THE EFFECTIVE PROBLEMSOLVER #082
When solving the world’s biggest problems, it’s easy to fall into the trap of breaking them into smaller, manageable pieces—optimize one aspect, get the best result, and move on.
This approach, popular in the effective altruism movement, seems efficient.
But what happens when focusing on a single part leads to unintended consequences?
Take GiveDirectly, which has been distributing cash transfers across African countries for over a decade. In Kenya, studies show that direct transfers can boost local economies, with each dollar generating $2.60 in economic growth.
It’s tempting to want to scale the heck out of this solution and forget all other ineffective global poverty solutions.
However, in places like Mexico and Iran, similar efforts led to inflation, erasing much of the benefit.
This raises an important question: Are cash transfers a universal solution, or do their outcomes depend more on local conditions?
The core issue is that problems rarely exist in isolation.
When we optimize one piece of the puzzle, like personal income, without considering the broader context—such as inflation or market structures—unexpected and harmful results can emerge.
Systems theorist Russell Ackoff captured this perfectly:
“The sum of the optimal solutions to each component problem is not an optimal solution to the whole.”
In this week’s newsletter, I’ll show you three examples where optimizing a single aspect led to unintended harm. Then I’ll give you a counterintuitive solution – sub-optimizing – and three practical tips to create a more sustainable, positive impact.
Let’s dive in.
Three Examples Where Optimizing Leads to Unintended Consequences
1. Malaria Nets in Africa
In the fight against malaria, organizations have distributed millions of mosquito nets to reduce transmission.
However, a significant percentage of these nets have been repurposed for fishing instead of preventing disease. In fact, a 2014 study found that in certain parts of Lake Tanganyika, a majority of the population had used malaria nets for fishing.
The result?
Overfishing and ecological damage, which threatens long-term food security and the livelihoods of local communities.
2. Shoes for Children Programs
TOMS Shoes became famous for its “buy one, give one” model, donating a pair of shoes to a child in need for every pair purchased.
But a World Bank study conducted in rural El Salvador found that while children generally appreciated and used the shoes, the overall impact of the program was negligible.
The study revealed that the donated shoes had little effect on children’s health, foot health, or self-esteem, though there was a small positive effect on school attendance for boys. However, a concerning outcome was that children who received the shoes were more likely to believe that outsiders should provide for their family’s needs, suggesting that the donations may have fostered a sense of dependency.
This highlights the potential harm of well-intended charity programs that fail to address deeper, systemic issues and underscores the importance of better targeting for in-kind donations.
3. Cash Transfers to the Poor
We’ve already touched on direct cash transfers.
But to dive deeper, a study in the Philippines found that a household-targeted cash transfer program aimed at improving child nutrition had unintended consequences.
While the transfers helped beneficiary households, the influx of cash caused local food prices—particularly for items crucial to child nutrition—to rise. This inflation led to a 34% increase in stunting among young children in non-beneficiary households, equivalent to an 11 percentage point rise.
Each of these examples illustrates a harsh truth: when we focus on solving one aspect of a problem, we often miss how that solution interacts with the broader system.
Why Sub-Optimizing is Better
Instead of seeking the optimal solution for one piece, we should focus on sub-optimizing—making incremental, balanced progress that considers the broader system.
What does this mean in a practical sense?
Well, in complex problems, we’re never aiming for just one goal. Instead, we have multiple, often competing objectives.
For instance, we want to raise incomes, but not at the expense of health or buying power. We strive to reduce the incidence of diseases like malaria, but we also need to maintain the health of local ecosystems, such as fisheries.
Optimizing for just one goal—whether it’s higher income or fewer malaria cases—can backfire if we ignore the ripple effects on other important areas.
This is why sub-optimizing can be a smarter approach.
Rather than trying to find a perfect solution for a single objective, sub-optimizing involves making trade-offs that balance various goals across the system.
For example, instead of focusing solely on cash transfers to boost income, a sub-optimized approach might include parallel efforts to stabilize local markets and improve public health. This way, the benefits of higher incomes are sustained without causing inflation or other negative side effects.
By keeping an eye on the bigger picture, sub-optimizing ensures that improvements in one area don’t come at the cost of harm in another.
It’s about recognizing that in complex systems, progress is often about balancing multiple goals rather than achieving the optimal solution for just one.
So, how can we sub-optimize effectively?
1. Take Time to Understand the System
Before rushing to solve one part of a problem, map out how that part interacts with other areas. Is it part of a larger ecosystem? Are there stakeholders or factors you’re overlooking? Ask these questions before implementing solutions.
2. Test Solutions in Context
Don’t assume your solution will work the same in every context. Pilot small changes and observe how they affect the surrounding system. This helps reveal unintended consequences before they become large-scale problems.
3. Embrace Trade-offs
Accept that not every solution will be a perfect win. Sometimes, avoiding large negative consequences means settling for “good enough” outcomes that keep the larger system intact.
By approaching problems with a sub-optimization mindset, we can navigate complex messes with greater humility and effectiveness. It’s not about winning one part of the problem—it’s about staying in the game long enough to make progress on the whole.
See you in two weeks.
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