Youth Gateway: Designing for Collective Action

(Hat tip to Mike Arauz for the thinking below.)

We spent yesterday afternoon in the Office of the Youth Envoy working together to flesh out a vision for a new platform with a goal to  create a central tool for young people to collectively engage with the Sustainable Development Goals.

They envision collective action - to provide a tool to mobilize the masses. We spent a time refocusing from content to strategies for facilitating real-world impact.

That in mind, we began to imagine a new strategy, based on a framework developed by Undercurrent's Mike Arauz.

The Internet at its best

Because most people in marketing and communications have seen the internet as an extension of their previous mode-of-operation, they tend of focus on content. Using the internet to “share” is often as deep as the strategy goes. But the internet is at its best when it is focused on collective action:

We began to ask questions about the root of this project: community organizing:

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• What are the Envoy's desired real world outcomes from the site?
• Who will be the actors?
• What organizing model will make it possible?
• What are the strategic, organizational, design, and technology needs to build community and facilitate collective action?

The Genome for Collective Intelligence

MIT’s Center for Collective Intelligence, a group which has studies over 250 collective projects that involved collective intelligence including Wikipedia, where thousands of contributors from across the world have collectively created the world’s largest encyclopedia, Google, which uses the judgements of millions of people to organize the web, and Threadless, which harnesses the intelligence of their 500,000+ person community to design and produce t-shirts.

The Center's basic research question is:

How can people and computers be connected so that - collectively - they act more intelligently than any person, group, or computer has ever done before?

Through their research, The Center mapped out what they call The Collective Intelligence Genome (highlight recommended reading), the collection of design patterns (or “genes”) that can be combined and recombined to create systems that harness the intelligence of crowds.

The patterns ultimately breakdown into four questions that are so simple they almost feels like common sense:

1. Who is performing the task? Why are they doing it?
2. What is being accomplished? How is it being done?

The report visualizes the framework like this:                  

Building a Strategy

How do you build a strategy around these questions? Let’s break it down:

What?

What are your goals for the community? The MIT studied boiled most organizational goals to two basic genes:

• Create: In this gene, the actors in the system generate something new - a piece of software code, a blog entry, a T-shirt design.  
• Decide: In this gene, the actors evaluate and select alternatives deciding whether a new module should be included in the next release of Linux, selecting which Tshirt design to manufacture, deciding whether to delete a Wikipedia article.
  (pg 5)

What does success look like from the community's point of view?

Social media tends to have two methods of measurement, but most groups stop short with the first one:

1. Process measures: These include things like “likes,” retweets, comments. They are indicators of how well your process is working. Are people engaging in the content? Good! But you might compare process measures to checking the oil or gas in your car. They’re good, the engine is running, the stage is set, but it doesn’t mean the car is taking you anywhere. That’s why you need...
2. Outcome measures: These are measurements of real change in the real world. FoldIt, the collective game that allowed thousands of players to solve a challenge in protein folding and unlock the structure of an AIDS-related enzyme that had baffled scientists for a decade wasn’t just measuring how engaged the players were, they measured this extremely important real-world outcome.

We began to work together brainstorming the beginnings of outcomes we could design and measure for, and a small number of process measures to track progress towards each outcome.

Who and Why?

These two “genes” ask us to understand our participants and what motivates them. You can begin by asking and answering a few questions:

• What is their shared interest? What brings these people to gather in the first place?
• What are their shared values? What is important to them?
• What is their shared vision? What inspires them to be part of the group?
• What is their shared behavior? What do these people like to do?

Next, what incentives will motivate their collective behavior?

Researcher and game designer Jane McGonnigal says people participate in order to:

1. accomplish satisfying work,
2. get better at something,
3. spend time with people we like, and
4. to feel part of something bigger than themselves

Her paper "Why I Love Bees : A Case Study in Collective Intelligence Gaming” shows us the power of using feedback loops to tangibly reinforce the reality that participants are contributing to something bigger than themselves.

Daren C. Brabham’s research of the Threadless community tells us that money, love, glory, expertise, and social experience are also powerful motivators.

How?

What methods and tools will we use to make this happen? We tend to want to start with this question, because it is the most concrete out of all of them. We can look and see what tools are available. We already having something to sink our teeth into. But if we haven’t started answering the above first, talking about tools and methods is a waste of time.

Going back to the “What” above, we’re now asking:

How will people create? and/orHow will people decide?

Specifically, will the community create or decide independently, or dependently?

Beginning to focus in on these adds constraints that help ideas and possible experiments to emerge more freely:

• Create: a contest, collection, or, if as a group, a collaboration.
• Group decision: Tools for voting, averaging, managing consensus, running prediction markets
• Individual decision: Markets and social networks

Bringing it all together

Genes are only useful when they come together as a genome. A few examples from the study show what a genome like this might look like:

Further reading:

• The Climate CoLab, "you can work with people from all over the world to create proposals for what to do about climate change."
• "Solving wicked social problems with socio-computational systems” (PDF)
• "Mobilizing resources for collective action and sustainable development: mobilizing interests or shared values?"