Erwin Knippenberg is a PhD student at Cornell’s Dyson School.
According to Nobel Prize winner Elinor Ostrom “Practitioners and scholars who fall into panacea traps falsely assume that all problems of resource governance can be represented by a small set of simple models, because they falsely perceive that the preferences and perceptions of most resource users are the same.” In Aid on the Edge of Chaos Ben Ramalingam offers concrete examples of how we can instead harness complexity to solve ongoing and future crises.
Ramalingam argues that development interventions tend to apply linear thinking to solve problems defined by their complexity. Consequently, interventions abound in unintended consequences, and when they do succeed, they are difficult if not impossible to replicate.
For example, in certain situations, such as the incredibly delicate social-ecological system of Bali, development interventions can prove destructive. Driven by the Green Revolution, in 1978 the government sought to maximize rice yields by introducing modern farming techniques. Instead, their interventions led to pest-infestations, water shortages and crop failure. Intrigued, researchers Stephen Lansing & Ben Kramer built a model incorporating data on rainfall, river flows, irrigation schedules, crop use and pest-population dynamics. They found that optimizing harvests subject to these many constraints required careful calibration at the local level. Traditional methods coordinated through the water temples led to near optimal outcomes, while a Green Revolution scenario led to pest-infestations and shortages.
The book highlights that even local successes, like mobile money, can prove hard to replicate. Pioneered by MPESA in Kenya, where it has met huge success and generated substantial positive externalities, mobile money has struggled in other environments. A notable example is India, where the necessary combination of ubiquitous cell-phone use, near monopoly by a telecom, and lax financial regulations that made MPESA successful in Kenya is lacking.
Ramalingam argues that many of the mistakes highlighted above stem from a preference for linear causality models based on restrictive assumptions. The traditional view is that relaxing those assumptions creates chaotic systems delivering little in the way of useful insights. But advances in the science of complex systems and improved computing power have opened up a new gray area ‘at the edge of chaos’. We can now map networks and simulate the behavior of interacting individual agents. These models allow us to better understand threshold effects and tipping points, where small tweaks can lead to massive changes. Such an approach also encourages tinkering and adjusting parameters and assumptions as the situation unfolds. By shifting from the prescriptive to the iterative, we open ourselves to discovery, making for better research and better policy.
Where most donors focused on the virus, Médecins sans Frontières (MSF, also known as Doctors Without Borders) focused on an even more contagious threat: fear. When someone got sick, healthcare workers dressed as astronauts would come to take them away. Once they disappeared into isolation, people would rarely re-emerge and relatives were denied even the solace of seeing the body. Fueled by mistrust, entire communities would bar access to their sick, believing that the health-workers themselves were responsible for the contagion. MSF launched a concerted education campaign involving local leaders, health promoters and a radio talk-show. They allowed visitors to the health centers, who could talk to their loved ones or view their bodies from a safe distance. Behavior changed. The epidemic isn’t over, but determination has replaced panic. MSF, informed by decades of experience, proved flexible enough to adapt and co-opt the local communities, turning them from a liability into an asset. In contrast, expensive outside interventions built around simple, linear metrics proved not only insufficient, but also distracting.
Harnessing these opportunities requires a shift in mind-set, from aid as a ‘big push’ to aid as a catalyst for internal change. For researchers, this means combining technical acumen with a deep-rooted understanding of the local context. For policymakers, this means learning from what is and what is not working and being willing to adapt policy in light of this information.
