Critical Predictability!?

BBVA CRITICAL PREDICTABILITY!? NICOLAS NOVA, 18.01.2012, MADRID WWW.NEARFUTURELABORATORY.COMHello, 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 potentprovocative sometimes preposterous ideas into material form. We split our agenda between client projects and self-supportedinitiatives. Among many other lines of investigations, we are fascinated by the interplay between people, technology and theurban life.This presentation offers a critical perspective on the “prediction” trope you often ﬁnd in Smart Cities projects.

2 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 tookmany forms... here are few examples (among others)

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

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

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

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

7 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. HallThis one (of many) deﬁnition. If you look at it, you can see that the verbs have a certain spin

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

9 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 (duringthe government of president Salvador Allende). It was essentially a network of telex machines that linked factories with a singlecomputer 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 citizensʼspleasures/displeasures to the government or television studio in real time. The government would then be able to respondrapidly to public demands based on these information” (“rather than repress opposing views” as proposed by Stafford Beer).

10NOT NEW > CYBERSYN (1970-1973) 25

11FEEDBACK LOOPS: THE RETURN OF CYBERNETICS? 25 Hypothesis: accumulating data enough data would allow to simulate (and predict!) sensors system new behavior

12WHAT’S NEW: MORE SENSORS 25

13WHAT’S NEW: MORE HUMAN DATA 25

14 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 reportedLetʼs 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

15PROBLEM 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 hasnt been revealed to us yet. In reality, no such thread exists. Duncan Watts

16 PROBLEM 3: A CITY IS A COMPLEX SYSTEM 25what 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 heterogeneouscomponents that interact nonlinearly, to give rise to emergent behavior.

17EXAMPLE 1: “THE FIRES” (JOE FLOOD) 25Missing parameter: a model that says traffic has noimpact on how quickly a fire company can respond toa fire...

18 EXAMPLE 2: THE GENEVA TRAM CASE 25Missing parameter: the type and the quality of changenodes

19SO WHAT? “SIM CITY IS NOT A CITY” 25

20 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 theNear Future Laboratory.

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

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

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

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

THANK YOU MERCI GRACIAS DANKE GRAZIENICOLAS NOVAnicolas@nearfuturelaboratory.comwww.nearfuturelaboratory.com

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Critical Predictability!?

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