations in order …

Mark Bidgood (Arup) talked of Arup’s work in civil engineering and infrastructure, overlaying a set of biological metaphors — buildings as organs; power,water, sewerage as vascular system; information and communications technologies as the nervous system … He talked of his work in Riyadh (with Carlo Ratti and others), and mentioned Masdar as an exception. He saw the real value in retrofitting existing cities. But in terms of the relationship between brains and cities, he noted that physical utilities infrastructure doesn’t tend to self-repair, learn or adapt (instead, focus is on robustness, reliability, repairability; and so huge networks are underground and highly simple.) He also talks about the biggest roadblocks: the commercial & regulatory side, and generating the political will for change whilst enabling people to have freedom of action and choice. (I like this last point in particular; it resonated with my talk, later.)

Duncan Wilson (Arup Foresight) talked about the internet of things, based on his long-standing interest in autonomous networks. He notes haven’t exactly become ubiquitous in the physical world, yet they have on the internet. In this context, he was interested in feedback loops and behaviour change, and so was looking for a better understanding of cognition to aid system design for behaviour change ie. how people might pick up, absorb and act upon cues. (I’ve worked with Duncan for years, and he understands as much about the potential of sensors as anyone; good to see him looking deeper into the psychology.)

Peter Latham (UCL) took the cue directly: giving what he described as a naive answer to “what brains tell us about urban planning.” Latham delivered a rapid-fire, entertaining talk, casually noting we have 100 billion neurons and around 8 million kilometers of wires (axons) in the brain. He then extrapolated to transport systems (which is the natural, if problematic, thing to do — given that information transfer does not necessarily imply physical transportation these days), so he quickly painted a picture of local roads within neurons, and large spaces (parks and trees, in Peter’s city) in between dense nodes of “highrises”, or concentrations of activity. So a brain scientist ends up making a case for density too, which is good to hear. (Peter was of course much smarter than his deliberately “naive” answer, and was constantly insightful and entertaining all evening.)

Robin Daniels (Living PlanIT) said his interest is in managing data, and particularly in smart urban environments. He talks about Living PlanIT’s work in big regeneration projects, aiming to use resources more efficiently, via sensors that collect data, and then manage it in better way. He talks of their platform, and makes an analogy to the iPhone: he says the applications are what make it interesting. Their apps (“PlaceApps”) could “control everything from luminaries to transportation systems”; the data could be self learning. (To be honest, I haven’t been impressed with Living PlanIT’s vision so far, and there was little this evening to change my mind; to be fair, it probably wasn’t the right setting for them.)

Semir Zeki (UCL) is developing the field of “neuroaesthetics” of UCL (and his paper, particularly “The Disunity of Consciousness”, was perhaps the most interesting reading sent around beforehand.) He noted that a quarter to a third of the brain is devoted to vision, and is interested in how these layered activities of perception combine to give us a uniform view of visual world. He describes how we see colour before we see visual motion, for instance, which then brings up the “binding problem”. How do we arrange a unity of vision? Are we asking the right question, even, he says. And then throws in a few examples: What are the minimum conditions necessary for visual consciousness? What does art tell us about the brain? If neural mechanisms are important in the experience of beauty, are there any common characteristics in our experience of desire, love, beauty …? What can we learn about the brain by looking at beauty?

Daniel Wolpert (Cambridge University) is researching how the brain controls movement; in fact, he sees the brain as essentially “about” movement. He talks rapidly of how control of motion is so hard to understand or reproduce, because of multiple interacting components, entirely non-linear processes are, long time delays (relatively) and noise, and so on. He then describes Bayes and his algorithms (which I remember from my Comp.Sci degree), and in particular Bayesian decision theory — how you deal with uncertainty. This enables a form of thinking about “probabilistic actions”, which perhaps underpin motion control. Wolpert then switches gear to the idea of “priors”, which may be genetically encoded. In other words, we may be hard coded for some priors — gravity, for example. He asks, intriguingly, whether it’s important to understand “urban priors” (what priors do you need to possess to instinctively understand Tokyo or Los Angeles, for example.) He suggests the richness in a statistical/probabilistic model of the world, noting you can’t control everything, and asks whether there are good generative probabilistic models of cities? (It’s been an emerging field for years, and still is). Is a Bayesian perspective used in urban planning? (With the Black Swan in mind, I’m wary but fascinated.)

Dmitri Kullmann (UCL) is a neurologist, and so a little different. His expertise is in how nerve cells talk teach other. Like Zeki, he’s also interested in this idea of a unity of consciousness. He vividly describes how the actual connectivity and wiring in the brain is entirely relevant to this question, and the brain’s ability to “flip between streams”. We hear of the plasticity of synapses, and the plasticity of functional component. He talks about a theory that oscillations can control information flow, as there is evidence that different areas of the brain start oscillating together (this is called “coherence”), such that they’re able to exchange information if they oscillate together. But he notes that there is no good science on how this works. Can we use computational models to “spike a neuron”, he asks? (As with the way that the scientists interact, I hugely enjoy the language at play here, and am immediately intrigued.)

Troy Margrie (National Institute for Medical Research/UCL) works in mitral cell diversity, exploring the intrinsic biophysical diversity, particularly in the olfactory bulb, which processes smell.) He shows us the immense diversity in the biophysical property of mitral cells, whereas the cells that wire together networks tend to look similar, in comparison (they are the same functional network.) They explore these things by creating transgenic mice, and looking at the homotypic property that reflects the processing of specific functions. (Margrie is another that lets slip some wonderful language, perhaps inadvertently eg. “The nice thing about working in mice is that we can make mice. So we made a mouse, a transgenic mouse.” And so on.)

Steve Wilson (also UCL) is a developmental biologist rather than neuroscientist as such — though again, the genres are blurring heavily here, which is good. He studies how the brain gets to be in its mature form, or in particular, how the vertebrate forebrain develops. He notes it starts simply but over time forms an incomprehensible number of connections.) He’s looking in particular at asymmetry and lateralisation in developing brain (how does left side become different to right, for instance.) They work in little fish embryos, he says, looking directly into the brain when it’s at its most simple, and only consists of a a few neurons (tens, hundreds etc), as opposed to the complexity of humans. Why does the brain work in an asymmetric way; language processing is left side, right-handedness is behavioral motor asymettry; etc). The “little fish brain” (a couple of days old, but has vision, can respond to sensory information) already has left-right asymmetric epithalamic circuitry (this is the major output pathway of limbic system, one of the older systems in our brain) (see zebrafishbrain.org for more.)

Last but not least, Geoffrey West (Santa Fe Institute). Many of you will know West through his research indicating a strong correlation between the “metabolic rate” of cities and certain indicators of urban performance. His background is actually physics (working on dark matter, string theory) and then as a physicist working in biology. He eventually ended up researching the idea of “a science of cities” — can there be probabilistic mechanistic science of cities? Are we just looking at biological metaphors, or is it serious science? (He describes the latter as quantified, mathematised and predictive, I seem to recall.) He starts with the now well-known ‘body mass against metabolic rate’ graph, and then extends this rapidly into the mathematics of networks. He says you can derive all scaling laws — in a “coarse-grained average sense” — across a wide range of indicators and essentially all the many cities he’s measured. You can read more about his work here.

Basically, West sees cities as networks; the physical city is a representation of networks. (Picking up my cue, he also talks about culture as a form of network of networks.) Looking at these scaling laws, he can be given the size of a city and accurately predict the number of petrel stations, say, alongside pretty much all other physical infrastructure, socio-economic qualities like wages, crime levels, and so on. He can predict how fast the average person will walk. Based on this consistent systemic scaling behaviour, he suggests that doubling the size of the city systematically increases income, wealth, patents, number of colleges, number of creative people, AIDS cases etc etc. by 15%.

(We immediately get into a discussion here, as West is a good communicator, the data is compelling at face value, and the correlation seems almost magical. West notes that density is not particularly taken into account in their calculations, because there is so little good data about cities (a general problem, he adds.) He knows it’s not irrelevant (though personally I think it’s even more relevant than he suggests.) He is also pressed on why the scaling is .85 for infrastructure and 1.15 for patents, for example. I also find his data to be like much traditional analysis ie. It tells you the way something is, without necessarily uncovering “why” in a really useful way, or suggesting what to do — if you were a mayor, you wouldn’t double the size of your city, even if you could, to gain a ‘15% increase in patents generated’ if it also meant a 15% increase in crime and AIDS cases, right? To take a step forward you need synthesis, not analysis. Still, this is not necessarily the job of science, of course, and in conversation, I found Geoffrey to be more engaging than his data — and he is of course right on the necessity of having more thorough, more insightful data on urban performance. What to do with it is another matter.)

Chris Luebkeman (Arup) then gave his take on the subject, and the discussion so far. Chris is interested in “What is normal?”. He’s fascinated by the next 20 years and trying to understand how “normal itself” develops over that timeframe. Partly this is Arup projects often take that length of time, if not to develop, but to mature, and so, as he put it, “How do we be sure that life of a project is going to be a full one?” Recalling what he had just heard, Chris said he was fascinated by how the brain is able to adapt, adjust, be plastic. So this adaptation was key: how do we adjust to the new normal? How do environmental factors impact on adaptation? How will a resource-constrained world affect us? What’s driving change — how we understand change? And how do our brains change? How do we perceive that?

Sarah Caddick, summing up, talked about her own interest in how the brain works in this context Ie. What can we learn, in terms of urban systems, by observing the brain’s ability to work as a finite entity that can bring resources online when it needs them, looking at the somewhat controversial “silent synapses” as potential points of connection coordinating this (I’m extrapolating a bit here, which is dangerous to say the least.) She ended on a poetic note, recalling how flying over Prague once, before coming into land at night, the city below looked like nothing more than a “basket cell” (a neuron) (See also the juxtaposed illustrations from Steven Johnson’s Emergence, reproduced above, with their allusion to the plan of Hamburg and an illustration of the brain.)

My own presentation, in between Wolpert and Kullmann, focused only on a few things and avoided images and movies (unusually for me). I briefly mentioned our own smart services work on Low2No, but my core point was that we need to step back and think about the question we’re trying to ask here — why were we gathered here today? I suggested that ideas themselves are not particularly relevant; that the idea of optimising urban infrastructure as a ‘no-brainer’ (an odd phrase to use in this setting, I admitted.) (I also nodded to Zeki’s paper, which I’d learned a lot from.)

But then I made the claim that the city is not psychological or biological, but cultural, and that if anything is holding us back from “better cities” (if that’s our goal), it’s not ideas or technology, but our cultures of decision-making (which is the focus of our work in the Strategic Design Unit at Sitra.)

In this sense, I mentioned my boss Marco’s contention that we have 18th century institutions facing 21st century problems; that we need to preference synthesis over analysis (analysis tells us why things are, assuming it’s not too narrowly focused, which it often is; synthesis tells us how things could be); that people are more convinced by narrative than data (if we’re trying to convince people of something); that we need to address the “dark matter” of organisations, culture, policy, as well as physical matter, and connect the two together via prototypes and projects. So data is not enough to actually get things done. I tried to position this as no challenge to the collective knowledge capital in the room, but instead to open up an angle oriented towards “what to do”. So my closing questions were: how can brain science help us better understand the architecture of problems, and what might we learn about cultures of decision-making?;

(I later expanded on these ideas in my book Dark Matter and Trojan Horses: A Strategic Design Vocabulary.)

To the discussion, which was freewheeling, entertaining, and well-hosted by Sarah and Chris. Ultimately it was the kind of conversation that is difficult to sum up, so I’ll list some key points I remembered (this is, of course, my interpretation.):

Ultimately, I’d say we failed to make any particularly deep insights or connections in terms of overlaps between brain science and urbanism. We all learned a lot, but it felt like an opening conversation which might lead somewhere, rather than an immediate destination. While it was fascinating to position two sets of thinking and experience alongside each other, while the connections leaped across the divide as if electric, they didn’t sustain a meaningful connection. While urban processes might look like neural processes if time-lapsed and sped up such that centuries last seconds, the differences — particularly between urban fabric’s inert and cumbersome physical nature and the brain’s elasticity — are fairly significant. Of course, looking at how culture happens would be more interesting, from both perspectives.

We came up with numerous metaphorical insights and correlations between brains and cities but as a way of communicating as much anything (which is immensely valuable of course.) For instance, as the brain scientists tried to describe their theories, they would have to resort to metaphor to convey the content to us — so this was “like an autobahn”, or this was “like a tree”, or this was “like a railway station”, and so on. In the ability to make the metaphor, though, it’s clear that cities have many similar characteristics. The interesting stuff would be the things that there was no metaphor for; yet how would it be communicated over dinner?!

Even when someone was stopped to explain something, they might say something like “Oh, well, a silent synapse is an excitatory glutamatergic synapse whose postsynaptic membrane contains NMDA-type glutamate receptors but no AMPA-type glutamate receptors, yes?”, which we all realised was unintentionally hilarious and ultimately slightly absurd. We tried to keep up the best we could, though the gulf was as deep as it ever has been, even in my experience of being parachuted deep in areas of expertise I initially knew little about.

Metaphors are useful, as they enable the thin skein of connectivity between bodies of thought; yet they are also a leaky mechanism, potentially losing much richness from original concept to translation. If we were able to spend more time sharing ideas, we might get somewhere. The danger, after all, is that systems and concepts are built around analogies, rather than anything truly deep.

Long, good discussion over whether the brain has, or is, substantively changed. Someone brought up how contemporary cities might be affecting people in a neuropyschological sense, and, of course, video games came up. Here, the neuroscientists were all clear that video games were not “changing the brain” in any substantive sense (that that would take tens of thousands of years), although what people were doing with their brain, on the same hardware, as it were, was different — and that an increased ability with multi-tasking, due to these new characteristics, was an indubitably positive thing.

Chris had an excellent question up his sleeve: if you could change one thing about the brain, what would that be? One immediate answer was “memory”, as “we have a terrible memory.” Interesting, but I also suggested that forgetting might be equally important. Probably something in Frances A Yates’s The Art of Memory about this; also, this line recently caught my eye: “How comforting it is, once or twice a year,/To get together and forget the old times.”

Another answer concerned deterministic versus probabilistic responses (as the latter seems inefficient and energy-intensive, perhaps) — I didn’t fully understand this, though hung on by my finger-tips. There may be something in this probabilistic (Bayesian) algorithms, at a basic level of infrastructural decision-making.

The fact the brain hasn’t changed much despite recent advances was underscored with the memorable line, “We basically have the same brain as a well-fed Roman”. (A friend of mine later tweeted, upon hearing this, that he also has the body of a well-fed Roman, which isn’t true but could be if he put his well-fed mind to it.)

While I focused here on the brain science side of the conversation, we did of course discuss cities a lot. It felt that there was often a mischaracterisation of contemporary urban planning in play; not with any intent — just exemplifying the popular notion that urban planning is still working in 1950s mode (to be fair, it often is in some places.) So people were often unaware that, for example, generative models of cities already exist and are used in some circumstances; that it is an increasingly multi-disciplinary and holistic process; that long-term engagement might be catered for; and that ideas of human-centredness are also taken on by the business (again though, to be fair, the entire built environment business has at least a couple of decades to catch up here.)

Several times the brain scientists reinforced what little they actually knew about the brain. It was quite humbling to hear this, given how much they did clearly know (collectively, hundreds of years’ worth of experience in the room) and yet how little they felt they really knew, compared to what the brain must actually be. This is an incredibly important point, given the tendency of expertise to overstate its value (see Taleb etc.) and the danger in building understanding based on limited information — so it was both reassuring and powerful to see this humility and reason on display.

Zeki and I had an interesting exchange about resilience and cities, after I brought up the example of Beirut of a resilient city, inspired by Adrian Lahoud’s theories of post-traumatic urbanism. Zeki spoke wonderfully about the human qualities of cities like Beirut, as I’d reinforced their essential resilience through network redundancy; yet he also felt London was terrible in several places I would disagree with. In fact, the subjectivity of the urban conversation was interesting, in comparison to the apparently more objective brain science; West’s theories of an “urban science” didn’t seem — to me — to be the way forward in terms of leading or decision-making, although his wider urban data projects might contribute incredibly valuable analysis. Ultimately, the subjectivity of cities is what makes them so compelling, perhaps. Slippery little buggers, in that respect.

The emerging discussion I personally found most interesting — and tested on Geoffrey West and others, who were receptive — was this idea of how we make public decisions. Given our cultures of decision-making, from the individual to the institutional, were designed in another time, is it any wonder these systems are struggling to deliver the kind of complex, longer-term, interdependent decisions we need to make today? Equally, we now know rather more about the way the individual and society works, and so have some idea that fundamental systems within the brain, such as the limbic system, seem to prefer short-term decisions, for example, amongst a series of other unhelpful characteristics.

So the thought occurred: how can we better design our approach to public decision-making, in such a way that the structures and cultures mitigates against our inherent “limitations”? (Please note the inverted commas there, indicating the obvious value judgement.)

In no way would I want to suggest that we construct systems around what we understand about the brain, given that a) we clearly still understand so little, and b) designing systems based on biological and psychological structures seems inherently dangerous — see note on ‘ecosystem thinking’ below; or Will Davies on the folly of pursuing self-organising decision-making structures derived from ants — given that culture and politics are “higher-order functions”, if I can put it like that, which differentiates us from, well, ants. (With all due respect to ants, and admitting none of us have ever checked with an ant what they think about all this.)

For instance, emergence is a powerful form of organisation for certain contexts, and we might learn much from such processes, but I’m yet to be convinced that it should be primary driver of our public cultures of decision-making. That’s quite a leap: potentially pointless; potentially dangerous.

However, perhaps it might be fruitful to use brain science as one core input into the redesign of our cultures of decision-making? (We are already looking into the contributing roles of space, experience, community, social interaction, and other facets.)

How to mitigate against our short-termism? How to understand our intrinsic irrationality in decision-making — as Daniel Kahneman’s book does (and see this review) — and yet build systems that enable coherent, responsible, decisive and resilient decision-making nonetheless? How could we construct approaches that mitigate against the likelihood that humans tend to feel greater sympathy for those that resemble them (racially, for instance)? How to compensate for the “planning fallacy”, the demonstrated over-confidence of experts in their abilities, and numerous other cognitive biases that might shape public, representative decision-making? Given research indicates these characteristics, are we sure our current approaches might absorb and compensate for these instincts appropriately? How do we foreground conscious and rational decision-making when our subconscious and irrationality apparently shape our decision-making? What kind of structures and cultures might flex smartly in tension with these forces?

A series of great conversations with Steven Johnson in Oslo last week reinforced my interest in these ideas, as did David Brooks’s recent book The Social Animal, which started to approach the idea of re-calibrating policy-making based on our advancing understanding of various aspects of brain science (though do read this excellent critical review in the New York Times, which also attempts to keep science in check, whilst learning from it; and I don’t buy Brooks’s perception of the limits of social policy, which seems very US-context-driven, put it that way.)

It’d be interesting to take such science and not have it tend towards “self-help” psychology on personal decision-making, but something more nuanced, public and systemic, (or indeed get misinterpreted into “techniques” like brushing your teeth left-handed to promote mental flexibility, or debating with a full bladder, a bizarrely medievalist notion endorsed by the current Prime Minister of the United Kingdom, and with such winning results.)

Note again the desire would be to re-engage with politics, policy, the state, and the richness of our various formal structures and informal cultures of decision-making, as a primary contribution of humanity, rather than deny it or side-step it as previous such approaches have. I see this as a design challenge, at least with a contemporary understanding of design which is not solely tied to the limitations of “problem-solving”: to design a series of prototypes that enable us to learn by doing, in properly blending the natural sciences with culture, social science and the reality of politics.

Quite a few of the neuroscientists in the room seemed intrigued enough to pursue this ideas, and I certainly mean to. Any critiques, ideas and leads welcome.

Finally, on the way out of the building, chatting with a couple of neuroscientists, I floated that loose critique of ecosystem thinking — as in, denying the idea that “nature” (however defined) is an intrinsically better way of organising. I asked something along the lines of whether it is the case that the brain, and other natural systems, tend towards any kind of balanced equilibrium, or efficient use of resources, or useful steady state?

They smiled, and said something along the lines of “Of course not”, that such systems are often very “wasteful” (even allowing for a construct of conscious thought to be applied to something that clearly isn’t.)

Cities are also systems that thrive on instability and imbalance. They never, or rarely, tend towards any kind of equilibrium or steady-state — which challenges much of the philosophy (though that’s hardly the right word) which underpins many models of urban sustainability, including smart cities with its banal emphasis on efficient use of resources through feedback loops.

(Do also watch Adam Curtis’ second episode in his BBC series “All Watched Over By Machines Of Loving Grace”, in which he carefully dissects — and then utterly trashes, with his inimitable VHS-spattered sturm und drang — the entire idea of “the ecosystem” as a useful metaphor, as well as most similar “systems thinking”. Curtis points out that, as opposed to efficient equilibrium, “the history of nature was full of radical dislocations and unpredictable change … a raw chaotic instability”. See also this piece in The Guardian.) It may be the brain is also this way — it would make sense if it was, after all — but rather than be disheartened by this, maybe that might be a fruitful avenue to take in terms of our understanding of equally unstable cities? To not search for harmony and equilibrium, but understand instead this raw chaotic instability, and find ways to work with that, within a resource-constrained, increasingly diverse and dynamic world with a greater need than ever to make intelligent long-term as well as short-term decisions.

This doesn’t mean that there will not be fruitful approaches drawn from the ideas of smart cities, of course, just as I gained immeasurably from this exploratory conversation with brain scientists — just that the insights will surely not be obvious, will not be immediate, and will require a more concerted, deeper engagement.