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The Opportunity for Agile Governance

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Google handles over 3 billion searches a day, Amazon offers a storefront with 600 million unique items, Facebook users post 6 billion pieces of content sailing, all with the aid of complex algorithmic systems that respond to a constant influx of new data, adversarial activity by those trying to game the system, and changing preferences of users. These systems represent breakthroughs in the governance of complex, interacting systems, with algorithms that must be constantly updated to respond to rapidly changing conditions. The economy as a whole is also full of complex, interacting systems, but we still try to manage those systems with 20th century tools and processes. This talk explores what we can learn from technology platforms about new approaches that the Fed might take to improve its historical mission using the tools of agile development, big data, and artificial intelligence. My talk at the San Francisco Federal Reserve Bank FedAgile conference on November 7, 2018. Download the PPT file to read the narrative in the speaker notes. (I wish slideshare did a better job of displaying these, but they don't.)

Publié dans : Direction et management
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The Opportunity for Agile Governance

  1. Tim O’Reilly Agile Governance
  2. 2 What the great technology platforms teach us about the future of work, business, and the economy. wtfeconomy.com
  3. 3
  4. 4 If we want to understand the future of business and the economy, we have to understand these platforms
  5. 5 We are all living and working inside a machine
  6. 6 It’s no longer happening just in the digital realm
  7. 7 An Amazon warehouse is a human-machine hybrid
  8. 8
  9. 9 It makes things like this possible 68 million monthly users 440,000 employees 336 million monthly active users ~3400 employees
  10. 10 “Gradually, then suddenly” Ernest Hemingway
  11. 11 Gradually, then suddenly Artificial Intelligence and algorithmic systems are everywhere, in new kinds of partnerships with humans
  12. 12 “The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.” - J.C.R. Licklider, Man-Machine Symbiosis,1960
  13. 13 We are building a global brain, composed of all of us, augmented and connected by technology. Central banks are responsible for the health of a critical part of its nervous tissue.
  14. 14 Government is also a platform!
  15. 15 So are financial markets
  16. 16
  17. 17
  18. 18 Delivery-driven Policy “This isn’t just how we should be developing software. It’s how we should be developing policy.” Cecilia Muñoz, Former Director, White House Domestic Policy Council
  19. 19 New skillsets are needed • User Centered Design • DevOps • Site Reliability Engineering • Data Science • Deep Learning • API Design • A/B Testing via tens of thousands of experiments
  20. 20 “My grandfather wouldn’t recognize what I do as work.” Hal Varian, Google Chief Economist
  21. 21
  22. 22 Many of today’s workers are programs. Developers are actually their managers. Every day, they are inspecting the performance of their workers and giving them instruction (in the form of code) about how to do a better job
  23. 23 A new kind of management “It’s the difference between ‘playing Caesar’ (deciding which projects live and die), and ‘playing the scientist’ (being perpetually open to search and discovery.)” - Eric Ries, The Startup Way
  24. 24 Software has become a set of ongoing business processes, not an artifact
  25. 25 The lessons of technology are also lessons for the organization of the business “Services not only represent a software structure but also the organizational structure.” Werner Vogels, Amazon CTO
  26. 26 “Doing digital is not the same as being digital.” Josh Bersin Deloitte
  27. 27 Internet-scale networked platforms managed by algorithm are fundamentally changing the economic equation and the very nature of markets
  28. 28 Gradually, then suddenly Large segments of the economy are governed not by free markets but by centrally managed platform monopolies
  29. 29 The invisible hand at work
  30. 30 What happens when there’s only one queue?
  31. 31
  32. 32
  33. 33 Amazon.com
  34. 34 And what happens when there’s only one price for everything?
  35. 35
  36. 36 Algorithms decide “who gets what – and why” Markets are outcomes. A better designed marketplace can have better outcomes.
  37. 37
  38. 38 “In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently.” Herbert Simon
  39. 39 Algorithms have become a battleground Security: “That word does not mean what you think it means.”
  40. 40 Users post 7 billion pieces of content to Facebook a day. Expecting human fact checkers to catch fake news is like asking workers to build a modern city with only picks and shovels. At internet scale, we now rely increasingly on algorithms to manage what we see and believe.
  41. 41 Real Time Digital Regulatory Systems • Google search quality • Social media feed organization • Email spam filtering • Credit card fraud detection • Risk management and hedging
  42. 42 Government and central bank statistics, economic modeling, and regulations are too slow for the pace and scale of the modern world “Would you cross the street with information that was five seconds old?” • -Jeff Jonas, CEO of Senzing, Former IBM Fellow
  43. 43 “Why is policy still educated guesswork with a feedback loop measured in years?” Tom Loosemore, Former Deputy Director, UK Government Digital Service
  44. 44 Governance in the age of algorithms • Must focus on outcomes, not on rules. • Must operate at the speed and scale of the systems it is trying to regulate. • Must incorporate real-time data feedback loops. • Must be robust in the face of failure. • Must address the incentives that lead to misbehavior. • Must be constantly refined to meet ever-changing conditions.
  45. 45 Managing an algorithm
  46. 46 Algorithmic systems have an “objective function” • Google: Relevance • Facebook: Engagement • Uber and Lyft: Passenger pick up time • Scheduling software used by McDonald’s, The Gap, or Walmart: Reduce employee costs and benefits • Central banks: Control inflation? Employment? Interest rates?
  47. 47 When platforms get their algorithms wrong, there can be serious consequences! When platforms get their objective function wrong, there can be serious consequences!
  48. 48 Like the djinn of Arabian mythology, our digital djinni do exactly what we tell them to do
  49. 49
  50. 50
  51. 51 Divergence of productivity and real median family income in the US
  52. 52 “The art of debugging is figuring out what you really told your program to do rather than what you thought you told it to do.” Andrew Singer Andrew Singer
  53. 53 Are the FED’s “algorithms” having the intended effect? Have the goals of central banks been captured by the equivalent of spammers?
  54. 54
  55. 55 The runaway objective function “Even robots with a seemingly benign task could indifferently harm us. ‘Let’s say you create a self-improving A.I. to pick strawberries,’ Musk said, ‘and it gets better and better at picking strawberries and picks more and more and it is self-improving, so all it really wants to do is pick strawberries. So then it would have all the world be strawberry fields. Strawberry fields forever.’ No room for human beings.” Elon Musk, quoted in Vanity Fair https://www.vanityfair.com/news/2017/03/elon-musk-billion-dollar-crusade-to-stop-ai-space-x
  56. 56 We’ve built one of these already
  57. 57 What is the objective function of our financial markets? “The Social Responsibility of Business Is to Increase Its Profits” Milton Friedman, 1970
  58. 58 Is there really nothing left for humans to do?
  59. 59
  60. 60 Dealing with climate change Rebuilding our infrastructure Feeding the world Ending disease Resettling refugees Caring for each other Educating the next generation Enjoying the fruits of shared prosperity
  61. 61 This is what technology wants “Prosperity in human societies is best understood as the accumulation of solutions to human problems. We won’t run out of work until we run out of problems.” Nick Hanauer
  62. 62 “A platform is when the economic value of everybody that uses it exceeds the value of the company that creates it. Then it's a platform.” – Bill Gates
  63. 63 Once a platform stops creating more value for others than it captures for itself, people migrate elsewhere.
  64. 64 Remember Microsoft’s competition with its ecosystem?
  65. 65 Google’s share of ad revenue over time O’Reilly Research
  66. 66 Nations fail for the same reason Inclusive economies outperform extractive economies. When inclusive economies fall prey to extractive elites, everyone is worse off.
  67. 67 Growth goes on forever? One of the key drivers of corporate bad behavior is the command given them by financial markets that they must constantly grow and increase their profits
  68. 68 An alternative: “Doughnut Economics” Kate Raworth
  69. 69 Oikonomia vs Chrematistike
  70. 70 O’Reilly Media ● Providing learning for almost 40 years ● Trends called – Open Source, Web 2.0, Maker Movement, Big Data ● 500 employees, thousands of contributors ● 5,000+ enterprise clients, 2.3m platform users globally ● 17 global technology events serving 20k individuals and 1,000 sponsor companies
  71. 71 Change the world by spreading the knowledge of innovators
  72. 72
  73. 73
  74. 74
  75. 75 “The opportunity for AI is to help humans model and manage complex interacting systems.” Paul R. Cohen
  76. 76
  77. 77 “Computational Sustainability is a new interdisciplinary research field, with the overarching goal of studying and providing solutions to computational problems for balancing environmental, economic, and societal needs for a sustainable future. Such problems are unique in scale, impact, complexity, and richness, often involving combinatorial decisions, in highly dynamic and uncertain environments, offering challenges but also opportunities for the advancement of the state-of-the-art of computer and information science. Work in Computational Sustainability integrates in a unique way various areas within computer science and applied mathematics, such as constraint reasoning, optimization, machine learning, and dynamical systems.” Carla Gomes
  78. 78 The great opportunity of the 21st century is to use our newfound cognitive tools to build sustainable networks and ecosystems
  79. 79 Can we build an economic flywheel that keeps us in the doughnut?
  80. 80 A Social Investment Stipend? We need “a new social contract, one that values and rewards socially beneficial activities in the same way that we currently reward economically productive activities.” - Kai Fu Lee, China’s most successful AI investor
  81. 81 “Economic Possibilities for Our Grandchildren” The world of his grandchildren—the world of those of us living today— would, “for the first time . . . be faced with [mankind’s] real, his permanent problem—how to use his freedom from pressing economic cares, how to occupy the leisure, which science and compound interest will have won for him, to live wisely and agreeably and well.” John Maynard Keynes
  82. 82 What would it take for us to • Put people to work tackling the world’s greatest problems? • Treat humans as assets, not liabilities? • Create an economy based on caring and creativity, while machines focus on repetitive tasks? • Give everyone access to knowledge on demand, whenever we need it? • Have fresh approaches to public policy based on what is possible now, and by learning what works, rather than picking from set political menus?
  83. 83 We will create the economy of the future when we remember that the function of technology is to empower people to do things that were previously impossible!
  84. 84 What’s the Future? It’s Up To us wtfeconomy.com
  85. 85 Tim O’Reilly Founder & CEO, O’Reilly Media @timoreilly • O’Reilly AI Conference • Strata: The Business of Data • JupyterCon • O’Reilly Open Source Summit • Maker Faire • Foo Camp • … • 40,000+ ebooks • Tens of thousands of hours of video training • Live training • Millions of customers • A platform for knowledge exchange • Commercial internet • Open source software • Web 2.0 • Maker movement • Government as a platform • AI and The Next Economy