“The true voyage of discovery lies not in seeking new landscapes, but in having new eyes” 
–Marcel Proust

“Aid on the Edge of Chaos” is an eye-opening book that examines international aid: when it works, why it fails, and how it can be improved.  Author Ben Ramalingam, a researcher with the Overseas Development Institute, focuses on how insights from “complexity science” can help practitioners re-think aid in the post-Millennium Development Goal (MDG) world.  Ramalingam reflects on the business model and institutions of aid, and how ideas from complexity science can—and have—been applied to development.  He concludes with some exciting prospects for the future of the aid industry.

Complexity science is an emerging inter-disciplinary field focused on the study of complex systems.  Such systems are composed of interconnected and interacting parts.  These parts have little significance individually, and instead must be analyzed in the broader context of the system to which they belong.  For real world examples think of an ant colony, a metropolitan city, or agriculture.  The objective of complexity science is to understand the collective behavior of these systems, and how they interact with and adapt to their environments.  (For a great overview of complexity science and its application to the global financial sector, I suggest viewing the TED Talk by James B. Glattfelder called “Who controls the world?”).   While complexity science is employed in several scientific fields, its application to the humanities is largely uncharted.

Before applying complex science to development, Ramalingam examines the inner workings of the aid industry and where it has failed.  He argues that aid itself, while well intentioned, often undermines development goals such as participation, ownership, and relevance.  Central to this problem are the numerous assumptions aid organizations make—for example, that a certain level of investment will lead to economic growth—that shape how the industry learns, makes decisions, and assesses itself.  Ramalingam cites the design and implementation of the MDGs as a classic example of prescriptive and ultimately ineffective development.  He claims, “At their worst, [the MDGs] are a donor-led, top-down, reductionist agenda—‘minimum development goals’—that pays little attention to locally defined and owned definitions of progress and development.”

While some of Ramalingam’s critiques of the MDGs—that they lack accountability, flexibility or local considerations—are not new, he does well framing them within the context of the aid industry.  He claims that the aid industry has a tendency to reinforce, rather than challenge, poor practices, and how this can manifests itself in development organizations.  Ramalingam’s insights here can benefit both students and practitioners of development as they reflect on the kind of work environment best suited to learning and professional development.  For example, does a potential employer emphasize learning and experimentation, or long standing “best” practices?  When projects fail, is the organization willing to abandon ineffective strategies, or does it simply refine existing processes? 

Ramalingam believes that complexity science can help us understand the shortcomings of development organizations and the aid industry.  He describes the evolution of cities to demonstrate the way complex systems develop through a variety of random, historic, and physical characteristics that interact with each other and adapt over time.  Like cities, Ramalingam argues that humans have adapted in much the same way, and that human behavior is a result of constant change.  He contrasts this theory with that of the ‘rational choice’ actors traditionally presumed in political science and economics.  Further, he contends that the problems facing humanity—such as poverty and disease—are also adaptive.  Once we recognize these challenges as complex systems, we realize that traditional aid blueprints that target the part rather than the whole are liable to fail.

While Ramalingam’s account of complexity is at times dense and rigorous, his central argument is clear: understanding complexity helps us question—and offer alternatives to—the core assumptions of the aid industry.  In the process of developing this thesis, he draws on a rich collection of interventions that have harnessed complexity science in ways that are surprisingly unknown.  For example, a complexity-based approach to analyzing and adapting farmer practices was employed to combat malaria in the Mwea region of Kenya.  Instead of spraying insecticides—which can cause disease resistance—this approach deployed interventions such as better coordination of farmers, intercropping, using cattle as bait, and mosquito-repelling plants, which were pursued in concert with an understanding of Mwea’s social and political environment.  This integrated approach has significantly reduced cases of malaria.

Furthermore, complexity does not just apply to aid interventions.  According to Ramalingam, development organizations themselves have much to learn from complexity science.  He cites work from Eva Schiffer, a researcher who developed an approach to analyzing what Ramalingam calls “complex governance systems.”  He argues that Schiffer’s network analysis tool can help groups of actors, such as multiple aid organizations working together in a complex humanitarian crisis, understand how they are linked and their level of influence.  This enables them to more effectively deploy aid.  Thus, complexity can tell us a great deal about the process of development, and how aid organizations can be best positioned in the development landscape.  I believe this insight has significant implications for the increasing fragmentation of aid, wherein bilateral, multilateral, public, and private donors are deploying efforts in uncoordinated and often conflicting ways.

After exploring numerous examples of applied complexity, Ramalingam proposes some broad advice for the aid industry.  He suggests that we focus on:
●    Developing tools to facilitate adaptive learning in the face of complex challenges, from poverty alleviation and food security to natural resource management;
●    Supporting organizational cultures of learning, with a willingness to accept change as inevitable, and an ability to engage in interventions as experiments;
●    Designing process-based approaches better suited to complex and unstructured problems; and
●    Striking a balance between no strategy at all and the inelasticity of universal blueprints.

Overall, Ramalingam’s recommendations for the future of aid are informed and inspiring.  He contends that the successes of interventions that have taken on a more systemic and adaptive approach point towards a fundamental shift in development philosophy.  Furthermore, I think Ramalingam would propose that static benchmarks—such like those outlined by the MDGs—be abandoned entirely.  Instead, the aid industry should focus on facilitating adaptive processes that strengthen—rather than replace—local systems, which can experiment, investigate, and respond as new learning emerges.  

But just as Ramalingam has dismissed pre-packaged best practices in aid, he cautions readers not to misinterpret complexity as the panacea.  While complexity science can guide the aid industry towards new possibilities and approaches, it cannot offer an exact blueprint for the future.  The challenge for readers, especially those working in the aid industry, is to apply the approach outlined by Ramalingam to encourage this type of thinking where it currently does not exist.  How can we as practitioners, after replacing our old eyes with new ones, begin the conversation regarding complexity in the face of aid’s current model?

Ramalingam, Benjamin. Aid on the Edge of Chaos. Oxford University Press, 2013