Organismic Computing



Herein we entertain the prospect that engineered approaches to human computation can foster more effective collaborations than are possible today. It is commonly known that adding more people to a group effort eventually produces diminishing returns. Need this be the case? Recent evidence suggests that group efficacy is related less to the individuals in a group and more to the quality of their interactions. Furthermore, each person added to a larger group creates many more possible pairwise relationships than adding a person to a smaller group does. Taken together, this would seem to suggest the opposite of what is observed, that there should be increasing returns when adding people to a group. That there are not implies that the costs associated with adding people to a group accrue faster than the benefits. These considerations compel an amelioration strategy that involves both increasing the value and decreasing the burden of group interactions. Toward that end, a new human computation paradigm is proposed, inspired by the successes of natural systems. This “organismic computing” approach seeks to improve collaboration efficacy via the affordances of shared sensing, collective reasoning, and coordinated action. In addition, a technique involving simulated augmented reality is introduced to enable a pilot study that compares organismic computing to other collaboration methods within a virtual environment. Results from this study point to increasing rather than decreasing returns for larger groups under this new collaboration model.


Group Size Cognitive Load Augmented Reality Cognitive Architecture Jigsaw Puzzle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author wishes to express deep gratitude to Kshanti Greene and Thomas Young of Social Logic Institute for their creative contributions and tireless execution of the present study as well as their helpful feedback on this chapter. The author would also like to acknowledge Geoffrey Bingham for his insightful comments regarding the application of ecological perception to distributed groups. Finally, the author would like to thank James Donlon for his enduring confidence and support of this speculative work. This research was funded under DARPA contract #D11AP00291.


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© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.FairfaxUSA

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