In the stark, crude mathematics of economics, production depends on
capital and labor; increases in either raises economic output, but at a
decreasing rate. Increases in per capita economic output, or economic
growth, therefore depends on raising the level or quality of capital,
or increasing the quality of labor, or ideally doing both
simultaneously. This basic formula explains why macroeconomic theorists
advise governments and companies alike to boost spending on research
and development and education, with perhaps a nod to targeted venture
capital as an additional qualitative spur to economic growth.
Dig deeper into the causes of growth, however, and this simple
explanation comes up short. No increases in the level or quality of
capital or labor map neatly into the invention of the steamboat, the
car, electricity, the vacuum tube, or the iPod. To understand
innovation, we need nuanced, micro-level models that enable us to
unpack its causes.
Let’s start with what we know. The macroeconomic approach to the
problem of innovation considers innovative ability as an asset. This
construction lies at the core of modern endogenous growth theory, in
which the stock of knowledge—like the level of capital or the amount of
labor—can be influenced by individuals, companies, or policymakers.
Optimal growth paths require balancing investments in innovative
ability with investments in capital and labor.
Not only do economies struggle to achieve this proper balance, so do
ecosystems, species, companies, and people. Yet for all the success of
endogenous growth theory, it still leaves us with the micro-level
question of the source of innovations. Countries cannot just throw
money into an innovation fund and expect to reap dividends. In fact,
constructing organizational and institutional structures that encourage
innovative activity has been one of the most vexing problems for
businesses and countries over the past half century.
To understand innovation, we must focus on diversity as well as
ability. A scan of the intellectual landscape as well as of the
policies of successful companies reveals a tacit understanding of
diversity’s role in innovation. George Mason University professor
Richard Florida’s work on the creative class, The Rise of the Creative Class and The Flight of the Creative Class,
touches on the link between diversity and innovation, as do Yale
University’s Barry Nalebuff and Ian Ayres in their book and
accompanying website Why Not? and whynot.net. Some of the
innovation policies of Toyota Motor Corp. and Google Inc. illustrate a
similar understanding that differences in the composition of their work
forces boosts their bottom lines.
To appreciate the full potential of the power of difference,
however, requires opening up the pumpkins. What we find inside people’s
heads is that people possess ways of seeing problems and
solutions—oftentimes different perspectives depending on the kinds of
people viewing particular problems and solutions. People’s perspectives
are accompanied by ways of searching for solutions to problems,
something scientists call heuristics. When confronted with a problem,
people encode their (often quite different) perspectives and then apply
their particular heuristics to locate new, possibly better, solutions.
A person whom we think of as smart is generally someone who has lots
of interesting perspectives and many effective heuristics. A smart
person performs well, and often innovates, because of the many tools
she possesses. Yet most of these tools won’t work on a given problem,
which is why innovation is 99 percent perspiration. That’s why Edison
once claimed that he knew “a thousand ways not to make a light bulb.”
But how would several dozen Edisons, or several dozen Edisons from
different social, racial and educational backgrounds, approach the
making of a light bulb? To answer that question requires a fuller grasp
of the pitfalls and idiosyncrasies of innovation and the power of
diversity, which in turn requires a slight detour into theory.
First, for any problem there exists a perspective that makes it easy
to grasp a solution, though that may mean waiting for a person as
unique as Edison to come along. Second, across all problems no
perspective or no heuristic is any better than any other. In plain
English, any approach may be just as good as any other until it is
tested.
Third, teams of problem solvers—viewed as bundles of perspectives
and heuristics brought together to solve a particular problem—do better
when the diversity of perspectives and heuristics is greater than the
overall ability or talent of the team’s members. In other words,
diverse teams outperform teams composed of the very best individuals.
Diversity trumps ability.
This last result requires further explanation. A team, a group, or
even an entire society innovates through iterative application of
perspectives and heuristics. Individuals who perform best obviously
possess good perspectives and heuristics (think Edison), yet 30 Edisons
each may have 20 useful heuristics while collectively possessing a mere
25. In contrast, the diverse team’s individual members may on average
only know 15 heuristics apiece but collectively know 40.
When the diverse team applies those diverse heuristics, the effects
can be super-additive. Watson plus Crick were far more impressive than
either in isolation. On a far larger scale, Silicon Valley’s breadth of
bright engineers from different academic disciplines and from almost
every corner of the globe out-innovates other technology hotspots with
equal brainpower but less diversity.
Innovation provides the seeds for economic growth, and for that
innovation to happen depends as much on collective difference as on
aggregate ability. If people think alike then no matter how smart they
are they most likely will get stuck at the same locally optimal
solutions. Finding new and better solutions, innovating, requires
thinking differently. That’s why diversity powers innovation.
