Slides from a talk given at BBVA Innovation (January 18, 2012). It covers my perspective on the problem of predictions based on networked data.

1. BBVA
CRITICAL PREDICTABILITY!?
NICOLAS NOVA, 18.01.2012, MADRID
WWW.NEARFUTURELABORATORY.COM
Hello, my name is Nicolas Nova, I work for the Near Future Laboratory a research and design practices located in Geneva,
Barcelona and Los Angeles. We combine insight and analysis with design and research with rapid prototyping to create potent
provocative sometimes preposterous ideas into material form. We split our agenda between client projects and self-supported
initiatives. Among many other lines of investigations, we are fascinated by the interplay between people, technology and the
urban life.
This presentation offers a critical perspective on the “prediction” trope you often find in Smart Cities projects.

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RESEARCH ON HOLY GRAILS AND TECHNOLOGICAL MYTHS 25
1950s: First computer- 1960s: Cybernetics and 1970: The Architecture
based urban planning urban planning Machine (Negroponte)
One of my research interest lies in holy grails and technology myths... that I analyze in two ways: user research on one side,
cultural history on the other side. The use of Information Technologies in architecture and urbanism is a long story... that took
many forms... here are few examples (among others)

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IV UN MUNDO DE MEDIAS 25
A good way to explore a topic such as Smart Cities consist in a typing the keywords in Google images and see what comes
out...

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IV UN MUNDO DE MEDIAS 25
Itʼs very green, with technical infrastructures...

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IV UN MUNDO DE MEDIAS 25
and there are often no human beings

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IV UN MUNDO DE MEDIAS 25
The 4th image result on Google is this slide... (overly) focused on infrastructures

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THE ‘SMART CITY’ TROPE 25
“A city that monitors and integrates conditions
of all its critical infrastructures, including roads,
bridges, tunnels, rails, subways, airports,
seaports, communications, water, power, even
major buildings, can better optimize its
resources, plan its preventive maintenance
activities, and monitor security aspects while
maximizing services to its citizens” R.E. Hall
This one (of many) definition. If you look at it, you can see that the verbs have a certain spin

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THE OLIGOPTICON (BRUNO LATOUR) 25
“oligos” = “what sees a
little bit” in Greek
Bruno Latour (1998), “Thought Experiments in Social Science: from the Social Contract to Virtual Society”: “That is not what
sees everything, but what sees a little bit, which is what “Oligos” means in Greek. For instance what is interesting, and
we have in our book lots of these examples, is a series of pictures on the Meteo, the French Meteorological Organisation
around Paris. Now what is amusing is that what we see from the office here is not the weather. We see just a little bit of the
weather, much less than what we see when we look at the map, which is published and printed by the machine; a little more
when we get at the instruments, which are in the garden. Now what is interesting in the notion of Oligopticon is that when you
get outside, what you see outside your office is nothing. You start to begin to see something just by looking on the screen of
your computer. Itʼs a reverse of Platoʼs Cave Myth. In Platoʼs Cave Myth you had to get outside of a cave in order to see
anything. Nowadays when you go outside, you see less and certainly not the weather of France as a region.”

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NOT NEW > CYBERSYN (1970-1973) 25
Real-time computer-
controlled planned economy
“Project Cybersyn was a Chilean attempt at real-time computer-controlled planned economy in the years 1970–1973 (during
the government of president Salvador Allende). It was essentially a network of telex machines that linked factories with a single
computer centre in Santiago, which controlled them using principles of cybernetics. (…) The idea was to have so-called
“algedonic meters” in peopleʼs home, i.e. warning public opinion meters that would be able to transmit Chilean citizensʼs
pleasures/displeasures to the government or television studio in real time. The government would then be able to respond
rapidly to public demands based on these information” (“rather than repress opposing views” as proposed by Stafford Beer).

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NOT NEW > CYBERSYN (1970-1973) 25

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FEEDBACK LOOPS: THE RETURN OF CYBERNETICS? 25
Hypothesis: accumulating
data enough data would allow to
simulate (and predict!)
sensors system
new behavior

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WHAT’S NEW: MORE SENSORS 25

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WHAT’S NEW: MORE HUMAN DATA 25

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PROBLEM 1: DATA QUALITY: CRIME MAPPING (B. BEAUDE) 25
No declaration (~60%) = no data
Even less declaration in places with high criminality
Crime location = often where crime is reported
Letʼs see the problem of this prediction trope.
The Metʼs Crime Mapping Website, initiated by the Mayor of London, the Mps(Metropolitan Police Service) and the Mpa
(Metropolitan Police Authority), found in Boris Beaude, "Crime Mapping, ou le réductionnisme bien intentionné.",
EspacesTemps.net,Mensuelles, 04.05.2009 http://espacestemps.net/document7733.html

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PROBLEM 2: PREDICTION & THE DATA DELUGE FALLACY 25
Our impressive ability to make sense of
behavior does not imply a corresponding
ability to predict it.
(...)
when we think about the future, we
imagine it to be a unique thread of events
that simply hasn't been revealed to us yet.
In reality, no such thread exists.
Duncan Watts

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PROBLEM 3: A CITY IS A COMPLEX SYSTEM 25
what parameters should we
pick in our simulation?
The whole is not just the sum of its component, itʼs more than that because a system is a network of heterogeneous
components that interact nonlinearly, to give rise to emergent behavior.

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EXAMPLE 1: “THE FIRES” (JOE FLOOD) 25
Missing parameter: a model that says traffic has no
impact on how quickly a fire company can respond to
a fire...

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EXAMPLE 2: THE GENEVA TRAM CASE 25
Missing parameter: the type and the quality of change
nodes

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SO WHAT? “SIM CITY IS NOT A CITY” 25

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BUT SHOULD WE STOP USING THESE DATA ? MODELS? 25
NO!
Letʼs have a more critical perspective on things we can do with these data.... more specifically what we are interested in at the
Near Future Laboratory.

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POTENTIAL AVENUE 1: EXPLORATORY PERSPECTIVE 25
New perspectives for
innovative services
BBVA 2011
For instance we have been exploring of the new roles of a retail bank in the smart city. Our contribution took the form of a
fairly advanced sketched dashboard for members of the working group to explore and interrogate their data with fresh
perspectives. (here aggregated credit card activity in Madrid)

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POTENTIAL AVENUE 1: EXPLORATORY PERSPECTIVE 25
Defining measures of
hypercongestion
Also, based on the sensor data we extracted information on the visiting sequences, travel times and staying times of visitors to
rapidly sketch indicators that helped us detected areas suffering from recurrent symptoms of hyper-congestion.

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POTENTIAL AVENUE 2: “ETHNO-MINING” (INTEL) 25
Another interesting perspective developed by researchers at Intel. Ethno-mining: the integration of ethnographic and data
mining techniques. This integration is carried out in iterative loops between the formation of interpretations of the data and the
development of processes for validating those interpretations OR “as a kind of an ink blot so people had an occasion to
create their own stories about what was going on, but from a different point of view” (Dawn Nafus)

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FINAL POINT: THE CITY’S SMART ALREADY 25
In conclusion, I just wanted to remind you this: a city is smart already! People find solutions for their own problems. This
example shows how roms in Geneva help people buying their transportation tickets (the vending machine does not give back
the change, so the rom lady use her own multi-course card to buy ticket for other people who give her the change; since
buying a multi-course card is less expensive than a single ticket, she can get a short income based on that).

25. THANK YOU MERCI GRACIAS DANKE GRAZIE
NICOLAS NOVA
nicolas@nearfuturelaboratory.com
www.nearfuturelaboratory.com