The Design Studio on Collective Intelligence for Climate Action supported 15 UNDP Accelerator labs in developing novel collective intelligence solutions to tackle climate problems in the Global South, such as heat waves or floods.

The studio was structured in four phases. During this journey, the participants learned to apply methodology and processes from the CI Design Playbook while working closely with communities. The programme included various touch points and activities such as in-person workshops, online learning, one-to-one mentoring and peer-learning.

From crowdmapping to citizen science, the participants learnt to apply various collective intelligence methods and principles. They also developed key capabilities and skills, such as:

• conducting research with communities
• community mobilisation
• working with data at scale
• iteratively prototyping and testing ideas with communities
• piloting with the community and elevating the learnings.

The outcome was a set of collective intelligence solutions that address real community needs, such as a deliberative process to include indigenous communities in forest management in Bolivia or an early-warning system for flooding in Panama.

One of the core skills that the participants developed during the programme was the ability to conduct research with the community to co-define a climate problem. We supported them to use creative methods to engage the community such as photovoice, perception mapping and urban sketches. 

Team Mozambique employed photovoice to investigate the effects of cyclical floods on agriculture and livelihoods. Community members were asked to take photos depicting how the climate issue was impacting them. One image that stood out was of a pigsty, initially perplexing the team (see image above). Yet, the story behind it unveiled a valuable insight: the household had transitioned from raising chickens to pigs because pigs can survive floods by swimming, whereas chickens are less resilient. This surprising discovery offered valuable insight into the adaptation strategies that locals are employing and potential downstream effects on food supplies.

We kicked off our second phase with an in-person workshop where participants went from research on complex climate problems to prototypes of CI solutions in just one week. We used prompt cards from the playbook as well as our ‘CI Design Canvas’ to help teams explore, ideate and define their focus. For each idea, they considered how they would:

1. gather data, information: they explored the different types of data that may be available to them, such as open data, citizen-generated data or satellite data
2. mobilise people: they defined who should be involved in the solution (for example, ONGs and public entities that have incidence in the area)
3. connect and interpret data: they considered options such as participatory monitoring or peer-to-peer exchange for connecting and crowd forecasting or natural language processing (NLP) for interpreting data
4. create change: they ideated on different methods for creating change, such as heat maps or emergency alerts.

We know that testing and prototyping with communities early in the process can allow for rapid iteration and ultimately better align the solution with the communities’ needs and context. 

The design studio was structured in short action learning cycles that supported participants to learn from communities during team field work, reflect with the other teams during and share learnings in our ‘retrospective sessions,’ and iterate at a fast pace with the support of our programme mentors.

The Guatemala team, for example, tried different types of sensors to determine water availability. Through this process they learnt that farmers saw the most value in understanding soil humidity to help them manage irrigation. In the words of a farmer: “I dream of installing a sensor that ‘tells me’ when my plants need to be irrigated.” 

The third phase of the design studio aimed to strengthen participants' skills in working with data, prototyping and scaling ideas for testing with communities as part of Collective Intelligence initiatives. We supported them in translating prototypes into practical solutions, which involved technical development and service design frameworks to plan the functionality and testing of the CI solution.

For instance, the Panama team used the 'Collective Intelligence Solution Blueprint' to outline each step of their intervention, identify areas for improvement, and plan technical work and live testing. They were also introduced to new data tools like Cognicity and Ushahidi, which helped them develop an early-warning system for floods.