The Three Variables of City-Scaling: May 6, 2018 Snippets
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This week’s theme: large cities gain something in common as they scale, while still differing greatly from one another. What rules do they follow as they grow? Plus news on Capital as a Service and more from the Social Capital family.
Welcome back to our Snippets series on Cities, where after last week’s introduction to cities and the abundance contained within them, we’re going to dive in over the next several weeks to try to understand the rules behind what makes them tick. Today we’re going to start with understanding scale as it applies to cities.
There’s no doubt that throughout the great and small cities of the world, they all have a distinct “city-ness” quality of scale in common that supersedes individual differences in how those cities might work. Human activity and behavior represent the atomic unit which villages, towns and metro areas fundamentally comprise, and cities represent a superlinear scaling up of that human activity. Twenty neighbourhoods and suburbs of 100,000 people each add up to a city of 2 million, with human activity and energy that exceeds the sum of its individual parts. All cities share this in common. Yet there’s no denying that different cities scale differently: the infrastructure, social rules and norms, and citizen activity that make Tokyo function at scale is very different from what makes Houston work at scale, or Sao Paulo work at scale.
Not all cities are alike: different regions abide by different scaling rules. Cities from the same region, but of different size, resemble each other more closely than do similar-sized cities on opposite sides of the world. Although New York is clearly larger than its East Coast peers, one can recognize certain “scaling factors” that it has more in common with Philadelphia than it does with Shanghai, Cairo or Los Angeles. In other words, even though New York and Cairo have approximately the same population, New York feels more like four superimposed Philadelphias than it does one Cairo, or half a Tokyo. What we’d like to be able to do is articulate what those essential scaling factors might be: what are the fundamental variables through which we can understand how New York city represents something like a scaled up version of Philadelphia, Houston a scaled up version of San Antonio, Paris a scaled up version of Lyon, or Lagos a scaled up version of Accra?
In Geoffrey West’s sweeping and powerful book Scale: the Universal Laws of Growth, Innovation, Sustainability and the Pace of Life, we get some answers to these questions. The book (which should be a part of every Snippets reader’s book list) tackles a challenging and fascinating subject: how living organisms, economies, businesses, cities and other populations of individual parts can successfully scale up into enormous networks, and what are the universal rules by which those scaled-up networks evolve and behave. In a section titled Network Principles and the Origins of Allometric Scaling, West layout three principles through which the evolution of scaled-up complex networks can be understood: whether we’re talking about groups of cells organizing into the cardiovascular system, or groups of people organizing into cities. These three principles are:
- Space Filling. Whatever the geometry, geography or topology of the network, its “tentacles” will fill all available space as it grows. As cities grow, they fill more space, and the networked services, businesses, infrastructure and people that make up the city expand to fill whatever volume is available. (Compare people, their houses and driveways, and gas and power lines filling up the space in a city to how blood vessels and neurons will extend outwards to supply every corner of the body, or how HR, sales and accounting in a large company will expand in order to serve every business unit.) All growing cities exhibit this behavior, but the rules by which space gets filled will vary widely from one to the other: San Francisco has different zoning rules, city planning, and wealth distribution from Houston; correspondingly, space gets filled rather differently.)
- The Invariance of Terminal Units. No matter how large the organism, network or city, the units we encounter at ground level tend to remain the same size: they do not scale up in size along with the network. Salesforce Tower may be 100 times as large as your house, but the doorknobs, chairs, water faucets and light bulbs inside remain the same size (there are just way more of them). Similarly, the individual living cells in an elephant are no larger than their peers in a mouse: they are not rescaled or reinvented as the animal gets larger, but rather are copied more times. The basic “ready-made” units within a city are the same way: they are not continuously reinvented as cities scale, but rather repeatedly copied and expanded. All cities have this property, but the specific terminal units involved can vary from one city to another: when it comes to scaling up their transportation capacity, for instance, Los Angeles typically adds more cars and roads, whereas Moscow adds more subways, and Amsterdam adds more bicycles.
- Optimization. Over time, the tendency for the city to expand and fill available space as it grows, combined with the availability of “ready-made” terminal units that are repeatedly copied to fill that space, along with the feedback loops that naturally arise as cities grow up, will settle into some optimized local maximum of productivity and efficiency of some kind. As West writes, describing living systems (but equally applicable to cities): “Continuous multiple feedback and fine-tuning mechanisms implicit in the ongoing processes of natural selection and which have been playing out over enormous periods of time have led to the network performance being ‘optimized’”. Eventually, some steady state is found that works, although the journey can be contentious.
Now we’re ready to see how these three rules together constitute the essential three variables for what gives cities their universal “city-ness” as well as their own distinct qualities: First, all growing cities grow to fill available space, but they follow different rules and forces as they do so; Second, all cities grow by repeatedly copying the “ready-made” building blocks, but the building blocks in question may vary from one to another; Third, all cities in the long run seek local productivity and efficiency maxima given (1) and (2); this is the universal characteristic that cities share the most in common with one another, and the dimension to which cities of given sizes (and also similar ages) will most resemble one another.
Next week, we’ll look at the next essential quality of cities — their superlinear scaling of activity, productivity and output — and see how these three variables help us categorize and understand how it is that cities came to become the most important engines for growth and abundance in the modern world.
Elsewhere in the world:
Walmart to buy 73% of Flipkart for $16B, Alphabet might put in $3B | Sunny Sen, Factor Daily
How the Beijing Elite see the world | Martin Wolf, FT
And here at home:
Can one street solve the San Francisco Bay area housing crisis? | Joe DiStefano, Urban Footprint
California now world’s 5th largest economy, surpassing UK | AP Online
The American Cloud: America still has a heartland, it’s just an artificial one | Venkatesh Rao, Aeon
A wave of resurgent epidemics has hit the US | Melinda Wenner Moyer, Scientific American
Alan Turing’s work continues to break new frontiers, 65 years later:
Water filter inspired by Alan Turing’s lone biology paper passes first test | Mark Zastrow, Nature
Alan Turing’s chemistry hypothesis turned into a desalination filter | John Timmer, Ars Technica
Genetic trickery:
Chronological cues to life’s early history lurk in gene transfers | Jordana Cepelewicz, Nautilus
Other reading from around the Internet:
Business and life lessons from Aileen Lee of Cowboy Ventures | Tren Griffin, 25iq
The inefficiency of large, infrequent transactions | Eugene Wei
The court of values and the bureau of boringness | David Chapman, Meaningness
The real villain behind our new gilded age | Eric Posner & Glen Weyl, NYT
Private equity firms targeting Amazon sellers for rollups | Priya Anand, The Information
If ETH isn’t a security then nothing is | Mechanical Markets
And just for fun:
Move over, Meyers-Briggs: how the Magic: The Gathering color wheel explains people | Duncan Sabien
In this week’s news and notes from the team at Social Capital, be sure to check out an excellent article on the ongoing progress that Ashley Carroll’s been making with Capital-as-a-Service, written by Joshua Brustein at Bloomberg. Brustein does a great job getting right to the heart of why diverse representation among startups and venture investment matters: given that so much of venture investing today is based on pattern recognition, then don’t automated investment initiatives like Capital-as-a-Service risk perpetuating existing biases? It’s a crucial question to ask:
It’s no surprise that bias and inequality in our hiring, selection and funding practices can be improved through more rigorous decision-making, if that decision-making can be done in an objective way. That kind of objectivity, though, may be easier to obtain in academic studies than in real-world scenarios. A tool built for entrepreneurs the real world, like Capital-as-a-Service is, requires an extra bit of thoughtfulness in its design and its implementation. As Brustein writes:
“The most famous example comes from the 1970s, when five major orchestras began requiring musicians to stand behind a screen when auditioning. According to a study by researchers at Harvard, the proportion of women performing in those orchestras have increased more than threefold from 1970 from 1993. Technologists have lauded automated decision-making as a way to further reduce human fallacy. But the optimism around supposedly objective algorithms has been challenged in recent years by evidence that some automated systems amplify bias because they’re trained on data reflecting historical inequities.”
This is a really crucial point to appreciate: automated decision-making can help reduce bias, but it can also codify and formalize the values and pattern-matching cues that an organization already holds as prior beliefs. At Social Capital, this is something we care very deeply about, and as Capital-as-a-Service sees more early users and collects more early data on who those users are, the early numbers could potentially show a lot about how those value systems hold up when automated. More Brustein: “Social Capital kicked off a trial of Carroll’s model last year with a referral program of sorts — the fund asked other venture capital firms to direct promising early-stage companies to apply through the system. Most of the hopefuls came from outside the standard VC stomping grounds of the Bay Area and New York, and many were based overseas. The fund has since assessed 5,000 startups and invested in 60. Eighteen of the companies are run by women, and about 80 percent have non-white founders. The checks Social Capital is writing are small by its standards, from $50,000 to $250,000. But the firm plans to throw open the doors to anyone with a company and a few spreadsheets of operational data by the spring of 2019.” As always, if you’re interested in finding out more about Capital as a Service as either an entrepreneur or an investor, you can sign up here.
In other news, Mike Ghaffary went on This Week in Startups this past week to share productivity tips for running early-stage startups as founders, CEOs and managers: be sure to check it out in order to hear Mike’s thorough and valuable lessons for how to sustainably and effectively squeeze the most out of your workday and your calendar:
“Productivity for your startup: an action plan” | Mike Ghaffary on This Week in Startups
And finally, Hustle announced their Series B funding round:
How we’ll put our $30M Series B funding to use | Roddy Lindsay, Hustle
The Series B round adds to what has already been a momentous year for Hustle: they’ve recently celebrated the 100 millionth text message sent across their P2P texting platform, grown their customer base by 10x over the last year, and have broadened their client base into new areas like universities and non-profits, helping their customers excel at Hustle’s core mission: to help organizations humanize the way they communicate with people. As CEO Roddy Lindsay writes, in addition to continuing to grow their existing business, the further funding will help Hustle build out their goals and analytics dashboards in more powerful directions, create new business use cases for sales and customer engagement teams, and improve the way they work with large, complex organizations around the world with huge missions at global scale. You can keep up with Hustle’s progress at their Blog, and if you or someone you know is interested, they’re currently hiring for positions in both New York and San Francisco.
Have a great week,
Alex & the team at Social Capital
