This year, our Good Problems team explored running workshops with funders as a way of sharing our tools for identifying and prioritising problems. This gave us an opportunity to understand how funders are thinking about their role and impact, and how we can evolve the support we offer.

This post features two workshops that we think reflect the sector’s shifting orientation toward problem-led approaches: a workshop with a group of Finnish foundations on how to collaborate around problems, and another with the Centre for Ageing Better on prioritising problems.

Collaborating around problems

We often work with funders who want to know who else is working on the problems they are interested in. It helps them understand where they fit in and prevents duplication of work. But some funders are taking steps to actively collaborate with other funders around particular problems. This helps them have more impact than they could have by acting independently.

Jouni Lounasmaa, Executive Director of the KAUTE Foundation, was interested in a more collaborative approach. He wanted to hold a workshop where Finnish foundations could meet, share best practices, and explore how they could work together to tackle climate change and other pressing problems. Working with Jouni, we co-designed activities to help them share and understand the problems they were interested in, and see how they could work together to solve them.

The workshop gathered a dozen foundations, each with its own distinct history, approach to grant-making, and set of priorities. To start with, we invited them to get to know each other’s interests by forming groups and sharing problems they were currently working on or would like to work on. We then asked them to cluster the problems, select a cluster they were particularly interested in, and prototype a shared programme to address that problem.

To help them scope out the potential programmes, we asked funders to fill out a Rapid research and validation plan. They wrote down what they knew about the problem, what they didn’t know, and what they could do to fill the gaps in their knowledge. We often use a version of this validation plan in our own research on problems because it works well to highlight what we still need to learn and to plan how we’ll do this, doubling as a to-do list we can revisit periodically as our understanding of a problem evolves.

Taking this problem-focussed approach made it easier for individual funders to understand what they could work on and how their resources and expertise might be complementary. When you lead with problems, conversations about potential collaborations become much more concrete and grounded.

Science Practice provided a pragmatic approach to an often philosophically slanted discussion. The workshop has inspired us to embark on a journey towards well-researched and problem-oriented co-funding. I have found the tools to be very useful in my own work.

– Jouni Lounasmaa, Executive Director of the KAUTE Foundation

Foundations don’t often have the opportunity to meet and explore how to work together on solving problems. However, creating space for these activities is becoming increasingly relevant given the urgency and complexity of the problems we are facing. These problems require a shared vision, action plan, and complementary programmes that build on each other to maximise impact. We were excited by this open and collaborative gathering in Finland and are looking to create similar opportunities for funders in the UK to come together and explore how to tackle important systemic issues.

Prioritising problems

Even when funders have decided on a problem area, it can be challenging to define specific opportunities to intervene. Having a clear set of criteria for problems can support more open and constructive conversations and facilitate decision-making. It can also help funders clearly articulate strengths and pinpoint the biggest opportunities for impact.

The Centre for Ageing Better’s goal is to create a society where everyone enjoys a good later life. One challenge they face is setting priorities among the diverse range of problems they could tackle. For example, should they focus on workers over 50 with health conditions struggling to get back into work – or on transforming UK workplaces into healthy places to increase disability-free life expectancy?

To inform their decision-making, we designed and ran a workshop with around 20 team members focused on developing internal criteria for identifying and defining suitable problems for Ageing Better and exploring how the team could use these criteria to focus their work.

Before the workshop, we shared a short survey with the Ageing Better team to learn more about how they make prioritisation decisions, who is involved, and what criteria they use (whether these are explicit or not). We used the responses as a starting point for the workshop content and activities. This was important because it meant that the workshop built on the team’s own ideas and allowed them to advance and challenge their thinking rather than start from a blank slate.

For the main activity, we created a long-list of the criteria mentioned in the surveys and asked the participants, in groups, to discuss and refine these before selecting the five they thought were most relevant for Ageing Better team members to consider when deciding on which problems to pursue.

We then asked groups to use their resulting criteria to prioritise between three problems they were currently struggling to choose between. The activity proved challenging but productive. The teams said that making decisions over which problem to pursue over others was difficult because the criteria didn’t feel right just yet – the criteria were either trying to cover too many things at once, weren’t as relevant in practice as they had seemed when proposed, or were missing completely. This exercise proved useful for quickly testing the emerging criteria and understanding how they could be used in practice.

By the end of the session, the teams were already thinking of the next iteration of the criteria and how they could use different subsets at different stages of the research and prioritisation.

Having a defined set of criteria for this type of work can facilitate more open conversations and highlight hidden assumptions or potential misunderstandings around an organisation’s role and priorities. Being able to clarify these and speak openly and confidently about them means that you can be more strategic about the problems you decide to prioritise and the impact you can achieve.

Changing the funding narrative

With each conversation and workshop that we have with funders, we see a change in the narrative. Funders are increasingly reflecting on their own activities and looking for more strategic and collaborative ways to increase their impact. While the intent exists, there is still a lot of work to be done to understand how to best achieve this, what works, and what doesn’t. However, this learning needs to happen at an accelerated pace as the global problems facing us are only growing in scale and complexity.

Within this space, we see our role as helping funders become more strategic about the problems they prioritise to achieve greater impact, sharing best practice from across the sector, and defining common goals to enable collaborations. If you’re interested in what a problem-led approach could do for your funding organisation, we’d love to help.

For a while we’ve been interested in collective intelligence and how it can be used to identify and tackle important problems. In a previous post, we looked at methods that could help democratise futures thinking, many of which have a collective intelligence aspect. To explore more, I went to Nesta’s conference 21st century common sense: Using collective intelligence to tackle complex social challenges. It gave a good overview of current activity and thinking on the topic, and I wanted to share some of the themes I came across there.

What is collective intelligence?

According to Nesta’s new collective intelligence playbook.

Collective intelligence is created when people work together, often with the help of technology, to mobilise a wider range of information, ideas and insights to address a social challenge.

This is not something new – parliaments, business meetings, and scientific institutions all enable collective intelligence. But new technologies offer new options – Wikipedia helps people collaborate to build an encyclopedia, Loomio helps people make decisions without meetings, and the Human Diagnosis Project brings together medical professionals to collaborate on diagnosis.

Interesting themes at the conference

How to experience the challenges of group decision making

Group decision making is central to collective intelligence. I got to experience the challenges of this first-hand through an immersive theatre experience called The Justice Syndicate.

I was one of a 12-member jury asked to decide whether a fictional top surgeon accused of a serious crime was guilty or not. Guided by instructions on tablet computers we read forensics documents, saw videoed witness testimony, and read out lawyers’ statements. Periodically we had discussions, and voted on whether we thought he was guilty. It was an intense experience - the subject matter was disturbing, and the realism of the situation gave me a sense of responsibility for making the right decision.

After we’d made our decision, neuroscientist Kris De Meyer shared what he’d found through his work on the project. As well as telling us about the science, he said that it was unusual for us to have converged so rapidly on a unanimous verdict. Often more than one final vote is needed to get to unanimity, and sometimes there is a lengthy and heated discussion between two opposing groups.

The experience of going through this process and hearing the variety of ways different juries made decisions lead me to question my own rationality. Although we can know intellectually about the biases we all have, experiencing it makes it more real. It would be great to see these immersive decision making experiences used more widely, so people can experience the ways in which their thinking can go wrong.

Questions we’d like to explore more

• How can we improve public engagement methods like citizens juries using this research?
• How else can we use participatory theatre to stimulate thinking about social decision making?

Why we need to fill data gaps

Several speakers thought it was important to draw on new sources of data such as social media, which can be more useful than survey data. For example, Maesy Angelina from Pulse Lab Jakarta described their project Haze Gazer, which identifies pollution from forest fires in Indonesia using both satellite imagery and data from social media.

Filling gaps in existing data is also important, especially if these gaps harm particular groups in society. For example, Jessica Sena the OpenStreetMap group GeoChicas mentioned that only 3% of OpenStreetMap contributors are women, which affects the information that gets added to their maps.

Maesy Angelina from Pulse Lab Jakarta also emphasised the importance of having “thick data” as well as “big data”. As Tricia Wang defines it

Thick Data is data brought to light using qualitative, ethnographic research methods that uncover people’s emotions, stories, and models of their world.

Combining thick data and big data is a good example of combining the strengths of machines and humans. Pulse Lab Jakarta did this effectively when they used ethnography to understand why they were seeing complaints on social media about school opening times in areas affected by forest fires. By sending ethnographers to the affected areas, they found that children ended up walking a long way through polluted air to get to school, only to find that the school was closed because of the haze.

Questions we’d like to explore more

• How can we use the data sources held by private companies for the public good, without violating people’s privacy?
• How can we encourage a wider variety of people to contribute to crowdsourced data projects?
• When should we not close a data gap? As one member of the audience suggested, sometimes people want to remain invisible.

How software can help group decision making

Several speakers were working on software to help groups make better decisions. For example, Vito Trianni has developed software to give feedback to groups who are making decisions. He hopes that the software can help overcome some of the challenges of group decision making such as herding and overconfidence. Another speaker, David Baltaxe from Unanimous AI presented software inspired by the dynamics of flocks and swarms of animals. It has has been used in forecasting, business decision making, and in helping groups with different political views to set priorities.

Questions we’d like to explore more

• How can we trust software that helps us make decisions? This is particularly a problem if the algorithms are proprietary. But even if they are open source they might be difficult for people to understand and see as legitimate.
• How can political science and political theory help us design these kinds of systems? For example, can we learn from existing research in social choice theory on the characteristics of different voting systems?

How this is relevant for Good Problems?

When designing problem-led funding programmes we have to draw on the knowledge and wisdom of many people. We read many documents, look at data, talk to experts, and help our clients’ teams make tough prioritisation decisions. We’d like to explore how we could use collective intelligence to help with this.

As well as informing our own work, we think collective intelligence is a useful way of thinking about how society as a whole can understand, prioritise, and solve problems. We look forward to seeing how the field evolves and what innovative projects emerge from funding programmes like the recent collective intelligence grants from Nesta, the Wellcome Trust, the Cloudera Foundation, and Omidyar Network.

Related posts

Ways to democratise futures thinking

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:

1. Lack of knowledge about a problem
2. Disagreement about values
3. Lack of potential solutions or evidence of their effectiveness
4. Insufficient or badly-structured funding
5. Skill shortages
6. Misaligned incentives
7. Missing infrastructural organisations
8. Coordination problems

1. Lack of knowledge about a problem

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.

2. Disagreement about values

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.

3. Lack of potential solutions or evidence of their effectiveness

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.

4. Insufficient or badly-structured funding

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

5. Skill shortages

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.

6. Misaligned incentives

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.

7. Missing infrastructural organisations

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.

8. Coordination problems

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

Building on this list

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 goodproblems@science-practice.com

References

Identifying the right problem to solve is an important part of having an impact, but it’s a hard task. Lists of clearly-described problems offer a starting point for finding the right one. They can also help organisations and individuals coordinate around a set of common priorities, as has happened with the UN Sustainable Development Goals

This is why we’ve been working on what we call problem briefs. These are short documents where we describe a problem, evaluate how important it is, and suggest what a philanthropic funder could do to help solve it. We’d like to develop these into a resource where people can explore problems and see how they could contribute towards solving them. We’ve looked for other organisations doing similar work, and wanted to share what we found:

• The Moonshot Catalog. A project funded by Schmidt Futures in collaboration with the American Association for the Advancement of Science that summarises science- and technology-related challenges. One of the moonshots is feeding 2050’s ten billion people.
• The 50 breakthroughs study. The Institute for Transformative Technologies developed a list of 50 scientific and technological advances needed for sustainable global development. These include new methods of improving fertilizers and low-cost off-grid refrigerators for preserving vaccines.
• Open Philanthropy Project cause reports. This foundation uses a systematic process to work out what problems to work on, which involves writing up reports on each of the problem areas they have looked into. They have investigated problems such as geoengineering research and criminal justice reform.
• Milken Institute Center for Strategic Philanthropy Giving Smarter Guides. These guides outline philanthropic opportunities in areas of health such as Alzheimers and colorectal cancer.
• University of Pennsylvania Center for High Impact Philanthropy issue areas. They develop guidance for philanthropists on a variety of issue areas such as disaster relief and strengthening democracy.
• The Encyclopedia of World Problems and Human Potential. This is a list of 56,000 world problems identified from periodicals and the documents of 70,000 non-profit organisations. It is organised into categories and problems are linked to related problems. Examples include economic underdevelopment and environmental degradation.
• Ashes Ashes. A left-wing podcast where each episode explores a global problem. Episodes include one on the ease with which truth is manipulated and the problems with forensic science.
• 80,000 Hours problem profiles. a nonprofit that provides advice for people on using their career to solve global problems. Their problem profiles include improving institutional decision-making and positively shaping the development of artificial intelligence.

Developing these kinds of resources is part of a wider project that many organisations are independently working on: finding, understanding, and prioritising problems to work on. We’d like to see more of a unified field develop around this kind of work. A first step towards this would be for organisations to share their work so others can build on it. For example, we’d encourage foundations to publish their analyses of problems, which they often keep as internal documents.

In the longer run, we like Bret Victor’s vision of tools for problem-finding. When thinking about how an engineer might find climate change problems to work on, he suggests that she needs “a tool that lets her skim across entire fields, browsing problems and discovering where she could be most useful.” This is not just something that engineers need - anyone wanting to have a large positive impact would benefit from a tool like this.

Thinking about the future is important for taking effective action in the present. While futures thinking by specialists and elites can be useful, it risks not taking account of the knowledge and values of the public. The field of participatory futures aims to correct this by developing democratic and inclusive processes for people to explore and develop the futures they want.

With this goal in mind, Nesta have been exploring the idea of participatory futures, and have collected many examples of how it can be done. A report currently being developed will push this further. It will clarify what participatory futures is and share available best practice and methods. We have done some initial thinking in this area as well and would like to contribute our findings to this work. In this post, we will outline a series of observed trends that are relevant to participatory futures, propose a way of categorising different methods depending on what one is trying to achieve, and share some future lines of inquiry.

Trends relevant to participatory futures work

Political and social trends provide new opportunities for the use of participatory methods, and new technologies offer new ways of participating. Digital tools can help scale participatory futures across large populations and can enable access to rich, interactive visions of the future.

Through our initial research, we came across the following interesting trends in participatory futures.

Collective intelligence

The field of collective intelligence could provide new ways of doing participatory futures that combine the capabilities of groups of people with machines. Emerging technologies such as machine learning help make this more possible. An example of this is Climate CoLab, an open problem-solving platform from MIT aimed at exploring and solving complex problems.

Example methods: hybrid forecasting, collaborative argument-mapping software.

Participatory governance

Movements around participatory local governance are gaining prominence, and are using digital technology to help with this. For example, the municipalist movement is a radical movement that seeks to build bottom-up forms of governance using participatory methods. For example, participatory budgeting projects in Paris, Madrid, and Mexico City have used digital methods. One such tool is Empatia, which provides an environment to test out participatory systems.

Example methods: citizens assemblies, participatory budgeting.

Immersive experiences

There is a strand of futures work that puts people in immersive environments so that they can experience the future and use that experience as a stimulus for thought. Emerging technologies such as virtual and augmented reality (VR and AR) are making these experiences much more immersive and can support more constructive discussions about the future. For example, VR and AR have been used in facilitating participatory urban planning decisions. Games also help with immersiveness. For example, IMPACT is a game where participants play different roles in the future and see how future changes could impact those roles. The Block by Block project uses the Minecraft game as a space for children to participate in designing their environment.

Example methods: serious games, speculative design, VR-enabled participatory urban planning.

Creative activism

There has been a trend towards using creative methods in activism. Not all of this is futures-focussed, but some is. For example, temporary autonomous zones such as Burning Man or Freetown Christiania in Copenhagen provide an enclave for a new way of living without having to change the whole of society.

Example methods: temporary autonomous zones, prefigurative intervention, legislative theatre.

Focusing participation on neglected voices

Although all participatory futures methods aim to widen participation, some are particularly focussed on including people that tend to be neglected in discussions about the future. For example, MH:2K involves young people in mental health work as citizen researchers. Similarly, the Guardian’s Gene Gap project involves five different UK communities to help identify different stories to tell about gene editing. Afrofuturism uses science fiction to imagine and explore science, technology, and cultures of the future from the perspectives of the African diaspora.

Example methods: citizen journalism, citizen science, participatory international development.

What types of participatory futures methods are there?

The abundance of different methods for engaging people in conversations about the future makes choosing an appropriate method challenging – where to begin? You could start by asking yourself two questions: Which type of question are you asking about the future? And which actors will be driving the process?

Together, these two variables form a framework in which we can place methods.

In addition to the type of question and driving actors that form these categories, there are several other variables that it might be useful to consider:

1. What are participants contributing? There are a wide variety of inputs that people could contribute, such as: prediction, observation, knowledge, value, goal, preference, concern, theory, vision, or framing.
2. Design of the process. The design of the process considers how people will be brought together and think together about the future. It will include: how participants are selected, how they participate and contribute, how they are coordinated, what the output of the process is, how this output is used.
3. Practical considerations. There are also practical variables such as: money and time cost of running the process, time and energy required from participants, knowledge requirements for participation, political complexity of the topic.

Where next?

This post summarises some initial ideas based on a small amount of research; more in-depth research will challenge and refine them. Further work could also explore:

• What can we learn from the long history of participatory methods more broadly?
• What outcomes do we want from participatory futures, and how do we measure them and build up an evidence base?
• How do we ensure that participatory futures methods are genuinely participatory, and are not co-opted by powerful groups and individuals?
• Which futures tasks are most suited to participatory methods, vs expert methods? How can experts and the public work together most effectively?

Interested in learning more about participatory futures? You could start by checking out Participedia, a repository of participatory projects and methods. Beautiful trouble similarly presents a database of creative activism techniques. Involve’s participation knowledge base has a wealth of information related to participatory methods. And finally, we’ve also made our own research spreadsheet available for you to download and modify as you wish.

Getting a better understanding of participatory futures methods is an important part of the wider project of democratising futures thinking. We’re glad that Nesta is pushing this field forward and are excited to see further work in this area.