Abstract
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.
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Notes
- 1.
Dunbar’s number (Sutcliffe et al. 2012), represents a cognitive limit to the number of social relationships that can be maintained, and has been proposed to have a value between 100 and 230 (Hernando et al. 2009). However, these values may not generalize to non-social relationships or social relationships in constrained environments.
- 2.
As an interesting digression, this degree of variance in neuron size across species does explain how elephant brains can be twice as large as human brains but contain only a quarter as many neurons.
- 3.
It turns out to be even a bit more complicated than that, as it has also been observed that proteomics may be a factor in intelligence. That is, the number and complexity of proteins in the synapse (the junction between neurons) tends to be greater in more intelligence species (Emes et al. 2008).
- 4.
Due to the existence of different types of neuronal connections (e.g., inhibitory and excitatory), this characterization of the underlying activation dynamics may not be entirely accurate, but the effective outcome still holds.
- 5.
Details concerning behavioral profiles and associated heuristics, as well as other game mechanics and design elements will be reported in a forthcoming paper that is geared more specifically experimentation platform, the empirical methods, and the repeatability of reported techniques. The present exposition is intended primarily to exemplify the core theoretical concepts and summarize key results.
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Acknowledgments
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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Michelucci, P. (2013). Organismic Computing. In: Michelucci, P. (eds) Handbook of Human Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8806-4_36
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