˲ A collaborative deliberation: The global brain can also be used to enact decision processes where people and software systems select issues to consider, enumerate and critique solution alternatives, and then choose some subset of these solutions. In this context, programming can be viewed as defining the rules for identifying issues and enumerating, critiquing, and selecting solutions.

˲ A radically fluid virtual organization: Sometimes it is useful to view the global brain as a collection of evanescent virtual organizations, which rapidly coalesce, perform, and disband in light-speed open markets. In this context, programming includes identifying the task requirements and selecting among the organizations that are offering to perform the task. Interesting examples of this idea include odesk.com and elance.com.

˲ A multi-user game: Many tasks can be presented as a multi-user game, where useful outcomes are achieved as a result, sometimes unintentional, of playing the game. In this context, programming consists of specifying the rules and incentives for game play. Interesting examples of this include fold.it and the Google Image Labeller.

Making such global brain programming metaphors a reality will, in turn, require progress along several fronts:

Creating “social operating systems.” An operating system, in the context of a single computer, manages the allocation of hardware and software resources such as memory, CPU time, disk space, and input/output devices. A social operating system, in addition to doing all these things, will also have to manage the mustering and allocation of human resources to tasks. This will require fast, robust infrastructures for contracts, payments, or other moti-

making global brain
metaphors a reality
will require progress
along several fronts.

vational elements, as well as scalable task-to-resource matchmaking such as markets. These will be challenging problems because people (unlike hardware resources) are diverse in all the ways we have described. But providing easy-to-use solutions for the problems of finding and motivating human participants—rather than requiring each system developer to solve this problem individually—will greatly facilitate programming the global brain.

Defining new programming languages. Conventional programming languages are, out of necessity, fully prescriptive, describing the algorithms to be executed in exhaustive detail. Such languages are often not a good match, however, for specifying tasks with human participants. The programming languages for the global brain will, therefore, need to support a “specificity frontier” of varying degrees of detail in task definition. 2 One end of this frontier involves defining programs that allocate highly specific micro-tasks to people and link them into larger workflows. In the middle ground, we may use constraint-based programs, which specify (for example, in a game setting) the goals as well as the limits on how they can be achieved, but not how they should be achieved. At the far end of the specificity frontier, programming may be limited to simply stating incomplete goal specifications. Additionally, we need to expand the range of abstractions such programming languages offer. While traditional programming languages incorporate constructs such as loops and recursion, a global brain programming language may also need to incorporate abstractions such as group decision processes, contests, and collaborative steps. 1, 9

Promulgating new software engi-
neering skills. Programmers will need
to develop new mind-sets about, for
example, such basic concepts as what
a “program” is and what “testing” and
“debugging” mean. They will need to
become not just software architects
and algorithm implementers, but also
organizational or even societal archi-
tects able to think systematically about
things like motivations, coalitions,
emergence, and so on. Perhaps most
fundamentally, they will need to transi-
tion from a purely “command-and-con-
trol” perspective for organizing people
to one oriented around cultivating and
coordinating7 societies made up of
many diverse independent players.

A Call to Arms

We have attempted to identify some of the key challenges, opportunities, and strategies involved in programming the emerging global brain. Learning to do this well is, perhaps, even more urgent than many people realize. Our world is faced with both existential threats of unprecedented seriousness (such as the environment) and huge opportunities (such as for scientific and social progress). We believe that our ability to face the threats and opportunities of the coming century will be profoundly affected by how well, and soon, we can master the art of programming our planet’s emerging global brain.

 

References

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2. bernstein, a. how can cooperative work tools support dynamic group processes? bridging the specificity frontier. in Proceedings of the International Conference on Computer Supported Cooperative Work (cSc W 2000) 2000, acM.

3. doan, a., ramakrishnan, r., and halevy, a. crowdsourcing systems on the World-Wide Web. Commun. ACM 54, 4 (apr. 2011), 86–96.

4. hand, e. citizen science: People power. Nature 466 (2010), 685–687.

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7. Malone, t. W. The Future of Work. harvard business School Press, boston, Ma, 2004.

8. Malone, t. W., laubacher, r., and dellarocas, c. the collective intelligence genome. Sloan Management Review 51, 3 (Spring 2010), 21–31.

9. Minder, P. and bernstein, a. crowdlang—first steps towards programmable human computers for general computation. in Proceedings of the 3rd Human Computation Workshop (HCOMP 2011), aaai-Press.

10. Quinn, a.J. and bederson, b.b. human computation: a survey and taxonomy of a growing field. in Proceedings of CHI 2011, (Vancouver, bc, canada, May 7–12, 2011).

11. Shirky, c. Cognitive Surplus: Creativity and Generosity in a Connected Age. Penguin Press, new york, 2010.

12. von ahn, l. et al. recaPtcha: human-based character recognition via Web security measures. Science 321 (2008), 1465–1468.

Abraham Bernstein ( [e-mail]) is a professor at the University of Zurich’s department of informatics and heads the dynamic and distributed information Systems group. Mark Klein ( [e-mail]) is a principal research scientist at the Mit center for collective intelligence in cambridge, Ma. Thomas W. Malone ( [e-mail]) is the director of the Mit center for collective intelligence and the Patrick J. Mcgovern Professor of Management at the Mit Sloan School of Management in cambridge, Ma.

copyright held by author.