Tag Archives: Disaster Resilience

Why Digital Social Capital Matters for Disaster Resilience and Response

Recent empirical studies have clearly demonstrated the importance of offline social capital for disaster resilience and response. I’ve blogged about some of this analysis here and here. Social capital is typically described as those “features of social organizations, such as networks, norms, and trust, that facilitate action and cooperation for mutual benefit.” In other words, social capital increases a group’s capacity for collective action and thus self-organization, which is a key driver of disaster resilience. What if those social organizations were virtual and the networks digital? Would these online communities “generate digital social capital”? And would this digital social capital have any impact on offline social capital, collective action and resilience?

Social Capital

A data-driven study published recently, “Social Capital and Pro-Social Behavior Online and Offline” (PDF), presents some fascinating insights. The study, carried out by Constantin M. Bosancianu, Steve Powell and Esad Bratovi, draws on their survey of 1,912 Internet users in Bosnia & Herzegovina, Croatia and Serbia. The authors specifically consider two types of social capital: bonding social capital and bridging social capital. “

“Bridging social capital is described as inclusive, fostered in networks where membership is not restricted to a particular group defined by strict racial, class, linguistic or ethnic criteria.  Regular interactions inside these networks would gradually build norms of generalized trust and reciprocity at the individual level. These relationships […] are able to offer the individual access to new information but are not very adept in providing emotional support in times of need.”

“Bonding social capital, on the other hand, is exclusive, fostered in tight-knit networks of family members and close friends. Although the degree of information redundancy in these networks is likely high (as most members occupy the same social space), they provide […] the “sociological superglue” which gets members through tough emotional stages in their lives.”

The study’s findings reveal that online and offline social capital were correlated with each other. More specifically, online bridging social capital was closely correlated with offline bridging social capital, while online binding social capital was closely correlated with offline binding social capital. Perhaps of most interest with respect to disaster resilience, the authors discovered that “offline bridging social capital can benefit from online interactions.”

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Using Social Media to Predict Disaster Resilience (Updated)

Social media is used to monitor and predict all kinds of social, economic, political and health-related behaviors these days. Could social media also help identify more disaster resilient communities? Recent empirical research reveals that social capital is the most important driver of disaster resilience; more so than economic and material resources. To this end, might a community’s social media footprint indicate how resilience it is to disasters? After all, “when extreme events at the scale of Hurricane Sandy happen, they leave an unquestionable mark on social media activity” (1). Could that mark be one of resilience?

Twitter Heatmap Hurricane

Sentiment analysis map of tweets posted during Hurricane Sandy.
Click on image to learn more.

In the immediate aftermath of a disaster, “social ties can serve as informal insurance, providing victims with information, financial help and physical assistance” (2). This informal insurance, “or mutual assistance involves friends and neighbors providing each other with information, tools, living space, and other help” (3). At the same time, social media platforms like Twitter are increasingly used to communicate during crises. In fact, data driven research on tweets posted during disasters reveal that many tweets provide victims with information, help, tools, living space, assistance and other more. Recent studies argue that “such interactions are not necessarily of inferior quality compared to simultaneous, face-to-face interactions” (4). What’s more, “In addition to the preservation and possible improvement of existing ties, interaction through social media can foster the creation of new relations” (5). Meanwhile, and “contrary to prevailing assumptions, there is evidence that the boom in social media that connects users globally may have simultaneously increased local connections” (6).

A recent study of 5 billion tweets found that Japan, Canada, Indonesia and South Korea have highest percentage of reciprocity on Twitter (6). This is important because “Network reciprocity tells us about the degree of cohesion, trust and social capital in sociology” (7). In terms of network density, “the highest values correspond to South Korea, Netherlands and Australia.” The findings further reveal that “communities which tend to be less hierarchical and more reciprocal, also displays happier language in their content updates. In this sense countries with high conversation levels … display higher levels of happiness too” (8).

A related study found that the language used in tweets can be used to predict the subjective well-being of those users (9). The same analysis revealed that the level of happiness expressed by Twitter users in a community are correlated with members of that same community who are not on social media. Data-driven studies on happiness also show that social bonds and social activities are more conducive to happiness than financial capital (10). Social media also includes blogs. A new study analyzed more than 18.5 million blog posts found that “bloggers with lower social capital have fewer positive moods and more negative moods [as revealed by their posts] than those with higher social capital” (11).

Collectivism vs Individualism countries

Finally, another recent study analyzed more than 2.3 million twitter users and found that users in collectivist countries engage with others more than those in individualistic countries (12). “In high collectivist cultures, users tend to focus more on the community to which they belong,” while  people in individualistic countries are “in a more loosely knit social network,” and so typically “look after themselves or only after immediate family members” (13). The map above displays collectivist and individualistic countries; with the former represented by lighter shades and the latter darker colors.

In sum, one should be able to measure “digital social capital” and thus disaster resilience by analyzing social media networks before, during and after disasters. “These disaster responses may determine survival, and we can measure the likelihood of them happening” via digital social capital dynamics reflected on social media (14). One could also combine social network analysis with sentiment analysis to formulate various indexes. Anyone interested in pursuing this line of research?

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Why the Share Economy is Important for Disaster Response and Resilience

A unique and detailed survey funded by the Rockefeller Foundation confirms the important role that social and community bonds play vis-à-vis disaster resilience. The new study, which focuses on resilience and social capital in the wake of Hurricane Sandy, reveals how disaster-affected communities self-organized, “with reports of many people sharing access to power, food and water, and providing shelter.” This mutual aid was primarily coordinated face-to-face. This may not always be possible, however. So the “Share Economy” can also play an important role in coordinating self-help during disasters.

In a share economy, “asset owners use digital clearinghouses to capitalize the unused capacity of things they already have, and consumers rent from their peers rather than rent or buy from a company” (1). During disasters, these asset owners can use the same digital clearinghouses to offer what they have at no cost. For example, over 1,400 kindhearted New Yorkers offered free housing to people heavily affected by the hurricane. They did this using AirBnB, as shown in the short video above. Meanwhile, on the West Coast, the City of San Francisco has just lunched a partnership with BayShare, a sharing economy advocacy group in the Bay Area. The partnership’s goal is to “harness the power of sharing to ensure the best response to future disasters in San Francisco” (2).

fon wifi sharing

While share economy platforms like AirBnB are still relatively new, many believe that “the share economy is a real trend and not some small blip (3). So it may be worth taking an inventory of share platforms out there that are likely to be useful for disaster response. Here’s a short list:

  • AirBnBA global travel rental platform with accommodations in 192 countries. This service has already been used for disaster response as described above.
  • FonEnables people to share some of their home Wi-Fi  in exchange for getting free Wi-Fi from 8 million people in Fon’s network. Access to information is always key during & after disasters. The map above  displays a subset of all Fon users in that part of Europe.
  • LendingClub: A cheaper service than credit cards for borrowers. Also provides better interest rates than savings accounts for investors. Access to liquidity is often necessary after a disaster.
  • LiquidSpaceProvides high quality temporary workspaces and office rentals. These can be rented by the hour and by the day.  Dedicated spaces are key for coordinating disaster response.
  • Lyft: An is on-demand ride-sharing smartphone app for cheaper, safer rides. This service could be used to transport people and supplies following a disaster. Similar to Sidecar.
  • RelayRides:  A car sharing marketplace where participants can rent out their own cars. Like Lyft, RelayRides could be used to transport goods and people. Similar to Getaround. Also, ParkingPanda is the parking equivalent.
  • TaskRabbit: Get your deliveries and errands completed easily & quickly by trusted individuals in your neighborhood. This service could be used to run quick errands following disasters. Similar to Zaarly, a marketplace that helps you discover and hire local services. 
  • Yerdle: An “eBay” for sharing items with your friends. This could be used to provide basic supplies to disaster-affected neighborhoods. Similar to SnapGood, which also allows for temporary sharing.

Feel free to add more examples via the comments section below if you know of other sharing economy platforms that could be helpful during disasters.

While these share tools don’t necessary reinforce bonding social capital since face-to-face interactions are not required, they do stand to increase levels of bridging social capital. The former refers to social capital within existing social networks while the latter refers to “cooperative connections with people from different walks of life,” and is often considered “more valuable than ‘bonding social capital'” (3). Bridging social capital is “closely related to thin trust, as opposed to the bonding social capital of thick trust” (4). Platforms that facilitate the sharing economy provide reassurance vis-à-vis the thin trust since they tend to vet participants. This extra reassurance can go a long way during disasters and may thus facilitate mutual-aid at a distance.

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Resilience = Anarchism = Resilience?

Resilience is often defined as the capacity for self-organization, which in essence is cooperation without hierarchy. In turn, such cooperation implies mutuality; reciprocation, mutual dependence. This is what the French politician, philo-sopher, economist and socialist “Pierre-Joseph Proudhon had in mind when he first used the term ‘anarchism,’ namely, mutuality, or cooperation without hierarchy or state rule” (1).

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As renowned Yale Professor James Scott explains in his latest bookTwo Cheers for Anarchism, “Forms of informal cooperation, coordination, and action that embody mutuality without hierarchy are the quotidian experience of most people.” To be sure, “most villages and neighborhoods function precisely be-cause of the informal, transient networks of coordination that do not require formal organization, let alone hierarchy. In other words, the experience of anar-chistic mutuality is ubiquitous. The existence, power and reach of the nation-state over the centuries may have undermined the self-organizing capacity (and hence resilience) of individuals and small communities.” Indeed, “so many functions that were once accomplished by mutuality among equals and informal coordination are now state organized or state supervised.” In other words, “the state, arguably, destroys the natural initiative and responsibility that arise from voluntary cooperation.”

This is goes to the heart what James Scott argues in his new book, and he does so  in a very compelling manner. Says Scott: “I am suggesting that two centuries of a strong state and liberal economies may have socialized us so that we have largely lost the habits of mutuality and are in danger now of becoming precisely the dangerous predators that Hobbes thought populated the state of nature. Leviathan may have given birth to its own justification.” And yet, we also see a very different picture of reality, one in which solidarity thrives and mutual-aid remains the norm: we see this reality surface over & over during major disasters—a reality facilitated by mobile technology and social media networks.

Recall Jürgen Habermas’s treatise that “those who take on the tools of open expression become a public, and the presence of a synchronized public increas-ingly constrains undemocratic rulers while expanding the right of that public.” One of the main instruments for synchronization is what the military refers to as “shared awareness.” As my colleague Clay Shirky notes in his excellent piece on The Political Power of Social Media, “shared awareness is the ability of each member of a group to not only understand the situation at hand but also under-stand that everyone else does, too. Social media increase shared awareness by propagating messages through social networks.” Moreover, while “Opinions are first transmitted by the media,” they are then “echoed by friends, family mem-bers, and colleagues. It is in this second, social step that political opinions are formed. This is the step in which the Internet in general, and social media in particular, can make a difference.”

metropiano

In 1990, James Scott published Domination and the Arts of Resistance: Hidden Transcripts, in which he distinguishes between public and hidden transcripts. The former describes the open, public interactions that take place between dominators and oppressed while hidden transcripts relate to the critique of power that “goes on offstage” and which the power elites cannot decode. This hidden transcript is comprised of the second step described above, i.e., the social conversations that ultimately change political behavior. Scott writes that when the oppressed classes publicize this “hidden transcript”, they become conscious of its common status. Borrowing from Habermas, the oppressed thereby become a public and more importantly a synchronized public. Social media is the metronome that can synchronize the collective publication of the hidden trans-cript, yielding greater shared awareness that feeds on itself, thereby threatening the balance of power between Leviathan and now-empowered and self-organized mutual-aid communities.

I have previously argued that social media and online social networks also can and do foster social capital, which increases capacity for self-organization and renders local communities more resilient & independent, thus sowing the seeds for future social movements. In other words, habits of mutuality are not all lost and the Leviathan may still face some surprisesAs Peter Kropotkin observed well over 100 years ago in his exhaustive study, Mutual Aid: A Factor of Evolution, cooperation and mutual aid are the most important factors in the evolution of species and their ability to survive. “There is an immense amount of warfare and extermination going on amidst various species; there is, at the same time, as much, or perhaps even more, of mutual support, mutual aid, and mutual defense… Sociability is as much a law of nature as mutual struggle.” 

Sociability is the tendency or property of being social, of interacting with others. Social media, meanwhile, has become the media for mass social interaction; enabling greater volumes of interactions than at any other time in human history. By definition, these mass social interactions radically increase the probability of mutuality and self-organization. And so, as James Scott puts it best, Two Cheers for Anarchism

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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.

Social Media = Social Capital = Disaster Resilience?

Do online social networks generate social capital, which, in turn, increases resilience to disasters? How might one answer this question? For example, could we analyze Twitter data to capture levels of social capital in a given country? If so, do countries with higher levels of social capital (as measured using Twitter) demonstrate greater resiliences to disasters?

Twitter Heatmap Hurricane

These causal loops are fraught with all kinds of intervening variables, daring assumptions and econometric nightmares. But the link between social capital and disaster resilience is increasingly accepted. In “Building Resilience: Social Capital in Post-Disaster Recover,” Daniel Aldrich draws on both qualitative and quantita-tive evidence to demonstrate that “social resources, at least as much as material ones, prove to be the foundation for resilience and recovery.” A concise summary of his book is available in my previous blog post.

So the question that follows is whether the link between social media, i.e., online social networks and social capital can be established. “Although real-world organizations […] have demonstrated their effectiveness at building bonds, virtual communities are the next frontier for social capital-based policies,” writes Aldrich. Before we jump into the role of online social networks, however, it is important to recognize the function of “offline” communities in disaster response and resilience.

iran-reliefs

“During the disaster and right after the crisis, neighbors and friends—not private firms, government agencies, or NGOs—provide the necessary resources for resilience.” To be sure, “the lack of systematic assistance from government and NGOs [means that] neighbors and community groups are best positioned to undertake efficient initial emergency aid after a disaster. Since ‘friends, family, or coworkers of victims and also passersby are always the first and most effective responders, “we should recognize their role on the front line of disasters.”

In sum, “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.” Data driven research on tweets posted during disasters reveal that many provide victims with information, help, tools, living space, assistance and other help. But this support is also provided to complete strangers since it is shared openly and publicly on Twitter. “[…] Despite—or perhaps because of—horrendous conditions after a crisis, survivors work together to solve their problems; […] the amount of (bounding) social capital seems to increase under difficult conditions.” Again, this bonding is not limited to offline dynamics but occurs also within and across online social networks. The tweet below was posted in the aftermath of Hurricane Sandy.

Sandy Tweets Mutual Aid

“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. So is the degree of activity on Twitter directly proportional to the level of community resilience?

This data-driven study, “Do All Birds Tweet the Same? Characterizing Twitter Around the World,” may shed some light in this respect. The authors, Barbara Poblete, Ruth Garcia, Marcelo Mendoza and Alejandro Jaimes, analyze various aspects of social media–such as network structure–for the ten most active countries on Twitter. In total, the working dataset consisted close to 5 million users and over 5 billion tweets. The study is the largest one carried out to date on Twitter data, “and the first one that specifically examines differences across different countries.”

Screen Shot 2012-11-30 at 6.19.45 AM

The network statistics per country above reveals that Japan, Canada, Indonesia and South Korea have highest percentage of reciprocity on Twitter. This is important because according to Poblet et al., “Network reciprocity tells us about the degree of cohesion, trust and social capital in sociology.” In terms of network density, “the highest values correspond to South Korea, Netherlands and Australia.” Incidentally, the authors find that “communities which tend to be less hierarchical and more reciprocal, also displays happier language in their content updates. In this sense countries with high conversation levels (@) … display higher levels of happiness too.”

If someone is looking for a possible dissertation topic, I would recommend the following comparative case study analysis. Select two of the four countries with highest percentage of reciprocity on Twitter: Japan, Canada, Indonesia and South Korea. The two you select should have a close “twin” country. By that I mean a country that has many social, economic and political factors in common. The twin countries should also be in geographic proximity to each other since we ultimately want to assess how they weather similar disasters. The paired can-didates that come to mind are thus: Canada & US and Indonesia & Malaysia.

Next, compare the countries’ Twitter networks, particularly degrees of  recipro-city since this metric appears to be a suitable proxy for social capital. For example, Canada’s reciprocity score is 26% compared to 19% for the US. In other words, quite a difference. Next, identify recent  disasters that both countries have experienced. Do the affected cities in the respective countries weather the disasters differently? Is one community more resilient than the other? If so, do you find a notable quantitative difference in their Twitter networks and degrees of reciprocity? If so, does a subsequent comparative qualitative analysis support these findings?

As cautioned earlier, these causal loops are fraught with all kinds of intervening variables, daring assumptions and econometric nightmares. But if anyone wants to brave the perils of applied social science research, and finds the above re-search questions of interest, then please do get in touch!

Does Social Capital Drive Disaster Resilience?

The link between social capital and disaster resilience is increasingly accepted. In “Building Resilience: Social Capital in Post-Disaster Recover,” 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 re-silience and recovery.” His case studies suggest that social capital is more important for disaster resilience than physical and financial capital, and more im-portant than conventional explanations.

Screen Shot 2012-11-30 at 6.03.23 AM

Aldrich argues that social capital catalyzes increased “participation among networked members; providing information and knowledge to individuals in the group; and creating trustworthiness.” The author goes so far as using “the phrases social capital and social networks nearly interchangeably.” He finds that “higher levels of social capital work together more effectively to guide resources to where they are needed.” Surveys confirm that “after disasters, most survivors see social connections and community as critical for their recovery.” To this end, “deeper reservoirs of social capital serve as informal insurance and mutual assistance for survivors,” helping them “overcome collective action constraints.”

Capacity for self-organization is thus intimately related to resilience since “social capital can overcome obstacles to collective action that often prevent groups from accomplishing their goals.” In other words, “higher levels of social capital reduce transaction costs, increase the probability of collective action, and make cooperation among individuals more likely.” Social capital is therefore “an asset, a functioning propensity for mutually beneficial collective action […].”

In contrast, communities exhibiting “less resilience fail to mobilize collectively and often must wait for recover guidance and assistance […].”  This implies that vulnerable populations are not solely characterized in terms of age, income, etc., but in terms of “their lack of connections and embeddedness in social networks.” Put differently, “the most effective—and perhaps least expensive—way to mitigate disasters is to create stronger bonds between individuals in vulnerable populations.”

Social Capital

The author brings conceptual clarity to the notion of social capital when he unpacks the term into Bonding Capital, Bridging Capital and Linking Capital. The figure above explains how these differ but relate to each other. The way this relates and applies to digital humanitarian response is explored in this blog post.