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Understanding and Rewiring Societies


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How can we create organizations and governments that are cooperative, productive, and creative? These questions are especially important right now, because of global competition, environmental challenges, and government failure. The engine that drives this possible revolution is big data: the newly ubiquitous digital data that is becoming available about all aspects of human life. By using these data to build a predictive, computational theory of human behavior we can hope to engineer better social systems. In this talk we will show how to improve companies, cities and societies through a deep understanding of human behaviors and targeted interventions that leverage human connections.

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Understanding and Rewiring Societies

  1. 1. Bruno Lepri Understanding and Rewiring Societies
  2. 2. Digital breadcrumbs October 2013, 33 TheDaTa- m at h e m at i cs annual salary of $153,000 Based on social circle, likely to repay a loan. according to cell phone GPs data, walks 5.7 miles per day. Recent shift in text-messaging pattern; new girlfriend likely. travels a mile out of way to work each day. Web and phone records suggest dissatisfaction with physician. search engine records indicate coming down with the flu. Pregnant, but doesn¹t know it yet. Purchases five cups of coffee (usually starbucks) per day Driven The digital traces we leave behind each day reveal more about us than we know. This could become a privacy nightmare—or it could be the foundation of a healthier, more prosperous world Just applied for a seventh credit card. By Alex “Sandy” Pentland SocieTy
  3. 3. Call Detail Records - Mobility: from cell towers we can reconstruct the movement patterns of a community - Social Interactions: from call and sms we can reconstruct social networks and interactions - Economic Activities: monitoring airtime expenses is useful for detecting impacts of economic changes and crisis
  4. 4. social and spatial network diversity is strongly associated with IMD rank (measure of prosperity) (Eagle et al., 2010, Science) Economic Development
  5. 5. Estimating Poverty Maps
  6. 6. !  Toole et al. show that it’s possible to observe mass layoffs and identify the users affected by them in mobile phone records !  job loss has a systematic dampening effect on their social and mobility behaviour Tracking Employment Shocks
  7. 7. Idea Flow and Unemployment
  8. 8. ! increased ratio of residents ---> more crime (in contrast with Newman’s thesis) !  high diversity of functions (home vs. work) and high diversity of people (gender and age) act as eyes on street decreasing crime (in line with Jacobs’ thesis) Predicting Crime Levels
  9. 9. How do you capture death & life of cities? Some cities are alive, others less so ALIVE DEAD DETROITNEW YORK
  10. 10. The systematic acceleration of urban life - Bettencourt, Luís MA, et al. "Growth, innovation, scaling, and the pace of life in cities." PNAS 104.17 (2007): 7301-7306. - Milgram, Stanley. "The experience of living in cities." Crowding and behavior 167 (1974): 41. + Social human-interactions + Economical GDP, wages, patents - Issues violent crimes, contagious diseases, pollution
  11. 11. The theory: Jane Jacobs One of the most influential books in city planning • Death: caused by the elimination of pedestrian activity • Life: created by a vital urban fabric at all times of the day
  12. 12. Jacobs’ diversity conditions Diversity => Urban vitality There are 4 diversity conditions To be ensured in each city’s district (10,000+ inhabitants) SMALL BLOCKS LAND USE AGED BUILDINGS DENSITY
  13. 13. Border Vacuums • Patches of land dedicated to one single use • They could be either bad and good: • Parks are good for pedestrian activity • But they are exposed to criminality and deprivation if not well managed (e.g. night) LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS
  14. 14. “Operationalize” Vitality MILAN
  15. 15. Jacobs’ theory holds and is still valid URBAN METRICS
  17. 17. Which place looks safer?
  18. 18. The theory: Broken Windows • City mismanagement • Dirty places • Poor infrastructure Lead to misbehavior => Crime 18 Wilson, James Q., and George L. Kelling. "Broken windows." Critical issues in policing: Contemporary readings (1982): 395-407.
  19. 19. The theory: Jane Jacobs + Oscar Newman Two of the most influential books in city planning • Lit streets • Street-facing windows • Physical demarcation private- public 19 Klemek, C. (2011) ‘Dead or Alive at Fifty? Reading Jane Jacobs on her Golden Anniversary’ Dissent, Vol. 58, No. 2, 75–79.
  20. 20. Appearance and liveliness SAFETY PERCEPTION LIVELINESS
  21. 21. Safety perception: MIT Place Pulse 21 1 2MULTI- MODAL APPROACH
  22. 22. Safety perception: aggregation 22 1 2MULTI- MODAL APPROACH
  23. 23. Liveliness: metrics 23 MILAN MULTI- MODAL APPROACH 1 2
  25. 25. Urban metric Beta coefficient % of women (from census) 0.001 Deprivation -0.005 Distance from the center -0.003 Safety appearance 0.020** 0.65 ** p-value < 0.001; * p-value < 0.01; Safety perception<-> women, young people
  26. 26. Safety perception <-> elements 26
  27. 27. Now that we have new tools to measure aesthetics, we can estimate its consequences … to understand the relative value of improving the aestetics of neighbourhoods
  28. 28. Planning Civic Systems
  29. 29. UN Data Revolution
  30. 30. The Tyranny of Data? The dark side of data-driven decision-making for social good: - computational violations of privacy - lack of transparency - social exclusion and discrimination
  31. 31. The Tyranny of Data? Requirements for positive disruption of data-driven policies: - user centric data ownership - algorithmic accountability - living labs to experiment data-driven policies
  32. 32. !  de Montjoye et al. (2013) study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. !  in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier’s antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. Unique in the Crowd
  33. 33. The New Deal of Data Right to: - possess - control - dispose
  34. 34. OPAL Project Bring the algorithms to the data, don’t share the data itself!
  35. 35. Data Challenge Initiatives ! Data for Development (D4D): Ivory Coast and Senegal ! Datathon for Social Good: London Data ! Telecom/Tim Italia Big Data Challenge
  36. 36. Thanks