It’s also probably the most radical vision of the future you’ll see today (if you read this today and you haven’t seen it before – I have no idea how long it’s been out there.)
From today’s Big Think and The Razor’s Edge between Nobility and Crime: Who is Julian Assange?
Former FBI Director Louis Freeh once put it like this: “Ask the American public if they want an FBI wiretap and they’ll say, ‘no.’ If you ask them do they want a feature on their phone that helps the FBI find their missing child they’ll say, ‘Yes.’” Just make us safe. Just assure us our children will be safe. Just tell us what we need to know. Let the rest be silence.
Imagine what it’ll be like when the police not only have to sift through your boring Social Media sillage but that of your, erm, chickens too:
Today Chief Futurist for Cisco Systems Dave Evans appeared on the company’s netcast, Talk2Cisco, to answer questions about the next 50 years and beyond via email and Twitter. Turns out one of the world’s biggest technology companies is betting the Internet of Things is going to be big. . .
Evans said humans generated more data in 2009 than in the previous 5,000 years combined, although a lot of it is useless – comparable to saving all 2,000 photos from your weekend trip to the beach.
It doesn’t have to be all about police work. Adam Greenfield imagines the world of the read/write city, where we go beyond the civic participation experiments of things like FixMyStreet and hook up bus stops and lampposts to Facebook:
But what if we took a single step further out? What if we imagined that the citizen-responsiveness system we’ve designed lives in a dense mesh of active, communicating public objects? Then the framework we’ve already deployed becomes something very different. To use another metaphor from the world of information technology, it begins to look a whole lot like an operating system for cities.
Then we can begin to treat the things we encounter in urban environments as system resources, rather than a mute collection of disarticulated buildings, vehicles, sewers and sidewalks. One prospect that seems fairly straightforward is letting these resources report on their own status. Information about failures would propagate not merely to other objects on the network but reach you and me as well, in terms we can relate to, via the provisions we’ve made for issue-tracking.
The smarter we are, the more complex the economy becomes, and the dumber we become.
Dynamic Stochastic General Equilibrium models assume we’re all playing a game where the goal is equilibrium. Unfortunately (or fortunately?), though we are all playing games, they’re not the same ones.
At least, that’s how I read this piece from The Economist, which is, frankly, a little beyond me, but contains the kind of phrasing that sets off pleasure nodes in my brain:
Agent-based modelling does not assume that the economy can achieve a settled equilibrium. No order or design is imposed on the economy from the top down. Unlike many models, ABMs are not populated with “representative agents”: identical traders, firms or households whose individual behaviour mirrors the economy as a whole. Rather, an ABM uses a bottom-up approach which assigns particular behavioural rules to each agent. For example, some may believe that prices reflect fundamentals whereas others may rely on empirical observations of past price trends.
Crucially, agents’ behaviour may be determined (and altered) by direct interactions between them, whereas in conventional models interaction happens only indirectly through pricing. This feature of ABMs enables, for example, the copycat behaviour that leads to “herding” among investors. The agents may learn from experience or switch their strategies according to majority opinion. They can aggregate into institutional structures such as banks and firms. These things are very hard, sometimes impossible, to build into conventional models. But in an agent-based model you simply run a computer simulation to see what emerges, free from any top-down assumptions.
Although DSGE models are also based on microeconomic foundations, they accept the traditional view that there exists some ideal equilibrium towards which all prices are drawn. That this is often approximately true is why DSGE models perform well enough in a business-as-usual economy. They do badly in a crisis, however, because their “dynamic stochastic” element only amounts to minor fluctuations around a state of equilibrium, and there is no equilibrium during crashes.
. . . ABMs contain feedback mechanisms that can amplify small effects, such as the herding and panic that generate bubbles and crashes. In mathematical terms the models are “non-linear”, meaning that effects need not be proportional to their causes.
These non-linearities were clearly on show in the credit crunch . . . These “network-based vulnerabilities” are just the kind of thing that ABMs are good at capturing.
Of course, ABMs need stacks and stacks of data. Of the real-time real-life variety, if possible. Which means the modellers are keen to plug in your phone, your Facebook, your Twitters and all that. Which I’m all for, to be honest. But we’ll see how it goes down with the tin-foil hatters.
Politics is tinkering. Cities are machines. More on the reasons I hate suburbs. And shopping malls. And some possibly naive things about Motown.
A place to live vs a place to exist
There’s a simple two-part test that any space-for-living needs to pass, in order to be habitable:
1. Are there neutral, public spaces for people to meet up, socialise and exchange information in both planned and spontaneous activities?
2. Are there run-down areas where people can open up new enterprises and experiment?
Most suburbs fail both of these tests.
Suburbs: the Worst of Both Worlds
In The Information Architecture of Cities, L. Andrew Coward and Nikos A. Salingaros explore the idea of the city as a complex system, or brain. People are neurons:
The networks of a city — the paths, roads, telecommunications, etc. — are the mechanisms that support information exchange. Nevertheless, a city processes information rather than merely moving it around. . .
The use of urban space is linked to the information field generated by surrounding surfaces, and to how easily the information can be received by pedestrians. A primary information exchange is a pedestrian going from one point to another. He or she observes things that are unrelated to the primary reason for movement. This information is functional; it can recommend secondary behaviors to the observer who is executing a primary information exchange. A successful city is one in which even simple movements are a rich and rewarding experience.
Suburbs, retail parks and other mono-zones appear simpler and thus cheaper. But they still need to ‘communicate’ in order to process information and solve problems. This becomes hugely expensive and needs expensive infrastructure which is unable to easily react to change, let alone create it.
The geographical separation of residences from workplaces (enforced by postwar monofunctional zoning) is a case in point. Because these two urban regions — apartment blocks or groups of suburban houses on the one hand, and office towers on the other — interact so strongly with each other as a whole, they do NOT define separate functional modules, despite the simplistic expectations due to spatial clustering. Instead, the geometry forces functional module formation of the most inconvenient kind, with information exchange that is very expensive to maintain because of long links. The modules that do form are too weak, and suffer from overextended transport connections and a lack of internal coherence.
Another problem with this example is that there is simply no way to form modules of intermediate size. A stable hierarchy of different modules that fit within larger modules can never evolve in a monofunctional urban region; yet we know this to be a crucial feature of any working complex system. The nuclear household and its immediate connections defines the smallest module containing work, school, office, and supermarket. In the majority of cases, there is no successively larger module that contains this elementary module — one immediately jumps from the nuclear household to the entire city. This lack of hierarchy is pathological from a systems point of view. . .
High-rise office buildings and horizontal “office parks” are not functional modules. Typically, there is very little to no interaction between different offices in the same building or “park”, compared to the exchange between each individual office and its headquarters, branch locations, customers, suppliers, bankers, etc. This elementary analysis invalidates both the office building and the “office park” as useful urban typologies, despite their recent proliferation. For similar reasons a region of suburban houses is not a functional module. Creating office blocks and suburban house groups makes all genuine functional exchange high cost (or imposes systemic isolation). This is the system force behind Jane Jacobs’ observation that successful city neighborhoods are always mixed usage. . .
Nodes that do not form part of a larger module are often parasitic to the city, since they use its infrastructure without contributing to an overall functional coherence. Nevertheless, that is how most restaurants, stores, supermarkets, and office buildings are built nowadays. Entirely surrounded by an isolating parking lot, they are designed to be built in the middle of a wilderness, yet they are forced right into the urban fabric, tearing it in the process. Restaurants designed to work as highway truck stops are routinely erected inside the city, and of course they don’t belong to it. People working in a nearby office building, which could provide clientele at lunchtime, have to drive their cars around a busy road to get to a restaurant that is literally next door.
Suburbs make it harder for people to both socialise and work. And they make it harder for societies to innovate; they promote a poverty of aspiration (if you discount the ambition of people like me to leave).
Modern societies have expended great deal of energy on managing and preserving ‘nature’ by creating reserves, National Parks and Areas of Outstanding [insert idea denoting societal value here]. We haven’t done as good a job at managing and preserving grime and run-down areas. Suburbs lack wabi-sabi; where is the space to open a business or start some new, as-yet-undreamed-of, enterprise or madcap scheme? If you felt like going on a wild goose chase or tinkering with your neighbourhood, where would you go? Politics and civic responsibility can never be about planning because the world is too chaotic. Politics is tinkering.
The late experimental filmmaker and pioneer of underground cinema, Jack Smith, was once asked “Have you ever thought of another type of society?” His response, in part, was:
“… Like in the middle of the city should be a repository of objects that people don’t want anymore, which they would take to this giant junkyard. That would form an organization, a way that the city would be organized…the city organized around that. I think this center of unused objects and unwanted objects would become a center of intellectual activity. Things would grow up around it.”If the world is equal to Smith’s “city,” Detroit is the repository of objects.
Detroit gets a lot of badpress. But I’d put money on more exciting things coming out of Detroit than the suburbs where I grew up.
More recursion (or is it embedding?) from the Buttonwood column in The Economist:
THE best bet for the long term is to buy shares and hold on to them. That was the lesson hammered into the heads of investors in the 1990s when the “cult of the equity” was at its peak. Unfortunately, they absorbed the message at precisely the wrong time.
The past decade has been disastrous for equities. Over the ten years to June 18th 2010 investors in developed-market equities earned a cumulative total return of minus 7.9%. By contrast medium-dated Treasury bonds returned 95.3% and high-yield American bonds 102.2%. Richard Cookson, the chief investment officer at Citi Private Bank (and a former journalist at The Economist), points out that the cumulative outperformance of high-yield bonds over equities dates back to 1995.
At first glance this seems rather odd. Bondholders have first claim on the corporate sector’s cashflow and shareholders take what is left. In theory, shareholders should earn the best returns over the long term provided profits keep growing. After slumping in 2008 profits have recently rebounded and, in the case of America, are close to a post-war high as a proportion of GDP. Even if profits had been terrible, owners of high-yield bonds would have suffered too because of a likely jump in corporate defaults.
The answer to the conundrum is valuation. As the cult of equity gained more and more adherents in the 1990s share prices were bid to stratospheric levels. On the best long-term measure, Robert Shiller’s cyclically adjusted price-earnings ratio (which averages profits over ten years), valuations in 1999 were more than a third higher than their previous peak, just before the great crash of 1929. It was a nice irony. Investors bought shares because they desired high returns but their enthusiasm pushed prices to a level from which high returns became impossible.
This is the proposition that people behave as if they have a perfect prediction of the economy’s future path, and therefore they collectively fulfil that prediction.
The above quote is from the Knowing and Making blog. Which goes on to say:
In addition, we can surely expect economic actors to learn over time. One reason that Keynesian deficit spending may have appeared to work from the 1930s to 1950s but fail by the 1970s is that people learned of the existence of inflation, and started to demand pay rises to match it. Prior to the 20th century inflation barely existed. Even until the 1950s – outside of Weimar Germany and a few other special cases – significant inflation was not considered an important risk. Now it’s a basic part of all macroeconomic calculations.
Rational expectations, therefore, are only likely to account for the phenomena that we have learned so far. What new phenomena might lurk in our future?
The degrees of recursiveness this implies in Economics is startling. I don’t know why I’ve never seen this before.
