The Collective Learning Lab (CLL) of the Asian School of Governance explores how people, organizations, communities, universities, and governments can learn together to solve complex public problems.
Today’s challenges are too interconnected for any single institution, discipline, or expert to understand fully. Knowledge is distributed across government agencies, universities, communities, businesses, civil society organizations, professionals, and citizens.
The challenge is not simply producing more knowledge.
The challenge is learning how to connect what different people know and turn it into collective action.
The Collective Learning Lab develops the systems, methods, technologies, and networks that make this possible.
A city may have excellent universities, capable public officials, active communities, innovative businesses, and large amounts of data, yet still struggle to solve persistent problems.
Why?
Often, the knowledge needed to act is fragmented.
One agency sees one part of the problem.
Communities experience another.
Researchers understand particular mechanisms.
Businesses possess technologies and operational capabilities.
Civil society organizations see populations and problems that formal institutions may overlook.
No one sees the whole system.
Collective learning creates ways for these different forms of knowledge to interact.
It asks:
Who knows what?
Whose knowledge is missing?
How does knowledge move across organizations and communities?
What prevents people from learning from one another?
How can experience from one place help another?
How can learning become sustained institutional capability?
The Collective Learning Lab approaches society as a network of knowledge.
Knowledge exists not only in experts and institutions. It also resides in:
communities and lived experience;
professional practice;
indigenous and local knowledge;
organizational routines;
relationships and networks;
research and scientific knowledge;
government data and administrative experience;
markets and enterprises;
technologies and digital platforms; and
experiments taking place in communities every day.
CLL creates mechanisms for connecting these different sources of knowledge.
The goal is not to make everyone think alike.
The goal is to enable people with different knowledge, experiences, disciplines, and ways of seeing the world to learn together.
What if we designed governments not only to administer programs, but also to continuously learn?
A learning government senses emerging problems, listens to communities, tests ideas, analyzes results, remembers what has been tried, and adapts.
The Collective Learning Lab helps governments and their partners build this capability.
Rather than assuming that solutions must come from the top, CLL creates environments where multiple actors can:
Sense → Interpret → Connect → Experiment → Learn → Adapt → Scale
Every intervention becomes an opportunity to generate knowledge.
Every community becomes a potential source of insight.
Every failure can become evidence.
Every successful experiment can inform the next experiment.
Gather signals, data, stories, experiences, research, and emerging concerns.
Identify people, organizations, communities, and knowledge relevant to the challenge.
Bring different perspectives together to understand the system and identify possible entry points.
Design small, practical interventions that can generate new knowledge through action.
Study what happened, including intended results, failures, surprises, and unintended consequences.
Move useful knowledge across teams, organizations, universities, communities, and countries.
Use what has been learned to improve the next intervention.
Embed accumulated learning into policies, organizations, technologies, curricula, routines, and institutions.
Then the cycle begins again.
We design systems that help organizations and networks capture, connect, interpret, and use distributed knowledge.
We bring diverse actors together around real public problems and support repeated cycles of experimentation, reflection, and adaptation.
We map where knowledge, expertise, experience, relationships, and capabilities reside within communities and institutional ecosystems.
We connect universities, governments, communities, businesses, and civil society organizations so that lessons can travel across institutional and geographic boundaries.
We develop approaches for combining multiple perspectives without eliminating disagreement, diversity, or epistemic plurality.
We help organizations develop systems for remembering experiments, failures, adaptations, decisions, and lessons over time.
We explore how artificial intelligence can strengthen institutional memory, synthesize distributed knowledge, detect patterns, connect related experiments, and make accumulated learning accessible to decision-makers.
Universities can become much more than producers of research.
They can become learning infrastructure for their cities, provinces, regions, and countries.
Through Collective Learning Hubs, universities can convene:
Students + Faculty + Government + Communities + Civil Society + Business + Technology
around real-world challenges.
Students can become researchers and problem-solvers.
Faculty can bring disciplinary and methodological expertise.
Communities contribute lived and local knowledge.
Government brings public problems, administrative knowledge, and institutional authority.
Businesses contribute technology, entrepreneurship, investment, and implementation capabilities.
Together, they create a continuously evolving ecosystem for public problem-solving.
The Collective Learning Lab serves as a horizontal learning platform connecting the different laboratories and initiatives of the Asian School of Governance.
Each ASG Lab generates experiments, experiences, data, relationships, and knowledge.
CLL helps that learning travel.
Climate Action Labs
↓
Sustainable Energy Labs
↓
Blue Economy Labs
↓
Zero Hunger Labs
↓
AI4Gov Labs
↓
Social Finance Labs
↓
Education Impact Labs
↓
City and Municipal Futures Labs
Rather than operating as isolated programs, these laboratories can become nodes within a larger collective learning ecosystem.
An innovation developed in one municipality can inspire experiments elsewhere.
A failure in one country can prevent another team from repeating it.
A pattern emerging across several communities can become a research question.
Research can generate a new intervention.
And the cycle continues.
Traditional projects ask:
Did the project achieve its targets?
Collective learning asks an additional question:
Is the system now better able to solve the next problem?
This changes how we think about development.
A successful intervention should leave behind more than outputs.
It should strengthen relationships, institutional memory, problem-solving capability, local knowledge, networks, and the ability to experiment and adapt.
The ultimate product is not simply a successful project.
It is a system that has learned how to learn.
Artificial intelligence creates new possibilities for collective learning.
AI can help communities and institutions:
analyze large volumes of information;
visualize complex systems;
identify patterns across multiple experiments;
connect people working on similar problems;
synthesize lessons across locations;
translate knowledge across languages;
maintain institutional memory;
explore scenarios;
surface overlooked perspectives; and
make accumulated knowledge easier to access.
But AI does not replace human intelligence.
CLL explores a different possibility:
Technology strengthens the learning system while human beings continue to provide judgment, values, relationships, imagination, and lived experience.
The Collective Learning Lab investigates questions such as:
How do groups know more than their individual members?
Why does useful knowledge sometimes fail to travel?
How do institutions learn, remember, and forget?
How can tacit and local knowledge inform public decision-making?
How can competing knowledge systems coexist productively?
How does experimentation generate institutional capability?
How can universities become learning infrastructure for society?
How can AI strengthen rather than narrow epistemic diversity?
How can learning travel between communities without simply copying solutions?
How do societies become better at solving problems over time?
CLL works with:
Universities | Governments | Cities | Communities | Foundations | Development Organizations | Civil Society | Businesses | Research Networks
Partners can establish a Collective Learning Hub, organize an Action-Learning initiative, develop a collective intelligence system, undertake knowledge and capability mapping, or join a cross-country learning network.
Imagine thousands of universities, municipalities, organizations, and communities continuously experimenting with solutions to public problems.
Imagine if their experiences did not disappear when projects ended.
Imagine if every experiment contributed to a growing commons of knowledge that others could interrogate, adapt, challenge, and improve.
The Collective Learning Lab works toward this possibility.
Because the capacity to address increasingly complex challenges will depend not simply on having smarter individuals or more powerful technologies.
It will depend on our capacity to learn together.
Distributed Knowledge. Collective Intelligence. Coordinated Action.
An initiative of the Asian School of Governance.