If you compare climate change and nuclear war, a lack of humanitarian innovation and a slowdown in scientific discovery, or technology in education and in transport, you might think that these problems are very different from one another. But take a step back and identify what's causing them, and you’ll find they follow similar patterns. We call these patterns problem archetypes.1 Identifying these archetypes can help us identify types of solutions that work across many similar problems.
We’ve been keeping track of problem archetypes as we notice them, and we’re sharing them here as a starting point for developing a more comprehensive list.
So far, we’ve identified eight different problem archetypes. These include:
If we don’t know a problem exists, we can’t make deliberate progress on solving it. Sometimes discovering the problem leads quickly to action, as when the 1985 discovery of the hole in the ozone layer led to the 1987 Montreal Protocol limiting ozone-depleting substances.
But sometimes this process isn’t so simple. Although smoking was suggested as a major cause of lung cancer in the early 20th century, it took multiple lines of evidence developed over the following decades to prove it in the 1940s and 50s. It took several decades more to convince doctors, politicians, and the public of the problem, in large part because of campaigns by the tobacco industry.
Sometimes people don’t see something as a problem because of their values. Differences in values are key reasons for disagreement over whether factory farming, economic inequality, and abortion are important problems.
Values also affect people’s approaches to solving problems. For example, anticapitalist climate campaigners often disagree with proposals such as carbon taxes, arguing for a more radical reconfiguration of the economy.
In the early stages of solving a problem, there may be a lack of ideas of what to do to get started. The burgeoning field of AI policy is going through this right now: actors in this field are working to identify the different facets of the problem, and propose initial solutions, but there isn't yet consensus over where efforts should be focused.
Later on, there may be plenty of solutions, but inadequate evidence as to what will work best. A lack of good evidence is one of the most common issues we come across. For example, Steve Higgins, Professor of Education at Durham University, says “Most things in education, we have no idea whether they work.” This issue even affects areas with relatively good evidence, such as medicine. For example, many evidence-based medical guidelines have limited applicability when patients have more than one condition.
Some problems lack sufficient funding. For example, an IPCC report suggests that to limit global warming to 1.5°C we need a $2.4 trillion investment in the energy system every year between 2016 and 2035, which is about 2.5% of global GDP.2 But 2016 saw a global $455 billion spend on addressing climate change overall, which is only 19% of the IPCC recommended investment.
Often there is insufficient funding because a problem affects people who can’t pay for solutions. For example, because snakebite mainly affects poor people in the developing world, there isn’t a big enough financial incentive to make antivenom for them. Similarly, the future people affected by climate change are unable to pay us to prevent it. If they could, there would be much more motivation for people to solve the problem now.
Funding amount is not the only problem - how it is allocated also matters. For example, the humanitarian sector is hampered by funders who are risk-averse, inflexible, and give short-term grants. There is a similar situation in science funding, which constrains research.
The nonprofit sector, in general, suffers from problems with the structure of funding. Donors often prefer charities with low administrative (aka overhead) costs. But this can make it difficult for charities to operate effectively. Another difficulty for charities arises when donors restrict their funding to a particular programme, rather than giving the charity a grant that can be used on any part of their work. This can fragment charities’ strategies.3
Money is not the only resource constraint. Skill is another major one. For example, the humanitarian sector struggles with scaling innovations partly because there is a lack of skill on scale in the sector. In EdTech, teachers and school leaders often lack the expertise to properly evaluate EdTech. In the low-income countries, there is a shortage of many skilled professionals such as psychiatrists.
Even if there are incentives to solve a problem, they can be misaligned. This often happens in situations where it is difficult to measure and incentivise what we want, but decisions are made based on these flawed metrics anyway.4 For example, judging teachers based on student’s exam results does fit with the goal of education, but too much emphasis on this metric can lead to teaching to the test.
Fields that form to tackle a problem often benefit from the involvement of organisations that provide the field with infrastructural services. Rather than working directly on the problem, these organisations help by coordinating work, developing and sharing evidence, and building networks.5 For example, in global health, there is the Disease Control Priorities Project, which reviews the evidence on interventions to address disease in low-resource settings.
The development of a new type of infrastructural organisation can have a big impact on a field. For example, startup accelerators have become a major part of the tech startup ecosystem, beginning with Y Combinator in 2005. In charity funding, the charity evaluator GiveWell has had a big impact – it estimates that it influences ~$150 million per year in donations.
Sometimes, people want to take action on a problem, but doing so would put them at a disadvantage compared to others. Without a way to trust each other, it’s difficult for any of the parties to take action. For example, this occurs in climate change, where “other industrialized nations such as the USA (as well as Australia and Canada) have balked at taking action for fear of ‘free riding’ on the part of major developing nations who have become trade competitors.”6
Once we build up our understanding of each problem archetype, we plan to draw on fields like systems science, the economics of market failure, and the study of coordination and cooperation to think about corresponding types of solutions that funders and other actors can develop to address these.
But for now, know of any problem archetypes we’ve missed? Drop us a line at firstname.lastname@example.org
This is a similar idea to Daniel Kim’s System Archetypes. While his approach is focussed on any kind of problem, ours is focussed specifically on large-scale problems that altruistically-motivated people might want to solve. His approach is also rooted more in systems science, whereas we draw on our own experience. We plan to investigate the systems approach more and may incorporate it into our problem archetypes analysis. ↩
“Global model pathways limiting global warming to 1.5°C are projected to involve the annual average investment needs in the energy system of around 2.4 trillion USD 2010 between 2016 and 2035, representing about 2.5% of the world GDP” p. 24 of the IPCC report Global Warming of 1.5°C ↩
Similar to the idea of field-building intermediaries outlined in the article When Building a Field Requires Building a New Organization ↩
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