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visit MIT OpenCourseWare at ocw.mit.edu. WILLIAM BONVILLIAN: Let
me just introduce myself. And then maybe you all
can introduce yourselves to me and to each other. So I'm Bill Bonvillian. And for the last 11 years,
I have been director of MIT's Washington DC office. And I've taught at
Georgetown University a course on science and tech
policy for about 12 years. And I teach a different course
on energy technology policy at Johns Hopkins SAIS. So I'm on the edge on faculty
at Georgetown and Hopkins SAIS. And after 11 years of being
director of the Washington office, I've stepped
down from that job. So I'm now a lecturer up here. And I've got an attachment
to MIT's Industrial Policy Center, which is one
of its policy centers. And I'm finishing my
third book actually, which is now at MIT press
on advanced manufacturing. So you're going to hear
more than your share about manufacturing
for this all over. You're stuck with
those realities. And I had a background in-- I was in the executive
branch as a child. And I was a Deputy Assistant
Secretary of Transportation in my youth long
before any of you were born and got
to really engage in the development
of major legislation that deregulated most of the
big transportation sectors. So aviation, trucking,
and railroads and then did a lot of work on
surface transportation. And then practiced law because
I've been trained as a lawyer. So I practiced law for a decade. And then somebody I knew and
had worked with at an earlier stage became US senator. So I get one of those proverbial
Washington phone calls-- why don't you come up
and we'll talk, Bill? And that conversation
lasted for over 15 years. So I couldn't resist
staying up there. It was much too
interesting and fun. So I had a long career
working in the US senate and worked on lots
of legislation but including a lot of work on
innovation policy and science policy and R&D policy. So that's kind of where I got
my training in this territory was very much learning by doing. And then 11 years
ago, I went to MIT to run their Washington office,
which has been a great joy. So anyway, that's
kind of-- and then I, just as I said,
just stepped down from that very
activist kind of job to this life of
leisurely MIT faculty. Supposedly, I'm quasi retired. I doubt it. But anyway, that's
my background. Let me just say a
few things about how this course is going to work. We know from a lot of studies,
that MIT's online crowd is now doing about learning science,
that the talking head is not an efficient way to learn. So we really need you all
participating in a class and coming into the discussion. So to further that-- and you saw my email kind
of introducing the course and you've seen the
discussion in the syllabus. Those of you who haven't been
able to get online yet, stick around after class and
let's make sure you get registered and get access
to the online materials and the background materials. So let's talk for a few
minutes after class. But essentially, we're going to
use a discussion leader system. And what that means is that
we'll all have five readings or so for each class. And I'm going to ask all
of you to do those readings and then do a short-- doesn't need to be
more than a page. It shouldn't really be more
than a page and a half-- some key bullet points
on the key findings that you drew from
those readings and a couple of questions. So that kind of gets
all of you thinking about the readings on the way
in and some cute questions you've got about the
readings so that you can help own the class. Because it only works
if you all own it. And then, in turn, we'll do
this discussion leader thing. So each week, I'll
ask a couple of you to be leaders of the
Q&A part of the class. So here's how it will work. So I'll talk about any given
reading for maybe 10 minutes, maybe a little more than that. And then I'm going to turn it
over to the discussion leader to kind of bring you all into
the review of the reading so that you get to
participate in this and get your thoughts
out on the table. And importantly, in terms
of your learning process, your thinking, your learning,
your speaking, all of those are key to the learning process. One thing which we know
from MIT's online classes and the learning science work
that's been going with them, is that the human memory fades
after more than 10 minutes of the talking head. I mean, it is just
not rememberable. And it's very important to
change settings after that. So we're going to
change settings, and it's going to
be you, and you all are going to kind of
lead the next phase, and then we'll go
to the next reading. After we've talked about
the initial one for a while, we'll go to the next reading,
and follow this pattern through. And then I will always
try to summarize, either at the beginning of the end
of the class, where we are, because we also know that
repetition is key to learning, too. So just a few elementary points
about learning science-- there really is, actually,
some design here. The discussion leader stuff
is going to be very informal. This is going to
be very relaxed. I don't want you to be
concerned about having to do fancy presentations. Just speak for a few minutes
about some key things that you found about
the particular reading, and then try and draw
out your classmates. Now, you'll get their
one-pagers, their reading summaries, in advance. They'll get you get you
those the day in advance. I'll get a copy, too. So I'm going to set up a
rough discussion leader agenda for the
first few classes, and then we'll formalize
it for the whole class. But all of you will be
discussion leaders, probably, at least a couple of times. But again, it's very
relaxed, it's very informal. And we're just going to be
sitting around the table, and I want you to
kind of really work to bring your classmates
into the talking. So questions about this? Let me talk about where
this class is going to go, so you get a sense of the
pieces that lie ahead. So the first couple of classes
are really introductory. And today's class,
as those of you who were able to
get to the site saw, is really about economic growth
theory, so innovation growth theory. And that's foundational. In other words, what
are the pillars on which studying innovation rests? Clearly, the economic
justification is one of the absolute central
and probably the critical one. So what does innovation
growth policy-- how does it think,
how is it organized? And we're going to be reading
two very famous growth economists and
talking about that-- three, actually,
and kind of trying to put together the
basics of growth theory so that we understand
that and can use that. And the second class moves
into kind of a second part of growth theory. And we could call this
innovation systems. In other words, innovation
occurs in a system, and you have to understand
the elements in that system. So we're going to talk
about that systems approach in next week's class. And then I'll give you two
kind of key tools to work with. In other words, based on
the first couple of classes, you're going to be able to look
at another country, at a state, at a region, and
you're going to be able to see how you can evaluate
that area's innovation system. What are the factors
that you look for, what are the capabilities
that you look for, how do you assess
them, and then, in particular, at the
end of next week's class, how do you look at
them as a system? And then we'll take
that framework and we're going to apply that framework
and add pieces to it-- to the rest of the class. So the next two classes are
really about manufacturing. So just in case you got
the idea that this is just policy esoterica, we're going
to have a very real dive into some very real
problems that are, frankly, at the heart of the
political disruption that's going on now in the United
States and elsewhere. So we're going to do
two classes on that. So a deep, very
practical, very real dive into a really
important and very current innovation system problem. And then we're going
to go back and pick up some pieces we started with
the first couple of classes. So we're going to look
hard at the US innovation system, particularly the
federal pieces of it-- the federal R&D agencies
and organizations that are part of US
innovation system. AUDIENCE: What is R&D? WILLIAM BONVILLIAN:
Research and development. Don't hesitate to ask
questions, by the way. Please bring those forward. So we'll look at the research
and development system-- [TRYING TO PRONOUNCE STUDENT'S
NAME] AUDIENCE: You can call me Steph. WILLIAM BONVILLIAN: Steph? That is easier, OK. Thanks, Steph. And where did it
come from, how does it think, how is it organized,
what are its origins. Because we're at MIT, I'll give
you a lot of insider MIT stuff. Because MIT historically was
involved in a lot of this. So you'll collect
some good MIT stories, I hope, particularly
in that class. But you know, MIT greats
like Vannevar Bush and Alfred Loomis will
come up in that class. Then, in the sixth
class, we're going to talk about the major policy
focus in science and technology policy, which is called
the valley of death-- how do you get from
the research side over to later-stage development. And that's been the major policy
emphasis in the US system. There's a big chasm
there, a valley. Building the bridging
mechanisms across has been a big
policy preoccupation. So we'll dive into
that literature. And we will also
look at the fact that the United States actually
runs two innovation systems. So we run the innovation system. Probably most of you
are familiar with places like NSF, the Office of
Science at the Department of Energy, NIH. So that's the kind
of civilian side, but then there's a whole
defense innovation system that is organized very differently. Lily is nodding her head,
because we spent time on this in the first class. So we'll talk about
that system, too, and kind of how that works. And then, up until
class 7, we've been talking about
innovation as though it has to do with institutions. But people innovate,
not institutions. So innovation is
owned by people. It occurs in a very
face-to-face environment. You can't just
understand innovation by understanding the
institutions that are sticking their hands into it. In the end, it's
a people system. And how do people-- what does innovation look like
when people are running it at the face-to-face level? And it turns out there's
literature on this. There are some rules
that innovation groups tend to follow when
they're undertaking their process of trying
to get to a breakthrough. So we'll look at the
organization of innovation systems at the face-to-face
level as well as the institutional level. And that class, you all
will own, because I'm going to have you all present-- a whole group of you-- on great innovation groups. And then we've got the
foundations of set, and we're going to start to
look at particular segments. So we'll take a deep dive into
DARPA, the Defense Advanced Research Agency, and
its innovation model. So that'll be kind
of a case study. We're going to take a close
look at energy technology. And here, Martha will help
us get through the class, because she'll know
much more than me. And we're going to look very
closely at the life science innovation system that,
on the federal side, is led by the National
Institutes of Health, and some of the big
challenges in that system. We're going to
develop an idea of how do legacy sectors innovate. Because the computing
revolution was brought about by a new
frontier-- creating a new frontier territory
and innovation. But most of the
economy-- more than 80%-- is owned by these
established sectors. And it's much harder to bring
innovation into those sectors. So energy is like the poster
child for that problem, and we'll take a
real look at that. And then, at the
close of the class, we're going to do a
couple of, I think, more fun and interesting
out-of-the-blue categories. So we're going to look at
the talent base, and science and technology education, and
all the challenges embedded in developing a strong
S&T talent base. And then we're going to
take a look at what we could call the future of work. So are you going
to have any jobs? Are you going to be displaced
by MIT-developed robotics? You aren't, don't worry. But that'll be one
of the challenges that we're going to look at. AUDIENCE: Is the
future of work-- WILLIAM BONVILLIAN:
Now, tell me your name so I get to know everybody. AUDIENCE: [INAUDIBLE] WILLIAM BONVILLIAN: OK, Martine. AUDIENCE: So is the future of
work based on Tom Malone's work at Sloan? WILLIAM BONVILLIAN:
We'll look at-- I tell you who we'll look at. We'll look at the kind of
techno-dystopia movement, which Erik Brynjolfsson and
Andrew McAfee at Sloan have been doing
a lot of work on. And IT is going to replace
a lot of work theory. We're going to look
at David Autor, who's a wonderful MIT economist. And we'll read several
things from David. But he's looking at all kinds
of economic effects on job creation, including
from manufacturing. And we're going to look
at some studies that try to take a deep dive into
how much work is actually going to be displaced. And there's a fairly
interesting new OECD study that came out this
summer that indicates it's not going to be that bad. So we're going to look at
all these kinds of issues, and kind of play them out. But I think it will be
particularly interesting, and a kind of
challenging session. One more thing we'll
do is, David Mindell, who teaches at STS, who is a
wonderful technology historian but also a terrific engineer. So David is on leave
from MIT this semester because he's doing
his robotics startup. But he has a book called
Our Robots, Ourselves. And he takes a really deep look. If robotics, in a way, is
the most threatening entry to human work, what does
that actually look like? What is actually going on? And he comes up
with a thesis that's much more about assistive
robotics and cobotics than about people displacement. So we'll look at all this
stuff, and try and lay it out. Anything so far--
questions so far? AUDIENCE: Will we
also be talking about economic competitiveness
re international relations for example,
manufacturing in China? WILLIAM BONVILLIAN: Yeah. So we're going to do snapshots
on international issues kind of throughout. So the basic focus
will be in the US. But Matt, coming back
to your question, there'll be a lot of underlying
questions about how economic growth-- innovation-based growth
works that's going to have a lot more
applicability worldwide, including the developed
and developing world. So you're going to
get a toolset that will enable you to look at
innovation much more broadly than just the US. You're going to pick up a
lot of pieces about the US, but it's a much broader
toolset than that, which I think you'll be able to apply. And we'll have a number of
readings that will pull us into the international issues. What else? AUDIENCE: Are you going to
have any current events, especially during
this [INAUDIBLE]?? [CHUCKLING] WILLIAM BONVILLIAN: I'm
always open to current events. When we talk about
manufacturing, I'll try and give you a
backdrop on some of that. So that's the third class. And you know, it's been a lot
of social disruption in the US. And we'll try,
through a reading by-- we'll talk about
David Autor, and some of his work and findings
in that area in particular. And there'll be a lot of stories
developing as the year goes on, I know. Anything else to start? AUDIENCE: Yeah, question. WILLIAM BONVILLIAN:
Rasheed, right? AUDIENCE: Yeah. WILLIAM BONVILLIAN: Good. AUDIENCE: So when we go
through innovation systems, are we going to talk
about methods or ways to kind of change and alter
them, or are we just going to-- WILLIAM BONVILLIAN:
You bet, you bet. None of this stuff
is locked in stone. You know, I hope all
of you will do startups at some point in your career. I hope all of you will be
involved in innovation policy and issues. And part of the tool
set you're going to get is how to think about
those innovation systems and how to think
about organizing them in an optimal way. If anything, that's
the key thing I want to convey in this
course, so that when you're running this
country, you've got an agenda ready to go. Anything else? All right, so I'm going to do
more talking in this class. I'll do less talking
in future classes. Hold me to that. But let me kind of
summarize what we're going to see in today's class. So we're going to talk
about Robert Solow. And his contribution is to
think about a critical factor in innovation, a
direct innovation factor, which he refers to
as technological and related innovation. So that's a term that I
want you all to recall-- technological and
related innovation. We're going to use
that a lot, and we'll talk in a minute
about what it means. And then we're going to talk
about another great growth economist, Paul Romer. He was in MIT for about
a year, and then ran out. He's a character. And we'll talk about him, too. But he comes up with our
second direct innovation factor, human capital
engaged in research. And we'll talk about
what that means. And then, third, do these
factors actually make sense? So Dale Jorgenson is a Harvard
economics professor of note, another great growth economist. And he takes a deep dive
into the IT revolution of the '90s and the
period leading up to that, and he essentially
concludes, yeah, this was technologically-driven,
innovation-based growth. It drove a huge
growth in the economy. And then we'll have fun with a
little study by Merrill Lynch, which is an investors'
look at innovation, and how do investors think
about investing in innovation. And then we'll look at some-- I asked you all to
look at NSF indicators. I'll explain what
that is in a bit. But we'll look at some ways
of looking at those two basic innovation factors-- the technology R&D
factor, the talent factor, and if you look at those, what
do those look like in the US? So part 1 here is the kind
of fundamental factors of innovation. But let me get some
general terms on the table. And I will post, by
the way, the lectures, after, on the stellar website,
so you all have access to them. But there's some terms
that will recur here. And you don't need
to memorize them now, but they'll be posted
today or tomorrow. So science-- science evolved
as a way of understanding the natural world. It came out of
natural philosophy. It's an 18th/19th century
kind of conceptual framework. It is observational at heart. It observes the natural
world, and attempts to understand how
it's put together. And it's organized
around discovery about that natural
world and its order. Technology is really different. It is a system to organize
scientific and technical knowledge to go after a
more practical purpose. And this systems includes the
technical advance plus models to implement that advance. So you move from observation
to implementation. And this is, obviously,
the historical boundary between science and engineering. And research-- another
term we'll use constantly-- means increasing the scientific
or technical knowledge or both. So research can pursue either
of these historic ends, or both simultaneously. Invention is about applying
research knowledge to create a practical idea or device. Invention is really
different than innovation. Innovation is built on
scientific discovery and on breakthrough
invention or inventions, but it is the system of research
invention development that uses both scientific
background, scientific knowledge and technology knowledge to
lead to the implementation and widespread applicability
of a technology area. So typically, in our society,
that means commercialization. So this class is about this. That's the stage
we're most focused on. But remember all the input
above that that goes into this. Now, an innovation system--
we talked about this briefly before-- is the ecosystem for
developing innovation. And as we discussed, briefly,
it operates at least two levels. It operates at the
institutional level of supporting the development
of the inventions and discovery that go into
innovation, but it also operates at the personal,
face-to-face level. Because in the end, people-- you all-- innovate, not
some fancy institution. Innovation wave
theory-- in economics, this is called
Kondratiev theory. And we'll talk more about
this as the class goes on. But let me just give kind
of a snapshot of what I'm talking about. Innovation tends
to come in a wave. And in your lifetime,
the big wave has been the IT revolution. If we were born
in 1800, railroads would be like a big
innovation wave. Early telegraph-based
communication would be a big innovation wave. Electricity would be
a big innovation wave. And you tend to have a
core technology advance, you pile on applications,
and it begins to move through
an entire society and affect the whole society. And to some extent,
the technology drives the nature
of the society. So that's what
Karl Marx believed. That's called determinism,
that the technology plays a deterministic role in
the organization of society. So it's big. It is very big. It's not just a pile of
new technology stuff, and it's fundamentally changing
the way in which this society is organized as well. So that's also part of
these innovation waves. So a wave hits an
economy, takes a long time to grow, eventually
it scales up, and affects a good part of
the economy at any given time. Eventually, you run out
of the technology menu, and it stabilizes. But it doesn't disappear,
it creates a new plateau in your economy. And then you do another wave. Yeah. AUDIENCE: What do you mean
by run out of the technology? WILLIAM BONVILLIAN: Let me see
if there's-- is there any chalk down at that end, Martha? I don't see any. It's all right. Oh, there is? MARTHA: [INAUDIBLE] WILLIAM BONVILLIAN: Great. We'll come back to this, but
it's an important enough idea. So there's a slow build-up, and
then there's a rapid build-up. Then there's a bubble. Then there's slower scaling. And then, eventually, you
reach technological maturity-- sort of three scales. This is where the bubble is. So the IT wave, right? Somebody-- I don't
know where this starts. Maybe it starts with
Babbage in the 19th century. But let's start it
with the ENIAC computer at the end of World War II. 1945, 46, right? Slow scale up. Then you hit 1990s. And then you have very
rapid scale up, right? And you probably had some sense
for what that era was like. But it was a period of
remarkably big growth rise, big increase in GDP growth
rate, accompanied and driven by a big gain in productivity. We'll come back to that
term in a bit, too. Then there's always
a bubble, right? So if you all are investing
in innovation waves-- if you go into the
financial sector, and you're investing
in innovation waves, trying to ride them-- you want to start about
here, ride it all the way up. But never forget that
there's going to be a bubble. There is always a bubble. In every innovation wave so far,
there's always been a bubble. So in the IT revolution,
that was the dot com bust of 2001, right? And then, we're in a long period
of continued technological advance, but not at the rate
of the 1990s growth period. And you can think about
different generations of companies that come along
and play different roles here. And then, at someday-- and IT may be different-- but someday we'll
reach a certain amount of technological maturity. And the growth rate
will stabilize. And then we'll do
something else, right? So that's kind of what
a wave looks like. And again, you build
into your economy a series of mesas or plateaus,
which don't disappear. You just go on to the
next innovation wave. This is the way economies grow,
through these innovation lives. So it's a pretty
important concept. And if you can get
your technology advance into an innovation
wave, then it kind of goes on autopilot, right? And all these things
start to occur. So that's what we're desperately
trying to do for energy. We're trying to get
it scaled up enough so that it can take
off, go on autopilot, and just kind of happen. AUDIENCE: So you mentioned
there is a bubble present. [INAUDIBLE] wouldn't that
affect [INAUDIBLE] this growth? WILLIAM BONVILLIAN: Yeah. It wipes out lots of the
dot com startups, right? They die. And only the stronger companies
with more solid enduring models survive. So always anticipate the bubble. And get out in time. That's the key. AUDIENCE: [INAUDIBLE] have
a different perspective, they see it as two bumps. They see it as one
baby bump, and then a huge bump So for
the 90s, it was, like, it would cost you
like $70,000 to buy servers. So those businesses took
a lot of capital to start. And so people get
really, really excited. And so they start to
invest a lot of money. And then they expect the bubble. And then, no one
wants to invest. Because there is just a bubble. And then, people keep
innovating in the technology until it's perfected. So an example of that is, like,
once we had the mobile phone, in 2010, then you're going
to have a lot of apps and have a lot of
disruptive innovation. So VCs usually just
look at it as two bumps. But the second bump is usually
just massive [INAUDIBLE].. WILLIAM BONVILLIAN:
Well, and you can look at this as
a couple of bumps, but not quite in
the way, Martine, that you're describing. But you could look at this
rise and then this phase. It's kind of two
different pieces, too. So that's another way
of looking at this. Rapid growth, more stable
growth, with different firms typically involved in the two. We're going to dig into this. But go ahead. AUDIENCE: I was
just going to ask, what are the key indicators
that a bubble is-- WILLIAM BONVILLIAN: You're
getting rich, right? A lot of people are
getting rich, right? That's the key indicator. In the IT revolution,
everybody got a lot better off. So all quintiles of
the society ended up with a significant gain. You know, the upper
middle class, as usual, gets the biggest gain. But everybody went up
in that time period. I'm stealing my own thunder
from later in the class. But actually, we'll
deal with some of this when we talk about Jorgenson. We'll get into productivity
and GDP growth. So make me come back to this. All right. So we got through
innovation ways, more or less, with more to come. And then we talked earlier about
this Valley of Death concept, right-- the gap between research
and later stage development that has been the main
focus of public policy in the innovation field. One of the things
about this class is that you're going
to realize this is only one part of a much more
complicated and richer story. So we're going to be telling
a lot of stories in addition to this story. But that's one of the
stories we're going to tell. So any further
questions about this? I just wanted to get some
basic vocabulary kind of out on the table. Let me say a few more
introductory things. The relationship between
science and technology-- really, before the
mid-19th century, technology wasn't
really based on science. It was based, in a
way, on tinkering. And that's not to say
that science didn't enter at some critical stages. It does. But the initial
invention moments are technology types
fiddling around in the 19th century and late
18th century-- the equivalent of a garage, right? So science is not
far enough along to be able to give
birth to technology, for at least a large
part of the 19th century. But now we're in an era where
basic science definitely can give rise to technology. Now somebody-- a friend
I know pretty well, named Lee Buchanan, who is
a former deputy director of DARPA-- would always insist that,
yeah, that's all fine. But I'm running DARPA. And I get nothing
out of basic science. I could drop all that
funding and never miss it, he explained to me one day. So I think he's wrong. But you should know there's
a debate about this, including in our most famous
advanced technology agency. But I think, in the
end, the evidence is strong that science can
now give rise to technology. But keep in mind that technology
still gives rise to science. And we'll talk about
what that means in a bit. Now here's our first MIT great. Forgive the
MIT-centric focus here, for our colleagues from
other institutions. You'll have to bear with me. Robert Solow won the Nobel
Prize in economics in 1987 for, essentially,
developing growth economics. I mean, he created an
entire field of economics. And he's just a
wonderful human being. He's still around. If you ever get a chance to
meet him or listen to him, don't miss it. He's one of the greats. Forget the Nobel Prize. The really important prize
is the President's Medal of Technology, right? The only economist who's ever
won the President's Medal of Technology. There he is getting
it from Clinton. It's really unusual. His work is so important in
its implications for technology development that he gets
the technology prize, too, from the president. I can't tell you what
a nice person he is. I once watched him testify
in front of the House Science Committee. And it was like, you know,
God has entered the room. Solow has come into the room. And the committee all knew that
innovation based growth theory comes from this guy. And they were just
incredibly complimentary. And the congresswoman
who was, like, the number two or three
on the committee-- so her questioning
was fairly early-- and she turned to
him and said, you know, doctor, you know, for
the purpose of this hearing, you know, what should
we call you, Nobleist? You know, what term
should we use for you? And he kind of leans back. And he says, well, you know, I
was a tech sergeant in the army and during the Korean War. And I really came to
like the title Sarge. So maybe you could
call me Sarge. [LAUGHTER] And there's this fancy
congressional committee calling Robert Solow, Sarge,
for the rest of the hearing. He had them eating
out of his hands. He was just funny and charming
and incredibly thoughtful, all at the same time. So he's quite an amazing figure. He wins the Nobel Prize
for essentially blowing up a large part of
classical economics. And the problem with
classical economics-- and look, I asked
you to read this. And it's his Nobel Prize talk. So for economics writing,
it's fairly accessible. But even then, it's not simple. If you're going
to be reading it-- and I'm going to ask everybody
who hasn't done the readings yet for this class, because I
know it's your first time with access to the Stellar site-- but just blow by the
economics formulation period. Just blow by the econometrics. Just get the basic ideas down
for both Solow and for Romer, in particular. The Romer one is particularly
complicated reading, for those of you who've
not studied economics. But he goes after
classical economic theory. And just to summarize,
in a simpler way than is really fair to
classical economics, classical economics
asked when an economy is capable of steady
economic growth. And they never came
up with a good answer. The formulation was when the
national savings rate, which means income saved
in the economy, equals the capital supply,
capital output ratio, and the rate of labor force
growth, that's labor supply, then you get growth. That was the theory, all right? So let me simplify
that even more. Essentially, you've
got two factors. Capital supply and labor supply. And it's not capital
supply, like, all the money. It's capitals like
plant equipment, as well as resources,
that's available. And labor supply includes not
just the number of workers, but education health
systems and the supporting systems for that, too. So these are bigger concepts. So essentially, their
theory was that these are the contributing factors,
capital supply and labor supply. And when you get those in
the right balance, supported by your national saving
rate, which is essentially the funder of those
systems, then you will get economic growth. It's a static view. It's an equilibrium system. And these factors have to
be in the right balance-- they constantly tend to
throw each other off-- for growth to occur. So you explain a
business cycle from this. I mean, not really. But that's what
it attempts to do. And capitalism
becomes just periods of alternating improvement
and gain and decline. So it's a business cycle
kind of explanation. Worsening unemployment and
then labor shortages, right? Everything is throwing
each other, over time, out of balance. It is not a dynamic model. I should explain that classical
economics basically starts with Adam Smith and
lasts until, really, the post World War II period. And the problem
with economics that was so frustrating to
economists, as well as policymakers, was
that it didn't really have a foundational set
of operating and working assumptions that were reliable. It didn't have a reliable base. So if you had 15 of you
were economists sitting around trying to explain
to me, a policymaker, how come there was
a Great Depression, you would have at least 30
ideas on the table, right? It wasn't really
terribly helpful to me. There were just
too many variables. So social science, in the
course of World War II, saw what physics did-- staring at our two
physics guys here. Well, there's more--
several physics books. Physics, in that pre
World War II period, attempts to get down to
basic known scientifically established currents
of fact that can be demonstrated and known So that's the whole particle
physics endeavor, right? We're going to weed
out extraneous stuff, get down to a fairly
small subset of stuff that we really
know, and then work with those as building blocks. So all of the social
sciences watch what happens to physics in
that pre World War II period, and that World War
II period, where eventually things like nuclear
weapons and power come about. And they think, gee, maybe
they're onto something. Maybe that's a practice
that we ought to do. Can we get down to a relatively
small number of known variables and, in effect, create
a known system here that's much more reliable
than the kind of pre World War II guesswork in economics-- that everybody would be working
from a similar kind of problem set? So that's the enterprise. Solow is a
neoclassical economist. So he's post
classical economics. He's part of a movement,
which Paul Samuelson, here, is one of the tiny handful
of the great leaders of that tries to get economics down
to a subset of known factors and known realities that can be
demonstrated and mathematically proven. So that's what neoclassical
economics is up against. That's what they're
trying to do. Solow is a
neoclassical economist. And he is working through
this problem of growth. So he had, what, 150, 200
years worth of economics? And they didn't have a
viable growth theory. I mean, what's with this? So, fortunately,
Solow comes along. And he forms one. And he said the story told by
these classical models felt wrong. And he noted that in one of the
classical economists, Harrod, there was a hint
and generalizations about entrepreneurial behavior. And he decided
that he could think about replacing the
capital and labor output with a richer
and more realistic representation of
technology, which would be a new
theory of production, not just an assessment
of output levels. That's the key
thought pattern here. So it's not that capital
supply and labor supply aren't important, right? They remain important factors. But they are not close to
as important as this more realistic representation of
technology, which he also calls, as I said earlier,
technological and related innovation. So it's not just the
technology, right? It includes that bundle of
stuff around the technology, like process and business
models that enable it. But in the end, it's
technology-based innovation that becomes the heart
of economic growth. And Solow does a 50 year
review of the US economy. And he concludes that in
that 50 year time period, technological and
related innovation is the dominant causative
factor of US economic growth to the tune of about 2/3. So in that range, somewhere
between 50% and higher. We may be on the
higher end of that, now, in this era
that we're in now. And that's his key breakthrough. So capital supply and labor
supply are significant. But they're down in the teens. Now he acknowledges
that the rate of growth is going to depend upon
the investment rate. Because that will be a
factor that helps drives it. So capital supply here
is still significant. But old growth theory, classical
growth theory, is mechanical. It is an equilibrium system. It's constantly going in
and out of balance, hence those business cycles. The great thing about
what Solow comes up with is that he brings
a dynamic factor into understanding growth. I'll skip some of this. This can be very good news, OK? So economics goes from like this
kind of dark and dismal science to a way in which a society
can grow and increase its well-being by introducing
more technological and related innovation. That's profoundly good news. It gets us out of this
old equilibrium system, this old dismal science on to
what, if you can get it going, what can be a dynamic
pattern, and improve societal well-being. We can see these
technological innovations in the way they changed
society in the past, right? So we think about what the
19th century looks like. And I mentioned some
of these before. But you know, canals and
railroads and electricity and the telegraph
and the telephone, and more recent times, aerospace
and computing and the internet, these are all growth
transformers, right? And you've seen a
couple of these. You've seen the IT
revolution in your lifetime. And you've seen, on a smaller
scale, but still significant, biotech revolution, both of
which are still playing out. So you can get a feel for
what these things look like. We kind of know they're real. This is not just
some Solow construct. We feel these. Because we see them around us. So what's the pattern? So there's a core
technology advance. That yields opportunities
for new applications which can pile on to that
core technology advance. And then that, in turn, can
become big enough that it enters society at scale, right? And we also the IT revolution
spreading into different parts of the economy. And then that can yield
productivity gains. So productivity gains,
for you non-economists, are when you're producing
more with less labor input-- so more for less, right? Workers spend less time
on the production stage. And they produce more, right? So those are the
efficiencies that come out of technology, enable
these productivity gains. The productivity gains-- that
is a real gain in this society. That creates real wealth. Because you're producing
more with less. So there's a real gain. And then, depending on how
your society is organized, you can distribute that wealth. And the society can move ahead. So that's what we saw happening
in that amazing 1990s period, right? And we'll talk about Jorgensen
and the pictures of that, in a minute. Follow me? Are you with me? So, great. We're out of dismal science. There's really good news here. You can do this innovation
stuff, and grow your society, grow your economy, and grow
societal well-being, right? That's the good news. The bad news is how the
heck do you do that? How do you do that? And that's what this
course is about-- how do you do that? For a long time,
economists thought that this technological
and related innovation stuff was the way in which
rich nations got much richer. And that's pretty much
the way it looked, for a long period of time. But then, funny things
started happening, right? Countries like Korea, Taiwan. But then, really
importantly, India and China hit on aspects of a
technological based innovation model and began to significantly
grow the middle classes in their societies. So we now know
this is not only-- Matt, this is for you-- this is not only a model
for developed countries, this is a developing
country model, too. This is really important, right? So technological
progress is key. Capital is a supporting role,
but still important, right? Labor supply is a supporting
role, but still important. And Solow brings us this
whole new set of ideas. But then he runs into a problem. Because remember, he's a
neoclassical economist. He wants to get down to a
small number of variables that you really know, right? Good luck with trying to
understand an innovation system based upon mathematically proven
small numbers of variables. It is much too rich
and complex a system. You can't put an
innovation system into supply and demand curves. So Solow sees the power
of technological advance. But he doesn't see
how to measure it. So he can't put it into his
neoclassical economic model. So he treats technological
innovation as Exogenous-- outside of the economic
analytical modeling process that he's involved in. That's pretty amazing,
when you think about it. This guy figures out the
dominant causative factor of growth, which
is, needless to say, a pretty important
thing in economics. And then he can't
play with it, right? Because it's outside his system. So that brings us
to this character. And this is Paul Romer. And you can see, you know,
very California, right? And he's got that laid-back kind
of relaxed look in his eyes. And he is a remarkable figure. He is a maverick
troublemaker, all right? You know, I got to know
him by actually working on some legislation
with him, back when I was working in the Senate. And we knew we needed to improve
the STEM, education science and technology,
work base that was going to be pretty
important for reasons I'll explain in a minute. And how would you
do that, right? So I'm a senate staffer. I'm thinking, gee,
we're already spending, you know, X billions of dollars
on fellowships for you guys. And we're going to have to
find billions more and increase the number and expand the base. And it's going to be
incredibly costly. And I'll never get this passed. So Romer is thinking
about all this stuff and writing about it. And we'll read something else
of his late in the class. But I sat down with him. And he said, Bill, you're not
thinking about this right. He said, think
like an economist. He said, you don't have
to subsidize everything. You just bribe the gatekeepers. That was his phrase. And his point was,
you don't have to throw a huge amount
of money and build up all kinds of
programmatic elements. You just figure out who's
not doing their job, and expanding the number
of scientists and engineers studying at universities. And you bribe them
to change their ways. That's the way you
do it, all right? Much cheaper. And sure enough, we
fashion legislation and eventually get it passed. I'm not sure it
changed all that much. But it was a fun exercise
working with him. Because he definitely
has this maverick troublemaking perspective. At one point in his
career at Stanford, he realizes the
incredible inefficiency of economics education,
that his students just aren't getting the big ideas
by listening to lectures and reading textbooks. It's just not happening. So he walks out of Stanford,
and sets up his own economics textbook company, which
is anti textbook, right, and essentially
develops a whole set of modules of learning by
doing exercises and problem sets and online elements. And so you don't get
a textbook, yeah. You get something
with a textbook. But you get all kinds
of online pieces that you do constantly
in getting through this. So he completely blew up
the whole economics textbook industry and has forced an
absolute fundamental reform. And then he went out and
proved, yes, my new system here works better than
Paul Samuelson's and everybody else's textbooks. Because people are
absorbing the core ideas and be able to work
with it much more efficiently. It's a whole learning by doing
problem based learning set that he brings in to economics. So you know, he just
walks out of his economics job at Stanford and does
this for four years. And now he's on the
latest of his exercises, which is, he went
to NYU a few years ago to work on sort of cluster
development theory in cities and metropolitan
areas and growth. But then he left that. So he's now chief economist
for the World Bank. And look out, right? Because there's going to
be trouble ahead here. He's going to change that
place, I'm confident. But let's talk about the ideas
that he brings to our problem. AUDIENCE: When did
he switch over, Bill? [INAUDIBLE]? WILLIAM BONVILLIAN:
This past summer. I think that's when he started. I haven't seen him
since he started. But sure enough,
one thing he does, he writes this attack on
economists saying they're completely in love with, you
know, metrics and mathematics. And it's not going
to get them anywhere. These systems are too complex. And it's just an assault
on the profession. Because this is a profession,
like all professions. It doesn't attack itself, right? It's not polite, right? So Romer just lifts
the veil of trouble. It's quite a piece. It hasn't formally
been published. I'm not sure any journal
dares publish it. But it's definitely
circulating around. I'll try and find it for you. Because it's a fun read. It's really something. Anyway, that's Romer. And Romer is on this project. His famous 1990
piece-- which, frankly, probably should win him the
Nobel Prize in economics, but he's such a troublemaker
that it probably won't-- is called Endogenous
technological change. So he's trying to reverse
Solow's exaggerate exogenous change
and make it go back, get these ideas
of economic growth back into an economics
thinking framework. That's his project here, right? And he starts on this pathway. I mean, let me just
summarize the basic points. He agrees with
Solow that growth is driven by technological change. And then he argues
that that in turn is driven by researchers, who
he describes as economic agents, profit maximizing agents. And then he looks at how
technology isn't really a conventional good, in the
normal sense of economic goods, but has its own set
of unique properties that make it really
quite different. But most important
for us, he looks at what he calls Human
Capital Engaged in Research. In other words,
you don't just have to do R&D to loosely
summarize Solow. You've got to have a
talent base doing that R&D. This whole project is to
take all these concepts and move them into an
endogenous theory of growth. So his growth model
is similar to Solow's. He's picking up on Solow's work. He sees that
technological change is the heart of economic
growth, as we've discussed. He sees that technological
change occurs, in turn, in a large part
because of people responding to market incentives. Again, he's trying to get
this into economic thinking. And this technological
knowledge, which is instructions for
working with materials, is inherently different
from other economic models. So developing a new and
better set of instructions, he argues can be treated as a
fixed cost in economic terms. And it's, therefore, a defining
economics characteristic of integration. So just for an example-- and this article is
richer than this. But I'll just cite a
few examples of what he's trying to think about. A Rival Good is property that,
if used by one person or firm, precludes use by another. And a Non-rival Good
is a kind of property that, if used by one
person or firm, in no way limits the use by the others. So technology, he argues, is
naturally non-rival, right? Because it can be readily
shared and adopted by others. In other words, once
you see this thing and understand how
it works, then you can make your own
version of it, right? But there's obviously-- how
would you get rich off this if it's completely non-rival? So capitalists move to
make it excludable, right, where the owner of the good-- Steve Jobs and his crew-- try to prevent
others from using it, for example, through trade
secrets or patents, right? So technology can be made
partially excludable. But in the end, technology
has these, remember this term, spillover features--
knowledge spillover features-- that make it pretty accessible. Because you get to
see the technology. And you can derive ideas
about how it's put together. So technology is unlike any
other kind of economic good. Because it can be
both excludable and non-excludable, rival
and non rival, right? It's not like anything else. It's not like owning
a farm, right? AUDIENCE: [INAUDIBLE] WILLIAM BONVILLIAN: And you are? AUDIENCE: Max. WILLIAM BONVILLIAN: Max, good. AUDIENCE: This is saying
that technology just really specific technology or
technology in general? WILLIAM BONVILLIAN:
Technology, in general. So he's developing a
broader way of looking at this whole category. All right. So that's one core idea. Let's try and develop
theories of economics by which we can better
describe what technology is, since it's so key to growth. But his really
important contribution is around the role
of human capital. And that's what I want you
to really remember today. How can I explain this? So it's Rome-- whatever it is-- 200, right? It's Rome. And Romans, they're
terrific engineers. They figured out
these amazing roads. And they stuck them
around all over Europe. And that's a technology advance. And it improves communication
systems and transport. So it's a technological advance
that carries with it some gain. Now the width of the Roman road
is the defining characteristic of the width of
railroads, right? That's the distance
between the rails is defined by the
Roman road distances, interestingly, enough. The Romans had a very primitive
kind of toy steam engine that Roman kids would play with. And it had an axle
and had this little-- you heat it up with a
candle or something. And then this little thing
would spin around, puffing away. Because you know, water
would boil, and so forth. And it would sort
of spin around. Romer would say that the
reason why Romans didn't take the step of putting
rails on their roads and sticking the steam
engines on the rails was that they did not have
enough human capital engaged in research. In other words, they didn't
have a big enough group of people that were
talented and well-educated that were figuring out
how to move this toy steam system to an actual
technology advance, moving through the invention
and innovation stages. So the human capital
engaged in research was the critical
determinant factor about why they didn't put these
pieces together, all right? So it's not just doing
a bunch of research. You've got to have the talent
base doing that research, and that that will be
a determining factor in whether or not
you're going to get to the stage of
technological advance. So you follow me? So again, that phrase is Human
Capital Engaged in Research. If you lack human capital
engaged in research, you get economic stagnation. All right. Let's do another picture. Medieval Europe. Let's face it. There's no human capital
engaged in research-- maybe a few alchemists poking
around in dungeons, all right? That's it. So is their economic
growth in medieval Europe? Not really, right? I mean, they learned something
about castle building. But a lot of it came
from the Romans, anyway. Economic growth
basically consisted of, you know, hiring a motorcycle
gang and putting on our horses and riding over to
the next guy's castle and looting the place. That's how you got gain, right? That was the economic
growth model. It was stealing, right? It was like piracy, right? There was no economic
growth model. So it's not until the
Industrial Revolution that you get a significant
amount of human capital engaged in research that's going
to be able to nurture these technology advances. So we have our second
innovation factor now. The first factor-- Solow. And again, I'm summarizing here. You got to do R&D. Technological
and related innovation, you got to do some R&D. Romer, the second
factor, human capital engaged in research--
kind of the talent base. Behind that R&D system, behind
that technological advance is going to be the talent base. So that gives us two factors. And you guys can now look at any
country or society or region. And you can now say, oh, I
can start to look at that. I can look at, hey, are
they doing a bunch of R&D? And two, what's their
talent base like, right? Romer developed something that
he calls Prospector Theory. Now that's not in this reading. It's in some of his
somewhat later work. But it's an intriguing idea. And it's very simple
and straightforward. But it'll help you in
making these assessments. So Romer looks at the chemical
engineering industry in Europe in the late 1800s. And he notices that there
are two countries that dominate these early
chemical industries, Germany and Britain. They have the big emerging
chemical industries. And other countries are
doing stuff in this area. But they don't have
anything like what Germany and Britain are up to. So then he looks
behind those industries and finds that both
Germany and Britain have very strong education
systems for chemical engineers. So they are creating
a talent base that enables the
advances that they are making in chemical engineering. So the idea is you've got
a field of prospectors. So let's take it back to
the origin of the idea. So it's the
California Gold Rush. And it's 1848. It's not 1849, yet. It's 1848. And you've got
five or six people hanging around the
Sacramento River, occasionally sticking
a pan into the river. All right. Then a year later, 1849,
you've got 250,000 people sticking pans into
the Sacramento River and every traceable,
you know, part of that river system, up every creek. What do you think? You find more gold
with 250,000 people? You bet. A staggering amount
of gold gets found. That's Prospector Theory. And it's a little more
sophisticated than that. But you're going
to find more gold if you've got more
prospectors on the problem. Now you have to train
those prospectors. It can't be totally
amateur hour. It's much better
if you have trained sophisticated prospectors,
like Britain and Germany did, with the chemical
engineering sector. So you need that human capital
engaged in the research system. If the human capital
is driving cabs, it doesn't do you any good. You've got to have that
human capital engaged in that system of research. So you can then, now, make
a further extrapolation on what Romer's
thinking about, right? You have a way, now, of looking
at fundamental strengths of an innovation system. It's an R&D system and
the talent in that system. Questions. AUDIENCE: To what
extent does his theories include questions of inclusion? WILLIAM BONVILLIAN: Really
good and timely point. So if Romer is
right and you want a large number of
well-trained prospectors, and you're fencing
off significant parts of your society from
that prospector pool, you're doing something
really stupid, right? It's just fundamental as that. That's how simple
and straightforward prospector theory is. That's why what's been happening
over the last few weeks makes me so nervous. Because the US developed
an innovation system that worked on
encouraging talent from everywhere to come here. And it's a huge
innovation advantage. It's huge. In other words, if you're
running an innovation system and sucking in talent from
everywhere to help field it, to help staff it, that's
really interesting compared to running
an innovation system where you put fences
around your borders. Not very smart. That's why 400 tech companies
that joined the amicus brief in the case that's
being argued today. Because they understand this. So there is a huge inclusion
piece to this fundamental idea. Rasheed. AUDIENCE: Is there too many
cooks in the kitchen, upper bound? WILLIAM BONVILLIAN: Yeah. I don't think we're there yet. It's always an
important question. Does a society have too much
innovation going on, right? And I think we're not
remotely close to that. I don't think we're
close to that, right? In other words, if technological
and related innovation is the key driver
for growth, and we've got 2% growth at the moment,
maybe improving the innovation system might be a way to
improve our growth rate. Therefore, one way
of looking at that is to get more prospectors
on the problem, right? So I mean, that's a
simple-minded idea. But the prospector theory
is pretty straightforward. AUDIENCE: Out of curiosity,
what would it look like, suppose we did have too many
like he was saying, too many cooks [INAUDIBLE] WILLIAM BONVILLIAN: I
think we're just so far away from this that it's not
really worthy of thought. I mean, look. Periodically, in the
science community there's panic about,
heavens, we've trained too many
physicists, right? They're only going to be
able to drive cabs, right? That we're in an
oversupply situation. And every once in
a while, you get cycles that, frankly,
largely resemble the business cycles that tell us
that we may be at those stages. And panic hits the
science community that we're training
too many numbers. But if we go back
and think about what we've been learning
here, which is that technological and
related innovation is a dynamic factor in your
society and your economy, it is core to the way
in which you grow, then the talent base is
a dynamic factor, too. And you don't want to
restrict the dynamic factor. Because you're going to limit
your dynamism, all right? Don't shut down dynamic factors. Led them thrive. The problem science
has, it's not able to extrapolate to a larger
use of its talent base, right? Just because there may not be
enough jobs in formal academia for all of the scientists
who are training doesn't mean that there aren't
really important functions and roles they can
play elsewhere, right? So 2/3 of US scientists
and engineers are employed by manufacturing
industries, right? That's where actually
most of the jobs are, not in the academy. And yet the scientific
community views it as, oh, it's the academy. And that's where the
oversupply problem is. But maybe the key here is
equipping that talent base with a whole new
set of tools that enable them to play a broader
role than simply supporting the academy, right? Maybe that would be a smarter
talent based training idea. Maybe you give them
entrepreneurship classes, so they can think about
starting their own startups. Maybe they get business minors,
so they can do startup stuff. Maybe they get other
kinds of training that gives them additional
skill sets in addition to kind of classical
research skill sets. I think that would probably be
a better answer than attempting to restrict the supply. The historical US
growth rate is 3%. That's starting to
look pretty good. Because we haven't been
there for quite some time. It makes a big difference. You all probably don't
remember this too well. But the dynamic quality and feel
of the US economy in the 1990s was very different. The sense of opportunity that
was there was very powerful. And it was pretty pervasive
throughout society. And if technological and related
innovation drives growth, and it's key to a higher
growth rate, than the task is, not how do you shut
that down, but how do you get back to a higher
level growth rate, I'd argue. AUDIENCE: My concern
would just be, like, not too many cooks in the
kitchen, but too many empty kitchens. Because debatably, the
most problems today are energy, water, [INAUDIBLE]. But most people right now, 60%
of the people studying at MIT study computer science. I'm pretty sure, like, energy,
nuclear [INAUDIBLE] It's just like, you know, there's
a lot of empty kitchens that are the most
important problems. But also, from the
capitalist perspective, it's like, you want a monopoly. So it's the place
you want to be. If you discover fusion,
[INAUDIBLE] $38 trillion dollar market for energy. Most likely it wouldn't
capture the whole market. It's too hard. So you want to go the
path of least resistance. But ideally, they get like
$10 trillion, $2 trillion [INAUDIBLE] So and
the next trillion will probably be, like, space
money or something like that. WILLIAM BONVILLIAN: Yeah. I mean, that's an
interesting idea, right? And I do think that we do
have too many empty kitchens, at this point, that need
staffing up, frankly. They need a path to growth. And getting to fourth generation
nuclear power, I mean, that's an absolute core
climate strategy, in my view. But I think it's increasingly
widespread at MIT, right? And we're not staffing
that revolution up. In Romer's terms, we're not
putting enough prospectors on the problem. It's increasingly
hard to organize startups to do something other
than software and biotech. Because venture capital
is just doing too well with those models. And look there's nothing wrong
with software and biotech. it's important stuff. So there's nothing wrong
with what we're doing. But we are not funding
other technology sectors. So we don't have a broad based
approach on technology advance. See your metaphor, Martine,
of some empty kitchens-- we're emptying our
kitchens on quote, "hard technologies," where you
have to manufacture something. Because the scale up process is
much harder for that financing system to muster than
scaling up a software startup where its infrastructure
is in the cloud and doesn't cost
anything, and can scale up very quickly with very
limited amounts of capital. It's much more complicated
to stand up and do energy technology. It's a longer term project. And it's a more expensive
scale a process. So we're not doing those. So we've got GAP. And we'll talk about
this in the next class, In the innovation system. It's yielding a bunch of empty
kitchens around our economy, whereas I'd argue what we want
to do is fill those kitchens up but they've got to have
a pathway to success as part of that. [INAUDIBLE], you follow me? Does that add up? Is that what you're after? AUDIENCE: Yeah. WILLIAM BONVILLIAN: OK. AUDIENCE: [INAUDIBLE] WILLIAM BONVILLIAN: Tara, right? AUDIENCE: Tara, yeah. I feel like it kind of plays
into this innovation wave idea, where people sort of flock
and leave different kitchens to go to the one kitchen. And then, while there's
all these empty kitchens [INAUDIBLE]. WILLIAM BONVILLIAN: Right. And, look. If we could get
energy onto the wave, it would make all
the difference. We could do it, all right? So if we could manage to
get it into this phase-- and we're not, we're still
in the build up phase-- but if he could get here, that's
gets to be really interesting. AUDIENCE: You mentioned
that first phase is, like, between 40 and 50 years . At least for fusion, it's
been like [INAUDIBLE].. WILLIAM BONVILLIAN: Yeah, Max. I follow fusion a lot. Because MIT has been
so involved in this. So that's been one of my more
fun adventures in recent years is working with your fusion
team on some of these issues. But you're right. The technological advances
that could actually scale this thing, actually now,
I think, for the first time, appear in sight,
become plausible. Still a long term project. But I think we're a lot closer
than we were a decade ago. AUDIENCE: [INAUDIBLE] is because
I took your science policy bootcamp, not this IP,
but the previous one. And I was disappointed in
the lack of consideration for cultural values in promoting
R&D in science and technology. And I've had some
conversations like this. And I'm always sort of prone
to think about the values that we promote as a society,
both in the United States and across the world, in the
societies that they propagate, and how those impact, I guess,
the projects that are funded are not [INAUDIBLE] in basic
theories of development, for instance development. There's a limited
number of resources that any organization can apply
to, if they're public, private, etc. And what we, as a society, value
and how we brand that value and make it appealing
and interesting to the regular consumer
is of great concern to me, especially, because
science is not something that's accessible
or exciting to most people. WILLIAM BONVILLIAN: Yeah. [INAUDIBLE],, you're
right on a whole series of big important
problems that we need to keep bringing into
this class discussion. And creating a
culture around science and creating culture around
technology and development, a culture around
entrepreneurship, and a culture around
startups, those turn out to be fairly key. It's hard for
economics that's trying to do mathematical
modeling to capture these cultural,
historical issues. But they're there. And you know, part of what
makes innovation systems theory so interesting is just the
sheer complexity of that system and the number of
variables operating there. And you're right on
an important point. Understanding the historical
context, for example, turns out to be pretty key when
you look at innovation systems in different regions
of the world, right? Different regions have different
ways of organizing innovation systems. The innovation system that
got organized in Japan, coming out of the
Second World War is a very different innovation
system than the US organized. And we'll try and talk about
some of that, actually, in the next class. But keep bringing us back
to these cultural factors. Thank you. Martha, you've got a question. AUDIENCE: I'm just
going to briefly say... It's not really a question but
politics and leadership really plays a big role here. And why haven't wenot to
dwell on energy too much been able to pull up a
Manhattan Project for energy or the [INAUDIBLE] getting
somebody to the moon. I mean, those were
clearly having the leader of the
free world stand up and say, this is a priority. WILLIAM BONVILLIAN: It would
have been useful, right? AUDIENCE: [INAUDIBLE] WILLIAM BONVILLIAN: Right. And I mean, in a society,
there in the end, there is no substitute
for leadership, right? You really need a big
dose of that as well. And we will talk more
about innovation systems and how can you nurture
change agents, particularly, for innovation in these legacy
established complex sectors, like energy, helping you
introduce change agents. And what tool sets can they
use to actually drive change. So we'll talk about
that, particularly, in the energy class. But it's relevant
to many others.
