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.)
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.
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
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
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
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.
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
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?
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
57. 57
What is the objective function of our financial markets?
“The Social Responsibility of Business Is to
Increase Its Profits”
Milton Friedman, 1970
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.
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
70. 70
O’Reilly Media
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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!
85. 85
Tim O’Reilly
Founder & CEO, O’Reilly Media
@timoreilly
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