Tag Archives: Rockefeller

Latest Findings on Disaster Resilience: From Burma to California via the Rockefeller Foundation

I’ve long been interested in disaster resilience particularly when considered through the lens of self-organization. To be sure, the capacity to self-organize is an important feature of resilient societies. So what facilitates self-organization? There are several factors, of course, but the two I’m most interested in are social capital and communication technologies. My interest in disaster resilience also explains why one of our Social Innovation Tracks at QCRI is specifically focused on resilience. So I’m always on the lookout for new research on resilience. The purpose of this blog post is to summarize the latest insights.

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This new report (PDF) on Burma assesses the influence of social capital on disaster resilience. More specifically, the report focuses on the influence of bonding, bridging and linking social capital on disaster resilience in remote rural communities in the Ayerwaddy Region of Myanmar. Bonding capital refers to ties that are shared between individuals with common characteristics characteristics such as religion or ethnicity. Bridging capital relates to ties that connect individuals with those outside their immediate communities. These ties could be the result of shared geographical space, for example. Linking capital refers to vertical links between a community and individuals or groups outside said community. The relationship between a village and the government or a donor and recipients, for example.

As the report notes, “a balance of bonding, bridging and linking capitals is important of social and economic stability as well as resilience. It will also play a large role in a community’s ability to reduce their risk of disaster and cope with external shocks as they play a role in resource management, sustainable livelihoods and coping strategies.” In fact, “social capital can be a substitute for a lack of government intervention in disaster planning, early warning and recovery.” The study also notes that “rural communities tend to have stronger social capital due to their geographical distance from government and decision-making structures necessitating them being more self-sufficient.”

Results of the study reveal that villages in the region are “mutually supportive, have strong bonding capital and reasonably strong bridging capital […].” This mutual support “plays a part in reducing vulnerability to disasters in these communities.” Indeed, “the strong bonding capital found in the villages not only mobilizes communities to assist each other in recovering from disasters and building community coping mechanisms, but is also vital for disaster risk reduction and knowledge and information sharing. However, the linking capital of villages is “limited and this is an issue when it comes to coping with larger scale problems such as disasters.”

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Meanwhile, in San Francisco, a low-income neighborhood is  building a culture of disaster preparedness founded on social capital. “No one had to die [during Hurricane Katrina]. No one had to even lose their home. It was all a cascading series of really bad decisions, bad planning, and corrupted social capital,” says Homsey, San Francisco’s director of neighborhood resiliency who spearheads the city’s Neighborhood Empowerment Network (NEN). The Network takes a different approach to disaster preparedness—it is reflective, not prescriptive. The group also works to “strengthen the relationships between governments and the community, nonprofits and other agencies [linking capital]. They make sure those relationships are full of trust and reciprocity between those that want to help and those that need help.” In short, they act as a local Match.com for disaster preparedness and response.

Providence Baptist Church of San Francisco is unusual because unlike most other American churches, this one has a line item for disaster preparedness. Hodge, who administrates the church, takes issue with the government’s disaster plan for San Francisco. “That plan is to evacuate the city. Our plan is to stay in the city. We aren’t going anywhere. We know that if we work together before a major catastrophe, we will be able to work together during a major catastrophe.” This explains why he’s teaming up with the Neighborhood Network (NEN) which will “activate immediately after an event. It will be entirely staffed and managed by the community, for the community. It will be a hyper-local, problem-solving platform where people can come with immediate issues they need collective support for,” such as “evacuations, medical care or water delivery.”

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Their early work has focused on “making plans to protect the neighborhood’s most vulnerable residents: its seniors and the disabled.” Many of these residents have thus received “kits that include a sealable plastic bag to stock with prescription medication, cash, phone numbers for family and friends. They also have door-hangers to help speed up search-and-rescue efforts (above pics).

Lastly, colleagues at the Rockefeller Foundation have just released their long-awaited City Resilience Framework after several months of extensive fieldwork, research and workshops in six cities: Cali, Columbia; Concepción, Chile; New Orleans, USA; Cape Town, South Africa; Surat, India; and Semarang, Indonesia. “The primary purpose of the fieldwork was to understand what contributes to resilience in cities, and how resilience is understood from the perspective of different city stakeholder groups in different contexts. The results are depicted in the graphic below, which figures the 12 categories identified by Rockefeller and team (in yellow).

City Resilience Framework

These 12 categories are important because “one must be able to relate resilience to other properties that one has some means of ascertaining, through observation.” The four categories that I’m most interested in observing are:

Collective identity and mutual support: this is observed as active community engagement, strong social networks and social integration. Sub-indicators include community and civic participation, social relationships and networks, local identity and culture and integrated communities.

Empowered stakeholders: this is underpinned by education for all, and relies on access to up-to-date information and knowledge to enable people and organizations to take appropriate action. Sub-indicators include risk monitoring & alerts and communication between government & citizens.

Reliable communications and mobility: this is enabled by diverse and affordable multi-modal transport systems and information and communication technology (ICT) networks, and contingency planning. Sub-indicators include emergency communication services.

Effective leadership and management: this relates to government, business and civil society and is recognizable in trusted individuals, multi-stakeholder consultation, and evidence-based decision-making. Sub-indicators include emergency capacity and coordination.

How am I interested in observing these drivers of resilience? Via social media. Why? Because that source of information is 1) available in real-time; 2) enables two-way communication; and 3) remains largely unexplored vis-a-vis disaster resilience. Whether or not social media can be used as a reliable proxy to measure resilience is still very much a  research question at this point—meaning more research is required to determine whether social media can indeed serve as a proxy for city resilience.

As noted above, one of our Social Innovation research tracks at QCRI is on resilience. So we’re currently reviewing the list of 32 cities that the Rockefeller Foundation’s 100 Resilient Cities project is partnering with to identify which have a relatively large social media footprint. We’ll then select three cities and begin to explore whether collective identity and mutual support can be captured via the social media activity in each city. In other words, we’ll be applying data science & advanced computing—specifically computational social science—to explore whether digital data can shed light on city resilience. Ultimately, we hope our research will support the Rockefeller Foundation’s next phase in their 100 Resilient Cities project: the development of a Resilient City Index.

Bio

See also:

  • How to Create Resilience Through Big Data [link]
  • Seven Principles for Big Data & Resilience Projects [link]
  • On Technology and Building Resilient Societies [link]
  • Using Social Media to Predict Disaster Resilience [link]
  • Social Media = Social Capital = Disaster Resilience? [link]
  • Does Social Capital Drive Disaster Resilience? [link]
  • Failing Gracefully in Complex Systems: A Note on Resilience [link]
  • Big Data, Lord of the Rings and Disaster Resilience [link]

Seven Principles for Big Data and Resilience Projects

Authored by Kate Crawford, Patrick MeierClaudia PerlichAmy Luers, Gustavo Faleiros and Jer Thorp, 2013 PopTech & Rockefeller Foundation Bellagio Fellows

Update: See also “Big Data, Communities and Ethical Resilience: A Framework for Action” written by the above Fellows and available here (PDF).

Bellagio Fellows

The following is a draft “Code of Conduct” that seeks to provide guidance on best practices for resilience building projects that leverage Big Data and Advanced Computing. These seven core principles serve to guide data projects to ensure they are socially just, encourage local wealth- & skill-creation, require informed consent, and be maintainable over long timeframes. This document is a work in progress, so we very much welcome feedback. Our aim is not to enforce these principles on others but rather to hold ourselves accountable and in the process encourage others to do the same. Initial versions of this draft were written during the 2013 PopTech & Rockefeller Foundation workshop in Bellagio, August 2013.

1. Open Source Data Tools

Wherever possible, data analytics and manipulation tools should be open source, architecture independent and broadly prevalent (R, python, etc.). Open source, hackable tools are generative, and building generative capacity is an important element of resilience. Data tools that are closed prevent end-users from customizing and localizing them freely. This creates dependency on external experts which is a major point of vulnerability. Open source tools generate a large user base and typically have a wider open knowledge base. Open source solutions are also more affordable and by definition more transparent. Open Data Tools should be highly accessible and intuitive to use by non-technical users and those with limited technology access in order to maximize the number of participants who can independently use and analyze Big Data.

2. Transparent Data Infrastructure

Infrastructure for data collection and storage should operate based on transparent standards to maximize the number of users that can interact with the infrastructure. Data infrastructure should strive for built-in documentation, be extensive and provide easy access. Data is only as useful to the data scientist as her/his understanding of its collection is correct. This is critical for projects to be maintained over time, regardless of team membership, otherwise projects will collapse when key members leave. To allow for continuity, the infrastructure has to be transparent and clear to a broad set of analysts – independent of the tools they bring to bear. Solutions such as hadoop, JSON formats and the use of clouds are potentially suitable.

3. Develop and Maintain Local Skills

Make “Data Literacy” more widespread. Leverage local data labor and build on existing skills. The key and most constraint ingredient to effective data solutions remains human skill/knowledge and needs to be retained locally. In doing so, consider cultural issues and language. Catalyze the next generation of data scientists and generate new required skills in the cities where the data is being collected. Provide members of local communities with hands-on experience; people who can draw on local understanding and socio-cultural context. Longevity of Big Data for Resilience projects depends on the continuity of local data science teams that maintain an active knowledge and skills base that can be passed on to other local groups. This means hiring local researchers and data scientists and getting them to build teams of the best established talent, as well as up-and-coming developers and designers. Risks emerge when non-resident companies are asked to spearhead data programs that are connected to local communities. They bring in their own employees, do not foster local talent over the long-term, and extract value from the data and the learning algorithms that are kept by the company rather than the local community.

4. Local Data Ownership

Use Creative Commons and licenses that state that data is not to be used for commercial purposes. The community directly owns the data it generates, along with the learning algorithms (machine learning classifiers) and derivatives. Strong data protection protocols need to be in place to protect identities and personally identifying information. Only the “Principle of Do No Harm” can trump consent, as explicitly stated by the International Committee of the Red Cross’s Data Protection Protocols (ICRC 2013). While the ICRC’s data protection standards are geared towards humanitarian professionals, their core protocols are equally applicable to the use of Big Data in resilience projects. Time limits on how long the data can be used for should be transparently stated. Shorter frameworks should always be preferred, unless there are compelling reasons to do otherwise. People can give consent for how their data might be used in the short to medium term, but after that, the possibilities for data analytics, predictive modelling and de-anonymization will have advanced to a state that cannot at this stage be predicted, let alone consented to.

5. Ethical Data Sharing

Adopt existing data sharing protocols like the ICRC’s (2013). Permission for sharing is essential. How the data will be used should be clearly articulated. An opt in approach should be the preference wherever possible, and the ability for individuals to remove themselves from a data set after it has been collected must always be an option. Projects should always explicitly state which third parties will get access to data, if any, so that it is clear who will be able to access and use the data. Sharing with NGOs, academics and humanitarian agencies should be carefully negotiated, and only shared with for-profit companies when there are clear and urgent reasons to do so. In that case, clear data protection policies must be in place that will bind those third parties in the same way as the initial data gatherers. Transparency here is key: communities should be able to see where their data goes, and a complete list of who has access to it and why.

6. Right Not To Be Sensed

Local communities have a right not to be sensed. Large scale city sensing projects must have a clear framework for how people are able to be involved or choose not to participate. All too often, sensing projects are established without any ethical framework or any commitment to informed consent. It is essential that the collection of any sensitive data, from social and mobile data to video and photographic records of houses, streets and individuals, is done with full public knowledge, community discussion, and the ability to opt out. One proposal is the #NoShare tag. In essence, this principle seeks to place “Data Philanthropy” in the hands of local communities and in particular individuals. Creating clear informed consent mechanisms is a requisite for data philanthropy.

7. Learning from Mistakes

Big Data and Resilience projects need to be open to face, report, and discuss failures. Big Data technology is still very much in a learning phase. Failure and the learning and insights resulting from it should be accepted and appreciated. Without admitting what does not work we are not learning effectively as a community. Quality control and assessment for data-driven solutions is notably harder than comparable efforts in other technology fields. The uncertainty about quality of the solution is created by the uncertainty inherent in data. Even good data scientist are struggling to assess the upside potential of incremental efforts on the quality of a solution. The correct analogy is more one a craft rather a science. Similar to traditional crafts, the most effective way is to excellence is to learn from ones mistakes under the guidance of a mentor with a collective knowledge of experiences of both failure and success.

Data Science for 100 Resilient Cities

The Rockefeller Foundation recently launched a major international initiative called “100 Resilient Cities.” The motivation behind this global project stems from the recognition that cities are facing increasing stresses driven by the unprecedented pace urbanization. More than 75% of people expected to live in cities by 2050. The Foundation is thus rightly concerned: “As natural and man-made shocks and stresses grow in frequency, impact and scale, with the ability to ripple across systems and geographies, cities are largely unprepared to respond to, withstand, and bounce back from disasters” (1).

Resilience is the capacity to self-organize, and smart self-organization requires social capital and robust feedback loops. I’ve discussed these issues and related linkages at lengths in the posts listed below and so shan’t repeat myself here. 

  • How to Create Resilience Through Big Data [link]
  • On Technology and Building Resilient Societies [link]
  • Using Social Media to Predict Disaster Resilience [link]
  • Social Media = Social Capital = Disaster Resilience? [link]
  • Does Social Capital Drive Disaster Resilience? [link]
  • Failing Gracefully in Complex Systems: A Note on Resilience [link]

Instead, I want to make a case for community-driven “tactical resilience” aided (not controlled) by data science. I came across the term “tactical urbanism” whilst at the “The City Resilient” conference co-organized by PopTech & Rockefeller in June. Tactical urbanism refers to small and temporary projects that demonstrate what could be. We also need people-centered tactical resilience initiatives to show small-scale resilience in action and demonstrate what these could mean at scale. Data science can play an important role in formulating and implementing tactical resilience interventions and in demonstrating their resulting impact at various scales.

Ultimately, if tactical resilience projects do not increase local capacity for smart and scalable self-organization, then they may not render cities more resilient. “Smart Cities” should mean “Resilient Neighborhoods” but the former concept takes a mostly top-down approach focused on the physical layer while the latter recognizes the importance of social capital and self-organization at the neighborhood level. “Indeed, neighborhoods have an impact on a surprisingly wide variety of outcomes, including child health, high-school graduation, teen births, adult mortality, social disorder and even IQ scores” (1).

So just like IBM is driving the data science behind their Smart Cities initiatives, I believe Rockefeller’s 100 Resilient Cities grantees would benefit from similar data science support and expertise but at the tactical and neighborhood level. This explains why my team and I plan to launch a Data Science for Resilience Program at the Qatar Foundation’s Computing Research Institute (QCRI). This program will focus on providing data science support to promising “tactical resilience” projects related to Rockefeller’s 100 Resilient Cities initiative.

The initial springboard for these conversations will be the PopTech & Rockefeller Fellows Program on “Community Resilience Through Big Data and Technology”. I’m really honored and excited to have been selected as one of the PopTech and Rockefeller Fellows to explore the intersections of Big Data, Technology and Resilience. As mentioned to the organizers, one of my objectives during this two-week brainstorming session is to produce a joint set of “tactical resilience” project proposals with well articulated research questions. My plan is to select the strongest questions and make them the basis for our initial data science for resilience research at QCRI.

bio

How to Create Resilience Through Big Data

Revised! I have edited this article several dozen times since posting the initial draft. I have also made a number of substantial changes to the flow of the article after discovering new connections, synergies and insights. In addition, I  have greatly benefited from reader feedback as well as the very rich conversa-tions that took place during the PopTech & Rockefeller workshop—a warm thank you to all participants for their important questions and feedback!

Introduction

I’ve been invited by PopTech and the Rockefeller Foundation to give the opening remarks at an upcoming event on interdisciplinary dimensions of resilience, which is  being hosted at Georgetown University. This event is connected to their new program focus on “Creating Resilience Through Big Data.” I’m absolutely de-lighted to be involved and am very much looking forward to the conversations. The purpose of this blog post is to summarize the presentation I intend to give and to solicit feedback from readers. So please feel free to use the comments section below to share your thoughts. My focus is primarily on disaster resilience. Why? Because understanding how to bolster resilience to extreme events will provide insights on how to also manage less extreme events, while the converse may not be true.

Big Data Resilience

terminology

One of the guiding questions for the meeting is this: “How do you understand resilience conceptually at present?” First, discourse matters.  The term resilience is important because it focuses not on us, the development and disaster response community, but rather on local at-risk communities. While “vulnerability” and “fragility” were used in past discourse, these terms focus on the negative and seem to invoke the need for external protection, overlooking the fact that many local coping mechanisms do exist. From the perspective of this top-down approach, international organizations are the rescuers and aid does not arrive until these institutions mobilize.

In contrast, the term resilience suggests radical self-sufficiency, and self-sufficiency implies a degree of autonomy; self-dependence rather than depen-dence on an external entity that may or may not arrive, that may or may not be effective, and that may or may not stay the course. The term “antifragile” just recently introduced by Nassim Taleb also appeals to me. Antifragile sys-tems thrive on disruption. But lets stick with the term resilience as anti-fragility will be the subject of a future blog post, i.e., I first need to finish reading Nassim’s book! I personally subscribe to the following definition of resilience: the capacity for self-organization; and shall expand on this shortly.

(See the Epilogue at the end of this blog post on political versus technical defini-tions of resilience and the role of the so-called “expert”. And keep in mind that poverty, cancer, terrorism etc., are also resilient systems. Hint: we have much to learn from pernicious resilience and the organizational & collective action models that render those systems so resilient. In their book on resilience, Andrew Zolli and Ann Marie Healy note the strong similarities between Al-Qaeda & tuber-culosis, one of which are the two systems’ ability to regulate their metabolism).

Hazards vs Disasters

In the meantime, I first began to study the notion of resilience from the context of complex systems and in particular the field of ecology, which defines resilience as “the capacity of an ecosystem to respond to a perturbation or disturbance by resisting damage and recovering quickly.” Now lets unpack this notion of perturbation. There is a subtle but fundamental difference between disasters (processes) and hazards (events); a distinction that Jean-Jacques Rousseau first articulated in 1755 when Portugal was shaken by an earthquake. In a letter to Voltaire one year later, Rousseau notes that, “nature had not built [process] the houses which collapsed and suggested that Lisbon’s high population density [process] contributed to the toll” (1). In other words, natural events are hazards and exogenous while disas-ters are the result of endogenous social processes. As Rousseau added in his note to Voltaire, “an earthquake occurring in wilderness would not be important to society” (2). That is, a hazard need not turn to disaster since the latter is strictly a product or calculus of social processes (structural violence).

And so, while disasters were traditionally perceived as “sudden and short lived events, there is now a tendency to look upon disasters in African countries in particular, as continuous processes of gradual deterioration and growing vulnerability,” which has important “implications on the way the response to disasters ought to be made” (3). (Strictly speaking, the technical difference between events and processes is one of scale, both temporal and spatial, but that need not distract us here). This shift towards disasters as processes is particularly profound for the creation of resilience, not least through Big Data. To under-stand why requires a basic introduction to complex systems.

complex systems

All complex systems tend to veer towards critical change. This is explained by the process of Self-Organized Criticality (SEO). Over time, non-equilibrium systems with extended degrees of freedom and a high level of nonlinearity become in-creasingly vulnerable to collapse. Social, economic and political systems certainly qualify as complex systems. As my “alma mater” the Santa Fe Institute (SFI) notes, “The archetype of a self-organized critical system is a sand pile. Sand is slowly dropped onto a surface, forming a pile. As the pile grows, avalanches occur which carry sand from the top to the bottom of the pile” (4). That is, the sand pile becomes increasingly unstable over time.

Consider an hourglass or sand clock as an illustration of self-organized criticality. Grains of sand sifting through the narrowest point of the hourglass represent individual events or natural hazards. Over time a sand pile starts to form. How this process unfolds depends on how society chooses to manage risk. A laisser-faire attitude will result in a steeper pile. And grain of sand falling on an in-creasingly steeper pile will eventually trigger an avalanche. Disaster ensues.

Why does the avalanche occur? One might ascribe the cause of the avalanche to that one grain of sand, i.e., a single event. On the other hand, a complex systems approach to resilience would associate the avalanche with the pile’s increasing slope, a historical process which renders the structure increasingly vulnerable to falling grains. From this perspective, “all disasters are slow onset when realisti-cally and locally related to conditions of susceptibility”. A hazard event might be rapid-onset, but the disaster, requiring much more than a hazard, is a long-term process, not a one-off event. The resilience of a given system is therefore not simply dependent on the outcome of future events. Resilience is the complex product of past social, political, economic and even cultural processes.

dealing with avalanches

Scholars like Thomas Homer-Dixon argue that we are becoming increasingly prone to domino effects or cascading changes across systems, thus increasing the likelihood of total synchronous failure. “A long view of human history reveals not regular change but spasmodic, catastrophic disruptions followed by long periods of reinvention and development.” We must therefore “reduce as much as we can the force of the underlying tectonic stresses in order to lower the risk of synchro-nous failure—that is, of catastrophic collapse that cascades across boundaries between technological, social and ecological systems” (5).

Unlike the clock’s lifeless grains of sand, human beings can adapt and maximize their resilience to exogenous shocks through disaster preparedness, mitigation and adaptation—which all require political will. As a colleague of mine recently noted, “I wish it were widely spread amongst society  how important being a grain of sand can be.” Individuals can “flatten” the structure of the sand pile into a less hierarchical but more resilience system, thereby distributing and diffusing the risk and size of an avalanche. Call it distributed adaptation.

operationalizing resilience

As already, the field of ecology defines  resilience as “the capacity of an ecosystem to respond to a perturbation or disturbance by resisting damage and recovering quickly.” Using this understanding of resilience, there are at least 2 ways create more resilient “social ecosystems”:

  1. Resist damage by absorbing and dampening the perturbation.
  2. Recover quickly by bouncing back or rather forward.

Resisting Damage

So how does a society resist damage from a disaster? As hinted earlier, there is no such thing as a “natural” disaster. There are natural hazards and there are social systems. If social systems are not sufficiently resilient to absorb the impact of a natural hazard such as an earthquake, then disaster unfolds. In other words, hazards are exogenous while disasters are the result of endogenous political, economic, social and cultural processes. Indeed, “it is generally accepted among environmental geographers that there is no such thing as a natural disaster. In every phase and aspect of a disaster—causes, vulnerability, preparedness, results and response, and reconstruction—the contours of disaster and the difference between who lives and dies is to a greater or lesser extent a social calculus” (6).

So how do we apply this understanding of disasters and build more resilient communities? Focusing on people-centered early warning systems is one way to do this. In 2006, the UN’s International Strategy for Disaster Reduction (ISDR) recognized that top-down early warning systems for disaster response were increasingly ineffective. They thus called for a more bottom-up approach in the form of people-centered early warning systems. The UN ISDR’s Global Survey of Early Warning Systems (PDF), defines the purpose of people-centered early warning systems as follows:

“… to empower individuals and communities threatened by hazards to act in sufficient time and in an appropriate manner so as to reduce the possibility of personal injury, loss of life, damage to property and the environment, and loss of livelihoods.”

Information plays a central role here. Acting in sufficient time requires having timely information about (1) the hazard/s, (2) our resilience and (3) how to respond. This is where information and communication technologies (ICTs), social media and Big Data play an important role. Take the latter, for example. One reason for the considerable interest in Big Data is prediction and anomaly detection. Weather and climatic sensors provide meteorologists with the copious amounts of data necessary for the timely prediction of weather patterns and  early detection of atmospheric hazards. In other words, Big Data Analytics can be used to anticipate the falling grains of sand.

Now, predictions are often not correct. But the analysis of Big Data can also help us characterize the sand pile itself, i.e., our resilience, along with the associated trends towards self-organized criticality. Recall that complex systems tend towards instability over time (think of the hourglass above). Thanks to ICTs, social media and Big Data, we now have the opportunity to better characterize in real-time the social, economic and political processes driving our sand pile. Now, this doesn’t mean that we have a perfect picture of the road to collapse; simply that our picture is clearer than ever before in human history. In other words, we can better measure our own resilience. Think of it as the Quantified Self move-ment applied to an entirely different scale, that of societies and cities. The point is that Big Data can provide us with more real-time feedback loops than ever before. And as scholars of complex systems know, feedback loops are critical for adaptation and change. Thanks to social media, these loops also include peer-to-peer feedback loops.

An example of monitoring resilience in real-time (and potentially anticipating future changes in resilience) is the UN Global Pulse’s project on food security in Indonesia. They partnered with Crimson Hexagon to forecast food prices in Indonesia by analyzing tweets referring to the price of rice. They found an inter-esting relationship between said tweets and government statistics on food price inflation. Some have described the rise of social media as a new nervous system for the planet, capturing the pulse of our social systems. My colleagues and I at QCRI are therefore in the process of appling this approach to the study of the Arabic Twittersphere. Incidentally, this is yet another critical reason why Open Data is so important (check out the work of OpenDRI, Open Data for Resilience Initiative. See also this post on Demo-cratizing ICT for Development with DIY Innovation and Open Data). More on open data and data philanthropy in the conclusion.

Finally, new technologies can also provide guidance on how to respond. Think of Foursquare but applied to disaster response. Instead of “Break Glass in Case of Emergency,” how about “Check-In in Case of Emergency”? Numerous smart-phone apps such as Waze already provide this kind of at-a-glance, real-time situational awareness. It is only a matter of time until humanitarian organiza-tions develop disaster response apps that will enable disaster-affected commu-nities to check-in for real time guidance on what to do given their current location and level of resilience. Several disaster preparedness apps already exist. Social computing and Big Data Analytics can power these apps in real-time.

Quick Recovery

As already noted, there are at least two ways create more resilient “social eco-systems”. We just discussed the first: resisting damage by absorbing and dam-pening the perturbation.  The second way to grow more resilient societies is by enabling them to rapidly recover following a disaster.

As Manyena writes, “increasing attention is now paid to the capacity of disaster-affected communities to ‘bounce back’ or to recover with little or no external assistance following a disaster.” So what factors accelerate recovery in eco-systems in general? In ecological terms, how quickly the damaged part of an ecosystem can repair itself depends on how many feedback loops it has to the non- (or less-) damaged parts of the ecosystem(s). These feedback loops are what enable adaptation and recovery. In social ecosystems, these feedback loops can be comprised of information in addition to the transfer of tangible resources.  As some scholars have argued, a disaster is first of all “a crisis in communicating within a community—that is, a difficulty for someone to get informed and to inform other people” (7).

Improving ways for local communities to communicate internally and externally is thus an important part of building more resilient societies. Indeed, as Homer-Dixon notes, “the part of the system that has been damaged recovers by drawing resources and information from undamaged parts.” Identifying needs following a disaster and matching them to available resources is an important part of the process. Indeed, accelerating the rate of (1) identification; (2) matching and, (3) allocation, are important ways to speed up overall recovery.

This explains why ICTs, social media and Big Data are central to growing more resilient societies. They can accelerate impact evaluations and needs assessments at the local level. Population displacement following disasters poses a serious public health risk. So rapidly identifying these risks can help affected populations recover more quickly. Take the work carried out by my colleagues at Flowminder, for example. They  empirically demonstrated that mobile phone data (Big Data!) can be used to predict population displacement after major disasters. Take also this study which analyzed call dynamics to demonstrate that telecommunications data could be used to rapidly assess the impact of earthquakes. A related study showed similar results when analyzing SMS’s and building damage Haiti after the 2010 earthquake.

haiti_overview_570

Resilience as Self-Organization and Emergence

Connection technologies such as mobile phones allow individual “grains of sand” in our societal “sand pile” to make necessary connections and decisions to self-organize and rapidly recover from disasters. With appropriate incentives, pre-paredness measures and policies, these local decisions can render a complex system more resilient. At the core here is behavior change and thus the importance of understanding behavior change models. Recall  also Thomas Schelling’s observation that micro-motives can lead to macro-behavior. To be sure, as Thomas Homer-Dixon rightly notes, “Resilience is an emergent property of a system—it’s not a result of any one of the system’s parts but of the synergy between all of its parts.  So as a rough and ready rule, boosting the ability of each part to take care of itself in a crisis boosts overall resilience.” (For complexity science readers, the notions of transforma-tion through phase transitions is relevant to this discussion).

In other words, “Resilience is the capacity of the affected community to self-organize, learn from and vigorously recover from adverse situations stronger than it was before” (8). This link between resilience and capacity for self-organization is very important, which explains why a recent and major evaluation of the 2010 Haiti Earthquake disaster response promotes the “attainment of self-sufficiency, rather than the ongoing dependency on standard humanitarian assistance.” Indeed, “focus groups indicated that solutions to help people help themselves were desired.”

The fact of the matter is that we are not all affected in the same way during a disaster. (Recall the distinction between hazards and disasters discussed earlier). Those of use who are less affected almost always want to help those in need. Herein lies the critical role of peer-to-peer feedback loops. To be sure, the speed at which the damaged part of an ecosystem can repair itself depends on how many feedback loops it has to the non- (or less-) damaged parts of the eco-system(s). These feedback loops are what enable adaptation and recovery.

Lastly, disaster response professionals cannot be every where at the same time. But the crowd is always there. Moreover, the vast majority of survivals following major disasters cannot be attributed to external aid. One study estimates that at most 10% of external aid contributes to saving lives. Why? Because the real first responders are the disaster-affected communities themselves, the local popula-tion. That is, the real first feedback loops are always local. This dynamic of mutual-aid facilitated by social media is certainly not new, however. My colleagues in Russia did this back in 2010 during the major forest fires that ravaged their country.

While I do have a bias towards people-centered interventions, this does not mean that I discount the importance of feedback loops to external actors such as traditional institutions and humanitarian organizations. I also don’t mean to romanticize the notion of “indigenous technical knowledge” or local coping mechanism. Some violate my own definition of human rights, for example. However, my bias stems from the fact that I am particularly interested in disaster resilience within the context of areas of limited statehood where said institutions and organizations are either absent are ineffective. But I certainly recognize the importance of scale jumping, particularly within the context of social capital and social media.

RESILIENCE THROUGH SOCIAL CAPITAL

Information-based feedback loops general social capital, and the latter has been shown to improve disaster resilience and recovery. In his recent book entitled “Building Resilience: Social Capital in Post-Disaster Recovery,” Daniel Aldrich draws on both qualitative and quantitative evidence to demonstrate that “social resources, at least as much as material ones, prove to be the foundation for resilience and recovery.” His case studies suggest that social capital is more important for disaster resilience than physical and financial capital, and more important than conventional explanations. So the question that naturally follows given our interest in resilience & technology is this: can social media (which is not restricted by geography) influence social capital?

Social Capital

Building on Daniel’s research and my own direct experience in digital humani-tarian response, I argue that social media does indeed nurture social capital during disasters. “By providing norms, information, and trust, denser social networks can implement a faster recovery.” Such norms also evolve on Twitter, as does information sharing and trust building. Indeed, “social ties can serve as informal insurance, providing victims with information, financial help and physical assistance.” This informal insurance, “or mutual assistance involves friends and neighbors providing each other with information, tools, living space, and other help.” Again, this bonding is not limited to offline dynamics but occurs also within and across online social networks. Recall the sand pile analogy. Social capital facilitates the transformation of the sand pile away (temporarily) from self-organized criticality. On a related note vis-a-vis open source software, “the least important part of open source software is the code.” Indeed, more important than the code is the fact that open source fosters social ties, networks, communities and thus social capital.

(Incidentally, social capital generated during disasters is social capital that can subsequently be used to facilitate self-organization for non-violent civil resistance and vice versa).

RESILIENCE through big data

My empirical research on tweets posted during disasters clearly shows that while many use twitter (and social media more generally) to post needs during a crisis, those who are less affected in the social ecosystem will often post offers to help. So where does Big Data fit into this particular equation? When disaster strikes, access to information is equally important as access to food and water. This link between information, disaster response and aid was officially recognized by the Secretary General of the International Federation of Red Cross & Red Crescent Societies in the World Disasters Report published in 2005. Since then, disaster-affected populations have become increasingly digital thanks to the very rapid and widespread adoption of mobile technologies. Indeed, as a result of these mobile technologies, affected populations are increasingly able to source, share and generate a vast amount of information, which is completely transforming disaster response.

In other words, disaster-affected communities are increasingly becoming the source of Big (Crisis) Data during and following major disasters. There were over 20 million tweets posted during Hurricane Sandy. And when the major earth-quake and Tsunami hit Japan in early 2011, over 5,000 tweets were being posted every secondThat is 1.5 million tweets every 5 minutes. So how can Big Data Analytics create more resilience in this respect? More specifically, how can Big Data Analytics accelerate disaster recovery? Manually monitoring millions of tweets per minute is hardly feasible. This explains why I often “joke” that we need a local Match.com for rapid disaster recovery. Thanks to social computing, artifi-cial intelligence, machine learning and Big Data Analytics, we can absolutely develop a “Match.com” for rapid recovery. In fact, I’m working on just such a project with my colleagues at QCRI. We are also developing algorithms to auto-matically identify informative and actionable information shared on Twitter, for example. (Incidentally, a by-product of developing a robust Match.com for disaster response could very well be an increase in social capital).

There are several other ways that advanced computing can create disaster resilience using Big Data. One major challenge is digital humanitarian response is the verification of crowdsourced, user-generated content. Indeed, misinforma-tion and rumors can be highly damaging. If access to information is tantamount to food access as noted by the Red Cross, then misinformation is like poisoned food. But Big Data Analytics has already shed some light on how to develop potential solutions. As it turns out, non-credible disaster information shared on Twitter propagates differently than credible information, which means that the credibility of tweets could be predicted automatically.

Conclusion

In sum, “resilience is the critical link between disaster and development; monitoring it [in real-time] will ensure that relief efforts are supporting, and not eroding [...] community capabilities” (9). While the focus of this blog post has been on disaster resilience, I believe the insights provided are equally informa-tive for less extreme events.  So I’d like to end on two major points. The first has to do with data philanthropy while the second emphasizes the critical importance of failing gracefully.

Big Data is Closed and Centralized

A considerable amount of “Big Data” is Big Closed and Centralized Data. Flow-minder’s study mentioned above draws on highly proprietary telecommunica-tions data. Facebook data, which has immense potential for humanitarian response, is also closed. The same is true of Twitter data, unless you have millions of dollars to pay for access to the full Firehose, or even Decahose. While access to the Twitter API is free, the number of tweets that can be downloaded and analyzed is limited to several thousand a day. Contrast this with the 5,000 tweets per second posted after the earthquake and Tsunami in Japan. We therefore need some serious political will from the corporate sector to engage in “data philanthropy”. Data philanthropy involves companies sharing proprietary datasets for social good. Call it Corporate Social Responsibility (CRS) for digital humanitarian response. More here on how this would work.

Failing Gracefully

Lastly, on failure. As noted, complex systems tend towards instability, i.e., self-organized criticality, which is why Homer-Dixon introduces the notion of failing gracefully. “Somehow we have to find the middle ground between dangerous rigidity and catastrophic collapse.” He adds that:

“In our organizations, social and political systems, and individual lives, we need to create the possibility for what computer programmers and disaster planners call ‘graceful’ failure. When a system fails gracefully, damage is limited, and options for recovery are preserved. Also, the part of the system that has been damaged recovers by drawing resources and information from undamaged parts.” Homer-Dixon explains that “breakdown is something that human social systems must go through to adapt successfully to changing conditions over the long term. But if we want to have any control over our direction in breakdown’s aftermath, we must keep breakdown constrained. Reducing as much as we can the force of underlying tectonic stresses helps, as does making our societies more resilient. We have to do other things too, and advance planning for breakdown is undoubtedly the most important.”

As Louis Pasteur famously noted, “Chance favors the prepared mind.” Preparing for breakdown is not defeatist or passive. Quite on the contrary, it is wise and pro-active. Our hubris—including our current infatuation with Bid Data—all too often clouds our better judgment. Like Macbeth, rarely do we seriously ask our-selves what we would do “if we should fail.” The answer “then we fail” is an option. But are we truly prepared to live with the devastating consequences of total synchronous failure?

In closing, some lingering (less rhetorical) questions:

  • How can resilience can be measured? Is there a lowest common denominator? What is the “atom” of resilience?
  • What are the triggers of resilience, creative capacity, local improvisation, regenerative capacity? Can these be monitored?
  • Where do the concepts of “lived reality” and “positive deviance” enter the conversation on resilience?
  • Is resiliency a right? Do we bear a responsibility to render systems more resilient? If so, recalling that resilience is the capacity to self-organize, do local communities have the right to self-organize? And how does this differ from democratic ideals and freedoms?
  • Recent research in social-psychology has demonstrated that mindfulness is an amplifier of resilience for individuals? How can be scaled up? Do cultures and religions play a role here?
  • Collective memory influences resilience. How can this be leveraged to catalyze more regenerative social systems?

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Epilogue: Some colleagues have rightfully pointed out that resilience is ultima-tely political. I certainly share that view, which is why this point came up in recent conversations with my PopTech colleagues Andrew Zolli & Leetha Filderman. Readers of my post will also have noted my emphasis on distinguishing between hazards and disasters; that the latter are the product of social, economic and political processes. As noted in my blog post, there are no natural disastersTo this end, some academics rightly warn that “Resilience is a very technical, neutral, apolitical term. It was initially designed to characterize systems, and it doesn’t address power, equity or agency…  Also, strengthening resilience is not free—you can have some winners and some losers.”

As it turns out, I have a lot say about the political versus technical argument. First of all, this is hardly a new or original argument but nevertheless an important one. Amartya Senn discussed this issue within the context of famines decades ago, noting that famines do not take place in democracies. In 1997, Alex de Waal published his seminal book, “Famine Crimes: Politics and the Disaster Relief In-dustry in Africa.” As he rightly notes, “Fighting famine is both a technical and political challenge.” Unfortunately, “one universal tendency stands out: technical solutions are promoted at the expense of political ones.” There is also a tendency to overlook the politics of technical actions, muddle or cover political actions with technical ones, or worse, to use technical measures as an excuse not to undertake needed political action.

De Waal argues that the use of the term “governance” was “an attempt to avoid making the political critique too explicit, and to enable a focus on specific technical aspects of government.” In some evaluations of development and humanitarian projects, “a caveat is sometimes inserted stating that politics lies beyond the scope of this study.” To this end, “there is often a weak call for ‘political will’ to bridge the gap between knowledge of technical measures and action to implement them.” As de Waal rightly notes, “the problem is not a ‘missing link’ but rather an entire political tradition, one manifestation of which is contemporary international humanitarianism.” In sum, “technical ‘solutions’ must be seen in the political context, and politics itself in the light of the domi-nance of a technocratic approach to problems such as famine.”

From a paper I presented back in 2007: “the technological approach almost always serves those who seek control from a distance.” As a result of this technological drive for pole position, a related “concern exists due to the separation of risk evaluation and risk reduction between science and political decision” so that which is inherently politically complex becomes depoliticized and mechanized. In Toward a Rational Society (1970), the German philosopher Jürgen Habermas describes “the colonization of the public sphere through the use of instrumental technical rationality. In this sphere, complex social problems are reduced to technical questions, effectively removing the plurality of contending perspectives.”

To be sure, Western science tends to pose the question “How?” as opposed to “Why?”What happens then is that “early warning systems tend to be largely conceived as hazard-focused, linear, topdown, expert driven systems, with little or no engagement of end-users or their representatives.” As De Waal rightly notes, “the technical sophistication of early warning systems is offset by a major flaw: response cannot be enforced by the populace. The early warning information is not normally made public.”  In other words, disaster prevention requires “not merely identifying causes and testing policy instruments but building a [social and] political movement” since “the framework for response is inherently political, and the task of advocacy for such response cannot be separated from the analytical tasks of warning.”

Recall my emphasis on people-centered early warning above and the definition of resilience as capacity for self-organization. Self-organization is political. Hence my efforts to promote greater linkages between the fields of nonviolent action and early warning years ago. I have a paper (dated 2008) specifically on this topic should anyone care to read. Anyone who has read my doctoral dissertation will also know that I have long been interested in the impact of technology on the balance of power in political contexts. A relevant summary is available here. Now, why did I not include all this in the main body of my blog post? Because this updated section already runs over 1,000 words.

In closing, I disagree with the over-used criticism that resilience is reactive and about returning to initial conditions. Why would we want to be reactive or return to initial conditions if the latter state contributed to the subsequent disaster we are recovering from? When my colleague Andrew Zolli talks about resilience, he talks about “bouncing forward”, not bouncing back. This is also true of Nassim Taleb’s term antifragility, the ability to thrive on disruption. As Homer-Dixon also notes, preparing to fail gracefully is hardly reactive either.