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.
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.
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
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
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
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.
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 email@example.com
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 ↩
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:
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.
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.
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.
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.
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.
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.
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.
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?
|Type of question||Ask||Example outputs|
|Predictive||What kind of future can we expect?||Predictions, scenarios, trends|
|Value-based||What kind of future do we want?||Values, visions, ideologies, speculative design|
|Strategic||How can we get the future we want?||Plans, strategies|
|Driving actors||Who initiates the process?||Who controls the process?|
|Top-down||Traditional authorities (e.g., local governments)||The initiating authority|
|Bottom-up||Members of the public||The public|
Together, these two variables form a framework in which we can place methods.
21st century town meetings
Temporary autonomous zones
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:
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:
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.
We helped the Humanitarian Innovation Fund translate research into actionable next steps for the humanitarian sector.
Research often leads to piles of information that are hard to act on. If you write this information up without synthesising and communicating it effectively, you will end up with an ineffective report. Because of this, we focus intensively on synthesis and communication in all of our work.
We recently did this kind of synthesis work for Elrha’s Humanitarian Innovation Fund (HIF). They support organisations developing innovations in humanitarian assistance and they’ve noticed that it's often difficult to scale these innovations. They wanted to write a report to help the humanitarian sector understand why scaling is difficult and take action to enable it. We helped them translate their experience and research findings into a set of clear and actionable challenges for the humanitarian sector.
Today we’ve launched our ‘Too tough to scale?’ report. Our report identifies the key barriers to scaling #humanitarian #innovations and calls for specific action to create transformative change. Read it! 👉 http://bit.ly/tootoughtoscale @DutchMFA @DFID_UK #tootoughtoscale— The HIF (@The_HIF) October 17, 2018
Using challenges to structure thinking
We structured the report around challenges because they are a good way to stimulate action. Challenges are brief statements of a problem, the reasons for the problem, and how it might be solved. They help the reader quickly understand the situation and provide focus for a community of practitioners.
We based our challenges on research that had identified barriers to scale and recommendations for the sector. This research drew on the HIF’s experience in helping innovators scale their projects and on research carried out by Spring Impact, who are experts in scaling social innovation. We analysed this research and proposed a set of challenges and a structure for the report that we refined with the HIF team.
Five key challenges stood out:
We developed the following structure to describe each challenge:
This structure gives humanitarian actors an understanding of the challenge, provides detail on what’s causing it, and gets them thinking about how they can solve it.
Opening up conversations
It might seem trivial, but something as simple as how research or insights are framed can shape the kind of conversations they enable. Identifying limitations and barriers is important, but advancing informed proposals on what needs to happen to address them can generate much more meaningful conversations.
This report represented an opportunity for the HIF to reflect on their work and consolidate their position as a leader in humanitarian innovation. By articulating concrete challenges and next steps for the sector, they now have a valuable tool they can use to work with stakeholders to unlock the systemic change needed to help innovations to scale.
To learn more about Too tough to scale? Challenges to scaling innovation in the humanitarian sector read the full report here.