How do we learn from a network of ecosystems to reinvent knowledge management?

Behind the scenes: Our laundry line of notes on how to create a learning network that surfaces, shares, and even creates knowledge.
  • How do we follow the emergence and dynamism of individual labs in a network to understand if and where learning is being accelerated?
  • How do we maintain subsidiarity through distributed, independent operations and still create space for convergence in knowledge creation across a network like this?
  • What is this network learning?
  • How do we fill in its blind spots with what our (current and future) partners know?

What knowledge is being created in the network of labs?

Our network of social innovation labs seems to be creating a range of forms of knowledge. For the purpose of creating clarity, I’ll frame the four types we are focused on:

  • We generate experiential knowledge created on how to deploy a range of innovation methods: what is the most effective way to convene a hackathon? What are the benefits and limits of behavioral insights experiments? What does a data powered approach to positive deviance look like? This is an essential kind of knowledge as we test out and codify new ways of working within global development.
  • Our solutions mappers and their teams also generate forms of ethnographic evidence that unpack the practices, knowledge, and inventions of women and men who are innovating around problems they face in their communities. Inspired by the Grassroots Innovation Augmentation Network, we try to learn from frugal innovation to inform the design and delivery of development programmes.
  • As a sensory platform, we see and cultivate weak signals of change to understand the real time state of sustainable development. How is the pandemic unfolding and affecting people’s livelihoods? What does the future of work look like? Weak signals of change are a form of intelligence that this lab network creates.
  • And when we run experiments, we probe systems to see what effect our interventions make. Here we create and share experimental knowledge. This is where we are trying to learn how to document tests and probes so that practitioners across the world can pick up where another lab team left off, modifying to suit the contexts that make or break the utility of knowledge transfer.

The Network Learning Prototype

With these forms of knowledge on our radar, and knowing that what we are trying to tap into is constantly changing and evolving, we set out a roadmap. It goes like this, although not necessarily in linear fashion:

  • The labs reflect on their work in free form. We’re tapping into what the labs write as a start, though there are multiple forms of reflection and audio is probably next on deck.
  • Machines help us find the signposts. Given the massively distributed way of working across the network, we need some help from our [machine] friends to know where to look to find patterns in all the written reflections.
  • Humans read for patterns. We read the reflections the labs create and try to draw out insights, nuggets of learning, and patterns across the 91 lab teams.
  • We set an R&D priority and learning questions. Our first priority is to learn more about informal economic activity — we’re currently seeing that it doesn’t go away with GDP growth and needs some new approaches.
  • We convene ‘learning circles’ to explore learning questions.
  • We blend the experiential knowledge coming from the labs with the body of knowledge in research circles.

Seeing the synapses created by a network

In the early days of 2019, when our Lead Learning Designer Bas Leurs was onboarding new teams, we brought them together in bootcamps. I’ll never forget the message the Mexico lab team stuck on the wall of the bootcamp room on their way out the door after being the first to undergo our onboarding. As they were about to become the first ever Heads of Exploration, Experimentation, and Solutions Mapping in their country’s UNDP office, they wisely conveyed that what they needed more than advice from us at HQ was a network of others in the same position. They shared their phone numbers and set up Whatsapp groups for explorers, experimenters, and mappers.

Machines point to patterns. Then we explore learning questions

So the synapses of the global lab network are firing, but what are the patterns? This is where the humans get back into the game. We use the advanced search tools of lots of unstructured text reflections (blogs, reflections, Mural boards, etc.) to see patterns.

  • What perceptions and motivations drive informality?
  • Is there an unseen contribution to environmental sustainability that comes from the informal sector?
  • What does the rapid digitization brought on by the pandemic mean for informal entrepreneurship?
  • Is there a way to approach informality through hybrid policies that protects people from hazardous labor and taps into informality’s ingenuity at the same time?

What do we produce out of all of this?

Admittedly, we are doing two things at once, which comes with some exasperation at times! We’re testing out a network learning prototype that meets the needs of the 21st century by tapping into distributed and often tacit intelligence. And at the same time we are seeking insights related to our R&D priority: informality as part of sustainable development. In truth, it sometimes feels like we are hacking our way through a jungle, changing tools and directions pretty frequently.



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UNDP Accelerator Labs

UNDP Accelerator Labs

Building the world’s largest learning network around development challenges. 91 Labs in 115 countries.