=Paper=
{{Paper
|id=None
|storemode=property
|title=An Analysis of the use of Cognitive Surplus in Disaster Relief Scenarios
|pdfUrl=https://ceur-ws.org/Vol-953/paper2.pdf
|volume=Vol-953
}}
==An Analysis of the use of Cognitive Surplus in Disaster Relief Scenarios==
https://ceur-ws.org/Vol-953/paper2.pdf
An Analysis of the use of Cognitive Surplus in Disaster
Relief Scenarios
Mark Roddy1
1
Telecommunications Software and Systems Group (TSSG), Waterford Institute of Technology,
Waterford, Ireland, mroddy@tssg.org
Abstract. In an increasingly connected world, can the cognitive surplus of the
online community be effectively harnessed to help in the assistance of
managing global disasters? Does this community even want to assist with
disaster relief? The relief experts on the ground are continually being
confronted with life and death scenarios, so how can they trust the veracity of
any assistance provided by the online community? By providing examples of
existing disaster management systems that have successfully leveraged the
online community to assist in disaster relief, this paper suggests that online
philanthropy exists, albeit this assistance does need to be manually verified.
The paper goes on to use the results from an online survey to hypothesize a
collective intelligence model for trusting this assistance. The potential impact of
this could be to reduce the burden that the disaster relief teams have to exert in
order to verify and validate this assistance.
1 Introduction
On the 26th December 2004 an earthquake in the Indian Ocean resulted in one of the
most destructive tsunamis ever to hit the islands of Indonesia. Within the first hours of
this tragic event some 150,000 people had died or were declared missing, and millions
were left homeless. Emergency services were fully stretched in trying to come to the
aid of the victims.
1.1 Objectives
The objective of this paper is to suggest to the reader a model for a next generation
disaster management system, which would be used to help alleviate the suffering of
future disaster victims.
The key objectives are to provide:
- Examples of the state of the art for disaster management systems
- Recommendations for the design of future disaster management systems
2 Cognitive Surplus and the Wisdom of Crowds
Shirky (2010) describes cognitive surplus as people’s free time and offers insights
into how this might be leveraged to impact changes around the globe. This free time
is separate from people’s work time, where the expectation from the former is not
necessarily market driven - people do not expect to be paid for any activity they are
engaged in during their free time.
The social scientist Dan Ariely (2008) explores this further - he discusses a scenario
of a Thanksgiving dinner where the son-in-law stands up at the end of the meal and
offers his mother-in-law payment for the services rendered, it was an artificial
scenario but served to highlight the dichotomy between free time and work time -
people in their free time do things for free, while people in their work time do things
for payment.
But the question still exists - how to harness this cognitive surplus and in particular
how can it be leveraged in disaster relief scenarios?
Watching television is an activity usually carried out in our free time, and Shirky
(2010, pp.9-10) writes, “imagine treating the free time of the world’s educated
citizenry as an aggregate, a kind of ‘cognitive surplus’”. Shirky uses the creation of
Wikipedia as a model to measure how big this surplus might be and estimates that the
creation of Wikipedia represents “something like one hundred million hours of human
thought”. He compares this to watching television, which in the US alone is about two
hundred billion hours every year, which is roughly equivalent to two thousand
Wikipedia projects every year from cognitive surplus.
Through the introduction of innovative online networking technologies it could be
possible to transition the passive usage of our cognitive surplus (e.g. watching
television) to more active engagement to help and support those in need.
The hit television game-show “Who Wants To Be A Millionaire?” asks contestants to
answer a question from four possible answers. If the contestant is unable to answer
the question they are able to rely on three lifelines: ‘Fifty-Fifty’, ‘Phone a Friend’, or
‘Ask the Audience’. An interesting statistic1 is that the ‘Ask the Audience’ lifeline has
a 95% success rate.
Why is this? It is an example of a phenomenon known as wisdom of the crowd.
Surowiecki (2004, p.70) cites, “The idea of wisdom of crowds also takes
decentralisation as a given and a good, since it implies that if you set a crowd of self
interested, independent people to work in a decentralised way on the same problem,
instead of trying to direct their efforts from the top down, their collective solution is
likely to be better than any other solution you could come up with”.
1
http://en.wikipedia.org/wiki/Who_Wants_to_Be_a_Millionaire%3F “Who Wants To Be A
Millionaire?”
Wisdom of crowds resonates with the cognitive surplus ideas. On the one hand there
is the potential to leverage the online communities’ cognitive surplus to assist in
disaster relief and on the other hand there is the ability to aggregate the crowd’s
(taken here to mean the online community) responses to arrive at the correct result.
Combining these concepts strongly suggests that a collective intelligence model might
exist that further increases trustworthiness and information veracity, which will be
discussed later in this paper.
3 Disaster Management Systems
This section provides some best in class examples of organisations (all voluntary) that
are using online tools to assist in the relief of disaster management scenarios. Some of
these organisations use collaborative cognitive surplus to provide online support back
into the disaster zone.
3.1 Ushahidi
Ushahidi2 is a not for profit organisation “that specializes in developing free and open
source software for information collection, visualisation and interactive mapping”.
Ushahidi was a response to the violence in the aftermath of the controversial Kenyan
elections of 2008.
Ushahidi started as a collaborative website set up by a group of Kenyan journalists
and was used to aggregate and map the reports of these violent events. It was seen as
an extremely powerful communication tool, and with over 45,000 users was the
catalyst for the design and development of today’s platform. The platform was
successfully used in many recent disasters, including as a relief response tool for the
Haiti earthquake, when it was used by online volunteers to create a visual crisis map
of the disaster zone, by clustering data mined tweets emanating from the disaster site.3
The volunteers then used Skype to relay the cluster details of their map back to relief
teams.
3.2 The Sahana Software Foundation
The Sahana Software Foundation, established in 2009, is another not for profit
organisation whose mission “is to help alleviate human suffering by giving
emergency managers, disaster response professionals and communities access to the
information that they need to better prepare for and respond to disasters through the
development and promotion of free and open source software and open standards”.
2 http://ushahidi.com/about-us “The Ushahidi Project”
3
http://usatoday30.usatoday.com/tech/news/2011-04-11-japan-social-media_N.htm “USA
Today”
Sahana originated in Sri Lanka as a response to the Indian Ocean tsunami disaster in
2005.4
The platform has had numerous deployments, including the 2011 earthquake in New
Zealand where it was used to help as a people locator.5
3.3 Crisis Commons
CrisisCommons6 is another example of a voluntary collaborative online community,
whose aim is to support the management of disaster and crisis relief. The community
emerged from so-called CrisisCamps, which are modelled on the
BarCamp/CodeCamp7 concept, to “connect a global network of volunteers who use
creative problem solving and open technologies to help people and communities in
times and places of crisis”. They provide an example of a Voluntary Technical
Community (VTC)8 and are supported directly by the US Federal Emergency
Management Agency (FEMA).
This community has also been very active in supporting disaster relief efforts, a
typical example being the collective support of the volunteers during the 2011
earthquake in Turkey where they successfully helped the relief agencies with support
response and recovery efforts.
4 Design Recommendations
The European Union Seventh Framework project, SOCIETIES9 has conducted some
initial evaluations with the European Union’s Civil Protection Mechanism (CPM),
using paper prototyping techniques. The objective of SOCIETIES is to design and
evaluate a next generation mobile platform that integrates existing Social Networking
sites with emerging Pervasive Computing frameworks, so as to create likeminded,
purpose driven communities. The paper prototypes were designed to receive feedback
from the CPM’s disaster experts on their views about using the cognitive surplus of
the online community to aid in the disaster relief. The experts were presented with
sample scenarios that attempted to describe how this online community might be
leveraged in a disaster. For example, one scenario described the disaster team being
4
http://wiki.sahanafoundation.org/doku.php “The Sahana Foundation”
5 https://pl.nlm.nih.gov/christchurch/index.php?mod=inw&act=default “People Locator for the
ChristChurch Earthquake”
6 http://wiki.crisiscommons.org/wiki/Main_Page “Crisis Commons”
7 http://en.wikipedia.org/wiki/BarCamp “Crisis Commons Bar Camp”
8 http://www.emergencymgmt.com/emergency-blogs/campus/Crisis-Commons-Monitors-
Turkey-Earthquake-102311.html “Voluntary Technical Community”
9 http://www.ict-societies.eu/ “FP7 SOCIETIES Project”
confronted by some street signage that they were unable to translate. A digital
photograph of the signage was taken and uploaded to the online community for
translation. Another example asked the volunteers to spot the difference between
satellite images of the disaster zone taken before and after the catastrophe, so roads or
bridges that were destroyed could be identified in advance and alternative routes
coursed. Two key findings10 resulted from this research:
• Trust: how could the experts in the field trust the veracity of the
results that they were receiving back from the online community?
• Automated decision-making: the experts said they would have to be
very wary about handing over life or death decision making to
machines, but were open to experimentation through simulation. They
saw the benefit of automating some of their processes but were
sceptical about where the veracity line would be drawn between
automated services and the traditional manual verification process,
particularly where lives are at stake.
In addition to this an online survey was undertaken in March 2012 (Roddy, 2012) and
the results showed that a strong willingness does exist for a community of online
volunteers to assist with disaster relief, and that this community would be willing to
offer significant amounts of their cognitive surplus to this philanthropic activity. The
survey also showed that this online community would be willing to provide personal
profile information and that they would also be prepared to operate as part of a
community of volunteers.
This is important because it indicates a potential model for establishing diversity. An
assumption can be made here that a diverse community of online volunteers exists,
which is at the heart of Surowiecki’s (2004) premise that diversity in the crowd will
provide more accurate results than an expert.
The next steps would be to prove the above through future experimentation. That
experiment would involve establishing an online user community of volunteers. These
volunteers would provide their profile information at a granularity level that correlates
to diversity; call this a ‘diversity factor’.
In total there are three components to be designed into this platform:
i. Firstly the platform will need to have some process for deciding whether
to send the data to an expert group or a diverse group. This could be
done using a ‘task tagging profile’ and an ontology or semantic
algorithm.
ii. Secondly the platform needs a process that discovers the appropriate list
of diverse volunteers; labelled as a ‘diversity factor’. Again, this could
be done using ontology assessment of the volunteer’s profile tags.
10
http://www.ict-societies.eu/files/2011/11/D8.1_public.pdf “SOCIETIES Paper Trial
Evaluation Report”
iii. Thirdly the platform needs to be able to predict the ‘certainty or
veracity level’ of the results, which is at the heart of Surowiecki’s
‘Wisdom of Crowds’ model. The problem here is to work out how many
volunteer responses are needed to solve just one problem. The platform
is trying to avoid: a) any mistakes being made, and b) volunteers
deliberately providing false responses. By asking ‘x’ amount of
volunteers to work on a problem and aggregating their responses,
increases the veracity of the feedback.
An example is summarised in the message sequence chart below:
Figure 1: Message sequence chart showing the three design components
The chart starts with a help request from the relief team working in the disaster zone.
This could be something like help with parsing through satellite images of the disaster
zone before and after the disaster, and reporting back on the amount of damage that
has been done. So these images are uploaded to the Disaster Management Platform
with a “Help Requested” tag, and a brief description of the profile of the task that they
need help with. In this particular example help is needed parsing the satellite images
for damage.
Using the “Task Profiler” component the platform now needs to figure out whether
this particular help request requires the attention of an expert group or a diverse group
and so sends the task profile to the Recommender System. The Recommender System
parses through the task profile information and because this particular task does not
require any particular skill advises back to the platform that a diverse group rather
than an expert group is required to solve this task.
The platform now sends a request to the Diversity System to supply a diverse list of
volunteers. So what does diverse mean here? The precise design of this component
will be a next step but at a high-level the “Diversity Factor” algorithm will data mine
the profiles of the complete list of volunteers (could be from their online social media
profiles) and present back a subset list that is diverse. Diversity here could include:
• 50% of the list could be women
• The age profiles could be evenly spread
• Their ethnicity could be evenly spread
• The educational profile could be evenly spread
The platform will now send the task to this volunteer list and collate back their
responses. Having aggregated the collated responses, which forms the “Veracity
Level” of the task, the platform forwards the task solution back to the disaster team.
5 Conclusions
This paper has made some recommendations that could aid the design of a collective
intelligence emergency responder tool (this could also be a plug-in to existing
systems, such as the Ushahidi platform). Use cases now need to be defined that list
typical problems encountered in disaster relief, and these use cases would be used as
input to the system design requirements.
The implemented design could be tested in a simulated environment, by setting up an
experiment with actual relief workers and asking them to send their simulated help
requests into the platform.
The experiment would continue by engaging on a real user (the online community)
evaluation that compared the results that used the ‘diversity factor’ with those using
the existing system (i.e. the manual verification process). Another important test will
be to prove whether or not diversity is actually needed at all. This could be tested by
setting up a controlled experiment that tests the use cases with the Recommender
System turned ‘off’ and then repeating this again with it turned ‘on’. The overall
objective here is to conclude that the system provides accurate enough results for the
onsite disaster experts to be able to trust the feedback given, and as such remove the
labour intensive manual verification process, thereby freeing up the valuable
resources of the relief teams in the disaster zones.
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