Complicated vs. Complex, Part II: Solving the World’s Most Difficult Challenges

Confronting the hardest problems on the planet requires humility to admit that we don’t know many answers when we start and sometimes we don’t even know the right problem to work on. If we address symptoms rather than root causes, we can exacerbate conditions. Penalizing teachers for example for not coming to school may ignore very real issues related to over-crowded classrooms, transport or meager wages for educators. If you start with the wrong problem, you’ll certainly propose the wrong solution.
 
What would it take to accept that most of the problems we encounter in development require listening better to end-users, learning about technical and political obstacles, and an ability to course correct when conditions change? That requires flexibility, faster response times, and treating beneficiaries as partners in solving complex problems.
 
I recently blogged on the difference between complicated and complex systems: the importance of identifying each to solve problems and particularly to scale solutions. What follows is a brief description of what makes a system complicated or complex and why it matters. 

	Complicated	Complex
In planning	Describe What; dictate How Focus on details Coordinate everything centrally Deliberate tradeoffs Solution is often reached through a series of algorithms	Describe What but not How Only key details—the fewer, the better Limit central coordination to what’s absolutely necessary Tradeoffs not always foreseeable, and they can shift over time
Goal	Optimal solution	Good enough to learn from and adjust
Focus on	All the details	Potential side effects
During execution	Make sure plan is adhered to Adjust to make things more efficient Compliance	Measure results against all desired outcomes Don’t get attached to any particular course of action Adjust constantly and learn

Scaling-up what works may have more to do with process expertise – figuring out what problems need to be solved in a given environment – than blueprints based on expert knowledge. Expert-driven organizations are too often hammers looking for nails rather than solution seekers looking to partner with local experts to solve local problems.
 
The risk with over-resourcing complicated problem-solving techniques in complex environments is that solutions feel forced, don’t resonate with end-users, and more importantly don’t solve underlying problems. A glaring example is the construction of the Choluteca Bridge in Honduras. Although the bridge was constructed by the U.S. Army Corps of Engineers in the 1930s and remains technically sound, the road it was connected to moved in 1998 after Hurricane Mitch; today, it’s a bridge to nowhere. Though structurally flawless, without attention to shifting realities on the ground, it serves no purpose.
 
And often solutions even when they do work, don’t scale. We’ve seen mobile telephony and micro-lending spread like wildfire while toilets and basic sanitation continue to be unavailable for more than a billion people. We can’t impose solutions on people. They have to want them and often demonstrate their demand through markets and other instruments.
 
But the biggest lesson is that of differentiating between solutions that can be replicated easily and usefully (say building a road) and those that require an understanding of local conditions and require an experimental approach driven by data, feedback, learning, and adaptation.

We are at such a fork in the road. The hardest problems of the world - climate change, food security, youth employment are complex and require data-driven experimentation and adaptation to solve. Solutions may not always scale. But the processes by which we experiment, learn, and adapt can scale. That’s the challenge for global development institutions in the 21^st century.