21st Century Common Sense: Collective intelligence for the Climate Crisis

Photo by Bob Blob on Unsplash

Time to see more crossovers: Machines, humans and wicked problems

Collective intelligence is people working together, sometimes with the help of machines to analyze issues, design solutions and inspire new forms of decision making. One way to think about it is that we are looking at a giant brain, a Big Mind, as Geoff Mulgan writes, where the functions of the brain — memory, analysis, creativity among others — are performed in a distributed way by many people together and often with the help of technology.

  • To predict displacement in Somalia, UNHCR is using machine learning through Project Jetson
  • UN Global Pulse has used natural language processing to learn from people calling in to talk radio shows to track new agricultural threats and learn about people’s attitudes towards refugees and is leading on privacy and ethical frameworks to govern the use of big data in development.
  • Here at UNDP, we have tested out ways of helping our Sudanese national partners gain real-time and detailed insights about poverty distribution using mobile phone metadata.
  • Satellite data is also enabling us to develop a more precise understanding of the socio-political and geographic changes that are driving people to migrate.
  • We have also seen crowdfunding evolve as an impact investment tool to finance startups in Egypt

Learning direction #1: Moving beyond using collective intelligence to understand the problem

From what I see, the current development work framed as collective intelligence experiments focus on getting a better understanding of what the problem is. We tend to get stuck on that part. Much less attention is paid to collectively developing options and ideas to solve problems and an even smaller portion is testing out ways to drive more inclusive decision making on what should be done. A key area of learning for our partnership with Nesta is how best to get to solutions and public accountability using collective intelligence methods. The learning question at hand is: How does the hive mind get better at making decisions together and overseeing public investments?

Learning direction #2: Balancing public sector promises with the flexibility to follow the unexpected

A core principle of instilling trust in the public sector is that promises made are kept. Here’s where open data about where public money goes is a good basis, but it is insufficient. In a world of constantly changing wicked problems and new opportunities, sticking with the plan is often not enough.

Learning direction #3: Unite the system thinkers, the machine designers and those who study the human brain

Sustainable development is a real-time, distributed problem. The likelihood of future generations living at least as well as we do is dependent on billions of choices and actions every second: what crops we grow, what we build our houses with, how we get around, who we vote for, how we heat our homes and cook our food….It’s a daunting set of actions, choices and interactions. This is why we think this problem demands collective intelligence.



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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. http://acceleratorlabs.undp.org/