Four questions on building a global network to learn about development challenges

Millie Begovic, UNDP Innovation Specialist, Kal Joffres, Innovation Advisor and Bas Leurs, Lead Learning Specialist

Tackling social and environmental challenges in a deeply interconnected world presents a special set of challenges. A simulation hosted by Johns Hopkins found that a ‘moderately contagious and moderately lethal’ virus would wipe out 150 million people within 20 months.

It is not just disease outbreaks that can create global shockwaves faster than ever. Financial crises and economic disruption, displacement of people by conflict and climate crisis — and many other development challenges are starting in one place and having an impact in many others with little regard for political borders. They’re spreading at speeds and in unpredictable ways we haven’t seen before.

Photo by Ryoji Iwata on Unsplash

How do we protect ourselves in a deeply interconnected world?

Some argue that isolation is the way to protect ourselves in an interconnected world. We believe that the global community represents significant benefits for all. We don’t amputate an arm just in case it gets infected at some point in the future. But if interconnection is making us more vulnerable, are we engaging it to become stronger and more responsive at the same rate?

At the heart of this question is our speed of learning. As new challenges emerge, we are continuously faced with development problems for which we don’t have locally proven solutions. When we test a solution for addressing marine plastic waste or helping people transition into new economy jobs, how long does it take us to learn about whether we are having an impact in the right direction? Does learning take years or months or weeks?

Developing a global “immune response” to 21st century social and environmental challenges that matches their scale and speed requires radically accelerating how quickly we learn about which solutions are working and which ones aren’t.

There is still much to be done. Today, we learn about development challenges in a siloed, country-by-country basis. We put what we learn (and more often, report our success) into PDFs that are sent to headquarters, publish it on the internet, or share through annual conferences in the hopes that someone will search the right term or the right person will be in the right conference session. This hardly takes full advantage of our interconnectedness. The consequence is that our global immune system falls short of the speed and scale of today’s challenges — not to mention their sheer number.

If interconnection is making us more vulnerable, are we engaging it to become stronger and more responsive at the same rate?

Four key questions to enable globally-networked learning

Learning as a global network in a way that leverages our interconnectedness requires that we remodel the way we learn. Part of UNDP’s answer to this is the Accelerator Labs, a network of teams covering 78 countries to help us accelerate our learning about what works and what doesn’t in development. The Labs are an experiment to build the world’s largest and fastest learning network — a network rapidly identifying and testing bold solutions for our most critical development challenges with a wide alliance of partners.

To build a global learning network that matches the scale and speed of today’s challenges, we’ll need to answer a few key questions. We are engaging in a set of initiatives ranging from convening people learning from others, to building and testing prototypes, and by drawing inspiration from a variety of other fields such as biology.

Most importantly, we’re sharing these questions in the hopes of finding, hearing from, and working with others also addressing these questions.

1. How do we ensure we get learning to the right people in the network — without deluging everyone with irrelevant information?

Taking advantage of the full intelligence of a global network rather than just those who are in our team and physically next to us requires breaking a trade-off between two conflicting needs. On one hand, the need to ensure that as soon as something is learned in one part of the network the right people in the rest of the network can take advantage of that learning. On the other hand, the need to ensure that people are not deluged with irrelevant information.

About 1,000 people are involved in the Accelerator Lab network — and we expect hundreds more over the coming months, not including numerous external collaborators. Imagine joining a Whatsapp group with that many problem solvers talking about what they’re learning. Chances are you aren’t able to catch up with the torrent of messages and you have muted the group. Isolating the network into smaller, thematic Whatsapp groups might lead to more relevant conversations, but in a world of interconnected challenges — where climate change is accelerating antibiotic resistance for example — the trade-off is that are will be missing some important learnings.

Research backs this up. ‘Small world networks’ are better predisposed to faster flow of information than fully integrated networks because people are likely to pay more attention to the smaller group of neighbors. We need ways of learning as if we were one big mind that get better as network size increases, not worse.

Photo by Maros Misove on Unsplash

2. How do we move from collecting knowledge “just in case”, to connecting people “just in time”?

Traditional approaches to knowledge management are premised on the collection of explicit and codified knowledge (e.g. guides, presentations, reports) stored in repositories “just in case” someone else finds it useful.

As soon as knowledge or learning needs to be packaged into a publication, we dramatically slow its journey to reaching the people who might need it most. We need to accumulate enough learning to make producing a report worthwhile, a narrative needs to be created around that learning, and the whole package has to be reviewed and vetted. Far too much time is invested in writing reports that too few people read.

We need to de-emphasise building “stocks” of highly codified knowledge and accelerate the flow of knowledge by finding ways of better connecting relevant people across the organisation, sharing smaller “nuggets” of learning, and shifting towards on-demand knowledge creation.

Dave Snowden’s mantra is that for every $1 invested in knowledge management, 90 cents should go toward enabling connections and only 10 cents toward building content. Connecting people allows us to leverage not just codified knowledge inside the network but also more tacit knowledge by providing the space for people to create things together. But how do we know who to connect when, to do what, and for how long?

Our friends at Edgeryders are doing interesting work using swarm-like behavior to attract unusual talent and niche expertise from the edges of organisations to solve issues such as social care, climate change, and urbanisation.

3. How do we operate as a boundaryless organization, seamlessly connecting with a variety of partners and pulling in expertise when and where needed, including those who are most affected by a development issue?

No matter how many smart people there are as a part of the network,there will always be more smart people outside of it . We have a responsibility to leverage not just knowledge within the organisation but from a variety of people and organisations who might know something about the challenges we’re working on. That might include other development actors but also mobile phone companies, social media networks, and the real experts — the people who are most affected by these challenges.

This implies being radically more open with what we are learning and exchanging that learning with others. But it doesn’t mean being completely open. This type of sharing can only happen on a foundation of trust and the reputation of the people involved. We need to learn how to effectively build platforms where trusted groups of people interested in particular issues can come together and co-create solutions.

Simone Cicero at the Platform Design Toolkit has been considering what the boundaryless organization might look like. When it comes to the public sector, Laboratorio de Gobierno in Chile has shown what networked learning could look like in public sector innovation with Innovadores Públicos.

4. How do we increase ‘return on attention’, help people sift signal from the noise, and better deal with information overload?

For every newsletter that lands in your inbox, you’re considering your “return on attention” when you think about whether to open it or to delete it straight away. Does this newsletter give you something relevant and interesting every time or open it? Or is it variable and you think of reading it only if you have some spare time?

Many corporate knowledge communication and knowledge sharing tools (e.g. Yammer) go straight to email trash because of their dismal return on attention. We need to solve for return attention across a wide diversity of interests.

This challenge is being worked on within and beyond UNDP. At UNDP, our colleague Alberto Lizzi is exploring using a knowledge graph — the idea that facts and learning can relate to each other like a graph, rather than sit in individual documents — to understand how to bring the relevant learning to people. Beyond UNDP, one of us (Kal) has applied artificial intelligence to the challenge of combing through thousands of lessons learned from projects to deliver the right lessons at the right time to people working on similar projects across an organisation.

Source: https://blogs.lse.ac.uk/impactofsocialsciences/2019/05/14/the-death-of-the-literature-review-and-the-rise-of-the-dynamic-knowledge-map/

Increasing return on attention is particularly challenging because we can’t just rely on traditional thematic fields (e.g. governance, livelihoods) or traditional knowledge sources to understand what might be relevant to someone. Just as velcro was discovered by a chemist who found seeds attached to his dog’s fur, development solutions may come from very different fields.

How we’re working on answering these questions

We have a few hunches about how to answer these questions — but no means all the answers.

Radically accelerating learning about what works and what doesn’t isn’t a technology problem. We expect successful approaches will blend interventions that accelerate learning at the level of (1) people and their relationships, (2) programmes of activities for sharing and co-creating, and (3) platforms that enable people to come together. While technology will play a role, it cannot accelerate learning on its own (social and connection as drivers of accelerated learning was also the key take away from a discussion we recently had with a group of interesting colleagues — Alberto Cottica, Elisabeth J. Altman, Tomer Sharon, Juan Felipe Lopez, Gorka Espiau to name a few).

To learn about building a faster, global learning network, we are pursuing three approaches.

First, we are convening people who are working in and around this challenge to work collaboratively and cross-pollinate our solutions. This month, we brought together a group of people who have been thinking about these questions in business, government, and development contexts to share what we’ve learned as well as shape some joint research questions and prototypes. This will be the first of more gatherings to come.

Second, we are creating and deploying prototypes to explore these questions within the Accelerator Labs context. We currently have a live prototype running within Labs network that is testing ways of collecting learnings from teams on a weekly basis rather than around specific project milestones.

Third, we are looking to inspire our thinking with approaches from disparate fields such as biodiversity and evolution, immunology, new and emerging technology, anthropology and psychology. This includes drawing lessons from people like Deborah Gordon, studying the “anternet” and the behaviour of ants, as well as Adrienne Maree Brown, who writes about ‘critical connections over critical mass’ and the power of sterlings, ferns and dandelions in shaping ecosystems.

Building a global learning network that matches the speed and scale of today’s challenges is more important than ever. We need to take full advantage of our interconnectedness so that what connects us doesn’t just make us more vulnerable — it also makes us stronger.

Over the coming months, we’ll be writing more to detail our hypotheses and what we’re learning.

Cracking this challenge isn’t something we can do alone. We’re looking to explore this challenge and these questions with others who are deeply passionate and invested in answering them.

If you’re interested in joining us on this journey, please drop us a line in the comments or on Twitter (@leursism, @ElaMi5, @kaliper, @UNDPAccLabs).

With thanks to John Hagel, Shreenath Regunathan, Glenn Fajardo, and Gina Lucarelli for their feedback to our thinking about this work.

Building the world’s largest learning network around development challenges. 91 Labs in 115 countries. http://acceleratorlabs.undp.org/

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