IEEE Technology and Society Magazine - Fall 2014 - 58

Balloon, but managed to identify
only three out of the five targets.

together on solving a given
problem). Human collectives
are "super-additive" examples
of systems where the whole
(in terms of ability to carry out
a task or solve a problem) is

Limitations and Challenges
After the non-exhaustive list of
examples described above, one may

While the notion of social collective
intelligence looks appealing at
an abstract level, the engineering
problem of how to effectively build
such systems is a daunting problem.
wonder whether there are still fundamental challenges in the design and
operation of such type of systems.
The answer is, yes indeed there are.
An in-depth analysis, carried out by
the authors, highlighted the fact that
current systems employing humans
as "computational units" are rather
primitive in five dimensions:
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58

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Social structure and dynamics: most of the aforementioned
examples deal with single
individuals. Only a few (e.g.,
the DARPA Red Balloon winning solution) actually leverage social dynamics (e.g., peer
pressure) from a computational perspective. This leaves
plenty of room for building
more effective systems, able to
efficiently leverage their deep
embedding in the social fabric.
Compositionality: in the examples above it is decided at design
time which tasks shall be carried
out by humans and which ones
by computers. It remains an interesting philosophical and technological question whether we can
design systems where humans
and machines can carry out transparently (if not interchangeably)
computational tasks.
Collectiveness: most existing solutions fail to leverage the power of collectives
(e.g., teams of people working

■■

■■

more than the sum of its constituencies, and collectives can
manage to successfully complete activities that cannot be
divided and assigned to single
individuals.
Workflows: if we look at the
workflow underpinning the
computation carried out by the
SCI system, in most cases it is
based on a rather basic (in most
cases automated) aggregation of
inputs provided by humans. This
is in line with the little attention
paid to the usage of SCI for fostering collective action.
Generality: all the solutions
presented above have been
built in an ad hoc fashion,
in many cases around some
domain-specific features that
make their design poorly extensible and scalable. We are still
lacking a principled approach
on how to design, manage, and
control SCI systems.

A Computational Perspective
Now, let us take one step back and
look at SCI systems from a computational perspective.
Since social computation is, at
some level, still computation, we
still have to worry about conventional computational properties
when shifting to a social context.
These properties, however, change
in nature as we make the shift.

Correctness requires social measures: whether or not the algorithm
gives the correct result is determined
by the aggregation of experience and
capabilities of people engaging in
the exercise. Completeness requires
social judgement: since social algorithms begin as incomplete problem
statements and change via interaction with the population, there is
not necessarily a specific point at
which we can claim that we have a
complete execution. The scalability
of the algorithm implemented by
a program requires social engagement, which requires (at least within
a sufficiently constrained context)
the existence of some self-reinforcing system of feedback such that the
incentive for an individual to engage
with the algorithm increases as more
individuals participate.
In purely algorithmic terms, the
key challenge clearly arises from
the human factor. Humans present
a high level of diversity, are very
sensitive to context, and express
a multidimensional value system,
which makes it extremely difficult
to predict their behavior a priori.
And this is the key issue to tackle
in order to overcome the limitations
and challenges outlined above.
So how can we "program" SCI?
We actually believe that the question
should be reframed, moving from
the idea of "programming" to the
concept of "incentives." The idea is
to shift the focus from obtaining a
deterministic or predictable behavior,
to focusing on the design and deployment of appropriate strategies for
obtaining a "good enough" behavior.
An incentive is, roughly speaking, something that motivates people
to perform a given action. Incentive
design represents a well-established
field in social sciences, but a relatively
new one in computing sciences [13].
Roughly speaking, we can divide
incentives into two categories: monetary (where a financial reward is provided), or non-monetary, which make
use of social dynamics (e.g., peer
pressure, sense of belonging/community), personal beliefs (activism),

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