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Transforming Sociotech Design (TSD) Tutorial

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Transforming Sociotech Design (TSD) tutorial covers conceptual frameworks for designing and evaluating Persuasive Technology (PT) aimed at achieving sustainable transformations of our lives towards wellbeing. The tutorial introduces and explains how TSD contributes to PT research by extending our understanding beyond limitations of traditional behavioral change designs and interventions.

TSD embodies a fundamental understanding of the PT components that are essential for designing successful transformations, known as:

Socially Influencing Systems
Computer-Supported Influence
Persuasive Cities
Persuasive Backfiring
Persuasive Design for Sustainability

Published in: Design
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Transforming Sociotech Design (TSD) Tutorial

  1. 1. PROF AGNIS STIBE ESLSCA BUSINESS SCHOOL PARIS MIT MEDIA LAB TEDX : TRANSCENDING INSTINCTS TEDX : PERSUASIVE CITIES FORTUNE 100 : ORACLE & HP AWARDS : NOKIA & SCHÖLLER
  2. 2. AGENDA 9:00 INTRODUCTION 9:15 DEFINING TRANSFORMATION 9:30 TRANSFORMING SOCIOTECH DESIGN 10:00 BREAK 10:15 TRANSFORMING MODEL 10:35 SOCIALLY INFLUENCING SYSTEMS 10:55 COMPUTER-SUPPORTED INFLUENCE 11:15 BREAK 11:30 PERSUASIVE CITIES 11:45 PERSUASIVE BACKFIRING & DARK PATTERNS 12:00 PERSUASIVE DESIGN FOR SUSTAINABILITY 12:15 NEXT STEPS
  3. 3. defining transformation transforms.me
  4. 4. TRANSFORMING SOCIOTECH DESIGN DESIGN : CREATING, INVENTING, DESIGNING SOCIO : PEOPLE, HUMAN, INTERACTION TECH : TECHNOLOGY, SYSTEMS, ENGINEERING TRANSFORMING : CHANGING, RESHAPING, MODIFYING
  5. 5. SPECIAL AIMS PERMANENT CHANGE LONG-TERM EFFECT PERSISTENT INFLUENCE CHARACTER TRANSFORMING CONTINUOUS OMNIPRESENT
  6. 6. https://scottbreslin.org/2016/08/transformation-vs-change/http://www.beyond-diet-reviews.com/5-foods-to-never-eat-and-why/
  7. 7. PEOPLE METRIC BEHAVIOR TODAY CHANGE FUTURE
  8. 8. MIT ML PEOPLE ONTHE 2nd FLOOR #TIMES ELEVATORS GO TOTHE 2nd FLOOR USE ELEVATORS 100 USE STAIRS 50
  9. 9. 120 2014 € 30 000 000 FROM/VON 2010 — TO/BIS 2014 Highlights Radwege-Bauprojekte Wichtige Radprojekte, in den Jahren 2010 bis 2014 umgesetzt: 1: Ottakringer Straße, 2: Ring-Rund-Radweg, 3: Radwege rund um den Hauptbahnhof, 4: Landstraßer Gürtel, 5: Zentrum Meidling, 6: Kagraner Platz, 7: fahrradfreundliche Hasnerstraße Generelle Radverkehrsplanung und Studien Auswahl aus Konzepten und Studien: Radlangstrecken und Lückenschlüsse, befahrbare Haltestellenkaps für RadfahrerInnen, Piktogramme und Pfeile zur Erhöhung der Verkehrssicherheit, Radfahren gegen die Einbahn 1 2 4 5 6 3 7 Radfahren gegen die Einbahn +16% StVO-Novelle umgesetzt Fahrradstraße: 1.650 m Benutzungspflicht bei Radwegen aufgehoben: 1.970 m Begegnungszonen: 1.200 m Detailplanung Mehr als 600 Einzelmaßnahmen für den (fließenden und ruhenden) Radverkehr pro Jahr, unter breiter interdisziplinärer Beteiligung am Planungs- und Umsetzungsprozess: Dienststellen, Bezirke, Wirtschaftskammer, Polizei etc. (bis zu 30 Beteiligte) Radfahrnetz Citybike-Stationen +96 km 2010 Budget für die Radinfrastruktur Millionen Euro (6 Mio. p.a.) 30 79 2010 Winterdienst 266 km prioritär geräumte Radwege Errichtete Radabstellplätze 2010 +9.588 27.329 Stück 2014 36.917 Stück 2014 1.270 km 1.174 km Radinfrastruktur 2010–2014 Tempo-30-Zonen in Wien Befahrbare Haltestellenkaps für RadfahrerInnen Radlangstrecken Piktogramme und Pfeile zur Erhöhung der Verkehrs- sicherheit Radfahren gegen die Einbahn Modal Split Radverkehr Anteil des Radverkehrs an den zurückgelegten Wegen der Wienerinnen und Wienern 2010: 1.472 km 2014: 1.657 km 4,6% 7,1% 2010 2014 EINBAHN ausgen. 2010 208.790 m ausgen. 242.420 m EINBAHN 2014 Impressum: Magistrat der Stadt Wien, Rathaus, A-1082 Wien, www.verkehr.wien.at
  10. 10. ATTITUDE BEHAVIOR CITY
  11. 11. 88% PEDALED Michael Lin
  12. 12. http://www.thisiscolossal.com/2014/10/lets-bike-it-bamboo-car-skeletons/
  13. 13. 1000 640 790 430 820
  14. 14. 6 WEEKS 14 COMPANIES 239 EMPLOYEES 29374 MILES
  15. 15. MY FUTURE IN THIS CITY I SEE HOW IT DEVELOPS IN LINE WITH NOVEL TECHNOLOGIES AND INNOVATIONS. I WILL HAVE A BALANCED LIFE IN IT. TO LIVE THERE WILL BE SAFE AS COMMUNITIES WILL HAVE STRONG COLLABORATIVE SPIRIT. I WILL BE ABLE TO FULFIL MYSELF. I WOULD SPEND MORE TIME OUTSIDE OF MY HOME TO INTERACT WITH OTHERS. WE WILL HAVE NICELY DESIGNED PUBLIC SPACES FOR THE RESIDENTS OF ALL AGES.
  16. 16. http://giphy.com/gifs/tetris-zirvjg7inwu2s
  17. 17. CITY CITY DISTRICT DISTRICT NEIGHBORHOOD NEIGHBORHOOD BUILDING BUILDING INDOORS INDOORS PERSON
  18. 18. https://echristensen42.com/2016/10/07/creating-your-own-pathways-through-the-cloud/usesidewalks/
  19. 19. http://performancecritical.com/handling-inattention-barrier-effective-communication/
  20. 20. https://twitter.com/lisakilker/status/794337117358981121
  21. 21. http://levieva.blogspot.fi/2011/06/social-psychology.html
  22. 22. transforming framework transforms.me
  23. 23. ATTITUDE BEHAVIOR ENVIRONMENT
  24. 24. SELF- CONTAINED SELF- DRIVEN JANUARY 1st SELF- DRIVEN SELF- CONTAINED JANUARY 1st
  25. 25. PEOPLE METRIC BEHAVIOR TODAY CHANGE FUTURE
  26. 26. MIT ML PEOPLE ONTHE 2nd FLOOR #TIMES ELEVATORS GO TOTHE 2nd FLOOR USE ELEVATORS 100 USE STAIRS 50
  27. 27. TRANSFORMING SOCIOTECH DESIGN SMART AI SENSING DATA
  28. 28. socially influencing systems (sis) transforms.me
  29. 29. Towards a Framework for Socially Influencing Systems: Meta-analysis of Four PLS-SEM Based Studies Agnis Stibe( ) MIT Media Lab, Cambridge, MA, USA agnis@mit.edu Abstract. People continuously experience various types of engagement through social media, mobile interaction, location-based applications, and other tech- nologically advanced environments. Often, integral parts of such socio- technical contexts often are information systems designed to change behaviors and attitudes of their users by leveraging powers of social influence, further de- fined as socially influencing systems (SIS). Drawing upon socio-psychological theories, this paper initially reviews and presents a typology of relevant social influence aspects. Following that, it analyzes four partial least squares structural equation modeling (PLS-SEM) based empirical studies to examine the inter- connectedness of their social influence aspects. As a result, the analysis pro- vides grounds for seminal steps towards the development and advancement of a framework for designing and evaluating socially influencing systems. The main findings can also deepen understanding of how to effectively harness social in- fluence for enhanced user engagement in socio-technical environments and guide persuasive engineering of future socially influencing systems.
  30. 30. CT Competition SL Social learning SC Social comparison CR Cooperation NI Normative influenceSF Social facilitation RE Recognition
  31. 31. CT Competition SL Social learning SC Social comparison CR Cooperation NI Normative influenceSF Social facilitation
  32. 32. CT Competition SL Social learning SC Social comparison CR Cooperation NI Normative influenceSF Social facilitation RE Recognition
  33. 33. CT Competition SL Social learning SC Social comparison CR Cooperation SF Social facilitation
  34. 34. CT Competition SL Social learning SC Social comparison CR Cooperation NI Normative influenceSF Social facilitation RE Recognition
  35. 35. CT Competition SL Social learning SC Social comparison CR Cooperation NI Normative influenceSF Social facilitation RE Recognition
  36. 36. CT Competition SL Social learning SC Social comparison CR Cooperation NI Normative influenceSF Social facilitation RE Recognition
  37. 37. CT COMPETITION SL LEARNING SC COMPARISON CR COOPERATION NI NORMATIVE SF FACILITATION RE RECOGNITION SOCIAL INFLUENCE Downward Upward Injunctive Descriptive Goal Gain Goal Together Participants Onlookers
  38. 38. CT COMPETITION SL LEARNING SC COMPARISON CR COOPERATION NI NORMATIVE SF FACILITATION RE RECOGNITION
  39. 39. AGNISANDREJSIEVAVOLDEMARS TOTAL SALES RESULT BROKER «IEDOD LETAK» EMPLOYEES OF BROKER «IEDOD LETAK» 50%50% Average for all brokersPolicies in Partneris 312 1025 POINTS EARNED POLICIES ISSUED IN SYSTEM 50% (18) 88% 25% 83% 33% BROKER «IEDOD LETAK» POSITION IN TOP
  40. 40. TOP EVALUATORS INVOLVED SPECIALLISTS JĀNIS BĒRZIŅŠ 467 564 EUR Saved 456 Twitter responses Project: GAISMAS PILS
  41. 41. MONEY SAVEDISSUES TRACKED
  42. 42. BEST TWITTER RESPONDERS#BIMSAVESMONEY BEST TWITTER RESPONDERS OF THE MONTH
  43. 43. Context of the project! More often sales team meetings start later, because people are late 73
  44. 44. Motivation of the project After project every sales team member comes to all scheduled meetings on time 74
  45. 45. 75 SELF- CONTAINED 0% SELF- DRIVEN 40% JANUARY 1st 60%
  46. 46. 76 ✗ Colleagues are busy ✗ Other meetings ended late ✗ Meeting isn`t important ✗ Other are also late ✗ Late is acceptable ✗ No one makes remark that somebody is late ✗ Meetings end and begin at the same time
  47. 47. 77 Show who are late
  48. 48. 0 10 20 30 40 50 60 70 80 90 Sanita Andris Gints Liena Ilze Ruta Inga Anita Aldis Juris Gunta Rita Meetings start on time 13.11. 14.11. 20.11. 27.11. 28.11. 01.12. 05.12. 06.12.
  49. 49. computer-supported influence (csi) transforms.me
  50. 50. Advancing Typology of Computer-Supported Influence: Moderation Effects in Socially Influencing Systems Agnis Stibe( ) MIT Media Lab, Cambridge, MA, USA agnis@mit.edu Abstract. Persuasive technologies are commonly engineered to change beha- vior and attitudes of users through persuasion and social influence without us- ing coercion and deception. While earlier research has been extensively focused on exploring the concept of persuasion, the present theory-refining study aims to explain the role of social influence and its distinctive characteristics in the field of persuasive technology. Based on a list of notable differences, this study outlines how both persuasion and social influence can be best supported through computing systems and introduces a notion of computer-moderated in- fluence, thus extending the influence typology. The novel type of influence tends to be more salient for socially influencing systems, which informs design- ers to be mindful when engineering such technologies. The study provides sharper conceptual representation of key terms in persuasive engineering, drafts a structured approach for better understanding of the influence typology, and presents how computers can be moderators of social influence.
  51. 51. PERSUASION SOCIAL INFLUENCE ACTION of causing someone to do something through REASONING CAPACITY to have an effect on the behavior of someone in a SOCIAL CONTEXT
  52. 52. Table 1. Persuasion and social influence in social psychology literature Reference Persuasion Social Influence Cialdini [3] Works by appealing to a set of deeply rooted human drives and needs, such as liking, reciprocity, consistency, authority, and scarcity. Humans look for social proof, therefor rely on the people around them for cues on how to think, feel, and act. Guadagno et al. [11] Refers to the changing of attitudes, beliefs, or behavior of an individual because of real or imagined external pressure. O’Keefe [21] Mainly relies on and is built upon reasoning and argument to shift attitudes and behavior of individuals towards a desired agenda. Commonly driven by the behavior and actions of surrounding people. Petty and Cacioppo [22] Two basic routes to persuasion. One is based on the thoughtful consideration of arguments central to the issue, whereas the other is based on peripheral cues. Rashotte [23] Focuses merely on written or spoken messages sent from source to recipient. Defined as change in thoughts, feelings, attitudes, or behavior of an individual that results from interaction with another individual or a group. Wood [31] Typically includes detailed argumentation that is presented to people in a context with only minimal social interaction. Usually enabled and facilitated by complex social settings.
  53. 53. PERSUASION SOCIAL INFLUENCE ORIGIN AGENDA PRESENCE DRIVER ARGUMENT BEHAVIOR IMPACT GUIDED UNCONTROLLED DIRECTION PUSH PULL
  54. 54. COMPUTER – MEDIATED (CME) FACE – TO – FACE (FTF) COMPUTER – HUMAN (CHU) COMPUTER – MODERATED (CMO) INTERPERSONAL INFLUENCE USER BEHAVIOR USER CONTENT DYNAMIC DESIGN DYNAMIC CONTENT PERSUASIVE DESIGN
  55. 55. many years. For example, Di Blasio and Milani [7] found that computer-mediated discussion could possibly activate the central route of persuasion [22] more easily than face-to-face interaction. This knowledge can be instrumental to explore more granular differences between the two types of influence. Table 3. Comparing the four types of influence Interpersonal Face-to-face (FTF) Computer- mediated (CME) Computer-moderated (CMO) Computer- human (CHU) Origin Human User User behavior Designer Description People can influence each other in the physical world. Users can influence each other through computers. Computers can amplify, decrease, or reverse influ- ence based on the pres- ence (or absence) of other users and their behavior. Computers can influence users when designed to do so. 3.3 Computer-Human (CHU) Influence
  56. 56. 12374 0
  57. 57. 260 A. Stibe Table 4. Components of computer-supported influence Content Design Fixed (FC) Preset by developers and owners Supports CHU influence (FD) Preset by designers Supports CHU influence Dynamic (DC) Generated by users Supports interpersonal CME influence (DD) Evolving through user behavior Supports interpersonal CMO influence Historically, computer systems were often built with fixed design that was preset by designers and fixed content that was predefined by system developers and owners. With the overall technological advancement, computer systems are becoming more social and dynamic by both allowing users to contribute own content and displaying their interactions with the systems.
  58. 58. persuasive cities transforms.me
  59. 59. http://blog.earnest-agency.com/blog/5-everyday-terrible-user-experiences-and-how-to-avoid-them
  60. 60. http://inspiration.goreapparel.com/cycling-to-work/
  61. 61. Persuasive Urban Mobility Dr. Agnis Stibe 96
  62. 62. J J J J J J 7AM
  63. 63. J J L L L L LL9AM
  64. 64. SENSITIVE READ FEEL SENSORS
  65. 65. SMART CLASSIFY UNDERSTAND BIG DATA SENSITIVE READ FEEL SENSORS
  66. 66. PERSUASIVE CITIES CHANGE CARE SOCIOTECH DESIGN SMART CLASSIFY UNDERSTAND BIG DATA SENSITIVE READ FEEL SENSORS
  67. 67. 6 WEEKS 14 COMPANIES 239 EMPLOYEES 29374 MILES
  68. 68. CT (58%) Competition RA Rankings PD Public Display CR (58%) Cooperation EN (26%) Engagement SF (21%) Social facilitation β = 0.23 ** (.13) β = 0.27 ** (.14) β = 0.62 *** (.45) β = 0.35 *** (.16) β = 0.23 ** (.10) β = 0.61 *** (.44) β = 0.32 ** (.13) β = 0.24 ** (.08)
  69. 69. Persuasive Cities for Sustainable Wellbeing: Quantified Communities Agnis Stibe(&) and Kent Larson MIT Media Lab, Cambridge, USA {agnis,kll}@mit.edu Abstract. Can you imagine a city that feels, understands, and cares about your wellbeing? Future cities will reshape human behavior in countless ways. New strategies and models are required for future urban spaces to properly respond to human activity, environmental conditions, and market dynamics. Persuasive urban systems will play an important role in making cities more livable and resource-efficient by addressing current environmental challenges and enabling healthier routines. Persuasive cities research aims at improving wellbeing across societies through applications of socio-psychological theories and their inte- gration with conceptually new urban designs. This research presents an ecosystem of future cities, describes three generic groups of people depending on their susceptibility to persuasive technology, explains the process of defining behavior change, and provides tools for social engineering of persuasive cities. Advancing this research is important as it scaffolds scientific knowledge on how to design persuasive cities and refines guidelines for practical applications in achieving their emergence.
  70. 70. persuasive backfiring transforms.me
  71. 71. CHANGE ATTITUDE BEHAVIOR EMPOWER REMIND POKE NUDGE PERSUADE ALTER MODIFY MANIPULATE COERCE DECEIVE INFLUENCE
  72. 72. Persuasive Backfiring: When Behavior Change Interventions Trigger Unintended Negative Outcomes Agnis Stibe1(&) and Brian Cugelman2,3 1 MIT Media Lab, Cambridge, MA, USA agnis@mit.edu 2 Statistical Cybermetrics Research Group, University of Wolverhampton, Wolverhampton, UK brian@alterspark.com 3 AlterSpark, Toronto, ON, Canada
  73. 73. POSITIVE OUTCOME NEGATIVE OUTCOME INTENDED UNINTENDED MAJOR SEVERITY MINOR SEVERITY HIGH LIKELIHOOD LOW LIKELIHOOD PERSUASIVE BACKFIRING DARK PATTERNS TARGET BEHAVIOR SURPRISE BEHAVIOR
  74. 74. MINOR SEVERITY MAJOR SEVERITY LOW LIKELIHOOD HIGH LIKELIHOOD Poor Judgment Mistailoring Mistargeting Misdiagnosing Misanticipating Social Psychology Anti-Modeling Reverse Norming Personality Responses Defiance Arousing Self-Licensing Fineprint Fallacy Overemphasizing Inexperience Superficializing Credibility Damage Self-Discrediting Message Hijacking
  75. 75. MINOR SEVERITY MAJOR SEVERITY LOW LIKELIHOOD HIGH LIKELIHOOD Poor Judgment Mistailoring Mistargeting Misdiagnosing Misanticipating Social Psychology Anti-Modeling Reverse Norming Personality Responses Defiance Arousing Self-Licensing Fineprint Fallacy Overemphasizing Inexperience Superficializing Credibility Damage Self-Discrediting Message Hijacking
  76. 76. MINOR SEVERITY MAJOR SEVERITY LOW LIKELIHOOD HIGH LIKELIHOOD Poor Judgment Mistailoring Mistargeting Misdiagnosing Misanticipating Social Psychology Anti-Modeling Reverse Norming Personality Responses Defiance Arousing Self-Licensing Fineprint Fallacy Overemphasizing Inexperience Superficializing Credibility Damage Self-Discrediting Message Hijacking
  77. 77. MINOR SEVERITY MAJOR SEVERITY LOW LIKELIHOOD HIGH LIKELIHOOD Poor Judgment Mistailoring Mistargeting Misdiagnosing Misanticipating Social Psychology Anti-Modeling Reverse Norming Personality Responses Defiance Arousing Self-Licensing Fineprint Fallacy Overemphasizing Inexperience Superficializing Credibility Damage Self-Discrediting Message Hijacking
  78. 78. MINOR SEVERITY MAJOR SEVERITY LOW LIKELIHOOD HIGH LIKELIHOOD Poor Judgment Mistailoring Mistargeting Misdiagnosing Misanticipating Social Psychology Anti-Modeling Reverse Norming Personality Responses Defiance Arousing Self-Licensing Fineprint Fallacy Overemphasizing Inexperience Superficializing Credibility Damage Self-Discrediting Message Hijacking
  79. 79. MINOR SEVERITY MAJOR SEVERITY LOW LIKELIHOOD HIGH LIKELIHOOD Poor Judgment Mistailoring Mistargeting Misdiagnosing Misanticipating Social Psychology Anti-Modeling Reverse Norming Personality Responses Defiance Arousing Self-Licensing Fineprint Fallacy Overemphasizing Inexperience Superficializing Credibility Damage Self-Discrediting Message Hijacking
  80. 80. MINOR SEVERITY MAJOR SEVERITY LOW LIKELIHOOD HIGH LIKELIHOOD Poor Judgment Mistailoring Mistargeting Misdiagnosing Misanticipating Social Psychology Anti-Modeling Reverse Norming Personality Responses Defiance Arousing Self-Licensing Fineprint Fallacy Overemphasizing Inexperience Superficializing Credibility Damage Self-Discrediting Message Hijacking
  81. 81. MINOR SEVERITY MAJOR SEVERITY LOW LIKELIHOOD HIGH LIKELIHOOD Poor Judgment Mistailoring Mistargeting Misdiagnosing Misanticipating Social Psychology Anti-Modeling Reverse Norming Personality Responses Defiance Arousing Self-Licensing Fineprint Fallacy Overemphasizing Inexperience Superficializing Credibility Damage Self-Discrediting Message Hijacking
  82. 82. MINOR SEVERITY MAJOR SEVERITY LOW LIKELIHOOD HIGH LIKELIHOOD Poor Judgment Mistailoring Mistargeting Misdiagnosing Misanticipating Social Psychology Anti-Modeling Reverse Norming Personality Responses Defiance Arousing Self-Licensing Fineprint Fallacy Overemphasizing Inexperience Superficializing Credibility Damage Self-Discrediting Message Hijacking
  83. 83. dark patterns transforms.me
  84. 84. POSITIVE OUTCOME NEGATIVE OUTCOME INTENDED UNINTENDED DARK GREY INVISIBLE VISIBLE DARK PATTERNS PERSUASIVE BACKFIRING TARGET BEHAVIOR SURPRISE BEHAVIOR
  85. 85. GREYDARK VISIBLE INVISIBLE EXTRA ITEM EMAIL SUBSCRIPTION TRAVEL INSURANCE NEVERENDING TRIAL
  86. 86. GREYDARK VISIBLE INVISIBLE FREQUENT FLYER TWO DOTSFARMVILLE FIFA 18: LOOT
  87. 87. experience transforms.me
  88. 88. persuasive design for sustainability transforms.me
  89. 89. A System Development Life Cycle for Persuasive Design for Sustainability Moyen M. Mustaquim( ) and Tobias Nyström Uppsala University, Uppsala, Sweden {moyen.mustaquim,tobias.nystrom}@im.uu.se Abstract. The impact of a system development lifecycle (SDLC) often deter- mines the success of a project from analysis to evolution. Although SDLC can be universally used design projects, a focused SDLC for a specific complex de- sign issue could be valuable for understanding diverse user needs. The impor- tance of sustainability elevation using a persuasive system is not new. Previous research presented frameworks and design principles for persuasive system de- sign for sustainability, while an SDLC of sustainable system development also exists. However, at present no SDLC for persuasive design aiming for sustaina- bility is evident, which was proposed in this paper. An existing sustainable SDLC established earlier by the authors was taken as the reference framework. A cognitive model with established persuasive design principles was then ana- lyzed and mapped within the context of the reference framework to come up with the resulting life cycle. Finally, extensive discussions and future work pos- sibilities were given. Keywords: Sustainability · SDLC · Persuasive System Design · Cognitive
  90. 90. text, e.g. a similar approach ize their platforms and offer Fig. 1. System Develo Ability. The ability phase h that enterprise resource-planning systems use to standa r standard solutions for companies. opment Life Cycle of Persuasive Design for Sustainability could be compared with the development phase in ard- the
  91. 91. Planning Analysis Design Implementation Maintainance PlanningMaintainance Implementation Purpose Provisioning Metrics Human-Centeredness Transforming strategies
  92. 92. next transforms.me
  93. 93. Roadmap for Autonomous Cities: Sustainable Transformation of Urban Spaces Roadmap for Autonomous Cities: Sustainable Transformation of Urban Spaces Full Paper Ariel Noyman MIT Media Lab noyman@mit.edu Agnis Stibe MIT Media Lab agnis@mit.edu Kent Larson MIT Media Lab kll@mit.edu Abstract Despite the inherent relationship between cars and their physical urban surroundings, many cities are hesitant to embrace the impact of autonomous mobility on urban design. Industry leaders envision autonomous vehicles soon penetrating global markets, although the relationship between autonomous vehicles and their urban context has been poorly discussed. Witnessing rapid technological advancement and tardiness of city planning and execution, the proposed research diverts discourse from intrinsic technology of autonomous vehicles to their impact on urban design. This paper offers a review of historical cars-oriented design and the global surrender to car-culture in the past century. Then, it elaborates on different autonomous technologies and their potential impact on urban form. Furthermore, it shares plural plausible future perspectives to initiate a discussion on tangible implications of autonomous vehicles on contemporary cities. Ultimately, this research suggests a preliminary roadmap to the way autonomous mobility might be incorporated within new and existing cities.
  94. 94. 421 3 5 6 87 109 11 12 13 14
  95. 95. Djurgården 2 Advice Accenture Latvia Magazine | Issue 5 | May 2017 Advicefrom AgnisStibe, Transformational Designer Nowadays, there are experts in technology, experts in sensors, Thus, for machines to be able to resemble anything similar Thepurpose ofinnovationsArtificial intelligence (AI) is going to be just another buzzword if we won’t try zooming out and locating it within a bigger picture of our lives. What is AI? What is the purpose of AI? This discussion is currently missing from the discourse around AI. To a certain degree, everyone is interested in talking about AI, many say it is important, but why? How? For whom? How AI will influence our daily routines? Finding answers to these questions is essential already today. Agnis is a Social Engineer at MIT Media Lab: transforms.me
  96. 96. homework transforms.me
  97. 97. ATTITUDE BEHAVIOR ENVIRONMENT
  98. 98. SELF- CONTAINED SELF- DRIVEN JANUARY 1st SELF- DRIVEN SELF- CONTAINED JANUARY 1st
  99. 99. transforms.me

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