from systems where individuals perform simple micro-tasks (http://mturk. com) to where they compete to solve complex engineering problems (http:// innocentive.com). Some systems harness the “wisdom of crowds” in contexts that range from “citizen science” 4 ( http://fold.it, http://galaxyzoo.org) to predicting box office performance ( http://hsx.com). Open idea ecologies (where crowds of people share, recombine, and refine each other’s creative outputs) have produced remarkable results for everything from videos ( http://youtube.com) and encyclopedias ( http://wikipedia.org) to software (Linux). Systems have been developed that look for individual task-focused “geniuses” ( http://marketocracy.com) or, conversely, datamine the activity traces of millions of Internet users (Google’s search engine). While these systems cover an enormous range of approaches, it has become clear from these experiences that programming the global brain is different from programming traditional computers in some fundamental ways. Some of the most important differences include:

˲ Motivational diversity: People, unlike current computational systems, are self-interested and therefore require appropriate incentives— anything from money, fame, and fun to altruism and community—to perform tasks. These incentives have to be carefully designed, moreover, to avoid people gaming the system or causing outright damage. In some cases, one may even use their motivation to do one task to accomplish another, as in reCAPTCHA, where people OCR documents as a side effect of passing a human versus bot test. 12

˲ Cognitive diversity: In most computer systems we deal with a limited range of diversity—in terms of memory, speed, and device access. People, by contrast, vary across many dimensions in the kinds of tasks they can do well, and their individual strengths are only incompletely understood at best. This implies qualitative differences in how (and how well) we can expect to match tasks and resources in a global brain context.

˲ Error diversity: With traditional computers, we worry much more about outright failure than other kinds of errors. And the other errors are usu-

Because people
are involved,
programming the
global brain is
deeply different
from programming
traditional computers.

ally highly deterministic and limited in diversity because a relatively small range of software is typically replicated across millions of computers. People, by contrast, are prone to a bewildering and inconsistent variety of idiosyncratic deviations from rational and accurate performance. The global brain, therefore, calls for a radically more capable quality assurance oriented toward the particular kinds of errors that occur with human participants. Fortunately, the global brain also provides access, at least currently, to a huge human “cognitive surplus,” 11 so that, for instance, quality mechanisms based on previously unthinkable levels of redundancy have become practical. 10

These attributes lead, in turn, to the possibility of new, and potentially troubling, forms of emergence. Crowds of people, when engaged in solving interdependent problems, can evince emergent behaviors that range from groupthink (where decision-makers converge prematurely on a small subset of the solution space) to balkaniza-tion (where decision-makers divide into intransigent competing cliques) to chaotic dynamics (for example, stock market bubbles and crashes). While emergence is, of course, not unique to the global brain, it is probably made much more challenging by the unprecedented combination of microsecond computer and communications speeds, globe-scale interdepen-dencies, and human diversity.

the Need for New
Programming metaphors
How, then, can we effectively program
a global brain, characterized as it is by
unique challenges (and opportunities)?
We believe a fundamental requirement
is developing powerful new program-
ming metaphors that more accurately
reflect the ways people and computers
can work together in the global brain.
For instance, today’s innovative collec-
tive intelligence systems embody a set
of common design patterns, 8 includ-
ing collections (where people create
independent items, such as YouTube
videos), collaborations (where people
create interdependent items, such as
Linux modules), and various kinds of
group and individual decisions (such as
voting, averaging, social networks, and
markets). These design patterns, in
turn, can be embodied in various pro-
gramming metaphors, such as: