The past month has been marked by a series of extraordinary events that would have been completely unforeseen only a year ago, or even in mid-summer. (In June, West Texas Intermediate crude oil futures contracts were selling at $60 a barrel, roughly twice the current price.) While this may be an unusual month, the larger question remains: how can human institutions evolve to better address both sudden and glacial change, in both positive and negative directions? Put another way, if we see what keeps surprising us, maybe we can adapt our practices and assumptions to be surprised less often, less acutely, or both.
Oil is certainly big news.
While the dynamics of a global market, controlled by a wide range of
political and business players, remain fascinating, “common knowledge”
in energy markets shifts dramatically. Recall how recently talk of “peak
oil” was common: according to Google Trends, searches for the phrase
spiked in August 2005 and, at a slightly lower index, May 2008. After
2011, interest dwindled to baseline noise, and today we wrestle with the
problems of sub-$2.00 gasoline. The precise events coming into play
right now have complex origins: innovations in drilling technology,
geopolitical forces (including bitter national and ethnic rivalries),
and national budgets whose planning assumptions have been obliterated.
Saudi Arabia, for example, can produce a barrel of oil for about $3 but
needs $93 to break even for budget purposes given its economic
monoculture; Venezuela needs $149 a barrel to break even, to take the
most extreme example. At $30, budgets in many places (including Alaska)
are a mess.
Given that oil is such big business in so many parts
of the world, considerable expertise is deployed in forecasting. Yet the
industry’s record, with regard to both estimates of oil reserves and
now prices, is consistently poor. Perhaps the lesson is that complex
systems cannot be predicted well, so the best answer might be to shorten
planning horizons — a tough call in light of the magnitude of
investment and concomitant project lead time required.
“shock’ is in some ways predictable: U.S. infrastructure investment has
lagged for so long that calamities on bridges, railroads, and water
supplies are unfortunately overdue. The particular politics of Flint,
Michigan’s mismanagement are also not surprising given the nature of
both large, overlapping bureaucracies and the governor’s high priority
on municipal budget repair to be performed by unelected “emergency
managers.” The competing agendas are difficult: if bondholders lose
trust, investment becomes prohibitive. At the same time, the dismissal
of known test results and risks, and the human consequences thereof, are
criminal: GM stopped using Flint water because it was destroying auto
parts while Flint’s citizens had to keep drinking it.
in Flint is not all that unusual, except in its impact: given the size
of federal and state governments, it’s hard to imagine who voters could
hold accountable for substandard ports, roads, and airports. Many are in
poor repair, but the constituencies are diffuse and/or politically
marginal, and so can be ignored. Who can one complain to (or vote out)
regarding connections inside Philadelphia’s airport, or Amtrak’s
unreliability, or Detroit’s crumbling schools? Conversely, what good
came to the Detroit mayor who supported that airport’s modernization?
Who is the primary constituency that benefits from New Jersey’s
extremely heavy spending on roads ($2 million per state-controlled mile)
that are consistently graded as among the nation’s worst (at both the
Interstate and local arterial levels)? Rather than planning horizons,
the issue here appears to center on accountability. The interconnections
of race, poverty, and party politics can also fuel tragedy: decisions
were made in Flint that would be unthinkable in more affluent Detroit
suburbs. (Another water issue, the one in California, could also amplify
class conflicts in the event the El Nino snowpack melts to last
summer’s levels in coming years.)
The third shock is a positive
one. Google’s DeepMind unit (acquired for $400 million in 2014)
announced that it had used machine learning to develop a computer
capable of defeating the European champion at Go, the ancient Chinese
game of strategy. AlphaGo, DeepMind’s program, will now play a
higher-ranked champion in March. If the machine can win, another
cognitive milestone will have been achieved with AI, about ten years
faster than had been generally predicted. Interestingly, Facebook had
previously announced that it had made significant progress at Go in a
purely machine tournament, but the Google news swamped the magnitude of
To their credit, DeepMind’s team
published the algorithmic architecture in Nature.
Two distinct neural networks are built: one, the “policy network,”
limits its scope to a small number of attractive options for each move,
while the “value network” rates the proximate choices in the context of
20 moves ahead. It’s likely the technology will be tested outside
abstract board games, potentially in climate forecasting, medical
diagnostics, and other fields.
In this case, the breakthrough is
so unexpected that nobody, including the scientists involved, knows what
it means. Even though Deep Blue won at championship chess and Watson
won at Jeopardy, neither advancement has translated into wide commercial
or humanitarian benefit even though the game wins were in 1997 and 2011
respectively. This is by no means a critique of IBM; rather, turning
technology breakthroughs in a specific domain into a more
general-purpose tool can in some cases be impossible when it is not
Elsewhere, however, giant strides are possible:
Velodyne lidar, the spinning sensor atop the first generation Google
car, has dropped from $75,000 per unit to a smaller unit costing under
$500, with further economies of mass production to come. Even more
astoundingly, the cost of human genomic sequencing continues to plummet:
the first human DNA sequence cost $2.7 billion, for the entire research
program. Shortly after, the cost was about $100,000 as of 2002; today
it’s approaching $1,000, outpacing Moore’s law by a factor of thousands
(depending on how one calculates) in a 15-year span.
In each of
these technological instances, people have yet to invent large markets,
business models, or related apparatus (liability law, quality metrics,
etc) for these breakthroughs. As the IBM example showed in regard to AI,
this is in some ways normal. At the same time, I believe we can create
better scaffolding for technology commercialization: patent law reform
comes immediately to mind. Erik Brynjolfsson and Andrew McAfee suggest
some other ideas in their essential book, The Second Machine Age.
is of course a piece of the puzzle, and there’s a lot of discussion
regarding STEM courses, including why more people should learn to code.
I’ve seen several people make the case that code is already the basis of
our loss of privacy, and there will be more deep questions emerging
soon: who owns my genomic information? who controls my digital
breadcrumbs? should big-data collection be opt-in or opt-out? Yes,
knowing _how to_ code can get you a job, but more and more, knowing
_about_ code will be essential for making informed choices as a citizen.
The widespread lack of understanding of what “net neutrality” actually
entails serves as a cautionary tale: few people understand the mechanics
of peering, CDNs, and now mobile ad tech so much of the debate misses
the core issue, which is lack of competition among Internet service
providers. “Broadband industry consolidation” isn’t on anyone’s top-5
agenda in the U.S., yet even comedian John Oliver identified it as the
major nut to crack with regard to information access.
In the end,
humans will continue to see the future as looking much like the
present, driven by psychological patterns we now understand better than
ever. As shocks increase in magnitude, for many reasons including
climatic ones, and impact, because so many aspects of life and commerce
are interconnected, it may be time to rethink some of our approaches to
planning for both the normal and the exceptional.