Tag Archives: PopTech

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?

bio

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.

SMS for Violence Prevention: PeaceTXT International Launches in Kenya

[Cross-posted from my post on the Ushahidi blog]

One of the main reasons I’m in Nairobi this month is to launch PeaceTXT International with PopTech, Praekelt Foundation, Sisi ni Amani and several other key partners. PeaceTXT International is a spin-off from the original PeaceTXT project that several of us began working on with CeaseFire Chicago last year. I began thinking about the many possible international applications of the PeaceTXT project during our very first meeting, which is why I am thrilled and honored to be spearheading the first PeaceTXT International pilot project.

The purpose of PeaceTXT is to leverage mobile messaging to catalyze behavior change around peace and conflict issues. In the context of Chicago, the joint project with CeaseFire aims to leverage SMS reminders to interrupt gun violence in marginalized neighborhoods. Several studies in other fields of public health have already shown the massive impact that SMS reminders can have on behavior change, e.g., improving drug adherence behavior among AIDS and TB patients in Africa, Asia and South America.

Our mobile messaging campaign in Chicago builds on another very successful one in the US: “Friends Don’t Let Friends Drink and Drive.” Inspired by this approach, the PeaceTXT Team is looking to launch a friends-don’t-let-friends-get-killed campaign. Focus groups recently conducted with high-risk individuals have resulted in rich content for several dozen reminder messages (see below) that could be disseminated via SMS. Note that CeaseFire has been directly credited for significantly reducing the number of gun-related killings in Chicago over the past 10 years. In other words, they have a successful and proven methodology; one being applied to several other cities and countries worldwide. PeaceTXT simply seeks to scale this success by introducing SMS.

These messages are user-generated in that the content was developed by high-risk individuals themselves—i.e., those most likely to get involved in gun violence. The messages are not limited to reminders. Some also prompt the community to get engaged by responding to various questions. Indeed, the project seeks to crowdsource community solutions to gun violence and thus greater participation. When high-risk individuals were asked how they’d feel if they were to receive these messages on their phones, they had the following to share: “makes me feel like no one is forgetting about me”; “message me once a day to make a difference.”

Given that both forwarding and saving text messages is very common among the population that CeaseFire works with, the team hopes that the text messages will circulate and recycle widely. Note that the project is still in prototype phase but going into implementation mode as of 2012. So we’ll have to wait and see how the project fares and what the initial impact looks like.

In the meantime, PeaceTXT is partnering with Sisi ni Amani (We are Peace) to launch its first international pilot project. Rachel Brown, who spearheads the initiative, first got in touch with me back in the Fall of 2009 whilst finishing her undergraduate studies at Tufts. Rachel was interested in crowdsourcing a peace map of Kenya, which I blogged about here shortly after our first conversation. Since then, Rachel and her team have set up the Kenyan NGO Sisi ni Amani Kenya (SnA-K) to leverage mobile technology for awareness raising and civic engagement with the aim of preventing possible violence during next year’s Presidential Elections.

SnA-K currently manages a ~10,000 member SMS subscriber list in Baba Dogo and Korogocho, Kamukunji and Narok. SnA-K’s SMS campaigns focus on voter education, community cohesion and rumor prevention. What SnA-K needs, how-ever, is the scalable SMS broadcasting technology, the type of focus that PeaceTXT brought to CeaseFire Chicago and the unique response methodology developed by the CeaseFire team. So I reached out to Rachel early on during the work in Chicago to let her know about PeaceTXT and to gain insights from her projects in Kenya. We set up regular conference calls throughout the year to keep each other informed of our respective progress and findings.

Soon enough, PopTech’s delightful Leetha Filderman asked me to put together a pitch for international applications of PeaceTXT’s work, an initiative I have “code-named” PeaceTXT International. I was absolutely thrilled when she shared the good news at PopTech 2011 that our donor, the Rita Allen Foundation, had provided us with additional funding, some of which could go towards an international pilot project. Naturally, Sisi ni Amani was a perfect fit.

So we organized a half-day brainstorming session at the iHub last week to chart the way forward on PeaceTXT Kenya. For example, what is the key behavioral change variable (like friendship in Chicago) that is most likely to succeed in Kenya? As for interrupting violence, how can the CeaseFire methodology be customized for the SnA-K context? Finally, what kind of SMS broadcasting technology do we need to have in place to provide maximum flexibility and scalability earlier rather than later? Answering these questions and implementing scalable solutions essentially forms the basis of the partnership between SnA-K and PeaceTXT (which also includes Mobile:Medic & Revolution Messaging). We have some exciting leads on next steps and will be sure to blog about them as we move forward to get feedback from the wider community.

Conflicts are often grounded in the stories and narratives that people tell themselves and the emotions that these stories generate. Narratives shape identity and the social construct of reality—we interpret our lives through stories. These have the power to transform relationships and communities. We believe the PeaceTXT model can be applied to catalyze behavior  change vis-a-vis peace and conflict issues at the community level by amplifying new narratives via SMS. There is considerable potential here and still much to learn, which is why I’m thrilled to be working with SnA, PopTech & partners on launching our first international pilot project: PeaceTXT Kenya.

Applying Earthquake Physics to Conflict Analysis

I really enjoyed speaking with Captain Wayner Porter whilst at PopTech 2011 last week. We both share a passion for applying insights from complexity science to different disciplines. I’ve long found the analogies between earthquakes and conflicts intriguing. We often talk of geopolitical fault lines, mounting tensions and social stress. “If this sounds at all like the processes at work in the Earth’s crust, where stresses build up slowly to be released in sudden earthquakes … it may be no coincidence” (Buchanan 2001).

To be sure, violent conflict is “often like an earthquake: it’s caused by the slow accumulation of deep and largely unseen pressures beneath the surface of our day-to-day affairs. At some point these pressures release their accumulated energy with catastrophic effect, creating shock waves that pulverize our habitual and often rigid ways of doing things…” (Homer-Dixon 2006).

But are fore shocks and aftershocks in social systems really as discernible as well? Like earthquakes, both inter-state and internal wars actually occur with the same statistical pattern (see my previous blog post on this). Since earthquakes and conflicts are complex systems, they also exhibit emergent features associated with critical states. In sum, “the science of earthquakes […] can help us understand sharp and sudden changes in types of complex systems that aren’t geological–including societies…” (Homer-Dixon 2006).

Back in 2006, I collaborated with Professor Didier Sornette and Dr. Ryan Woodard from the Swiss Federal Institute of Technology (ETHZ) to assess whether a mathematical technique developed for earthquake prediction might shed light on conflict dynamics. I presented this study along with our findings at the American Political Science Association (APSA) convention last year (PDF). This geophysics technique, “superposed epoch analysis,” is used to identify statistical signatures before and after earthquakes. In other words, this technique allows us to discern whether any patterns are discernible in the data during foreshocks and aftershocks. Earthquake physicists work from global spatial time series data of seismic events to develop models for earthquake prediction. We used a global time series dataset of conflict events generated from newswires over a 15-year period. The graph below explains the “superposed epoch analysis” technique as applied to conflict data.

eqphysics

The curve above represents a time series of conflict events (frequency) over a particular period of time. We select arbitrary threshold, such as “threshold A” denoted by the dotted line. Every peak that crosses this threshold is then “copied” and “pasted” into a new graph. That is, the peak, together with the data points 25 days prior to and following the peak is selected.

The peaks in the new graph are then superimposed and aligned such that the peaks overlap precisely. With “threshold A”, two events cross the threshold, five for “threshold B”. We then vary the thresholds to look for consistent behavior and examine the statistical behavior of the 25 days before and after the “extreme” conflict event. For this study, we performed the computational technique described above on the conflict data for the US, UK, Afghanistan, Columbia and Iraq.

Picture 4Picture 5Picture 6

The foreshock and aftershock behaviors in Iraq and Afghanistan appear to be similar. Is this because the conflicts in both countries were the result of external intervention, i.e., invasion by US forces (exogenous shock)?

In the case of Colombia, an internal low intensity and protracted conflict, the statistical behavior of foreshocks and aftershocks are visibly different from those of Iraq and Afghanistan. Do the different statistical behaviors point to specific signature associated with exogenous and endogenous causes of extreme events? Does one set of behavior contrast with another one in the same way that old wars and new wars differ?

Are certain extreme events endogenous or exogenous in nature? Can endogenous or exogenous signatures be identified? In other words, are extreme events just part of the fat tail of a power law due to self-organized criticality (endogeneity)? Or is catastrophism in action, extreme events require extreme causes outside the system (exogeneity)?

Another possibility still is that extreme events are the product of both endo-genous and exogenous effects. How would this dynamic unfold? To answer these questions, we need to go beyond political science. The distinction between responses to endogenous and exogenous processes is a fundamental property of physics and is quantified as the fluctuation-dissipation theorem in statistical mechanics. This theory has been successfully applied to social systems (such as books sales) as a way to help understand different classes of causes and effects.

Questions for future research: Do conflict among actors in social systems display measurable endogenous and exogenous behavior? If so, can a quantitative signature of precursory (endogenous) behavior be used to help recognize and then reduce growing conflict? The next phase of this research will be to apply the above techniques to the conflict dataset already used to examine the statistical behavior of foreshocks and aftershocks.

The Mathematics of War: On Earthquakes and Conflicts

A conversation with my colleague Sinan Aral at PopTech 2011 reminded me of some earlier research I had carried out on the mathematics of war. So this is a good time to share some of the findings from this research. The story begins some 60 years ago, when British physicist Lewis Fry Richardson found that international wars follow what is called a power law distribution. A power law distribution relates the frequency and “magnitude” of events. For example, the Richter scale, relates the size of earthquakes to their frequency. Richardson found that the frequency of international wars and the number of causalities each produced followed a power law.

More recently, my colleague Erik-Lars Cederman sought to explain Richardson’s findings in his 2003 peer-reviewed publication “Modeling the Size of Wars: From Billiard Balls to Sandpiles.” However, Lars used an invalid statistical technique to test for power law distributions. In 2005, I began collaborating with Pro-fessors Neil Johnson and Michael Spagat on related research after I came across their fascinating co-authored study that tested casualty distributions in new wars (internal conflicts) for power laws. Though he was not a co-author on the 2005 study, my colleague Sean Gourely presented this research at TED in 2009.

In any case, I invited Michael to present his research at The Fletcher School in the Fall of 2005 to generate interest here. Shortly after, I suggested to Michael that we test whether conflict events, in addition to casualties, followed a power law distribution. I had access to an otherwise proprietary dataset on conflict events that spanned a longer time period than the casualty datasets that he and Neils were working off. I also suggested we try to test whether casualties from natural disasters follow a power law distribution.

We chose to pursue the latter first and I submitted an abstract to the 2006 American Political Science Association (APSA) conference to present our findings. Soon after, I was accepted to the Santa Fe Institute’s Complex Systems Summer Institute for PhD students and took the opportunity to pursue my original research in testing conflict events for power law distributions with my colleague Dr. Ryan Woodard.

The APSA paper, presented in August 2006, was entitled “Natural Disasters, Casualties and Power Laws:  A Comparative Analysis with Armed Conflict” (PDF). Here is the paper’s abstract and findings:

Power-law relationships, relating events with magnitudes to their frequency, are common in natural disasters and violent conflict. Compared to many statistical distributions, power laws drop off more gradually, i.e. they have “fat tails”. Existing studies on natural disaster power laws are mostly confined to physical measurements, e.g., the Richter scale, and seldom cover casualty distributions. Drawing on the Center for Research on the Epidemiology of Disasters (CRED) International Disaster Database, 1980 to 2005, we find strong evidence for power laws in casualty distributions for all disasters combined, both globally and by continent except for North America and non-EU Europe. This finding is timely and gives useful guidance for disaster preparedness and response since natural catastrophes are increasing in frequency and affecting larger numbers of people.  We also find that the slopes of the disaster casualty power laws are much smaller than those for modern wars and terrorism, raising an open question of how to explain the differences. We show that many standard risk quantification methods fail in the case of natural disasters.

apsa1

Dr. Woodard and I presented our research on power laws and conflict events at SFI in June 2006. We produced a paper in August of that year entitled “Concerning Critical Correlations in Conflict, Cooperation and Casualties” (PDF). As the title implies, we also tested whether cooperative events followed a power law. As far as I know, we were the first to test conflict events not to mention cooperative events for power laws. In addition, we looked at conflict/cooperation (C/C) events in Western countries.

The abstract and some findings are included below:

Knowing that the number of casualties of war are distributed as a power law and given a rich data set of conflict and cooperation (C/C) events, we ask: Are there correlations among C/C events? Is there a correlation between C/C events and war casualties? Can C/C data be used as proxy for (potentially) less reliable casualty data? Can C/C data be used in conflict early warning systems? To begin to answer these questions we analyze the distribution of C/C event data for the period 1990–2004 in Afghanistan, Colombia, Iran, Iraq, North Korea, Switzerland, UK and USA. We find that the distributions of individual C/C event types scale as power laws, but only over approximately a single decade, leaving open the possibility of a more appropriate fit (for which we have not yet tested). However, the average exponent of the power law (2.5) is the same as that found in recent studies of casualties of war. We find low levels of correlations between C/C events in Iraq and Afghanistan but not in the other countries studied. We find that the distribution of the sum of all conflict or cooperation events scales exponentially. Finally, we find low levels of correlations between a two year time series of casualties in Afghanistan and the corresponding conflict events.

sfi1sfi2sfi3

I’m looking to discuss all this further with Sinan and learning more about his fascinating area of research.

The Best of PopTech2011 in Tweets and Pics

@CauseGlobal: Zolli opens PopTech2011 , A World ReBalancing:
“We are not in Kansas, nor are we in Oz.
We are in the whirlwind”

@eileenlambert: Tablet in US $300, tablet in India $35.
Eastern countries are innovating
for radical affordability.
@andrew_zolli

@storylaura: the rural poor only exist as numbers.
by taking pictures they are removed from anonymity.
Shahidul Alam

@rperezzz: At PopTech2011 @shahidul: introduced term “The Majority World” – better than third world. I don’t want to be third of anything.

@SarahNelson: Check out majorityworld.com to see the work of photographers from developing nations

@frogdesign: American dream is alive and well -
just not in U.S -it is in India. -Anand G.

@dgilford: “Destiny is something you make rather than inherit.” @AnandWrites on India’s revolution against tradition of “know your place

@rperezzz: PopTech2011: @AnandWrites :In India, we-centric societies
are moving toward me-centric societies.

@thedelk: “China’s economic dominance is more imminent, larger in magnitude, and broader in scope than is currently believed.”

@wlabar: “By 2030 there will be a G1 – China” – Arvind Subramanian

@priyaparker: Cover of @arvindsubraman‘s book #eclipse is photo of
Obama bowing to a fully-standing Hu.

@brainpicker: Ooh! @PopTech launches fantastic new iPad app,
visualizing the World Rebalancing theme j.mp/oUY853

@artate: check out @unglobalpulse piece in the new @poptech ipad app:
mobile surveys to get a pulse of the planet http://bit.ly/pdwKRm

 

@poptech: “Rebalancing is not something you do once,
it’s a way of life” -@StephanieCoontz

@rperezzz: Countries most resistant to women’s rights are countries where women have least access to labor force, says @StephanieCoontz

@xtinem: Coontz #PopTech2011 “US is dead last of all western countries
in work family policies”

@storylaura: I used to say US neanderthal in family/work policy. I’ve studied Neanderthals & they took great family care. Stephanie Coontz 

@storylauraIf we redefine gender to include women’s right to work,
we must redefine work to include workers’ rights to family life.

@bookpickings: Stealth of Nations: The Global Rise of the Informal Economy – intriguing new book by Robert Neuwirth http://j.mp/pIXUh0

@AnandWrites: 1.8 billion people, half of world workers,
work off the books in informal economy: Robert Neuwirth

@dgilford: Informal economy is profitable: avg.
Lagos street shoe seller has higher margin than Payless Shoe Stores.
-Robert Neuwith

priyaparker: @nils_gilman predicts rise of “survival entrepreneurship”
in places like Greece, Egypt, Syria

@poptech: “Reminders that we’re not masters of the universe.”
- President of Iceland on the events of past decade

@AnandWrites: How Iceland dealt with its crisis so different from US,
acc. to president. He says they purged all the people responsible

@brainpicker: “Bank failure should not become the responsibility of
the people.” The president of Iceland tells it like it is

@AnandWrites: “What we are now seeing is people power on its purest form”: Iceland president on power of social media

@storylaura: protests and action in Iceland successful because
1) mobilized thru internet
2) demands concrete and measurable.
Prez Grimson

@JMathewAllen: Pres of Iceland Social Media has empowered the people
and made institutions a “sideshow”

@AnandWritesPrez: When Iceland had financial crisis, China was more helpful than West. He hosts more delegations from China than Europe.

@poptechAnnouncing PopTech Reykjavik 2012: Toward Resilience,
June 27-29, 2012 http://poptech.org/iceland

@PatrickMeier: On the Role of Technology in Building Resilient Societies http://tinyurl.com/3aw4tsb

@wlabar: Best estimate by IPCC is an increase in temp. of 1.8 to 4C

@AnandWrites2.7 billion humans have no access to financial services,
even merely to save: Bhagwan Chowdhry http://pic.twitter.com/beNS0bot

@AnandWritesCellphones will become the banks for the poor

@rperezzz: PopTech2011 Social Innovation Fellow Rose Goslinga takes stage.
“I insure the rains” – micro-insurance for Kenyan farmers.

@brainpicker: “850,000 girls in Kenya miss school because
they don’t have sanitary pads.”

@ZanaAfrica: Spread the word: pads + health education
can break cycles of poverty for girls.

@rperezzz: I <3 Rothberg’s PopTech2011 preso title: High Speed DNA Sequencing: Outbreaks, Honey Bees, Neanderthals, Watson, Moore and Your Genome.

@deliciousblur: High speed genome sequencing offers a new way
to develop therapeutic drugs

@storylaura: It’s okay to be down, it’s a chance to step back and say,
“maybe we did it wrong.” Rothman

@storylaura: By sequencing Neaderthal DNA we learned ~200 places different
btwn human and Neanderthal and chimpanzee. Cool! Rothman
@ConnectMinds: Did you know that the current spacesuit weighs 140 kilos?
MIT’s Dava Newman is out to make things slimmer and more mobile
@audreylinnloves: imagine using a space suit to help kids with cerebral palsy partake in day to day activities
@colincolin: “Maybe the future of science will be in creating puzzles,
then handling them to the world to solve.” – Adrien Treuille
@jdsutterFoldIt game creator: “We’ve in fact crowdsourced the entire scientific method, from hypothesis to experiment to results”

@brainpicker: Wow. 6 months into Eterna experiment
the worst player design was better than
the best computer design. http://j.mp/pBSq2g

@patrickmeier: The next frontier: time-critical #crowdsolving

@brainpicker: “Crowdsourcing has the potential to democratize the economics
and the joys of basic science.”
@ChristieNic: CDC is going to do real time #crowdsourcing to find solutions during next disease outbreak. (wow!) —Rothberg

@rperezzz: PopTech2011 Social Innovation Fellow Michael Murphy from @MASSDesignLab talks about buildings that heal. twitpic.com/739o8n